WorldWideScience

Sample records for accurately predicts clinical

  1. Ethics and epistemology of accurate prediction in clinical research.

    Hey, Spencer Phillips

    2015-07-01

    All major research ethics policies assert that the ethical review of clinical trial protocols should include a systematic assessment of risks and benefits. But despite this policy, protocols do not typically contain explicit probability statements about the likely risks or benefits involved in the proposed research. In this essay, I articulate a range of ethical and epistemic advantages that explicit forecasting would offer to the health research enterprise. I then consider how some particular confidence levels may come into conflict with the principles of ethical research.

  2. High IFIT1 expression predicts improved clinical outcome, and IFIT1 along with MGMT more accurately predicts prognosis in newly diagnosed glioblastoma.

    Zhang, Jin-Feng; Chen, Yao; Lin, Guo-Shi; Zhang, Jian-Dong; Tang, Wen-Long; Huang, Jian-Huang; Chen, Jin-Shou; Wang, Xing-Fu; Lin, Zhi-Xiong

    2016-06-01

    Interferon-induced protein with tetratricopeptide repeat 1 (IFIT1) plays a key role in growth suppression and apoptosis promotion in cancer cells. Interferon was reported to induce the expression of IFIT1 and inhibit the expression of O-6-methylguanine-DNA methyltransferase (MGMT).This study aimed to investigate the expression of IFIT1, the correlation between IFIT1 and MGMT, and their impact on the clinical outcome in newly diagnosed glioblastoma. The expression of IFIT1 and MGMT and their correlation were investigated in the tumor tissues from 70 patients with newly diagnosed glioblastoma. The effects on progression-free survival and overall survival were evaluated. Of 70 cases, 57 (81.4%) tissue samples showed high expression of IFIT1 by immunostaining. The χ(2) test indicated that the expression of IFIT1 and MGMT was negatively correlated (r = -0.288, P = .016). Univariate and multivariate analyses confirmed high IFIT1 expression as a favorable prognostic indicator for progression-free survival (P = .005 and .017) and overall survival (P = .001 and .001), respectively. Patients with 2 favorable factors (high IFIT1 and low MGMT) had an improved prognosis as compared with others. The results demonstrated significantly increased expression of IFIT1 in newly diagnosed glioblastoma tissue. The negative correlation between IFIT1 and MGMT expression may be triggered by interferon. High IFIT1 can be a predictive biomarker of favorable clinical outcome, and IFIT1 along with MGMT more accurately predicts prognosis in newly diagnosed glioblastoma.

  3. Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

    Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.; Fournier, Marcia V.

    2008-10-20

    One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic

  4. Women's age and embryo developmental speed accurately predict clinical pregnancy after single vitrified-warmed blastocyst transfer.

    Kato, Keiichi; Ueno, Satoshi; Yabuuchi, Akiko; Uchiyama, Kazuo; Okuno, Takashi; Kobayashi, Tamotsu; Segawa, Tomoya; Teramoto, Shokichi

    2014-10-01

    The aim of this study was to establish a simple, objective blastocyst grading system using women's age and embryo developmental speed to predict clinical pregnancy after single vitrified-warmed blastocyst transfer. A 6-year retrospective cohort study was conducted in a private infertility centre. A total of 7341 single vitrified-armed blastocyst transfer cycles were included, divided into those carried out between 2006 and 2011 (6046 cycles) and 2012 (1295 cycles). Clinical pregnancy rate, ongoing pregnancy rate and delivery rates were stratified by women's age (149 h) as embryo developmental speed. In all the age groups, clinical pregnancy rate, ongoing pregnancy rate and delivery rates decreased as the embryo developmental speed decreased (P pregnancy rates observed in the 2006-2011 cohort. Subsequently, the novel grading score was validated in the 2012 cohort (1295 cycles), finding an excellent association. In conclusion, we established a novel blastocyst grading system using women's age and embryo developmental speed as objective parameters.

  5. How accurate can genetic predictions be?

    Dreyfuss Jonathan M

    2012-07-01

    Full Text Available Abstract Background Pre-symptomatic prediction of disease and drug response based on genetic testing is a critical component of personalized medicine. Previous work has demonstrated that the predictive capacity of genetic testing is constrained by the heritability and prevalence of the tested trait, although these constraints have only been approximated under the assumption of a normally distributed genetic risk distribution. Results Here, we mathematically derive the absolute limits that these factors impose on test accuracy in the absence of any distributional assumptions on risk. We present these limits in terms of the best-case receiver-operating characteristic (ROC curve, consisting of the best-case test sensitivities and specificities, and the AUC (area under the curve measure of accuracy. We apply our method to genetic prediction of type 2 diabetes and breast cancer, and we additionally show the best possible accuracy that can be obtained from integrated predictors, which can incorporate non-genetic features. Conclusion Knowledge of such limits is valuable in understanding the implications of genetic testing even before additional associations are identified.

  6. Customised birthweight standards accurately predict perinatal morbidity

    Figueras, Francesc; Figueras, Josep; Meler, Eva; Eixarch, Elisenda; Coll, Oriol; Gratacos, Eduard; Gardosi, Jason; Carbonell, Xavier

    2007-01-01

    Objective Fetal growth restriction is associated with adverse perinatal outcome but is often not recognised antenatally, and low birthweight centiles based on population norms are used as a proxy instead. This study compared the association between neonatal morbidity and fetal growth status at birth as determined by customised birthweight centiles and currently used centiles based on population standards. Design Retrospective cohort study. Setting Referral hospital, Barcelona, Spain. Patients A cohort of 13 661 non‐malformed singleton deliveries. Interventions Both population‐based and customised standards for birth weight were applied to the study cohort. Customised weight centiles were calculated by adjusting for maternal height, booking weight, parity, ethnic origin, gestational age at delivery and fetal sex. Main outcome measures Newborn morbidity and perinatal death. Results The association between smallness for gestational age (SGA) and perinatal morbidity was stronger when birthweight limits were customised, and resulted in an additional 4.1% (n = 565) neonates being classified as SGA. Compared with non‐SGA neonates, this newly identified group had an increased risk of perinatal mortality (OR 3.2; 95% CI 1.6 to 6.2), neurological morbidity (OR 3.2; 95% CI 1.7 to 6.1) and non‐neurological morbidity (OR 8; 95% CI 4.8 to 13.6). Conclusion Customised standards improve the prediction of adverse neonatal outcome. The association between SGA and adverse outcome is independent of the gestational age at delivery. PMID:17251224

  7. Accurate torque-speed performance prediction for brushless dc motors

    Gipper, Patrick D.

    Desirable characteristics of the brushless dc motor (BLDCM) have resulted in their application for electrohydrostatic (EH) and electromechanical (EM) actuation systems. But to effectively apply the BLDCM requires accurate prediction of performance. The minimum necessary performance characteristics are motor torque versus speed, peak and average supply current and efficiency. BLDCM nonlinear simulation software specifically adapted for torque-speed prediction is presented. The capability of the software to quickly and accurately predict performance has been verified on fractional to integral HP motor sizes, and is presented. Additionally, the capability of torque-speed prediction with commutation angle advance is demonstrated.

  8. Accurate Multisteps Traffic Flow Prediction Based on SVM

    Zhang Mingheng

    2013-01-01

    Full Text Available Accurate traffic flow prediction is prerequisite and important for realizing intelligent traffic control and guidance, and it is also the objective requirement for intelligent traffic management. Due to the strong nonlinear, stochastic, time-varying characteristics of urban transport system, artificial intelligence methods such as support vector machine (SVM are now receiving more and more attentions in this research field. Compared with the traditional single-step prediction method, the multisteps prediction has the ability that can predict the traffic state trends over a certain period in the future. From the perspective of dynamic decision, it is far important than the current traffic condition obtained. Thus, in this paper, an accurate multi-steps traffic flow prediction model based on SVM was proposed. In which, the input vectors were comprised of actual traffic volume and four different types of input vectors were compared to verify their prediction performance with each other. Finally, the model was verified with actual data in the empirical analysis phase and the test results showed that the proposed SVM model had a good ability for traffic flow prediction and the SVM-HPT model outperformed the other three models for prediction.

  9. Inverter Modeling For Accurate Energy Predictions Of Tracking HCPV Installations

    Bowman, J.; Jensen, S.; McDonald, Mark

    2010-10-01

    High efficiency high concentration photovoltaic (HCPV) solar plants of megawatt scale are now operational, and opportunities for expanded adoption are plentiful. However, effective bidding for sites requires reliable prediction of energy production. HCPV module nameplate power is rated for specific test conditions; however, instantaneous HCPV power varies due to site specific irradiance and operating temperature, and is degraded by soiling, protective stowing, shading, and electrical connectivity. These factors interact with the selection of equipment typically supplied by third parties, e.g., wire gauge and inverters. We describe a time sequence model accurately accounting for these effects that predicts annual energy production, with specific reference to the impact of the inverter on energy output and interactions between system-level design decisions and the inverter. We will also show two examples, based on an actual field design, of inverter efficiency calculations and the interaction between string arrangements and inverter selection.

  10. Accurate prediction of secondary metabolite gene clusters in filamentous fungi.

    Andersen, Mikael R; Nielsen, Jakob B; Klitgaard, Andreas; Petersen, Lene M; Zachariasen, Mia; Hansen, Tilde J; Blicher, Lene H; Gotfredsen, Charlotte H; Larsen, Thomas O; Nielsen, Kristian F; Mortensen, Uffe H

    2013-01-02

    Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify supporting enzymes for key synthases one cluster at a time. In this study, we design and apply a DNA expression array for Aspergillus nidulans in combination with legacy data to form a comprehensive gene expression compendium. We apply a guilt-by-association-based analysis to predict the extent of the biosynthetic clusters for the 58 synthases active in our set of experimental conditions. A comparison with legacy data shows the method to be accurate in 13 of 16 known clusters and nearly accurate for the remaining 3 clusters. Furthermore, we apply a data clustering approach, which identifies cross-chemistry between physically separate gene clusters (superclusters), and validate this both with legacy data and experimentally by prediction and verification of a supercluster consisting of the synthase AN1242 and the prenyltransferase AN11080, as well as identification of the product compound nidulanin A. We have used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom.

  11. Standardized EEG interpretation accurately predicts prognosis after cardiac arrest

    Rossetti, Andrea O.; van Rootselaar, Anne-Fleur; Wesenberg Kjaer, Troels; Horn, Janneke; Ullén, Susann; Friberg, Hans; Nielsen, Niklas; Rosén, Ingmar; Åneman, Anders; Erlinge, David; Gasche, Yvan; Hassager, Christian; Hovdenes, Jan; Kjaergaard, Jesper; Kuiper, Michael; Pellis, Tommaso; Stammet, Pascal; Wanscher, Michael; Wetterslev, Jørn; Wise, Matt P.; Cronberg, Tobias

    2016-01-01

    Objective: To identify reliable predictors of outcome in comatose patients after cardiac arrest using a single routine EEG and standardized interpretation according to the terminology proposed by the American Clinical Neurophysiology Society. Methods: In this cohort study, 4 EEG specialists, blinded to outcome, evaluated prospectively recorded EEGs in the Target Temperature Management trial (TTM trial) that randomized patients to 33°C vs 36°C. Routine EEG was performed in patients still comatose after rewarming. EEGs were classified into highly malignant (suppression, suppression with periodic discharges, burst-suppression), malignant (periodic or rhythmic patterns, pathological or nonreactive background), and benign EEG (absence of malignant features). Poor outcome was defined as best Cerebral Performance Category score 3–5 until 180 days. Results: Eight TTM sites randomized 202 patients. EEGs were recorded in 103 patients at a median 77 hours after cardiac arrest; 37% had a highly malignant EEG and all had a poor outcome (specificity 100%, sensitivity 50%). Any malignant EEG feature had a low specificity to predict poor prognosis (48%) but if 2 malignant EEG features were present specificity increased to 96% (p EEG was found in 1% of the patients with a poor outcome. Conclusions: Highly malignant EEG after rewarming reliably predicted poor outcome in half of patients without false predictions. An isolated finding of a single malignant feature did not predict poor outcome whereas a benign EEG was highly predictive of a good outcome. PMID:26865516

  12. Standardized EEG interpretation accurately predicts prognosis after cardiac arrest

    Westhall, Erik; Rossetti, Andrea O; van Rootselaar, Anne-Fleur;

    2016-01-01

    OBJECTIVE: To identify reliable predictors of outcome in comatose patients after cardiac arrest using a single routine EEG and standardized interpretation according to the terminology proposed by the American Clinical Neurophysiology Society. METHODS: In this cohort study, 4 EEG specialists...... patients. EEGs were recorded in 103 patients at a median 77 hours after cardiac arrest; 37% had a highly malignant EEG and all had a poor outcome (specificity 100%, sensitivity 50%). Any malignant EEG feature had a low specificity to predict poor prognosis (48%) but if 2 malignant EEG features were present...

  13. Simple Mathematical Models Do Not Accurately Predict Early SIV Dynamics

    Cecilia Noecker

    2015-03-01

    Full Text Available Upon infection of a new host, human immunodeficiency virus (HIV replicates in the mucosal tissues and is generally undetectable in circulation for 1–2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection. Mathematical models have been used to understand how HIV spreads from mucosal tissues systemically and what impact vaccination and/or antiretroviral prophylaxis has on viral eradication. Because predictions of such models have been rarely compared to experimental data, it remains unclear which processes included in these models are critical for predicting early HIV dynamics. Here we modified the “standard” mathematical model of HIV infection to include two populations of infected cells: cells that are actively producing the virus and cells that are transitioning into virus production mode. We evaluated the effects of several poorly known parameters on infection outcomes in this model and compared model predictions to experimental data on infection of non-human primates with variable doses of simian immunodifficiency virus (SIV. First, we found that the mode of virus production by infected cells (budding vs. bursting has a minimal impact on the early virus dynamics for a wide range of model parameters, as long as the parameters are constrained to provide the observed rate of SIV load increase in the blood of infected animals. Interestingly and in contrast with previous results, we found that the bursting mode of virus production generally results in a higher probability of viral extinction than the budding mode of virus production. Second, this mathematical model was not able to accurately describe the change in experimentally determined probability of host infection with increasing viral doses. Third and finally, the model was also unable to accurately explain the decline in the time to virus detection with increasing viral

  14. Artificial neural network accurately predicts hepatitis B surface antigen seroclearance.

    Ming-Hua Zheng

    Full Text Available BACKGROUND & AIMS: Hepatitis B surface antigen (HBsAg seroclearance and seroconversion are regarded as favorable outcomes of chronic hepatitis B (CHB. This study aimed to develop artificial neural networks (ANNs that could accurately predict HBsAg seroclearance or seroconversion on the basis of available serum variables. METHODS: Data from 203 untreated, HBeAg-negative CHB patients with spontaneous HBsAg seroclearance (63 with HBsAg seroconversion, and 203 age- and sex-matched HBeAg-negative controls were analyzed. ANNs and logistic regression models (LRMs were built and tested according to HBsAg seroclearance and seroconversion. Predictive accuracy was assessed with area under the receiver operating characteristic curve (AUROC. RESULTS: Serum quantitative HBsAg (qHBsAg and HBV DNA levels, qHBsAg and HBV DNA reduction were related to HBsAg seroclearance (P<0.001 and were used for ANN/LRM-HBsAg seroclearance building, whereas, qHBsAg reduction was not associated with ANN-HBsAg seroconversion (P = 0.197 and LRM-HBsAg seroconversion was solely based on qHBsAg (P = 0.01. For HBsAg seroclearance, AUROCs of ANN were 0.96, 0.93 and 0.95 for the training, testing and genotype B subgroups respectively. They were significantly higher than those of LRM, qHBsAg and HBV DNA (all P<0.05. Although the performance of ANN-HBsAg seroconversion (AUROC 0.757 was inferior to that for HBsAg seroclearance, it tended to be better than those of LRM, qHBsAg and HBV DNA. CONCLUSIONS: ANN identifies spontaneous HBsAg seroclearance in HBeAg-negative CHB patients with better accuracy, on the basis of easily available serum data. More useful predictors for HBsAg seroconversion are still needed to be explored in the future.

  15. Fast and accurate predictions of covalent bonds in chemical space

    Chang, K. Y. Samuel; Fias, Stijn; Ramakrishnan, Raghunathan; von Lilienfeld, O. Anatole

    2016-05-01

    We assess the predictive accuracy of perturbation theory based estimates of changes in covalent bonding due to linear alchemical interpolations among molecules. We have investigated σ bonding to hydrogen, as well as σ and π bonding between main-group elements, occurring in small sets of iso-valence-electronic molecules with elements drawn from second to fourth rows in the p-block of the periodic table. Numerical evidence suggests that first order Taylor expansions of covalent bonding potentials can achieve high accuracy if (i) the alchemical interpolation is vertical (fixed geometry), (ii) it involves elements from the third and fourth rows of the periodic table, and (iii) an optimal reference geometry is used. This leads to near linear changes in the bonding potential, resulting in analytical predictions with chemical accuracy (˜1 kcal/mol). Second order estimates deteriorate the prediction. If initial and final molecules differ not only in composition but also in geometry, all estimates become substantially worse, with second order being slightly more accurate than first order. The independent particle approximation based second order perturbation theory performs poorly when compared to the coupled perturbed or finite difference approach. Taylor series expansions up to fourth order of the potential energy curve of highly symmetric systems indicate a finite radius of convergence, as illustrated for the alchemical stretching of H 2+ . Results are presented for (i) covalent bonds to hydrogen in 12 molecules with 8 valence electrons (CH4, NH3, H2O, HF, SiH4, PH3, H2S, HCl, GeH4, AsH3, H2Se, HBr); (ii) main-group single bonds in 9 molecules with 14 valence electrons (CH3F, CH3Cl, CH3Br, SiH3F, SiH3Cl, SiH3Br, GeH3F, GeH3Cl, GeH3Br); (iii) main-group double bonds in 9 molecules with 12 valence electrons (CH2O, CH2S, CH2Se, SiH2O, SiH2S, SiH2Se, GeH2O, GeH2S, GeH2Se); (iv) main-group triple bonds in 9 molecules with 10 valence electrons (HCN, HCP, HCAs, HSiN, HSi

  16. An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions

    Xin Deng

    2015-07-01

    Full Text Available Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale.

  17. Accurate skin dose measurements using radiochromic film in clinical applications.

    Devic, S; Seuntjens, J; Abdel-Rahman, W; Evans, M; Olivares, M; Podgorsak, E B; Vuong, Té; Soares, Christopher G

    2006-04-01

    Megavoltage x-ray beams exhibit the well-known phenomena of dose buildup within the first few millimeters of the incident phantom surface, or the skin. Results of the surface dose measurements, however, depend vastly on the measurement technique employed. Our goal in this study was to determine a correction procedure in order to obtain an accurate skin dose estimate at the clinically relevant depth based on radiochromic film measurements. To illustrate this correction, we have used as a reference point a depth of 70 micron. We used the new GAFCHROMIC dosimetry films (HS, XR-T, and EBT) that have effective points of measurement at depths slightly larger than 70 micron. In addition to films, we also used an Attix parallel-plate chamber and a home-built extrapolation chamber to cover tissue-equivalent depths in the range from 4 micron to 1 mm of water-equivalent depth. Our measurements suggest that within the first millimeter of the skin region, the PDD for a 6 MV photon beam and field size of 10 x 10 cm2 increases from 14% to 43%. For the three GAFCHROMIC dosimetry film models, the 6 MV beam entrance skin dose measurement corrections due to their effective point of measurement are as follows: 15% for the EBT, 15% for the HS, and 16% for the XR-T model GAFCHROMIC films. The correction factors for the exit skin dose due to the build-down region are negligible. There is a small field size dependence for the entrance skin dose correction factor when using the EBT GAFCHROMIC film model. Finally, a procedure that uses EBT model GAFCHROMIC film for an accurate measurement of the skin dose in a parallel-opposed pair 6 MV photon beam arrangement is described.

  18. Is Three-Dimensional Soft Tissue Prediction by Software Accurate?

    Nam, Ki-Uk; Hong, Jongrak

    2015-11-01

    The authors assessed whether virtual surgery, performed with a soft tissue prediction program, could correctly simulate the actual surgical outcome, focusing on soft tissue movement. Preoperative and postoperative computed tomography (CT) data for 29 patients, who had undergone orthognathic surgery, were obtained and analyzed using the Simplant Pro software. The program made a predicted soft tissue image (A) based on presurgical CT data. After the operation, we obtained actual postoperative CT data and an actual soft tissue image (B) was generated. Finally, the 2 images (A and B) were superimposed and analyzed differences between the A and B. Results were grouped in 2 classes: absolute values and vector values. In the absolute values, the left mouth corner was the most significant error point (2.36 mm). The right mouth corner (2.28 mm), labrale inferius (2.08 mm), and the pogonion (2.03 mm) also had significant errors. In vector values, prediction of the right-left side had a left-sided tendency, the superior-inferior had a superior tendency, and the anterior-posterior showed an anterior tendency. As a result, with this program, the position of points tended to be located more left, anterior, and superior than the "real" situation. There is a need to improve the prediction accuracy for soft tissue images. Such software is particularly valuable in predicting craniofacial soft tissues landmarks, such as the pronasale. With this software, landmark positions were most inaccurate in terms of anterior-posterior predictions.

  19. Fast and accurate automatic structure prediction with HHpred.

    Hildebrand, Andrea; Remmert, Michael; Biegert, Andreas; Söding, Johannes

    2009-01-01

    Automated protein structure prediction is becoming a mainstream tool for biological research. This has been fueled by steady improvements of publicly available automated servers over the last decade, in particular their ability to build good homology models for an increasing number of targets by reliably detecting and aligning more and more remotely homologous templates. Here, we describe the three fully automated versions of the HHpred server that participated in the community-wide blind protein structure prediction competition CASP8. What makes HHpred unique is the combination of usability, short response times (typically under 15 min) and a model accuracy that is competitive with those of the best servers in CASP8.

  20. Predicting accurate absolute binding energies in aqueous solution

    Jensen, Jan Halborg

    2015-01-01

    Recent predictions of absolute binding free energies of host-guest complexes in aqueous solution using electronic structure theory have been encouraging for some systems, while other systems remain problematic. In this paper I summarize some of the many factors that could easily contribute 1-3 kcal......-represented by continuum models. While I focus on binding free energies in aqueous solution the approach also applies (with minor adjustments) to any free energy difference such as conformational or reaction free energy differences or activation free energies in any solvent....

  1. Objective criteria accurately predict amputation following lower extremity trauma.

    Johansen, K; Daines, M; Howey, T; Helfet, D; Hansen, S T

    1990-05-01

    MESS (Mangled Extremity Severity Score) is a simple rating scale for lower extremity trauma, based on skeletal/soft-tissue damage, limb ischemia, shock, and age. Retrospective analysis of severe lower extremity injuries in 25 trauma victims demonstrated a significant difference between MESS values for 17 limbs ultimately salvaged (mean, 4.88 +/- 0.27) and nine requiring amputation (mean, 9.11 +/- 0.51) (p less than 0.01). A prospective trial of MESS in lower extremity injuries managed at two trauma centers again demonstrated a significant difference between MESS values of 14 salvaged (mean, 4.00 +/- 0.28) and 12 doomed (mean, 8.83 +/- 0.53) limbs (p less than 0.01). In both the retrospective survey and the prospective trial, a MESS value greater than or equal to 7 predicted amputation with 100% accuracy. MESS may be useful in selecting trauma victims whose irretrievably injured lower extremities warrant primary amputation.

  2. Predicting accurate absolute binding energies in aqueous solution

    Jensen, Jan Halborg

    2015-01-01

    Recent predictions of absolute binding free energies of host-guest complexes in aqueous solution using electronic structure theory have been encouraging for some systems, while other systems remain problematic. In this paper I summarize some of the many factors that could easily contribute 1-3 kcal...... mol(-1) errors at 298 K: three-body dispersion effects, molecular symmetry, anharmonicity, spurious imaginary frequencies, insufficient conformational sampling, wrong or changing ionization states, errors in the solvation free energy of ions, and explicit solvent (and ion) effects that are not well......-represented by continuum models. While I focus on binding free energies in aqueous solution the approach also applies (with minor adjustments) to any free energy difference such as conformational or reaction free energy differences or activation free energies in any solvent....

  3. An accurate empirical correlation for predicting natural gas viscosity

    Ehsan Sanjari; Ebrahim Nemati Lay; Mohammad Peymani

    2011-01-01

    Natural gas viscosity is an important parameter in many gas and petroleum engineering calculations.This study presents a new empirical model for quickly calculating the natural gas viscosity.The model was derived from 4089 experimental viscosity data with varieties ranging from 0.01to 21,and 1 to 3 of pseudo reduced pressure and temperature,respectively.The accuracy of this new empirical correlation has been compared with commonly used empirical models,including Lee et al.,Heidaryan et al.,Carr et al.,and Adel Elsharkawy correlations.The comparison indicates that this new empirical model can predict viscosity of natural gas with average absolute relative deviation percentage AARD (%) of 2.173.

  4. Accurate theoretical prediction on positron lifetime of bulk materials

    Zhang, Wenshuai; Liu, Jiandang; Ye, Bangjiao

    2015-01-01

    Based on the first-principles calculations, we perform an initiatory statistical assessment on the reliability level of theoretical positron lifetime of bulk material. We found the original generalized gradient approximation (GGA) form of the enhancement factor and correlation potentials overestimates the effect of the gradient factor. Furthermore, an excellent agreement between model and data with the difference being the noise level of the data is found in this work. In addition, we suggest a new GGA form of the correlation scheme which gives the best performance. This work demonstrates that a brand-new reliability level is achieved for the theoretical prediction on positron lifetime of bulk material and the accuracy of the best theoretical scheme can be independent on the type of materials.

  5. Standardized EEG interpretation accurately predicts prognosis after cardiac arrest

    Westhall, Erik; Rossetti, Andrea O; van Rootselaar, Anne-Fleur

    2016-01-01

    OBJECTIVE: To identify reliable predictors of outcome in comatose patients after cardiac arrest using a single routine EEG and standardized interpretation according to the terminology proposed by the American Clinical Neurophysiology Society. METHODS: In this cohort study, 4 EEG specialists......, blinded to outcome, evaluated prospectively recorded EEGs in the Target Temperature Management trial (TTM trial) that randomized patients to 33°C vs 36°C. Routine EEG was performed in patients still comatose after rewarming. EEGs were classified into highly malignant (suppression, suppression...... with periodic discharges, burst-suppression), malignant (periodic or rhythmic patterns, pathological or nonreactive background), and benign EEG (absence of malignant features). Poor outcome was defined as best Cerebral Performance Category score 3-5 until 180 days. RESULTS: Eight TTM sites randomized 202...

  6. Change in BMI accurately predicted by social exposure to acquaintances.

    Rahman O Oloritun

    Full Text Available Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC and R(2. This study found a model that explains 68% (p<0.0001 of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as

  7. A machine learned classifier that uses gene expression data to accurately predict estrogen receptor status.

    Meysam Bastani

    Full Text Available BACKGROUND: Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. METHODS: To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. RESULTS: This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. CONCLUSIONS: Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions.

  8. Can radiation therapy treatment planning system accurately predict surface doses in postmastectomy radiation therapy patients?

    Wong, Sharon [National University of Singapore, Yong Loo Lin School of Medicine (Singapore); Back, Michael [Northern Sydney Cancer Centre, Royal North Shore Hospital, St Leonards, New South Wales (Australia); Tan, Poh Wee; Lee, Khai Mun; Baggarley, Shaun [National University, Cancer Institute, Department of Radiation Oncology, National University, Hospital, Tower Block (Singapore); Lu, Jaide Jay, E-mail: mdcljj@nus.edu.sg [National University of Singapore, Yong Loo Lin School of Medicine (Singapore); National University, Cancer Institute, Department of Radiation Oncology, National University, Hospital, Tower Block (Singapore)

    2012-07-01

    Skin doses have been an important factor in the dose prescription for breast radiotherapy. Recent advances in radiotherapy treatment techniques, such as intensity-modulated radiation therapy (IMRT) and new treatment schemes such as hypofractionated breast therapy have made the precise determination of the surface dose necessary. Detailed information of the dose at various depths of the skin is also critical in designing new treatment strategies. The purpose of this work was to assess the accuracy of surface dose calculation by a clinically used treatment planning system and those measured by thermoluminescence dosimeters (TLDs) in a customized chest wall phantom. This study involved the construction of a chest wall phantom for skin dose assessment. Seven TLDs were distributed throughout each right chest wall phantom to give adequate representation of measured radiation doses. Point doses from the CMS Xio Registered-Sign treatment planning system (TPS) were calculated for each relevant TLD positions and results correlated. There were no significant difference between measured absorbed dose by TLD and calculated doses by the TPS (p > 0.05 (1-tailed). Dose accuracy of up to 2.21% was found. The deviations from the calculated absorbed doses were overall larger (3.4%) when wedges and bolus were used. 3D radiotherapy TPS is a useful and accurate tool to assess the accuracy of surface dose. Our studies have shown that radiation treatment accuracy expressed as a comparison between calculated doses (by TPS) and measured doses (by TLD dosimetry) can be accurately predicted for tangential treatment of the chest wall after mastectomy.

  9. Accurate Prediction of One-Dimensional Protein Structure Features Using SPINE-X.

    Faraggi, Eshel; Kloczkowski, Andrzej

    2017-01-01

    Accurate prediction of protein secondary structure and other one-dimensional structure features is essential for accurate sequence alignment, three-dimensional structure modeling, and function prediction. SPINE-X is a software package to predict secondary structure as well as accessible surface area and dihedral angles ϕ and ψ. For secondary structure SPINE-X achieves an accuracy of between 81 and 84 % depending on the dataset and choice of tests. The Pearson correlation coefficient for accessible surface area prediction is 0.75 and the mean absolute error from the ϕ and ψ dihedral angles are 20(∘) and 33(∘), respectively. The source code and a Linux executables for SPINE-X are available from Research and Information Systems at http://mamiris.com .

  10. Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

    Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

    2016-07-01

    The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug.

  11. Mind-set and close relationships: when bias leads to (In)accurate predictions.

    Gagné, F M; Lydon, J E

    2001-07-01

    The authors investigated whether mind-set influences the accuracy of relationship predictions. Because people are more biased in their information processing when thinking about implementing an important goal, relationship predictions made in an implemental mind-set were expected to be less accurate than those made in a more impartial deliberative mind-set. In Study 1, open-ended thoughts of students about to leave for university were coded for mind-set. In Study 2, mind-set about a major life goal was assessed using a self-report measure. In Study 3, mind-set was experimentally manipulated. Overall, mind-set interacted with forecasts to predict relationship survival. Forecasts were more accurate in a deliberative mind-set than in an implemental mind-set. This effect was more pronounced for long-term than for short-term relationship survival. Finally, deliberatives were not pessimistic; implementals were unduly optimistic.

  12. Seizure semiology inferred from clinical descriptions and from video recordings. How accurate are they?

    Beniczky, Simona Alexandra; Fogarasi, András; Neufeld, Miri;

    2012-01-01

    To assess how accurate the interpretation of seizure semiology is when inferred from witnessed seizure descriptions and from video recordings, five epileptologists analyzed 41 seizures from 30 consecutive patients who had clinical episodes in the epilepsy monitoring unit. For each clinical episode...

  13. SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

    Chen Ke

    2008-05-01

    Full Text Available Abstract Background Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. Results SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is

  14. Rapid yet accurate first principle based predictions of alkali halide crystal phases using alchemical perturbation

    Solovyeva, Alisa

    2016-01-01

    We assess the predictive power of alchemical perturbations for estimating fundamental properties in ionic crystals. Using density functional theory we have calculated formation energies, lattice constants, and bulk moduli for all sixteen iso-valence-electronic combinations of pure pristine alkali halides involving elements $A \\in \\{$Na, K, Rb, Cs$\\}$ and $X \\in \\{$F, Cl, Br, I$\\}$. For rock salt, zincblende and cesium chloride symmetry, alchemical Hellmann-Feynman derivatives, evaluated along lattice scans of sixteen reference crystals, have been obtained for all respective 16$\\times$15 combinations of reference and predicted target crystals. Mean absolute errors (MAE) are on par with density functional theory level of accuracy for energies and bulk modulus. Predicted lattice constants are less accurate. NaCl is the best reference salt for alchemical estimates of relative energies (MAE $<$ 40 meV/atom) while alkali fluorides are the worst. By contrast, lattice constants are predicted best using NaF as a re...

  15. A New Method for Accurate Prediction of Ship’s Inertial Stopping Distance

    Langxiong Gan

    2013-10-01

    Full Text Available This study aims to research the prediction of ship’s inertial stopping distance. Accurate prediction of a ship’s inertial stopping distance helps the duty officers to make the collision avoidance decisions effectively. In this study ship’s inertial stopping distance is calculated using the ALE (Arbitrary Lagrangian Eulerian algorithm implemented in the FLUENT code. Firstly, a method for predicting the inertial stopping distance of a floating body based on the FLUENT code is established. Then, the results calculated by the method are compared with those obtained from the empirical formulae and the physical model tests. The comparison result indicates that the proposed method is robust and can be used effectively to predict the ship’s inertial stopping distance.

  16. PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif

    Tang, Shengnan; Li, Tonghua; Cong, Peisheng; Xiong, Wenwei; Wang, Zhiheng; Sun, Jiangming

    2013-01-01

    Knowledge of subcellular localizations (SCLs) of plant proteins relates to their functions and aids in understanding the regulation of biological processes at the cellular level. We present PlantLoc, a highly accurate and fast webserver for predicting the multi-label SCLs of plant proteins. The PlantLoc server has two innovative characters: building localization motif libraries by a recursive method without alignment and Gene Ontology information; and establishing simple architecture for rapi...

  17. Fast and Accurate Prediction of Stratified Steel Temperature During Holding Period of Ladle

    Deodhar, Anirudh; Singh, Umesh; Shukla, Rishabh; Gautham, B. P.; Singh, Amarendra K.

    2016-12-01

    Thermal stratification of liquid steel in a ladle during the holding period and the teeming operation has a direct bearing on the superheat available at the caster and hence on the caster set points such as casting speed and cooling rates. The changes in the caster set points are typically carried out based on temperature measurements at the end of tundish outlet. Thermal prediction models provide advance knowledge of the influence of process and design parameters on the steel temperature at various stages. Therefore, they can be used in making accurate decisions about the caster set points in real time. However, this requires both fast and accurate thermal prediction models. In this work, we develop a surrogate model for the prediction of thermal stratification using data extracted from a set of computational fluid dynamics (CFD) simulations, pre-determined using design of experiments technique. Regression method is used for training the predictor. The model predicts the stratified temperature profile instantaneously, for a given set of process parameters such as initial steel temperature, refractory heat content, slag thickness, and holding time. More than 96 pct of the predicted values are within an error range of ±5 K (±5 °C), when compared against corresponding CFD results. Considering its accuracy and computational efficiency, the model can be extended for thermal control of casting operations. This work also sets a benchmark for developing similar thermal models for downstream processes such as tundish and caster.

  18. How to Establish Clinical Prediction Models

    Yong-ho Lee

    2016-03-01

    Full Text Available A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice.

  19. A Single Linear Prediction Filter that Accurately Predicts the AL Index

    McPherron, R. L.; Chu, X.

    2015-12-01

    The AL index is a measure of the strength of the westward electrojet flowing along the auroral oval. It has two components: one from the global DP-2 current system and a second from the DP-1 current that is more localized near midnight. It is generally believed that the index a very poor measure of these currents because of its dependence on the distance of stations from the source of the two currents. In fact over season and solar cycle the coupling strength defined as the steady state ratio of the output AL to the input coupling function varies by a factor of four. There are four factors that lead to this variation. First is the equinoctial effect that modulates coupling strength with peaks (strongest coupling) at the equinoxes. Second is the saturation of the polar cap potential which decreases coupling strength as the strength of the driver increases. Since saturation occurs more frequently at solar maximum we obtain the result that maximum coupling strength occurs at equinox at solar minimum. A third factor is ionospheric conductivity with stronger coupling at summer solstice as compared to winter. The fourth factor is the definition of a solar wind coupling function appropriate to a given index. We have developed an optimum coupling function depending on solar wind speed, density, transverse magnetic field, and IMF clock angle which is better than previous functions. Using this we have determined the seasonal variation of coupling strength and developed an inverse function that modulates the optimum coupling function so that all seasonal variation is removed. In a similar manner we have determined the dependence of coupling strength on solar wind driver strength. The inverse of this function is used to scale a linear prediction filter thus eliminating the dependence on driver strength. Our result is a single linear filter that is adjusted in a nonlinear manner by driver strength and an optimum coupling function that is seasonal modulated. Together this

  20. Accurately Estimating the State of a Geophysical System with Sparse Observations: Predicting the Weather

    An, Zhe; Abarbanel, Henry D I

    2014-01-01

    Utilizing the information in observations of a complex system to make accurate predictions through a quantitative model when observations are completed at time $T$, requires an accurate estimate of the full state of the model at time $T$. When the number of measurements $L$ at each observation time within the observation window is larger than a sufficient minimum value $L_s$, the impediments in the estimation procedure are removed. As the number of available observations is typically such that $L \\ll L_s$, additional information from the observations must be presented to the model. We show how, using the time delays of the measurements at each observation time, one can augment the information transferred from the data to the model, removing the impediments to accurate estimation and permitting dependable prediction. We do this in a core geophysical fluid dynamics model, the shallow water equations, at the heart of numerical weather prediction. The method is quite general, however, and can be utilized in the a...

  1. More accurate recombination prediction in HIV-1 using a robust decoding algorithm for HMMs

    Brown Daniel G

    2011-05-01

    Full Text Available Abstract Background Identifying recombinations in HIV is important for studying the epidemiology of the virus and aids in the design of potential vaccines and treatments. The previous widely-used tool for this task uses the Viterbi algorithm in a hidden Markov model to model recombinant sequences. Results We apply a new decoding algorithm for this HMM that improves prediction accuracy. Exactly locating breakpoints is usually impossible, since different subtypes are highly conserved in some sequence regions. Our algorithm identifies these sites up to a certain error tolerance. Our new algorithm is more accurate in predicting the location of recombination breakpoints. Our implementation of the algorithm is available at http://www.cs.uwaterloo.ca/~jmtruszk/jphmm_balls.tar.gz. Conclusions By explicitly accounting for uncertainty in breakpoint positions, our algorithm offers more reliable predictions of recombination breakpoints in HIV-1. We also document a new domain of use for our new decoding approach in HMMs.

  2. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers

    Lundegaard, Claus; Lund, Ole; Nielsen, Morten

    2008-01-01

    Several accurate prediction systems have been developed for prediction of class I major histocompatibility complex (MHC):peptide binding. Most of these are trained on binding affinity data of primarily 9mer peptides. Here, we show how prediction methods trained on 9mer data can be used for accurate...

  3. Planar Near-Field Phase Retrieval Using GPUs for Accurate THz Far-Field Prediction

    Junkin, Gary

    2013-04-01

    With a view to using Phase Retrieval to accurately predict Terahertz antenna far-field from near-field intensity measurements, this paper reports on three fundamental advances that achieve very low algorithmic error penalties. The first is a new Gaussian beam analysis that provides accurate initial complex aperture estimates including defocus and astigmatic phase errors, based only on first and second moment calculations. The second is a powerful noise tolerant near-field Phase Retrieval algorithm that combines Anderson's Plane-to-Plane (PTP) with Fienup's Hybrid-Input-Output (HIO) and Successive Over-Relaxation (SOR) to achieve increased accuracy at reduced scan separations. The third advance employs teraflop Graphical Processing Units (GPUs) to achieve practically real time near-field phase retrieval and to obtain the optimum aperture constraint without any a priori information.

  4. A Novel Method for Accurate Operon Predictions in All SequencedProkaryotes

    Price, Morgan N.; Huang, Katherine H.; Alm, Eric J.; Arkin, Adam P.

    2004-12-01

    We combine comparative genomic measures and the distance separating adjacent genes to predict operons in 124 completely sequenced prokaryotic genomes. Our method automatically tailors itself to each genome using sequence information alone, and thus can be applied to any prokaryote. For Escherichia coli K12 and Bacillus subtilis, our method is 85 and 83% accurate, respectively, which is similar to the accuracy of methods that use the same features but are trained on experimentally characterized transcripts. In Halobacterium NRC-1 and in Helicobacterpylori, our method correctly infers that genes in operons are separated by shorter distances than they are in E.coli, and its predictions using distance alone are more accurate than distance-only predictions trained on a database of E.coli transcripts. We use microarray data from sixphylogenetically diverse prokaryotes to show that combining intergenic distance with comparative genomic measures further improves accuracy and that our method is broadly effective. Finally, we survey operon structure across 124 genomes, and find several surprises: H.pylori has many operons, contrary to previous reports; Bacillus anthracis has an unusual number of pseudogenes within conserved operons; and Synechocystis PCC6803 has many operons even though it has unusually wide spacings between conserved adjacent genes.

  5. The Clinical Prediction of Dangerousness.

    1985-05-01

    executive potential, psychopathy , suicidality and so forth. Unfor- tunately, this is not the case. There tend to be substantial dif- ferences among...Prediction from case material to personality data. New York Archives of Psychology, 29 (No. 207). Hare, R. D. (1970). Psychopathy : Theory and research. New...1967). Psychopathy , mental deficiency, aggressiveness, and the XYY syndrome. Nature, 214, (5087), 500-501. Wexler, D. (1979). Patients, therapists

  6. LogGPO: An accurate communication model for performance prediction of MPI programs

    CHEN WenGuang; ZHAI JiDong; ZHANG Jin; ZHENG WeiMin

    2009-01-01

    Message passing interface (MPI) is the de facto standard in writing parallel scientific applications on distributed memory systems. Performance prediction of MPI programs on current or future parallel sys-terns can help to find system bottleneck or optimize programs. To effectively analyze and predict per-formance of a large and complex MPI program, an efficient and accurate communication model is highly needed. A series of communication models have been proposed, such as the LogP model family, which assume that the sending overhead, message transmission, and receiving overhead of a communication is not overlapped and there is a maximum overlap degree between computation and communication. However, this assumption does not always hold for MPI programs because either sending or receiving overhead introduced by MPI implementations can decrease potential overlap for large messages. In this paper, we present a new communication model, named LogGPO, which captures the potential overlap between computation with communication of MPI programs. We design and implement a trace-driven simulator to verify the LogGPO model by predicting performance of point-to-point communication and two real applications CG and Sweep3D. The average prediction errors of LogGPO model are 2.4% and 2.0% for these two applications respectively, while the average prediction errors of LogGP model are 38.3% and 9.1% respectively.

  7. Special purpose hybrid transfinite elements and unified computational methodology for accurately predicting thermoelastic stress waves

    Tamma, Kumar K.; Railkar, Sudhir B.

    1988-01-01

    This paper represents an attempt to apply extensions of a hybrid transfinite element computational approach for accurately predicting thermoelastic stress waves. The applicability of the present formulations for capturing the thermal stress waves induced by boundary heating for the well known Danilovskaya problems is demonstrated. A unique feature of the proposed formulations for applicability to the Danilovskaya problem of thermal stress waves in elastic solids lies in the hybrid nature of the unified formulations and the development of special purpose transfinite elements in conjunction with the classical Galerkin techniques and transformation concepts. Numerical test cases validate the applicability and superior capability to capture the thermal stress waves induced due to boundary heating.

  8. The MIDAS touch for Accurately Predicting the Stress-Strain Behavior of Tantalum

    Jorgensen, S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2016-03-02

    Testing the behavior of metals in extreme environments is not always feasible, so material scientists use models to try and predict the behavior. To achieve accurate results it is necessary to use the appropriate model and material-specific parameters. This research evaluated the performance of six material models available in the MIDAS database [1] to determine at which temperatures and strain-rates they perform best, and to determine to which experimental data their parameters were optimized. Additionally, parameters were optimized for the Johnson-Cook model using experimental data from Lassila et al [2].

  9. An accurate and efficient numerical framework for adaptive numerical weather prediction

    Tumolo, G

    2014-01-01

    We present an accurate and efficient discretization approach for the adaptive discretization of typical model equations employed in numerical weather prediction. A semi-Lagrangian approach is combined with the TR-BDF2 semi-implicit time discretization method and with a spatial discretization based on adaptive discontinuous finite elements. The resulting method has full second order accuracy in time and can employ polynomial bases of arbitrarily high degree in space, is unconditionally stable and can effectively adapt the number of degrees of freedom employed in each element, in order to balance accuracy and computational cost. The p-adaptivity approach employed does not require remeshing, therefore it is especially suitable for applications, such as numerical weather prediction, in which a large number of physical quantities are associated with a given mesh. Furthermore, although the proposed method can be implemented on arbitrary unstructured and nonconforming meshes, even its application on simple Cartesian...

  10. Physical modeling of real-world slingshots for accurate speed predictions

    Yeats, Bob

    2016-01-01

    We discuss the physics and modeling of latex-rubber slingshots. The goal is to get accurate speed predictions inspite of the significant real world difficulties of force drift, force hysteresis, rubber ageing, and the very non- linear, non-ideal, force vs. pull distance curves of slingshot rubber bands. Slingshots are known to shoot faster under some circumstances when the bands are tapered rather than having constant width and stiffness. We give both qualitative understanding and numerical predictions of this effect. We consider two models. The first is based on conservation of energy and is easier to implement, but cannot determine the speeds along the rubber bands without making assumptions. The second, treats the bands as a series of mass points subject to being pulled by immediately adjacent mass points according to how much the rubber has been stretched on the two adjacent sides. This is a classic many-body F=ma problem but convergence requires using a particular numerical technique. It gives accurate p...

  11. Easy-to-use, general, and accurate multi-Kinect calibration and its application to gait monitoring for fall prediction.

    Staranowicz, Aaron N; Ray, Christopher; Mariottini, Gian-Luca

    2015-01-01

    Falls are the most-common causes of unintentional injury and death in older adults. Many clinics, hospitals, and health-care providers are urgently seeking accurate, low-cost, and easy-to-use technology to predict falls before they happen, e.g., by monitoring the human walking pattern (or "gait"). Despite the wide popularity of Microsoft's Kinect and the plethora of solutions for gait monitoring, no strategy has been proposed to date to allow non-expert users to calibrate the cameras, which is essential to accurately fuse the body motion observed by each camera in a single frame of reference. In this paper, we present a novel multi-Kinect calibration algorithm that has advanced features when compared to existing methods: 1) is easy to use, 2) it can be used in any generic Kinect arrangement, and 3) it provides accurate calibration. Extensive real-world experiments have been conducted to validate our algorithm and to compare its performance against other multi-Kinect calibration approaches, especially to show the improved estimate of gait parameters. Finally, a MATLAB Toolbox has been made publicly available for the entire research community.

  12. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K.; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G.; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H.

    2017-01-01

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. PMID:27899623

  13. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H

    2017-01-09

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

  14. DYNAMICALLY EVOLVING CLINICAL PRACTICES AND IMPLICATIONS FOR PREDICTING MEDICAL DECISIONS

    CHEN, JONATHAN H; GOLDSTEIN, MARY K; ASCH, STEVEN M; ALTMAN, RUSS B

    2015-01-01

    Automatically data-mining clinical practice patterns from electronic health records (EHR) can enable prediction of future practices as a form of clinical decision support (CDS). Our objective is to determine the stability of learned clinical practice patterns over time and what implication this has when using varying longitudinal historical data sources towards predicting future decisions. We trained an association rule engine for clinical orders (e.g., labs, imaging, medications) using structured inpatient data from a tertiary academic hospital. Comparing top order associations per admission diagnosis from training data in 2009 vs. 2012, we find practice variability from unstable diagnoses with rank biased overlap (RBO)0.6. Predicting admission orders for future (2013) patients with associations trained on recent (2012) vs. older (2009) data improved accuracy evaluated by area under the receiver operating characteristic curve (ROC-AUC) 0.89 to 0.92, precision at ten (positive predictive value of the top ten predictions against actual orders) 30% to 37%, and weighted recall (sensitivity) at ten 2.4% to 13%, (P<10−10). Training with more longitudinal data (2009-2012) was no better than only using recent (2012) data. Secular trends in practice patterns likely explain why smaller but more recent training data is more accurate at predicting future practices. PMID:26776186

  15. ILT based defect simulation of inspection images accurately predicts mask defect printability on wafer

    Deep, Prakash; Paninjath, Sankaranarayanan; Pereira, Mark; Buck, Peter

    2016-05-01

    At advanced technology nodes mask complexity has been increased because of large-scale use of resolution enhancement technologies (RET) which includes Optical Proximity Correction (OPC), Inverse Lithography Technology (ILT) and Source Mask Optimization (SMO). The number of defects detected during inspection of such mask increased drastically and differentiation of critical and non-critical defects are more challenging, complex and time consuming. Because of significant defectivity of EUVL masks and non-availability of actinic inspection, it is important and also challenging to predict the criticality of defects for printability on wafer. This is one of the significant barriers for the adoption of EUVL for semiconductor manufacturing. Techniques to decide criticality of defects from images captured using non actinic inspection images is desired till actinic inspection is not available. High resolution inspection of photomask images detects many defects which are used for process and mask qualification. Repairing all defects is not practical and probably not required, however it's imperative to know which defects are severe enough to impact wafer before repair. Additionally, wafer printability check is always desired after repairing a defect. AIMSTM review is the industry standard for this, however doing AIMSTM review for all defects is expensive and very time consuming. Fast, accurate and an economical mechanism is desired which can predict defect printability on wafer accurately and quickly from images captured using high resolution inspection machine. Predicting defect printability from such images is challenging due to the fact that the high resolution images do not correlate with actual mask contours. The challenge is increased due to use of different optical condition during inspection other than actual scanner condition, and defects found in such images do not have correlation with actual impact on wafer. Our automated defect simulation tool predicts

  16. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

    Li, Zhen; Zhang, Renyu

    2017-01-01

    Motivation Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. Method This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Results Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact

  17. Genetic crossovers are predicted accurately by the computed human recombination map.

    Pavel P Khil

    2010-01-01

    Full Text Available Hotspots of meiotic recombination can change rapidly over time. This instability and the reported high level of inter-individual variation in meiotic recombination puts in question the accuracy of the calculated hotspot map, which is based on the summation of past genetic crossovers. To estimate the accuracy of the computed recombination rate map, we have mapped genetic crossovers to a median resolution of 70 Kb in 10 CEPH pedigrees. We then compared the positions of crossovers with the hotspots computed from HapMap data and performed extensive computer simulations to compare the observed distributions of crossovers with the distributions expected from the calculated recombination rate maps. Here we show that a population-averaged hotspot map computed from linkage disequilibrium data predicts well present-day genetic crossovers. We find that computed hotspot maps accurately estimate both the strength and the position of meiotic hotspots. An in-depth examination of not-predicted crossovers shows that they are preferentially located in regions where hotspots are found in other populations. In summary, we find that by combining several computed population-specific maps we can capture the variation in individual hotspots to generate a hotspot map that can predict almost all present-day genetic crossovers.

  18. Intermolecular potentials and the accurate prediction of the thermodynamic properties of water

    Shvab, I.; Sadus, Richard J., E-mail: rsadus@swin.edu.au [Centre for Molecular Simulation, Swinburne University of Technology, PO Box 218, Hawthorn, Victoria 3122 (Australia)

    2013-11-21

    The ability of intermolecular potentials to correctly predict the thermodynamic properties of liquid water at a density of 0.998 g/cm{sup 3} for a wide range of temperatures (298–650 K) and pressures (0.1–700 MPa) is investigated. Molecular dynamics simulations are reported for the pressure, thermal pressure coefficient, thermal expansion coefficient, isothermal and adiabatic compressibilities, isobaric and isochoric heat capacities, and Joule-Thomson coefficient of liquid water using the non-polarizable SPC/E and TIP4P/2005 potentials. The results are compared with both experiment data and results obtained from the ab initio-based Matsuoka-Clementi-Yoshimine non-additive (MCYna) [J. Li, Z. Zhou, and R. J. Sadus, J. Chem. Phys. 127, 154509 (2007)] potential, which includes polarization contributions. The data clearly indicate that both the SPC/E and TIP4P/2005 potentials are only in qualitative agreement with experiment, whereas the polarizable MCYna potential predicts some properties within experimental uncertainty. This highlights the importance of polarizability for the accurate prediction of the thermodynamic properties of water, particularly at temperatures beyond 298 K.

  19. Intermolecular potentials and the accurate prediction of the thermodynamic properties of water

    Shvab, I.; Sadus, Richard J.

    2013-11-01

    The ability of intermolecular potentials to correctly predict the thermodynamic properties of liquid water at a density of 0.998 g/cm3 for a wide range of temperatures (298-650 K) and pressures (0.1-700 MPa) is investigated. Molecular dynamics simulations are reported for the pressure, thermal pressure coefficient, thermal expansion coefficient, isothermal and adiabatic compressibilities, isobaric and isochoric heat capacities, and Joule-Thomson coefficient of liquid water using the non-polarizable SPC/E and TIP4P/2005 potentials. The results are compared with both experiment data and results obtained from the ab initio-based Matsuoka-Clementi-Yoshimine non-additive (MCYna) [J. Li, Z. Zhou, and R. J. Sadus, J. Chem. Phys. 127, 154509 (2007)] potential, which includes polarization contributions. The data clearly indicate that both the SPC/E and TIP4P/2005 potentials are only in qualitative agreement with experiment, whereas the polarizable MCYna potential predicts some properties within experimental uncertainty. This highlights the importance of polarizability for the accurate prediction of the thermodynamic properties of water, particularly at temperatures beyond 298 K.

  20. Differential contribution of visual and auditory information to accurately predict the direction and rotational motion of a visual stimulus.

    Park, Seoung Hoon; Kim, Seonjin; Kwon, MinHyuk; Christou, Evangelos A

    2016-03-01

    Vision and auditory information are critical for perception and to enhance the ability of an individual to respond accurately to a stimulus. However, it is unknown whether visual and auditory information contribute differentially to identify the direction and rotational motion of the stimulus. The purpose of this study was to determine the ability of an individual to accurately predict the direction and rotational motion of the stimulus based on visual and auditory information. In this study, we recruited 9 expert table-tennis players and used table-tennis service as our experimental model. Participants watched recorded services with different levels of visual and auditory information. The goal was to anticipate the direction of the service (left or right) and the rotational motion of service (topspin, sidespin, or cut). We recorded their responses and quantified the following outcomes: (i) directional accuracy and (ii) rotational motion accuracy. The response accuracy was the accurate predictions relative to the total number of trials. The ability of the participants to predict the direction of the service accurately increased with additional visual information but not with auditory information. In contrast, the ability of the participants to predict the rotational motion of the service accurately increased with the addition of auditory information to visual information but not with additional visual information alone. In conclusion, this finding demonstrates that visual information enhances the ability of an individual to accurately predict the direction of the stimulus, whereas additional auditory information enhances the ability of an individual to accurately predict the rotational motion of stimulus.

  1. Accurate Mobility Modeling and Location Prediction Based on Pattern Analysis of Handover Series in Mobile Networks

    Péter Fülöp

    2009-01-01

    Full Text Available The efficient dimensioning of cellular wireless access networks depends highly on the accuracy of the underlying mathematical models of user distribution and traffic estimations. Mobility prediction also considered as an effective method contributing to the accuracy of IP multicast based multimedia transmissions, and ad hoc routing algorithms. In this paper we focus on the tradeoff between the accuracy and the complexity of the mathematical models used to describe user movements in the network. We propose mobility model extension, in order to utilize user's movement history thus providing more accurate results than other widely used models in the literature. The new models are applicable in real-life scenarios, because these rely on additional information effectively available in cellular networks (e.g. handover history, too. The complexity of the proposed models is analyzed, and the accuracy is justified by means of simulation.

  2. Fast and accurate prediction of numerical relativity waveforms from binary black hole mergers using surrogate models

    Blackman, Jonathan; Galley, Chad R; Szilagyi, Bela; Scheel, Mark A; Tiglio, Manuel; Hemberger, Daniel A

    2015-01-01

    Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. In this paper, we construct an accurate and fast-to-evaluate surrogate model for numerical relativity (NR) waveforms from non-spinning binary black hole coalescences with mass ratios from $1$ to $10$ and durations corresponding to about $15$ orbits before merger. Our surrogate, which is built using reduced order modeling techniques, is distinct from traditional modeling efforts. We find that the full multi-mode surrogate model agrees with waveforms generated by NR to within the numerical error of the NR code. In particular, we show that our modeling strategy produces surrogates which can correctly predict NR waveforms that were {\\em not} used for the surrogate's training. For all practical purposes, then, the surrogate waveform model is equivalent to the high-accuracy, large-scale simulation waveform but can be evaluated in a millisecond to a second dependin...

  3. Distance scaling method for accurate prediction of slowly varying magnetic fields in satellite missions

    Zacharias, Panagiotis P.; Chatzineofytou, Elpida G.; Spantideas, Sotirios T.; Capsalis, Christos N.

    2016-07-01

    In the present work, the determination of the magnetic behavior of localized magnetic sources from near-field measurements is examined. The distance power law of the magnetic field fall-off is used in various cases to accurately predict the magnetic signature of an equipment under test (EUT) consisting of multiple alternating current (AC) magnetic sources. Therefore, parameters concerning the location of the observation points (magnetometers) are studied towards this scope. The results clearly show that these parameters are independent of the EUT's size and layout. Additionally, the techniques developed in the present study enable the placing of the magnetometers close to the EUT, thus achieving high signal-to-noise ratio (SNR). Finally, the proposed method is verified by real measurements, using a mobile phone as an EUT.

  4. Mass transport and direction dependent battery modeling for accurate on-line power capability prediction

    Wiegman, H.L.N. [General Electric Corporate Research and Development, Schenectady, NY (United States)

    2000-07-01

    Some recent advances in battery modeling were discussed with reference to on-line impedance estimates and power performance predictions for aqueous solution, porous electrode cell structures. The objective was to determine which methods accurately estimate a battery's internal state and power capability while operating a charge and sustaining a hybrid electric vehicle (HEV) over a wide range of driving conditions. The enhancements to the Randles-Ershler equivalent electrical model of common cells with lead-acid, nickel-cadmium and nickel-metal hydride chemistries were described. This study also investigated which impedances are sensitive to boundary layer charge concentrations and mass transport limitations. Non-linear impedances were shown to significantly affect the battery's ability to process power. The main advantage of on-line estimating a battery's impedance state and power capability is that the battery can be optimally sized for any application. refs., tabs., figs., append.

  5. In vitro transcription accurately predicts lac repressor phenotype in vivo in Escherichia coli

    Matthew Almond Sochor

    2014-07-01

    Full Text Available A multitude of studies have looked at the in vivo and in vitro behavior of the lac repressor binding to DNA and effector molecules in order to study transcriptional repression, however these studies are not always reconcilable. Here we use in vitro transcription to directly mimic the in vivo system in order to build a self consistent set of experiments to directly compare in vivo and in vitro genetic repression. A thermodynamic model of the lac repressor binding to operator DNA and effector is used to link DNA occupancy to either normalized in vitro mRNA product or normalized in vivo fluorescence of a regulated gene, YFP. An accurate measurement of repressor, DNA and effector concentrations were made both in vivo and in vitro allowing for direct modeling of the entire thermodynamic equilibrium. In vivo repression profiles are accurately predicted from the given in vitro parameters when molecular crowding is considered. Interestingly, our measured repressor–operator DNA affinity differs significantly from previous in vitro measurements. The literature values are unable to replicate in vivo binding data. We therefore conclude that the repressor-DNA affinity is much weaker than previously thought. This finding would suggest that in vitro techniques that are specifically designed to mimic the in vivo process may be necessary to replicate the native system.

  6. Exchange-Hole Dipole Dispersion Model for Accurate Energy Ranking in Molecular Crystal Structure Prediction.

    Whittleton, Sarah R; Otero-de-la-Roza, A; Johnson, Erin R

    2017-02-14

    Accurate energy ranking is a key facet to the problem of first-principles crystal-structure prediction (CSP) of molecular crystals. This work presents a systematic assessment of B86bPBE-XDM, a semilocal density functional combined with the exchange-hole dipole moment (XDM) dispersion model, for energy ranking using 14 compounds from the first five CSP blind tests. Specifically, the set of crystals studied comprises 11 rigid, planar compounds and 3 co-crystals. The experimental structure was correctly identified as the lowest in lattice energy for 12 of the 14 total crystals. One of the exceptions is 4-hydroxythiophene-2-carbonitrile, for which the experimental structure was correctly identified once a quasi-harmonic estimate of the vibrational free-energy contribution was included, evidencing the occasional importance of thermal corrections for accurate energy ranking. The other exception is an organic salt, where charge-transfer error (also called delocalization error) is expected to cause the base density functional to be unreliable. Provided the choice of base density functional is appropriate and an estimate of temperature effects is used, XDM-corrected density-functional theory is highly reliable for the energetic ranking of competing crystal structures.

  7. Measuring solar reflectance Part I: Defining a metric that accurately predicts solar heat gain

    Levinson, Ronnen; Akbari, Hashem; Berdahl, Paul

    2010-05-14

    Solar reflectance can vary with the spectral and angular distributions of incident sunlight, which in turn depend on surface orientation, solar position and atmospheric conditions. A widely used solar reflectance metric based on the ASTM Standard E891 beam-normal solar spectral irradiance underestimates the solar heat gain of a spectrally selective 'cool colored' surface because this irradiance contains a greater fraction of near-infrared light than typically found in ordinary (unconcentrated) global sunlight. At mainland U.S. latitudes, this metric RE891BN can underestimate the annual peak solar heat gain of a typical roof or pavement (slope {le} 5:12 [23{sup o}]) by as much as 89 W m{sup -2}, and underestimate its peak surface temperature by up to 5 K. Using R{sub E891BN} to characterize roofs in a building energy simulation can exaggerate the economic value N of annual cool-roof net energy savings by as much as 23%. We define clear-sky air mass one global horizontal ('AM1GH') solar reflectance R{sub g,0}, a simple and easily measured property that more accurately predicts solar heat gain. R{sub g,0} predicts the annual peak solar heat gain of a roof or pavement to within 2 W m{sup -2}, and overestimates N by no more than 3%. R{sub g,0} is well suited to rating the solar reflectances of roofs, pavements and walls. We show in Part II that R{sub g,0} can be easily and accurately measured with a pyranometer, a solar spectrophotometer or version 6 of the Solar Spectrum Reflectometer.

  8. Predicting Clinical Outcomes Using Molecular Biomarkers.

    Burke, Harry B

    2016-01-01

    Over the past 20 years, there has been an exponential increase in the number of biomarkers. At the last count, there were 768,259 papers indexed in PubMed.gov directly related to biomarkers. Although many of these papers claim to report clinically useful molecular biomarkers, embarrassingly few are currently in clinical use. It is suggested that a failure to properly understand, clinically assess, and utilize molecular biomarkers has prevented their widespread adoption in treatment, in comparative benefit analyses, and their integration into individualized patient outcome predictions for clinical decision-making and therapy. A straightforward, general approach to understanding how to predict clinical outcomes using risk, diagnostic, and prognostic molecular biomarkers is presented. In the future, molecular biomarkers will drive advances in risk, diagnosis, and prognosis, they will be the targets of powerful molecular therapies, and they will individualize and optimize therapy. Furthermore, clinical predictions based on molecular biomarkers will be displayed on the clinician's screen during the physician-patient interaction, they will be an integral part of physician-patient-shared decision-making, and they will improve clinical care and patient outcomes.

  9. Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics

    Zheng-Wei Li

    2016-08-01

    Full Text Available Protein-protein interactions (PPIs occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research.

  10. Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics.

    Li, Zheng-Wei; You, Zhu-Hong; Chen, Xing; Gui, Jie; Nie, Ru

    2016-01-01

    Protein-protein interactions (PPIs) occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM) classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori) datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research.

  11. A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

    Alanazi, Hamdan O; Abdullah, Abdul Hanan; Qureshi, Kashif Naseer

    2017-04-01

    Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

  12. Accurate load prediction by BEM with airfoil data from 3D RANS simulations

    Schneider, Marc S.; Nitzsche, Jens; Hennings, Holger

    2016-09-01

    In this paper, two methods for the extraction of airfoil coefficients from 3D CFD simulations of a wind turbine rotor are investigated, and these coefficients are used to improve the load prediction of a BEM code. The coefficients are extracted from a number of steady RANS simulations, using either averaging of velocities in annular sections, or an inverse BEM approach for determination of the induction factors in the rotor plane. It is shown that these 3D rotor polars are able to capture the rotational augmentation at the inner part of the blade as well as the load reduction by 3D effects close to the blade tip. They are used as input to a simple BEM code and the results of this BEM with 3D rotor polars are compared to the predictions of BEM with 2D airfoil coefficients plus common empirical corrections for stall delay and tip loss. While BEM with 2D airfoil coefficients produces a very different radial distribution of loads than the RANS simulation, the BEM with 3D rotor polars manages to reproduce the loads from RANS very accurately for a variety of load cases, as long as the blade pitch angle is not too different from the cases from which the polars were extracted.

  13. ChIP-seq Accurately Predicts Tissue-Specific Activity of Enhancers

    Visel, Axel; Blow, Matthew J.; Li, Zirong; Zhang, Tao; Akiyama, Jennifer A.; Holt, Amy; Plajzer-Frick, Ingrid; Shoukry, Malak; Wright, Crystal; Chen, Feng; Afzal, Veena; Ren, Bing; Rubin, Edward M.; Pennacchio, Len A.

    2009-02-01

    A major yet unresolved quest in decoding the human genome is the identification of the regulatory sequences that control the spatial and temporal expression of genes. Distant-acting transcriptional enhancers are particularly challenging to uncover since they are scattered amongst the vast non-coding portion of the genome. Evolutionary sequence constraint can facilitate the discovery of enhancers, but fails to predict when and where they are active in vivo. Here, we performed chromatin immunoprecipitation with the enhancer-associated protein p300, followed by massively-parallel sequencing, to map several thousand in vivo binding sites of p300 in mouse embryonic forebrain, midbrain, and limb tissue. We tested 86 of these sequences in a transgenic mouse assay, which in nearly all cases revealed reproducible enhancer activity in those tissues predicted by p300 binding. Our results indicate that in vivo mapping of p300 binding is a highly accurate means for identifying enhancers and their associated activities and suggest that such datasets will be useful to study the role of tissue-specific enhancers in human biology and disease on a genome-wide scale.

  14. Simplified versus geometrically accurate models of forefoot anatomy to predict plantar pressures: A finite element study.

    Telfer, Scott; Erdemir, Ahmet; Woodburn, James; Cavanagh, Peter R

    2016-01-25

    Integration of patient-specific biomechanical measurements into the design of therapeutic footwear has been shown to improve clinical outcomes in patients with diabetic foot disease. The addition of numerical simulations intended to optimise intervention design may help to build on these advances, however at present the time and labour required to generate and run personalised models of foot anatomy restrict their routine clinical utility. In this study we developed second-generation personalised simple finite element (FE) models of the forefoot with varying geometric fidelities. Plantar pressure predictions from barefoot, shod, and shod with insole simulations using simplified models were compared to those obtained from CT-based FE models incorporating more detailed representations of bone and tissue geometry. A simplified model including representations of metatarsals based on simple geometric shapes, embedded within a contoured soft tissue block with outer geometry acquired from a 3D surface scan was found to provide pressure predictions closest to the more complex model, with mean differences of 13.3kPa (SD 13.4), 12.52kPa (SD 11.9) and 9.6kPa (SD 9.3) for barefoot, shod, and insole conditions respectively. The simplified model design could be produced in 3h in the case of the more detailed model, and solved on average 24% faster. FE models of the forefoot based on simplified geometric representations of the metatarsal bones and soft tissue surface geometry from 3D surface scans may potentially provide a simulation approach with improved clinical utility, however further validity testing around a range of therapeutic footwear types is required.

  15. Radiogenomics: predicting clinical normal tissue radiosensitivity

    Alsner, Jan

    2006-01-01

    of subcutaneous fibrosis in breast cancer patients will be presented and discussed in relation to possible future studies in radiogenomics. One important and necessary basis for future studies is the collection of carefully designed clinical studies with the accrual of very large numbers of patients (the ESTRO......Studies on the genetic basis of normal tissue radiosensitivity, or  'radiogenomics', aims at predicting clinical radiosensitivity and optimize treatment from individual genetic profiles. Several studies have now reported links between variations in certain genes related to the biological response...... to radiation injury and risk of normal tissue morbidity in cancer patients treated with radiotherapy. However, after these initial association studies including few genes, we are still far from being able to predict clinical radiosensitivity on an individual level. Recent data from our own studies on risk...

  16. Predicting accurate fluorescent spectra for high molecular weight polycyclic aromatic hydrocarbons using density functional theory

    Powell, Jacob; Heider, Emily C.; Campiglia, Andres; Harper, James K.

    2016-10-01

    The ability of density functional theory (DFT) methods to predict accurate fluorescence spectra for polycyclic aromatic hydrocarbons (PAHs) is explored. Two methods, PBE0 and CAM-B3LYP, are evaluated both in the gas phase and in solution. Spectra for several of the most toxic PAHs are predicted and compared to experiment, including three isomers of C24H14 and a PAH containing heteroatoms. Unusually high-resolution experimental spectra are obtained for comparison by analyzing each PAH at 4.2 K in an n-alkane matrix. All theoretical spectra visually conform to the profiles of the experimental data but are systematically offset by a small amount. Specifically, when solvent is included the PBE0 functional overestimates peaks by 16.1 ± 6.6 nm while CAM-B3LYP underestimates the same transitions by 14.5 ± 7.6 nm. These calculated spectra can be empirically corrected to decrease the uncertainties to 6.5 ± 5.1 and 5.7 ± 5.1 nm for the PBE0 and CAM-B3LYP methods, respectively. A comparison of computed spectra in the gas phase indicates that the inclusion of n-octane shifts peaks by +11 nm on average and this change is roughly equivalent for PBE0 and CAM-B3LYP. An automated approach for comparing spectra is also described that minimizes residuals between a given theoretical spectrum and all available experimental spectra. This approach identifies the correct spectrum in all cases and excludes approximately 80% of the incorrect spectra, demonstrating that an automated search of theoretical libraries of spectra may eventually become feasible.

  17. Towards accurate cosmological predictions for rapidly oscillating scalar fields as dark matter

    Ureña-López, L Arturo

    2015-01-01

    As we are entering the era of precision cosmology, it is necessary to count on accurate cosmological predictions from any proposed model of dark matter. In this paper we present a novel approach to the cosmological evolution of scalar fields that eases their analytic and numerical analysis at the background and at the linear order of perturbations. We apply the method to a scalar field endowed with a quadratic potential and revisit its properties as dark matter. Some of the results known in the literature are recovered, and a better understanding of the physical properties of the model is provided. It is shown that the Jeans wavenumber defined as $k_J = a \\sqrt{mH}$ is directly related to the suppression of linear perturbations at wavenumbers $k>k_J$. We also discuss some semi-analytical results that are well satisfied by the full numerical solutions obtained from an amended version of the CMB code CLASS. Finally we draw some of the implications that this new treatment of the equations of motion may have in t...

  18. Accurate prediction of DnaK-peptide binding via homology modelling and experimental data.

    Joost Van Durme

    2009-08-01

    Full Text Available Molecular chaperones are essential elements of the protein quality control machinery that governs translocation and folding of nascent polypeptides, refolding and degradation of misfolded proteins, and activation of a wide range of client proteins. The prokaryotic heat-shock protein DnaK is the E. coli representative of the ubiquitous Hsp70 family, which specializes in the binding of exposed hydrophobic regions in unfolded polypeptides. Accurate prediction of DnaK binding sites in E. coli proteins is an essential prerequisite to understand the precise function of this chaperone and the properties of its substrate proteins. In order to map DnaK binding sites in protein sequences, we have developed an algorithm that combines sequence information from peptide binding experiments and structural parameters from homology modelling. We show that this combination significantly outperforms either single approach. The final predictor had a Matthews correlation coefficient (MCC of 0.819 when assessed over the 144 tested peptide sequences to detect true positives and true negatives. To test the robustness of the learning set, we have conducted a simulated cross-validation, where we omit sequences from the learning sets and calculate the rate of repredicting them. This resulted in a surprisingly good MCC of 0.703. The algorithm was also able to perform equally well on a blind test set of binders and non-binders, of which there was no prior knowledge in the learning sets. The algorithm is freely available at http://limbo.vib.be.

  19. Cluster abundance in chameleon f(R) gravity I: toward an accurate halo mass function prediction

    Cataneo, Matteo; Rapetti, David; Lombriser, Lucas; Li, Baojiu

    2016-12-01

    We refine the mass and environment dependent spherical collapse model of chameleon f(R) gravity by calibrating a phenomenological correction inspired by the parameterized post-Friedmann framework against high-resolution N-body simulations. We employ our method to predict the corresponding modified halo mass function, and provide fitting formulas to calculate the enhancement of the f(R) halo abundance with respect to that of General Relativity (GR) within a precision of lesssim 5% from the results obtained in the simulations. Similar accuracy can be achieved for the full f(R) mass function on the condition that the modeling of the reference GR abundance of halos is accurate at the percent level. We use our fits to forecast constraints on the additional scalar degree of freedom of the theory, finding that upper bounds competitive with current Solar System tests are within reach of cluster number count analyses from ongoing and upcoming surveys at much larger scales. Importantly, the flexibility of our method allows also for this to be applied to other scalar-tensor theories characterized by a mass and environment dependent spherical collapse.

  20. Accurate prediction of band gaps and optical properties of HfO2

    Ondračka, Pavel; Holec, David; Nečas, David; Zajíčková, Lenka

    2016-10-01

    We report on optical properties of various polymorphs of hafnia predicted within the framework of density functional theory. The full potential linearised augmented plane wave method was employed together with the Tran-Blaha modified Becke-Johnson potential (TB-mBJ) for exchange and local density approximation for correlation. Unit cells of monoclinic, cubic and tetragonal crystalline, and a simulated annealing-based model of amorphous hafnia were fully relaxed with respect to internal positions and lattice parameters. Electronic structures and band gaps for monoclinic, cubic, tetragonal and amorphous hafnia were calculated using three different TB-mBJ parametrisations and the results were critically compared with the available experimental and theoretical reports. Conceptual differences between a straightforward comparison of experimental measurements to a calculated band gap on the one hand and to a whole electronic structure (density of electronic states) on the other hand, were pointed out, suggesting the latter should be used whenever possible. Finally, dielectric functions were calculated at two levels, using the random phase approximation without local field effects and with a more accurate Bethe-Salpether equation (BSE) to account for excitonic effects. We conclude that a satisfactory agreement with experimental data for HfO2 was obtained only in the latter case.

  1. Cluster abundance in chameleon $f(R)$ gravity I: toward an accurate halo mass function prediction

    Cataneo, Matteo; Lombriser, Lucas; Li, Baojiu

    2016-01-01

    We refine the mass and environment dependent spherical collapse model of chameleon $f(R)$ gravity by calibrating a phenomenological correction inspired by the parameterized post-Friedmann framework against high-resolution $N$-body simulations. We employ our method to predict the corresponding modified halo mass function, and provide fitting formulas to calculate the fractional enhancement of the $f(R)$ halo abundance with respect to that of General Relativity (GR) within a precision of $\\lesssim 5\\%$ from the results obtained in the simulations. Similar accuracy can be achieved for the full $f(R)$ mass function on the condition that the modeling of the reference GR abundance of halos is accurate at the percent level. We use our fits to forecast constraints on the additional scalar degree of freedom of the theory, finding that upper bounds competitive with current Solar System tests are within reach of cluster number count analyses from ongoing and upcoming surveys at much larger scales. Importantly, the flexi...

  2. Accurate and Simplified Prediction of AVF for Delay and Energy Efficient Cache Design

    An-Guo Ma; Yu Cheng; Zuo-Cheng Xing

    2011-01-01

    With continuous technology scaling, on-chip structures are becoming more and more susceptible to soft errors. Architectural vulnerability factor (AVF) has been introduced to quantify the architectural vulnerability of on-chip structures to soft errors. Recent studies have found that designing soft error protection techniques with the awareness of AVF is greatly helpful to achieve a tradeoff between performance and reliability for several structures (i.e., issue queue, reorder buffer). Cache is one of the most susceptible components to soft errors and is commonly protected with error correcting codes (ECC). However, protecting caches closer to the processor (i.e., L1 data cache (L1D)) using ECC could result in high overhead. Protecting caches without accurate knowledge of the vulnerability characteristics may lead to over-protection. Therefore, designing AVF-aware ECC is attractive for designers to balance among performance, power and reliability for cache, especially at early design stage. In this paper, we improve the methodology of cache AVF computation and develop a new AVF estimation framework, soft error reliability analysis based on SimpleScalar. Then we characterize dynamic vulnerability behavior of L1D and detect the correlations between LID AVF and various performance metrics. We propose to employ Bayesian additive regression trees to accurately model the variation of L1D AVF and to quantitatively explain the important effects of several key performance metrics on L1D AVF. Then, we employ bump hunting technique to reduce the complexity of L1D AVF prediction and extract some simple selecting rules based on several key performance metrics, thus enabling a simplified and fast estimation of L1D AVF. Based on the simplified and fast estimation of L1D AVF, intervals of high L1D AVF can be identified online, enabling us to develop the AVF-aware ECC technique to reduce the overhead of ECC. Experimental results show that compared with traditional ECC technique

  3. ACE-I Angioedema: Accurate Clinical Diagnosis May Prevent Epinephrine-Induced Harm

    R. Mason Curtis

    2016-06-01

    Full Text Available Introduction: Upper airway angioedema is a life-threatening emergency department (ED presentation with increasing incidence. Angiotensin-converting enzyme inhibitor induced angioedema (AAE is a non-mast cell mediated etiology of angioedema. Accurate diagnosis by clinical examination can optimize patient management and reduce morbidity from inappropriate treatment with epinephrine. The aim of this study is to describe the incidence of angioedema subtypes and the management of AAE. We evaluate the appropriateness of treatments and highlight preventable iatrogenic morbidity. Methods: We conducted a retrospective chart review of consecutive angioedema patients presenting to two tertiary care EDs between July 2007 and March 2012. Results: Of 1,702 medical records screened, 527 were included. The cause of angioedema was identified in 48.8% (n=257 of cases. The most common identifiable etiology was AAE (33.1%, n=85, with a 60.0% male predominance. The most common AAE management strategies included diphenhydramine (63.5%, n=54, corticosteroids (50.6%, n=43 and ranitidine (31.8%, n=27. Epinephrine was administered in 21.2% (n=18 of AAE patients, five of whom received repeated doses. Four AAE patients required admission (4.7% and one required endotracheal intubation. Epinephrine induced morbidity in two patients, causing myocardial ischemia or dysrhythmia shortly after administration. Conclusion: AAE is the most common identifiable etiology of angioedema and can be accurately diagnosed by physical examination. It is easily confused with anaphylaxis and mismanaged with antihistamines, corticosteroids and epinephrine. There is little physiologic rationale for epinephrine use in AAE and much risk. Improved clinical differentiation of mast cell and non-mast cell mediated angioedema can optimize patient management.

  4. Towards more accurate wind and solar power prediction by improving NWP model physics

    Steiner, Andrea; Köhler, Carmen; von Schumann, Jonas; Ritter, Bodo

    2014-05-01

    The growing importance and successive expansion of renewable energies raise new challenges for decision makers, economists, transmission system operators, scientists and many more. In this interdisciplinary field, the role of Numerical Weather Prediction (NWP) is to reduce the errors and provide an a priori estimate of remaining uncertainties associated with the large share of weather-dependent power sources. For this purpose it is essential to optimize NWP model forecasts with respect to those prognostic variables which are relevant for wind and solar power plants. An improved weather forecast serves as the basis for a sophisticated power forecasts. Consequently, a well-timed energy trading on the stock market, and electrical grid stability can be maintained. The German Weather Service (DWD) currently is involved with two projects concerning research in the field of renewable energy, namely ORKA*) and EWeLiNE**). Whereas the latter is in collaboration with the Fraunhofer Institute (IWES), the project ORKA is led by energy & meteo systems (emsys). Both cooperate with German transmission system operators. The goal of the projects is to improve wind and photovoltaic (PV) power forecasts by combining optimized NWP and enhanced power forecast models. In this context, the German Weather Service aims to improve its model system, including the ensemble forecasting system, by working on data assimilation, model physics and statistical post processing. This presentation is focused on the identification of critical weather situations and the associated errors in the German regional NWP model COSMO-DE. First steps leading to improved physical parameterization schemes within the NWP-model are presented. Wind mast measurements reaching up to 200 m height above ground are used for the estimation of the (NWP) wind forecast error at heights relevant for wind energy plants. One particular problem is the daily cycle in wind speed. The transition from stable stratification during

  5. The development and verification of a highly accurate collision prediction model for automated noncoplanar plan delivery

    Yu, Victoria Y.; Tran, Angelia; Nguyen, Dan; Cao, Minsong; Ruan, Dan; Low, Daniel A.; Sheng, Ke, E-mail: ksheng@mednet.ucla.edu [Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90024 (United States)

    2015-11-15

    attributed to phantom setup errors due to the slightly deformable and flexible phantom extremities. The estimated site-specific safety buffer distance with 0.001% probability of collision for (gantry-to-couch, gantry-to-phantom) was (1.23 cm, 3.35 cm), (1.01 cm, 3.99 cm), and (2.19 cm, 5.73 cm) for treatment to the head, lung, and prostate, respectively. Automated delivery to all three treatment sites was completed in 15 min and collision free using a digital Linac. Conclusions: An individualized collision prediction model for the purpose of noncoplanar beam delivery was developed and verified. With the model, the study has demonstrated the feasibility of predicting deliverable beams for an individual patient and then guiding fully automated noncoplanar treatment delivery. This work motivates development of clinical workflows and quality assurance procedures to allow more extensive use and automation of noncoplanar beam geometries.

  6. Accurate wavelength prediction of photonic crystal resonant reflection and applications in refractive index measurement

    Hermannsson, Pétur Gordon; Vannahme, Christoph; Smith, Cameron L. C.

    2014-01-01

    and superstrate materials. The importance of accounting for material dispersion in order to obtain accurate simulation results is highlighted, and a method for doing so using an iterative approach is demonstrated. Furthermore, an application for the model is demonstrated, in which the material dispersion...... of a liquid is extracted from measured resonance wavelengths....

  7. Improved Ecosystem Predictions of the California Current System via Accurate Light Calculations

    2011-09-30

    absorption , scatter, and backscatter coefficients) effects. However, once an accurate value of the scalar irradiance Eo(z,λ) has been computed to... photosynthesis . It is possible to compute PAR to the bottom of the euphotic zone in a fraction of a second of computer time, with errors of no more than a few

  8. Clinical predictive factors of pathologic tumor response

    Choi, Chi Hwan; Kim, Won Dong; Lee, Sang Jeon; Park, Woo Yoon [Chungbuk National University College of Medicine, Cheongju (Korea, Republic of)

    2012-09-15

    The aim of this study was to identify clinical predictive factors for tumor response after preoperative chemoradiotherapy (CRT) in rectal cancer. The study involved 51 patients who underwent preoperative CRT followed by surgery between January 2005 and February 2012. Radiotherapy was delivered to the whole pelvis at a dose of 45 Gy in 25 fractions, followed by a boost of 5.4 Gy in 3 fractions to the primary tumor with 5 fractions per week. Three different chemotherapy regimens were used. Tumor responses to preoperative CRT were assessed in terms of tumor downstaging and pathologic complete response (ypCR). Statistical analyses were performed to identify clinical factors associated with pathologic tumor response. Tumor downstaging was observed in 28 patients (54.9%), whereas ypCR was observed in 6 patients (11.8%). Multivariate analysis found that predictors of downstaging was pretreatment relative lymphocyte count (p = 0.023) and that none of clinical factors was significantly associated with ypCR. Pretreatment relative lymphocyte count (%) has a significant impact on the pathologic tumor response (tumor downstaging) after preoperative CRT for locally advanced rectal cancer. Enhancement of lymphocyte-mediated immune reactions may improve the effect of preoperative CRT for rectal cancer.

  9. Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction.

    Tatjana Braun

    2015-12-01

    Full Text Available Recent work has shown that the accuracy of ab initio structure prediction can be significantly improved by integrating evolutionary information in form of intra-protein residue-residue contacts. Following this seminal result, much effort is put into the improvement of contact predictions. However, there is also a substantial need to develop structure prediction protocols tailored to the type of restraints gained by contact predictions. Here, we present a structure prediction protocol that combines evolutionary information with the resolution-adapted structural recombination approach of Rosetta, called RASREC. Compared to the classic Rosetta ab initio protocol, RASREC achieves improved sampling, better convergence and higher robustness against incorrect distance restraints, making it the ideal sampling strategy for the stated problem. To demonstrate the accuracy of our protocol, we tested the approach on a diverse set of 28 globular proteins. Our method is able to converge for 26 out of the 28 targets and improves the average TM-score of the entire benchmark set from 0.55 to 0.72 when compared to the top ranked models obtained by the EVFold web server using identical contact predictions. Using a smaller benchmark, we furthermore show that the prediction accuracy of our method is only slightly reduced when the contact prediction accuracy is comparatively low. This observation is of special interest for protein sequences that only have a limited number of homologs.

  10. A machine learning approach to the accurate prediction of multi-leaf collimator positional errors

    Carlson, Joel N. K.; Park, Jong Min; Park, So-Yeon; In Park, Jong; Choi, Yunseok; Ye, Sung-Joon

    2016-03-01

    Discrepancies between planned and delivered movements of multi-leaf collimators (MLCs) are an important source of errors in dose distributions during radiotherapy. In this work we used machine learning techniques to train models to predict these discrepancies, assessed the accuracy of the model predictions, and examined the impact these errors have on quality assurance (QA) procedures and dosimetry. Predictive leaf motion parameters for the models were calculated from the plan files, such as leaf position and velocity, whether the leaf was moving towards or away from the isocenter of the MLC, and many others. Differences in positions between synchronized DICOM-RT planning files and DynaLog files reported during QA delivery were used as a target response for training of the models. The final model is capable of predicting MLC positions during delivery to a high degree of accuracy. For moving MLC leaves, predicted positions were shown to be significantly closer to delivered positions than were planned positions. By incorporating predicted positions into dose calculations in the TPS, increases were shown in gamma passing rates against measured dose distributions recorded during QA delivery. For instance, head and neck plans with 1%/2 mm gamma criteria had an average increase in passing rate of 4.17% (SD  =  1.54%). This indicates that the inclusion of predictions during dose calculation leads to a more realistic representation of plan delivery. To assess impact on the patient, dose volumetric histograms (DVH) using delivered positions were calculated for comparison with planned and predicted DVHs. In all cases, predicted dose volumetric parameters were in closer agreement to the delivered parameters than were the planned parameters, particularly for organs at risk on the periphery of the treatment area. By incorporating the predicted positions into the TPS, the treatment planner is given a more realistic view of the dose distribution as it will truly be

  11. Comparison of statistical and clinical predictions of functional outcome after ischemic stroke.

    Douglas D Thompson

    Full Text Available To determine whether the predictions of functional outcome after ischemic stroke made at the bedside using a doctor's clinical experience were more or less accurate than the predictions made by clinical prediction models (CPMs.A prospective cohort study of nine hundred and thirty one ischemic stroke patients recruited consecutively at the outpatient, inpatient and emergency departments of the Western General Hospital, Edinburgh between 2002 and 2005. Doctors made informal predictions of six month functional outcome on the Oxford Handicap Scale (OHS. Patients were followed up at six months with a validated postal questionnaire. For each patient we calculated the absolute predicted risk of death or dependence (OHS≥3 using five previously described CPMs. The specificity of a doctor's informal predictions of OHS≥3 at six months was good 0.96 (95% CI: 0.94 to 0.97 and similar to CPMs (range 0.94 to 0.96; however the sensitivity of both informal clinical predictions 0.44 (95% CI: 0.39 to 0.49 and clinical prediction models (range 0.38 to 0.45 was poor. The prediction of the level of disability after stroke was similar for informal clinical predictions (ordinal c-statistic 0.74 with 95% CI 0.72 to 0.76 and CPMs (range 0.69 to 0.75. No patient or clinician characteristic affected the accuracy of informal predictions, though predictions were more accurate in outpatients.CPMs are at least as good as informal clinical predictions in discriminating between good and bad functional outcome after ischemic stroke. The place of these models in clinical practice has yet to be determined.

  12. Accurate Prediction of the Ammonia Probes of a Variable Proton-to-Electron Mass Ratio

    Owens, Alec; Thiel, Walter; Špirko, Vladimir

    2015-01-01

    A comprehensive study of the mass sensitivity of the vibration-rotation-inversion transitions of $^{14}$NH$_3$, $^{15}$NH$_3$, $^{14}$ND$_3$, and $^{15}$ND$_3$ is carried out variationally using the TROVE approach. Variational calculations are robust and accurate, offering a new way to compute sensitivity coefficients. Particular attention is paid to the $\\Delta k=\\pm 3$ transitions between the accidentally coinciding rotation-inversion energy levels of the $\

  13. Enabling Computational Technologies for the Accurate Prediction/Description of Molecular Interactions in Condensed Phases

    2014-10-08

    models to compute accurately the molecular interactions between a mobile or stationary phase and a target substrate or analyte , which are fundamental...mobile or stationary phase and a target substrate or analyte , which are fundamental to diverse technologies, e.g., sensor or separation design. With...D. G., New Orleans, LA, April 9, 2013. 223rd Electrochemical Society Meeting, Continuum Solvation Models for Computational Electrochemistry

  14. Accurate microRNA target prediction correlates with protein repression levels

    Simossis Victor A

    2009-09-01

    Full Text Available Abstract Background MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease. Results DIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction. Conclusion Recently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at http://www.microrna.gr/microT

  15. Sensor Data Fusion for Accurate Cloud Presence Prediction Using Dempster-Shafer Evidence Theory

    Jesse S. Jin

    2010-10-01

    Full Text Available Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. This paper introduces a novel data fusion approach in a multiple radiation sensor environment using Dempster-Shafer evidence theory. The methodology is used to predict cloud presence based on the inputs of radiation sensors. Different radiation data have been used for the cloud prediction. The potential application areas of the algorithm include renewable power for virtual power station where the prediction of cloud presence is the most challenging issue for its photovoltaic output. The algorithm is validated by comparing the predicted cloud presence with the corresponding sunshine occurrence data that were recorded as the benchmark. Our experiments have indicated that comparing to the approaches using individual sensors, the proposed data fusion approach can increase correct rate of cloud prediction by ten percent, and decrease unknown rate of cloud prediction by twenty three percent.

  16. Sensor data fusion for accurate cloud presence prediction using Dempster-Shafer evidence theory.

    Li, Jiaming; Luo, Suhuai; Jin, Jesse S

    2010-01-01

    Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. This paper introduces a novel data fusion approach in a multiple radiation sensor environment using Dempster-Shafer evidence theory. The methodology is used to predict cloud presence based on the inputs of radiation sensors. Different radiation data have been used for the cloud prediction. The potential application areas of the algorithm include renewable power for virtual power station where the prediction of cloud presence is the most challenging issue for its photovoltaic output. The algorithm is validated by comparing the predicted cloud presence with the corresponding sunshine occurrence data that were recorded as the benchmark. Our experiments have indicated that comparing to the approaches using individual sensors, the proposed data fusion approach can increase correct rate of cloud prediction by ten percent, and decrease unknown rate of cloud prediction by twenty three percent.

  17. Clinical and MRI models predicting amyloid deposition in progressive aphasia and apraxia of speech.

    Whitwell, Jennifer L; Weigand, Stephen D; Duffy, Joseph R; Strand, Edythe A; Machulda, Mary M; Senjem, Matthew L; Gunter, Jeffrey L; Lowe, Val J; Jack, Clifford R; Josephs, Keith A

    2016-01-01

    Beta-amyloid (Aβ) deposition can be observed in primary progressive aphasia (PPA) and progressive apraxia of speech (PAOS). While it is typically associated with logopenic PPA, there are exceptions that make predicting Aβ status challenging based on clinical diagnosis alone. We aimed to determine whether MRI regional volumes or clinical data could help predict Aβ deposition. One hundred and thirty-nine PPA (n = 97; 15 agrammatic, 53 logopenic, 13 semantic and 16 unclassified) and PAOS (n = 42) subjects were prospectively recruited into a cross-sectional study and underwent speech/language assessments, 3.0 T MRI and C11-Pittsburgh Compound B PET. The presence of Aβ was determined using a 1.5 SUVR cut-point. Atlas-based parcellation was used to calculate gray matter volumes of 42 regions-of-interest across the brain. Penalized binary logistic regression was utilized to determine what combination of MRI regions, and what combination of speech and language tests, best predicts Aβ (+) status. The optimal MRI model and optimal clinical model both performed comparably in their ability to accurately classify subjects according to Aβ status. MRI accurately classified 81% of subjects using 14 regions. Small left superior temporal and inferior parietal volumes and large left Broca's area volumes were particularly predictive of Aβ (+) status. Clinical scores accurately classified 83% of subjects using 12 tests. Phonological errors and repetition deficits, and absence of agrammatism and motor speech deficits were particularly predictive of Aβ (+) status. In comparison, clinical diagnosis was able to accurately classify 89% of subjects. However, the MRI model performed well in predicting Aβ deposition in unclassified PPA. Clinical diagnosis provides optimum prediction of Aβ status at the group level, although regional MRI measurements and speech and language testing also performed well and could have advantages in predicting Aβ status in unclassified PPA subjects.

  18. Clinical and MRI models predicting amyloid deposition in progressive aphasia and apraxia of speech

    Jennifer L. Whitwell

    2016-01-01

    Full Text Available Beta-amyloid (Aβ deposition can be observed in primary progressive aphasia (PPA and progressive apraxia of speech (PAOS. While it is typically associated with logopenic PPA, there are exceptions that make predicting Aβ status challenging based on clinical diagnosis alone. We aimed to determine whether MRI regional volumes or clinical data could help predict Aβ deposition. One hundred and thirty-nine PPA (n = 97; 15 agrammatic, 53 logopenic, 13 semantic and 16 unclassified and PAOS (n = 42 subjects were prospectively recruited into a cross-sectional study and underwent speech/language assessments, 3.0 T MRI and C11-Pittsburgh Compound B PET. The presence of Aβ was determined using a 1.5 SUVR cut-point. Atlas-based parcellation was used to calculate gray matter volumes of 42 regions-of-interest across the brain. Penalized binary logistic regression was utilized to determine what combination of MRI regions, and what combination of speech and language tests, best predicts Aβ (+ status. The optimal MRI model and optimal clinical model both performed comparably in their ability to accurately classify subjects according to Aβ status. MRI accurately classified 81% of subjects using 14 regions. Small left superior temporal and inferior parietal volumes and large left Broca's area volumes were particularly predictive of Aβ (+ status. Clinical scores accurately classified 83% of subjects using 12 tests. Phonological errors and repetition deficits, and absence of agrammatism and motor speech deficits were particularly predictive of Aβ (+ status. In comparison, clinical diagnosis was able to accurately classify 89% of subjects. However, the MRI model performed well in predicting Aβ deposition in unclassified PPA. Clinical diagnosis provides optimum prediction of Aβ status at the group level, although regional MRI measurements and speech and language testing also performed well and could have advantages in predicting Aβ status in unclassified

  19. Toward accurate prediction of pKa values for internal protein residues: the importance of conformational relaxation and desolvation energy.

    Wallace, Jason A; Wang, Yuhang; Shi, Chuanyin; Pastoor, Kevin J; Nguyen, Bao-Linh; Xia, Kai; Shen, Jana K

    2011-12-01

    Proton uptake or release controls many important biological processes, such as energy transduction, virus replication, and catalysis. Accurate pK(a) prediction informs about proton pathways, thereby revealing detailed acid-base mechanisms. Physics-based methods in the framework of molecular dynamics simulations not only offer pK(a) predictions but also inform about the physical origins of pK(a) shifts and provide details of ionization-induced conformational relaxation and large-scale transitions. One such method is the recently developed continuous constant pH molecular dynamics (CPHMD) method, which has been shown to be an accurate and robust pK(a) prediction tool for naturally occurring titratable residues. To further examine the accuracy and limitations of CPHMD, we blindly predicted the pK(a) values for 87 titratable residues introduced in various hydrophobic regions of staphylococcal nuclease and variants. The predictions gave a root-mean-square deviation of 1.69 pK units from experiment, and there were only two pK(a)'s with errors greater than 3.5 pK units. Analysis of the conformational fluctuation of titrating side-chains in the context of the errors of calculated pK(a) values indicate that explicit treatment of conformational flexibility and the associated dielectric relaxation gives CPHMD a distinct advantage. Analysis of the sources of errors suggests that more accurate pK(a) predictions can be obtained for the most deeply buried residues by improving the accuracy in calculating desolvation energies. Furthermore, it is found that the generalized Born implicit-solvent model underlying the current CPHMD implementation slightly distorts the local conformational environment such that the inclusion of an explicit-solvent representation may offer improvement of accuracy.

  20. Hybrid exchange-correlation functional for accurate prediction of the electronic and structural properties of ferroelectric oxides

    D., I. Bilc; R., Orlando; R., Shaltaf; G., M. Rignanese; J., Íñiguez; Ph., Ghosez

    2008-01-01

    Using a linear combination of atomic orbitals approach, we report a systematic comparison of various Density Functional Theory (DFT) and hybrid exchange-correlation functionals for the prediction of the electronic and structural properties of prototypical ferroelectric oxides. It is found that none of the available functionals is able to provide, at the same time, accurate electronic and structural properties of the cubic and tetragonal phases of BaTiO$_3$ and PbTiO$_3$. Some, although not al...

  1. An endometrial gene expression signature accurately predicts recurrent implantation failure after IVF

    Koot, Yvonne E. M.; van Hooff, Sander R.; Boomsma, Carolien M.; van Leenen, Dik; Groot Koerkamp, Marian J. A.; Goddijn, Mariëtte; Eijkemans, Marinus J. C.; Fauser, Bart C. J. M.; Holstege, Frank C. P.; Macklon, Nick S.

    2016-01-01

    The primary limiting factor for effective IVF treatment is successful embryo implantation. Recurrent implantation failure (RIF) is a condition whereby couples fail to achieve pregnancy despite consecutive embryo transfers. Here we describe the collection of gene expression profiles from mid-luteal phase endometrial biopsies (n = 115) from women experiencing RIF and healthy controls. Using a signature discovery set (n = 81) we identify a signature containing 303 genes predictive of RIF. Independent validation in 34 samples shows that the gene signature predicts RIF with 100% positive predictive value (PPV). The strength of the RIF associated expression signature also stratifies RIF patients into distinct groups with different subsequent implantation success rates. Exploration of the expression changes suggests that RIF is primarily associated with reduced cellular proliferation. The gene signature will be of value in counselling and guiding further treatment of women who fail to conceive upon IVF and suggests new avenues for developing intervention. PMID:26797113

  2. Empirical approaches to more accurately predict benthic-pelagic coupling in biogeochemical ocean models

    Dale, Andy; Stolpovsky, Konstantin; Wallmann, Klaus

    2016-04-01

    The recycling and burial of biogenic material in the sea floor plays a key role in the regulation of ocean chemistry. Proper consideration of these processes in ocean biogeochemical models is becoming increasingly recognized as an important step in model validation and prediction. However, the rate of organic matter remineralization in sediments and the benthic flux of redox-sensitive elements are difficult to predict a priori. In this communication, examples of empirical benthic flux models that can be coupled to earth system models to predict sediment-water exchange in the open ocean are presented. Large uncertainties hindering further progress in this field include knowledge of the reactivity of organic carbon reaching the sediment, the importance of episodic variability in bottom water chemistry and particle rain rates (for both the deep-sea and margins) and the role of benthic fauna. How do we meet the challenge?

  3. Achieving accurate and efficient prediction of HVAC diaphragm noise at realistic Reynolds and Mach numbers

    Guilloud, G.; Schram, C.; Golliard, J.

    2009-01-01

    Despite the aeroacoustic expertise reached nowadays in air and ground transportation, energy sector or domestic appliances, reaching a decibel accuracy of an acoustic prediction for industrial cases is still challenging. Strong investments are made nowadays by oil and gas companies to determine and

  4. Safe surgery: how accurate are we at predicting intra-operative blood loss?

    2012-02-01

    Introduction Preoperative estimation of intra-operative blood loss by both anaesthetist and operating surgeon is a criterion of the World Health Organization\\'s surgical safety checklist. The checklist requires specific preoperative planning when anticipated blood loss is greater than 500 mL. The aim of this study was to assess the accuracy of surgeons and anaesthetists at predicting intra-operative blood loss. Methods A 6-week prospective study of intermediate and major operations in an academic medical centre was performed. An independent observer interviewed surgical and anaesthetic consultants and registrars, preoperatively asking each to predict expected blood loss in millilitre. Intra-operative blood loss was measured and compared with these predictions. Parameters including the use of anticoagulation and anti-platelet therapy as well as intra-operative hypothermia and hypotension were recorded. Results One hundred sixty-eight operations were included in the study, including 142 elective and 26 emergency operations. Blood loss was predicted to within 500 mL of measured blood loss in 89% of cases. Consultant surgeons tended to underestimate blood loss, doing so in 43% of all cases, while consultant anaesthetists were more likely to overestimate (60% of all operations). Twelve patients (7%) had underestimation of blood loss of more than 500 mL by both surgeon and anaesthetist. Thirty per cent (n = 6\\/20) of patients requiring transfusion of a blood product within 24 hours of surgery had blood loss underestimated by more than 500 mL by both surgeon and anaesthetist. There was no significant difference in prediction between patients on anti-platelet or anticoagulation therapy preoperatively and those not on the said therapies. Conclusion Predicted intra-operative blood loss was within 500 mL of measured blood loss in 89% of operations. In 30% of patients who ultimately receive a blood transfusion, both the surgeon and anaesthetist significantly underestimate

  5. Accurate Prediction of Phase Transitions in Compressed Sensing via a Connection to Minimax Denoising

    Donoho, David; Montanari, Andrea

    2011-01-01

    Compressed sensing posits that, within limits, one can undersample a sparse signal and yet reconstruct it accurately. Knowing the precise limits to such undersampling is important both for theory and practice. We present a formula precisely delineating the allowable degree of of undersampling of generalized sparse objects. The formula applies to Approximate Message Passing (AMP) algorithms for compressed sensing, which are here generalized to employ denoising operators besides the traditional scalar shrinkers (soft thresholding, positive soft thresholding and capping). This paper gives several examples including scalar shrinkers not derivable from convex optimization -- the firm shrinkage nonlinearity and the minimax} nonlinearity -- and also nonscalar denoisers -- block thresholding (both block soft and block James-Stein), monotone regression, and total variation minimization. Let the variables \\epsilon = k/N and \\delta = n/N denote the generalized sparsity and undersampling fractions for sampling the k-gene...

  6. An Integrative Pathway-based Clinical-genomic Model for Cancer Survival Prediction.

    Chen, Xi; Wang, Lily; Ishwaran, Hemant

    2010-09-01

    Prediction models that use gene expression levels are now being proposed for personalized treatment of cancer, but building accurate models that are easy to interpret remains a challenge. In this paper, we describe an integrative clinical-genomic approach that combines both genomic pathway and clinical information. First, we summarize information from genes in each pathway using Supervised Principal Components (SPCA) to obtain pathway-based genomic predictors. Next, we build a prediction model based on clinical variables and pathway-based genomic predictors using Random Survival Forests (RSF). Our rationale for this two-stage procedure is that the underlying disease process may be influenced by environmental exposure (measured by clinical variables) and perturbations in different pathways (measured by pathway-based genomic variables), as well as their interactions. Using two cancer microarray datasets, we show that the pathway-based clinical-genomic model outperforms gene-based clinical-genomic models, with improved prediction accuracy and interpretability.

  7. FastRNABindR: Fast and Accurate Prediction of Protein-RNA Interface Residues.

    Yasser El-Manzalawy

    Full Text Available A wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses are mediated by RNA-protein interactions. However, experimental determination of the structures of protein-RNA complexes is expensive and technically challenging. Hence, a number of computational tools have been developed for predicting protein-RNA interfaces. Some of the state-of-the-art protein-RNA interface predictors rely on position-specific scoring matrix (PSSM-based encoding of the protein sequences. The computational efforts needed for generating PSSMs severely limits the practical utility of protein-RNA interface prediction servers. In this work, we experiment with two approaches, random sampling and sequence similarity reduction, for extracting a representative reference database of protein sequences from more than 50 million protein sequences in UniRef100. Our results suggest that random sampled databases produce better PSSM profiles (in terms of the number of hits used to generate the profile and the distance of the generated profile to the corresponding profile generated using the entire UniRef100 data as well as the accuracy of the machine learning classifier trained using these profiles. Based on our results, we developed FastRNABindR, an improved version of RNABindR for predicting protein-RNA interface residues using PSSM profiles generated using 1% of the UniRef100 sequences sampled uniformly at random. To the best of our knowledge, FastRNABindR is the only protein-RNA interface residue prediction online server that requires generation of PSSM profiles for query sequences and accepts hundreds of protein sequences per submission. Our approach for determining the optimal BLAST database for a protein-RNA interface residue classification task has the potential of substantially speeding up, and hence increasing the practical utility of, other amino acid sequence based predictors of protein

  8. Accurate single-sequence prediction of solvent accessible surface area using local and global features.

    Faraggi, Eshel; Zhou, Yaoqi; Kloczkowski, Andrzej

    2014-11-01

    We present a new approach for predicting the Accessible Surface Area (ASA) using a General Neural Network (GENN). The novelty of the new approach lies in not using residue mutation profiles generated by multiple sequence alignments as descriptive inputs. Instead we use solely sequential window information and global features such as single-residue and two-residue compositions of the chain. The resulting predictor is both highly more efficient than sequence alignment-based predictors and of comparable accuracy to them. Introduction of the global inputs significantly helps achieve this comparable accuracy. The predictor, termed ASAquick, is tested on predicting the ASA of globular proteins and found to perform similarly well for so-called easy and hard cases indicating generalizability and possible usability for de-novo protein structure prediction. The source code and a Linux executables for GENN and ASAquick are available from Research and Information Systems at http://mamiris.com, from the SPARKS Lab at http://sparks-lab.org, and from the Battelle Center for Mathematical Medicine at http://mathmed.org.

  9. Accurate structure prediction of peptide–MHC complexes for identifying highly immunogenic antigens

    Park, Min-Sun; Park, Sung Yong; Miller, Keith R.; Collins, Edward J.; Lee, Ha Youn

    2013-11-01

    Designing an optimal HIV-1 vaccine faces the challenge of identifying antigens that induce a broad immune capacity. One factor to control the breadth of T cell responses is the surface morphology of a peptide–MHC complex. Here, we present an in silico protocol for predicting peptide–MHC structure. A robust signature of a conformational transition was identified during all-atom molecular dynamics, which results in a model with high accuracy. A large test set was used in constructing our protocol and we went another step further using a blind test with a wild-type peptide and two highly immunogenic mutants, which predicted substantial conformational changes in both mutants. The center residues at position five of the analogs were configured to be accessible to solvent, forming a prominent surface, while the residue of the wild-type peptide was to point laterally toward the side of the binding cleft. We then experimentally determined the structures of the blind test set, using high resolution of X-ray crystallography, which verified predicted conformational changes. Our observation strongly supports a positive association of the surface morphology of a peptide–MHC complex to its immunogenicity. Our study offers the prospect of enhancing immunogenicity of vaccines by identifying MHC binding immunogens.

  10. Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data

    Pagán, Josué; Irene De Orbe, M.; Gago, Ana; Sobrado, Mónica; Risco-Martín, José L.; Vivancos Mora, J.; Moya, José M.; Ayala, José L.

    2015-01-01

    Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives. PMID:26134103

  11. Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data

    Josué Pagán

    2015-06-01

    Full Text Available Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN. The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID that are capable of providing average forecast windows of 47 min and a low rate of false positives.

  12. Revisiting the blind tests in crystal structure prediction: accurate energy ranking of molecular crystals.

    Asmadi, Aldi; Neumann, Marcus A; Kendrick, John; Girard, Pascale; Perrin, Marc-Antoine; Leusen, Frank J J

    2009-12-24

    In the 2007 blind test of crystal structure prediction hosted by the Cambridge Crystallographic Data Centre (CCDC), a hybrid DFT/MM method correctly ranked each of the four experimental structures as having the lowest lattice energy of all the crystal structures predicted for each molecule. The work presented here further validates this hybrid method by optimizing the crystal structures (experimental and submitted) of the first three CCDC blind tests held in 1999, 2001, and 2004. Except for the crystal structures of compound IX, all structures were reminimized and ranked according to their lattice energies. The hybrid method computes the lattice energy of a crystal structure as the sum of the DFT total energy and a van der Waals (dispersion) energy correction. Considering all four blind tests, the crystal structure with the lowest lattice energy corresponds to the experimentally observed structure for 12 out of 14 molecules. Moreover, good geometrical agreement is observed between the structures determined by the hybrid method and those measured experimentally. In comparison with the correct submissions made by the blind test participants, all hybrid optimized crystal structures (apart from compound II) have the smallest calculated root mean squared deviations from the experimentally observed structures. It is predicted that a new polymorph of compound V exists under pressure.

  13. Accurate prediction of interfacial residues in two-domain proteins using evolutionary information: implications for three-dimensional modeling.

    Bhaskara, Ramachandra M; Padhi, Amrita; Srinivasan, Narayanaswamy

    2014-07-01

    With the preponderance of multidomain proteins in eukaryotic genomes, it is essential to recognize the constituent domains and their functions. Often function involves communications across the domain interfaces, and the knowledge of the interacting sites is essential to our understanding of the structure-function relationship. Using evolutionary information extracted from homologous domains in at least two diverse domain architectures (single and multidomain), we predict the interface residues corresponding to domains from the two-domain proteins. We also use information from the three-dimensional structures of individual domains of two-domain proteins to train naïve Bayes classifier model to predict the interfacial residues. Our predictions are highly accurate (∼85%) and specific (∼95%) to the domain-domain interfaces. This method is specific to multidomain proteins which contain domains in at least more than one protein architectural context. Using predicted residues to constrain domain-domain interaction, rigid-body docking was able to provide us with accurate full-length protein structures with correct orientation of domains. We believe that these results can be of considerable interest toward rational protein and interaction design, apart from providing us with valuable information on the nature of interactions.

  14. Does preoperative cross-sectional imaging accurately predict main duct involvement in intraductal papillary mucinous neoplasm?

    Barron, M R; Roch, A M; Waters, J A; Parikh, J A; DeWitt, J M; Al-Haddad, M A; Ceppa, E P; House, M G; Zyromski, N J; Nakeeb, A; Pitt, H A; Schmidt, C Max

    2014-03-01

    Main pancreatic duct (MPD) involvement is a well-demonstrated risk factor for malignancy in intraductal papillary mucinous neoplasm (IPMN). Preoperative radiographic determination of IPMN type is heavily relied upon in oncologic risk stratification. We hypothesized that radiographic assessment of MPD involvement in IPMN is an accurate predictor of pathological MPD involvement. Data regarding all patients undergoing resection for IPMN at a single academic institution between 1992 and 2012 were gathered prospectively. Retrospective analysis of imaging and pathologic data was undertaken. Preoperative classification of IPMN type was based on cross-sectional imaging (MRI/magnetic resonance cholangiopancreatography (MRCP) and/or CT). Three hundred sixty-two patients underwent resection for IPMN. Of these, 334 had complete data for analysis. Of 164 suspected branch duct (BD) IPMN, 34 (20.7%) demonstrated MPD involvement on final pathology. Of 170 patients with suspicion of MPD involvement, 50 (29.4%) demonstrated no MPD involvement. Of 34 patients with suspected BD-IPMN who were found to have MPD involvement on pathology, 10 (29.4%) had invasive carcinoma. Alternatively, 2/50 (4%) of the patients with suspected MPD involvement who ultimately had isolated BD-IPMN demonstrated invasive carcinoma. Preoperative radiographic IPMN type did not correlate with final pathology in 25% of the patients. In addition, risk of invasive carcinoma correlates with pathologic presence of MPD involvement.

  15. DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach.

    Zhiheng Wang

    Full Text Available The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database.The DisoMCS is available at http://cal.tongji.edu.cn/disorder/.

  16. Computational methods toward accurate RNA structure prediction using coarse-grained and all-atom models.

    Krokhotin, Andrey; Dokholyan, Nikolay V

    2015-01-01

    Computational methods can provide significant insights into RNA structure and dynamics, bridging the gap in our understanding of the relationship between structure and biological function. Simulations enrich and enhance our understanding of data derived on the bench, as well as provide feasible alternatives to costly or technically challenging experiments. Coarse-grained computational models of RNA are especially important in this regard, as they allow analysis of events occurring in timescales relevant to RNA biological function, which are inaccessible through experimental methods alone. We have developed a three-bead coarse-grained model of RNA for discrete molecular dynamics simulations. This model is efficient in de novo prediction of short RNA tertiary structure, starting from RNA primary sequences of less than 50 nucleotides. To complement this model, we have incorporated additional base-pairing constraints and have developed a bias potential reliant on data obtained from hydroxyl probing experiments that guide RNA folding to its correct state. By introducing experimentally derived constraints to our computer simulations, we are able to make reliable predictions of RNA tertiary structures up to a few hundred nucleotides. Our refined model exemplifies a valuable benefit achieved through integration of computation and experimental methods.

  17. Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences

    Chen, Peng

    2013-07-23

    Hot spot residues of proteins are fundamental interface residues that help proteins perform their functions. Detecting hot spots by experimental methods is costly and time-consuming. Sequential and structural information has been widely used in the computational prediction of hot spots. However, structural information is not always available. In this article, we investigated the problem of identifying hot spots using only physicochemical characteristics extracted from amino acid sequences. We first extracted 132 relatively independent physicochemical features from a set of the 544 properties in AAindex1, an amino acid index database. Each feature was utilized to train a classification model with a novel encoding schema for hot spot prediction by the IBk algorithm, an extension of the K-nearest neighbor algorithm. The combinations of the individual classifiers were explored and the classifiers that appeared frequently in the top performing combinations were selected. The hot spot predictor was built based on an ensemble of these classifiers and to work in a voting manner. Experimental results demonstrated that our method effectively exploited the feature space and allowed flexible weights of features for different queries. On the commonly used hot spot benchmark sets, our method significantly outperformed other machine learning algorithms and state-of-the-art hot spot predictors. The program is available at http://sfb.kaust.edu.sa/pages/software.aspx. © 2013 Wiley Periodicals, Inc.

  18. How Five Student Characteristics Accurately Predict For-Profit University Graduation Odds

    Tim Gramling

    2013-07-01

    Full Text Available President Obama’s goal is for America to lead the world in college graduates by 2020. Although for-profit institutions have increased their output of graduates at ten times the rate of nonprofits over the past decade, Congress and the U.S. Department of Education have argued that these institutions exploit the ambitions of lower-performing students. In response, this study examined how student characteristics predicted graduation odds at a large, regionally accredited for-profit institution campus. A logistic regression predicted graduation for the full population of 2,548 undergraduate students enrolled from 2005 to 2009 with scheduled graduation by June 30, 2011. Sixteen independent predictors were identified from school records and organized in the Bean and Metzner framework. The regression model was more robust than any in the literature, with a Nagelkerke R2 of .663. Only five factors had a significant impact on log odds: (a grade point average (GPA, where higher values increased odds; (b half time enrollment, which had lower odds than full time; (c Blacks, who had higher odds than Whites; (d credits required, where fewer credits increased odds; and (e primary expected family contribution, where higher values increased odds. These findings imply that public policy will not increase college graduates by focusing on institution characteristics.

  19. Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology.

    Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil

    2014-09-07

    Due to the central roles of lipid binding proteins (LBPs) in many biological processes, sequence based identification of LBPs is of great interest. The major challenge is that LBPs are diverse in sequence, structure, and function which results in low accuracy of sequence homology based methods. Therefore, there is a need for developing alternative functional prediction methods irrespective of sequence similarity. To identify LBPs from non-LBPs, the performances of support vector machine (SVM) and neural network were compared in this study. Comprehensive protein features and various techniques were employed to create datasets. Five-fold cross-validation (CV) and independent evaluation (IE) tests were used to assess the validity of the two methods. The results indicated that SVM outperforms neural network. SVM achieved 89.28% (CV) and 89.55% (IE) overall accuracy in identification of LBPs from non-LBPs and 92.06% (CV) and 92.90% (IE) (in average) for classification of different LBPs classes. Increasing the number and the range of extracted protein features as well as optimization of the SVM parameters significantly increased the efficiency of LBPs class prediction in comparison to the only previous report in this field. Altogether, the results showed that the SVM algorithm can be run on broad, computationally calculated protein features and offers a promising tool in detection of LBPs classes. The proposed approach has the potential to integrate and improve the common sequence alignment based methods.

  20. Fast and Accurate Accessible Surface Area Prediction Without a Sequence Profile.

    Faraggi, Eshel; Kouza, Maksim; Zhou, Yaoqi; Kloczkowski, Andrzej

    2017-01-01

    A fast accessible surface area (ASA) predictor is presented. In this new approach no residue mutation profiles generated by multiple sequence alignments are used as inputs. Instead, we use only single sequence information and global features such as single-residue and two-residue compositions of the chain. The resulting predictor is both highly more efficient than sequence alignment based predictors and of comparable accuracy to them. Introduction of the global inputs significantly helps achieve this comparable accuracy. The predictor, termed ASAquick, is found to perform similarly well for so-called easy and hard cases indicating generalizability and possible usability for de-novo protein structure prediction. The source code and a Linux executables for ASAquick are available from Research and Information Systems at http://mamiris.com and from the Battelle Center for Mathematical Medicine at http://mathmed.org .

  1. A Foundation for the Accurate Prediction of the Soft Error Vulnerability of Scientific Applications

    Bronevetsky, G; de Supinski, B; Schulz, M

    2009-02-13

    Understanding the soft error vulnerability of supercomputer applications is critical as these systems are using ever larger numbers of devices that have decreasing feature sizes and, thus, increasing frequency of soft errors. As many large scale parallel scientific applications use BLAS and LAPACK linear algebra routines, the soft error vulnerability of these methods constitutes a large fraction of the applications overall vulnerability. This paper analyzes the vulnerability of these routines to soft errors by characterizing how their outputs are affected by injected errors and by evaluating several techniques for predicting how errors propagate from the input to the output of each routine. The resulting error profiles can be used to understand the fault vulnerability of full applications that use these routines.

  2. nuMap:A Web Platform for Accurate Prediction of Nucleosome Positioning

    Bader A Alharbi; Thamir H Alshammari; Nathan L Felton; Victor B Zhurkin; Feng Cui

    2014-01-01

    Nucleosome positioning is critical for gene expression and of major biological interest. The high cost of experimentally mapping nucleosomal arrangement signifies the need for computational approaches to predict nucleosome positions at high resolution. Here, we present a web-based application to fulfill this need by implementing two models, YR and W/S schemes, for the translational and rotational positioning of nucleosomes, respectively. Our methods are based on sequence-dependent anisotropic bending that dictates how DNA is wrapped around a histone octamer. This application allows users to specify a number of options such as schemes and param-eters for threading calculation and provides multiple layout formats. The nuMap is implemented in Java/Perl/MySQL and is freely available for public use at http://numap.rit.edu. The user manual, implementation notes, description of the methodology and examples are available at the site.

  3. Fast and accurate prediction for aerodynamic forces and moments acting on satellites flying in Low-Earth Orbit

    Jin, Xuhon; Huang, Fei; Hu, Pengju; Cheng, Xiaoli

    2016-11-01

    A fundamental prerequisite for satellites operating in a Low Earth Orbit (LEO) is the availability of fast and accurate prediction of non-gravitational aerodynamic forces, which is characterised by the free molecular flow regime. However, conventional computational methods like the analytical integral method and direct simulation Monte Carlo (DSMC) technique are found failing to deal with flow shadowing and multiple reflections or computationally expensive. This work develops a general computer program for the accurate calculation of aerodynamic forces in the free molecular flow regime using the test particle Monte Carlo (TPMC) method, and non-gravitational aerodynamic forces actiong on the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite is calculated for different freestream conditions and gas-surface interaction models by the computer program.

  4. Non-Empirically Tuned Range-Separated DFT Accurately Predicts Both Fundamental and Excitation Gaps in DNA and RNA Nucleobases

    Foster, Michael E; 10.1021/ct300420f

    2012-01-01

    Using a non-empirically tuned range-separated DFT approach, we study both the quasiparticle properties (HOMO-LUMO fundamental gaps) and excitation energies of DNA and RNA nucleobases (adenine, thymine, cytosine, guanine, and uracil). Our calculations demonstrate that a physically-motivated, first-principles tuned DFT approach accurately reproduces results from both experimental benchmarks and more computationally intensive techniques such as many-body GW theory. Furthermore, in the same set of nucleobases, we show that the non-empirical range-separated procedure also leads to significantly improved results for excitation energies compared to conventional DFT methods. The present results emphasize the importance of a non-empirically tuned range-separation approach for accurately predicting both fundamental and excitation gaps in DNA and RNA nucleobases.

  5. Use of Feedback in Clinical Prediction

    Schroeder, Harold E.

    1972-01-01

    Results indicated that predictive accuracy is greater when feedback is applied to the basis for the prediction than when applied to gut" impressions. Judges forming hypotheses were also able to learn from experience. (Author)

  6. Predicting Out-of-Office Blood Pressure in the Clinic (PROOF-BP)

    Stevens, Richard; Gill, Paramjit; Martin, Una; Godwin, Marshall; Hanley, Janet; Heneghan, Carl; Hobbs, F.D. Richard; Mant, Jonathan; McKinstry, Brian; Myers, Martin; Nunan, David; Ward, Alison; Williams, Bryan; McManus, Richard J.

    2016-01-01

    Patients often have lower (white coat effect) or higher (masked effect) ambulatory/home blood pressure readings compared with clinic measurements, resulting in misdiagnosis of hypertension. The present study assessed whether blood pressure and patient characteristics from a single clinic visit can accurately predict the difference between ambulatory/home and clinic blood pressure readings (the home–clinic difference). A linear regression model predicting the home–clinic blood pressure difference was derived in 2 data sets measuring automated clinic and ambulatory/home blood pressure (n=991) using candidate predictors identified from a literature review. The model was validated in 4 further data sets (n=1172) using area under the receiver operator characteristic curve analysis. A masked effect was associated with male sex, a positive clinic blood pressure change (difference between consecutive measurements during a single visit), and a diagnosis of hypertension. Increasing age, clinic blood pressure level, and pulse pressure were associated with a white coat effect. The model showed good calibration across data sets (Pearson correlation, 0.48–0.80) and performed well-predicting ambulatory hypertension (area under the receiver operator characteristic curve, 0.75; 95% confidence interval, 0.72–0.79 [systolic]; 0.87; 0.85–0.89 [diastolic]). Used as a triaging tool for ambulatory monitoring, the model improved classification of a patient’s blood pressure status compared with other guideline recommended approaches (93% [92% to 95%] classified correctly; United States, 73% [70% to 75%]; Canada, 74% [71% to 77%]; United Kingdom, 78% [76% to 81%]). This study demonstrates that patient characteristics from a single clinic visit can accurately predict a patient’s ambulatory blood pressure. Usage of this prediction tool for triaging of ambulatory monitoring could result in more accurate diagnosis of hypertension and hence more appropriate treatment. PMID:27001299

  7. Towards Relaxing the Spherical Solar Radiation Pressure Model for Accurate Orbit Predictions

    Lachut, M.; Bennett, J.

    2016-09-01

    The well-known cannonball model has been used ubiquitously to capture the effects of atmospheric drag and solar radiation pressure on satellites and/or space debris for decades. While it lends itself naturally to spherical objects, its validity in the case of non-spherical objects has been debated heavily for years throughout the space situational awareness community. One of the leading motivations to improve orbit predictions by relaxing the spherical assumption, is the ongoing demand for more robust and reliable conjunction assessments. In this study, we explore the orbit propagation of a flat plate in a near-GEO orbit under the influence of solar radiation pressure, using a Lambertian BRDF model. Consequently, this approach will account for the spin rate and orientation of the object, which is typically determined in practice using a light curve analysis. Here, simulations will be performed which systematically reduces the spin rate to demonstrate the point at which the spherical model no longer describes the orbital elements of the spinning plate. Further understanding of this threshold would provide insight into when a higher fidelity model should be used, thus resulting in improved orbit propagations. Therefore, the work presented here is of particular interest to organizations and researchers that maintain their own catalog, and/or perform conjunction analyses.

  8. The human skin/chick chorioallantoic membrane model accurately predicts the potency of cosmetic allergens.

    Slodownik, Dan; Grinberg, Igor; Spira, Ram M; Skornik, Yehuda; Goldstein, Ronald S

    2009-04-01

    The current standard method for predicting contact allergenicity is the murine local lymph node assay (LLNA). Public objection to the use of animals in testing of cosmetics makes the development of a system that does not use sentient animals highly desirable. The chorioallantoic membrane (CAM) of the chick egg has been extensively used for the growth of normal and transformed mammalian tissues. The CAM is not innervated, and embryos are sacrificed before the development of pain perception. The aim of this study was to determine whether the sensitization phase of contact dermatitis to known cosmetic allergens can be quantified using CAM-engrafted human skin and how these results compare with published EC3 data obtained with the LLNA. We studied six common molecules used in allergen testing and quantified migration of epidermal Langerhans cells (LC) as a measure of their allergic potency. All agents with known allergic potential induced statistically significant migration of LC. The data obtained correlated well with published data for these allergens generated using the LLNA test. The human-skin CAM model therefore has great potential as an inexpensive, non-radioactive, in vivo alternative to the LLNA, which does not require the use of sentient animals. In addition, this system has the advantage of testing the allergic response of human, rather than animal skin.

  9. Clinical Prediction Rule of Drug Resistant Epilepsy in Children

    2015-01-01

    Background and Purpose: Clinical prediction rules (CPR) are clinical decision-making tools containing variables such as history, physical examination, diagnostic tests by developing scoring model from potential risk factors. This study is to establish clinical prediction scoring of drug-resistant epilepsy (DRE) in children using clinical manifestationa and only basic electroencephalography (EEG). Methods: Retrospective cohort study was conducted. A total of 308 children with diagnosed epileps...

  10. Protein corona composition does not accurately predict hematocompatibility of colloidal gold nanoparticles.

    Dobrovolskaia, Marina A; Neun, Barry W; Man, Sonny; Ye, Xiaoying; Hansen, Matthew; Patri, Anil K; Crist, Rachael M; McNeil, Scott E

    2014-10-01

    Proteins bound to nanoparticle surfaces are known to affect particle clearance by influencing immune cell uptake and distribution to the organs of the mononuclear phagocytic system. The composition of the protein corona has been described for several types of nanomaterials, but the role of the corona in nanoparticle biocompatibility is not well established. In this study we investigate the role of nanoparticle surface properties (PEGylation) and incubation times on the protein coronas of colloidal gold nanoparticles. While neither incubation time nor PEG molecular weight affected the specific proteins in the protein corona, the total amount of protein binding was governed by the molecular weight of PEG coating. Furthermore, the composition of the protein corona did not correlate with nanoparticle hematocompatibility. Specialized hematological tests should be used to deduce nanoparticle hematotoxicity. From the clinical editor: It is overall unclear how the protein corona associated with colloidal gold nanoparticles may influence hematotoxicity. This study warns that PEGylation itself may be insufficient, because composition of the protein corona does not directly correlate with nanoparticle hematocompatibility. The authors suggest that specialized hematological tests must be used to deduce nanoparticle hematotoxicity.

  11. Industrial Compositional Streamline Simulation for Efficient and Accurate Prediction of Gas Injection and WAG Processes

    Margot Gerritsen

    2008-10-31

    Gas-injection processes are widely and increasingly used for enhanced oil recovery (EOR). In the United States, for example, EOR production by gas injection accounts for approximately 45% of total EOR production and has tripled since 1986. The understanding of the multiphase, multicomponent flow taking place in any displacement process is essential for successful design of gas-injection projects. Due to complex reservoir geometry, reservoir fluid properties and phase behavior, the design of accurate and efficient numerical simulations for the multiphase, multicomponent flow governing these processes is nontrivial. In this work, we developed, implemented and tested a streamline based solver for gas injection processes that is computationally very attractive: as compared to traditional Eulerian solvers in use by industry it computes solutions with a computational speed orders of magnitude higher and a comparable accuracy provided that cross-flow effects do not dominate. We contributed to the development of compositional streamline solvers in three significant ways: improvement of the overall framework allowing improved streamline coverage and partial streamline tracing, amongst others; parallelization of the streamline code, which significantly improves wall clock time; and development of new compositional solvers that can be implemented along streamlines as well as in existing Eulerian codes used by industry. We designed several novel ideas in the streamline framework. First, we developed an adaptive streamline coverage algorithm. Adding streamlines locally can reduce computational costs by concentrating computational efforts where needed, and reduce mapping errors. Adapting streamline coverage effectively controls mass balance errors that mostly result from the mapping from streamlines to pressure grid. We also introduced the concept of partial streamlines: streamlines that do not necessarily start and/or end at wells. This allows more efficient coverage and avoids

  12. Accurate prediction of a minimal region around a genetic association signal that contains the causal variant.

    Bochdanovits, Zoltán; Simón-Sánchez, Javier; Jonker, Marianne; Hoogendijk, Witte J; van der Vaart, Aad; Heutink, Peter

    2014-02-01

    In recent years, genome-wide association studies have been very successful in identifying loci for complex traits. However, typically these findings involve noncoding and/or intergenic SNPs without a clear functional effect that do not directly point to a gene. Hence, the challenge is to identify the causal variant responsible for the association signal. Typically, the first step is to identify all genetic variation in the locus region, usually by resequencing a large number of case chromosomes. Among all variants, the causal one needs to be identified in further functional studies. Because the experimental follow up can be very laborious, restricting the number of variants to be scrutinized can yield a great advantage. An objective method for choosing the size of the region to be followed up would be highly valuable. Here, we propose a simple method to call the minimal region around a significant association peak that is very likely to contain the causal variant. We model linkage disequilibrium (LD) in cases from the observed single SNP association signals, and predict the location of the causal variant by quantifying how well this relationship fits the data. Simulations showed that our approach identifies genomic regions of on average ∼50 kb with up to 90% probability to contain the causal variant. We apply our method to two genome-wide association data sets and localize both the functional variant REP1 in the α-synuclein gene that conveys susceptibility to Parkinson's disease and the APOE gene responsible for the association signal in the Alzheimer's disease data set.

  13. How Accurate Are the Anthropometry Equations in in Iranian Military Men in Predicting Body Composition?

    Shakibaee, Abolfazl; Faghihzadeh, Soghrat; Alishiri, Gholam Hossein; Ebrahimpour, Zeynab; Faradjzadeh, Shahram; Sobhani, Vahid; Asgari, Alireza

    2015-01-01

    Background: The body composition varies according to different life styles (i.e. intake calories and caloric expenditure). Therefore, it is wise to record military personnel’s body composition periodically and encourage those who abide to the regulations. Different methods have been introduced for body composition assessment: invasive and non-invasive. Amongst them, the Jackson and Pollock equation is most popular. Objectives: The recommended anthropometric prediction equations for assessing men’s body composition were compared with dual-energy X-ray absorptiometry (DEXA) gold standard to develop a modified equation to assess body composition and obesity quantitatively among Iranian military men. Patients and Methods: A total of 101 military men aged 23 - 52 years old with a mean age of 35.5 years were recruited and evaluated in the present study (average height, 173.9 cm and weight, 81.5 kg). The body-fat percentages of subjects were assessed both with anthropometric assessment and DEXA scan. The data obtained from these two methods were then compared using multiple regression analysis. Results: The mean and standard deviation of body fat percentage of the DEXA assessment was 21.2 ± 4.3 and body fat percentage obtained from three Jackson and Pollock 3-, 4- and 7-site equations were 21.1 ± 5.8, 22.2 ± 6.0 and 20.9 ± 5.7, respectively. There was a strong correlation between these three equations and DEXA (R² = 0.98). Conclusions: The mean percentage of body fat obtained from the three equations of Jackson and Pollock was very close to that of body fat obtained from DEXA; however, we suggest using a modified Jackson-Pollock 3-site equation for volunteer military men because the 3-site equation analysis method is simpler and faster than other methods. PMID:26715964

  14. Industrial Compositional Streamline Simulation for Efficient and Accurate Prediction of Gas Injection and WAG Processes

    Margot Gerritsen

    2008-10-31

    Gas-injection processes are widely and increasingly used for enhanced oil recovery (EOR). In the United States, for example, EOR production by gas injection accounts for approximately 45% of total EOR production and has tripled since 1986. The understanding of the multiphase, multicomponent flow taking place in any displacement process is essential for successful design of gas-injection projects. Due to complex reservoir geometry, reservoir fluid properties and phase behavior, the design of accurate and efficient numerical simulations for the multiphase, multicomponent flow governing these processes is nontrivial. In this work, we developed, implemented and tested a streamline based solver for gas injection processes that is computationally very attractive: as compared to traditional Eulerian solvers in use by industry it computes solutions with a computational speed orders of magnitude higher and a comparable accuracy provided that cross-flow effects do not dominate. We contributed to the development of compositional streamline solvers in three significant ways: improvement of the overall framework allowing improved streamline coverage and partial streamline tracing, amongst others; parallelization of the streamline code, which significantly improves wall clock time; and development of new compositional solvers that can be implemented along streamlines as well as in existing Eulerian codes used by industry. We designed several novel ideas in the streamline framework. First, we developed an adaptive streamline coverage algorithm. Adding streamlines locally can reduce computational costs by concentrating computational efforts where needed, and reduce mapping errors. Adapting streamline coverage effectively controls mass balance errors that mostly result from the mapping from streamlines to pressure grid. We also introduced the concept of partial streamlines: streamlines that do not necessarily start and/or end at wells. This allows more efficient coverage and avoids

  15. Clinical prediction of 5-year survival in systemic sclerosis

    Fransen, Julie Munk; Popa-Diaconu, D; Hesselstrand, R;

    2011-01-01

    Systemic sclerosis (SSc) is associated with a significant reduction in life expectancy. A simple prognostic model to predict 5-year survival in SSc was developed in 1999 in 280 patients, but it has not been validated in other patients. The predictions of a prognostic model are usually less accurate...... in other patients, especially from other centres or countries. A study was undertaken to validate the prognostic model to predict 5-year survival in SSc in other centres throughout Europe....

  16. A cross-race effect in metamemory: Predictions of face recognition are more accurate for members of our own race.

    Hourihan, Kathleen L; Benjamin, Aaron S; Liu, Xiping

    2012-09-01

    The Cross-Race Effect (CRE) in face recognition is the well-replicated finding that people are better at recognizing faces from their own race, relative to other races. The CRE reveals systematic limitations on eyewitness identification accuracy and suggests that some caution is warranted in evaluating cross-race identification. The CRE is a problem because jurors value eyewitness identification highly in verdict decisions. In the present paper, we explore how accurate people are in predicting their ability to recognize own-race and other-race faces. Caucasian and Asian participants viewed photographs of Caucasian and Asian faces, and made immediate judgments of learning during study. An old/new recognition test replicated the CRE: both groups displayed superior discriminability of own-race faces, relative to other-race faces. Importantly, relative metamnemonic accuracy was also greater for own-race faces, indicating that the accuracy of predictions about face recognition is influenced by race. This result indicates another source of concern when eliciting or evaluating eyewitness identification: people are less accurate in judging whether they will or will not recognize a face when that face is of a different race than they are. This new result suggests that a witness's claim of being likely to recognize a suspect from a lineup should be interpreted with caution when the suspect is of a different race than the witness.

  17. Long‐Term Post‐CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions

    Carr, Brendan M.; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C.; Zhu, Wei

    2015-01-01

    Abstract Background/aim Clinical risk models are commonly used to predict short‐term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long‐term mortality. The added value of long‐term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long‐term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Methods Long‐term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c‐index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Results Mortality rates were 3%, 9%, and 17% at one‐, three‐, and five years, respectively (median follow‐up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long‐term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Conclusions Long‐term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long‐term mortality risk can be accurately assessed and subgroups of higher‐risk patients can be identified for enhanced follow‐up care. More research appears warranted to refine long‐term CABG clinical risk models. doi: 10.1111/jocs.12665 (J Card Surg 2016;31:23–30) PMID:26543019

  18. Accurate prediction of polarised high order electrostatic interactions for hydrogen bonded complexes using the machine learning method kriging

    Hughes, Timothy J.; Kandathil, Shaun M.; Popelier, Paul L. A.

    2015-02-01

    As intermolecular interactions such as the hydrogen bond are electrostatic in origin, rigorous treatment of this term within force field methodologies should be mandatory. We present a method able of accurately reproducing such interactions for seven van der Waals complexes. It uses atomic multipole moments up to hexadecupole moment mapped to the positions of the nuclear coordinates by the machine learning method kriging. Models were built at three levels of theory: HF/6-31G**, B3LYP/aug-cc-pVDZ and M06-2X/aug-cc-pVDZ. The quality of the kriging models was measured by their ability to predict the electrostatic interaction energy between atoms in external test examples for which the true energies are known. At all levels of theory, >90% of test cases for small van der Waals complexes were predicted within 1 kJ mol-1, decreasing to 60-70% of test cases for larger base pair complexes. Models built on moments obtained at B3LYP and M06-2X level generally outperformed those at HF level. For all systems the individual interactions were predicted with a mean unsigned error of less than 1 kJ mol-1.

  19. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response.

    Gao, Hui; Korn, Joshua M; Ferretti, Stéphane; Monahan, John E; Wang, Youzhen; Singh, Mallika; Zhang, Chao; Schnell, Christian; Yang, Guizhi; Zhang, Yun; Balbin, O Alejandro; Barbe, Stéphanie; Cai, Hongbo; Casey, Fergal; Chatterjee, Susmita; Chiang, Derek Y; Chuai, Shannon; Cogan, Shawn M; Collins, Scott D; Dammassa, Ernesta; Ebel, Nicolas; Embry, Millicent; Green, John; Kauffmann, Audrey; Kowal, Colleen; Leary, Rebecca J; Lehar, Joseph; Liang, Ying; Loo, Alice; Lorenzana, Edward; Robert McDonald, E; McLaughlin, Margaret E; Merkin, Jason; Meyer, Ronald; Naylor, Tara L; Patawaran, Montesa; Reddy, Anupama; Röelli, Claudia; Ruddy, David A; Salangsang, Fernando; Santacroce, Francesca; Singh, Angad P; Tang, Yan; Tinetto, Walter; Tobler, Sonja; Velazquez, Roberto; Venkatesan, Kavitha; Von Arx, Fabian; Wang, Hui Qin; Wang, Zongyao; Wiesmann, Marion; Wyss, Daniel; Xu, Fiona; Bitter, Hans; Atadja, Peter; Lees, Emma; Hofmann, Francesco; Li, En; Keen, Nicholas; Cozens, Robert; Jensen, Michael Rugaard; Pryer, Nancy K; Williams, Juliet A; Sellers, William R

    2015-11-01

    Profiling candidate therapeutics with limited cancer models during preclinical development hinders predictions of clinical efficacy and identifying factors that underlie heterogeneous patient responses for patient-selection strategies. We established ∼1,000 patient-derived tumor xenograft models (PDXs) with a diverse set of driver mutations. With these PDXs, we performed in vivo compound screens using a 1 × 1 × 1 experimental design (PDX clinical trial or PCT) to assess the population responses to 62 treatments across six indications. We demonstrate both the reproducibility and the clinical translatability of this approach by identifying associations between a genotype and drug response, and established mechanisms of resistance. In addition, our results suggest that PCTs may represent a more accurate approach than cell line models for assessing the clinical potential of some therapeutic modalities. We therefore propose that this experimental paradigm could potentially improve preclinical evaluation of treatment modalities and enhance our ability to predict clinical trial responses.

  20. Outcome prediction in mild traumatic brain injury : age and clinical variables are stronger predictors than CT abnormalities

    Jacobs, Bram; Beems, Tjemme; Stulemeijer, Maja; van Vugt, Arie B; van der Vliet, Ton M; Borm, George F; Vos, Pieter E

    2010-01-01

    Mild traumatic brain injury (mTBI) is a common heterogeneous neurological disorder with a wide range of possible clinical outcomes. Accurate prediction of outcome is desirable for optimal treatment. This study aimed both to identify the demographic, clinical, and computed tomographic (CT) characteri

  1. A clinical prediction rule for histological chorioamnionitis in preterm newborns.

    Jasper V Been

    Full Text Available BACKGROUND: Histological chorioamnionitis (HC is an intrauterine inflammatory process highly associated with preterm birth and adverse neonatal outcome. HC is often clinically silent and diagnosed postnatally by placental histology. Earlier identification could facilitate treatment individualisation to improve outcome in preterm newborns. AIM: Develop a clinical prediction rule at birth for HC and HC with fetal involvement (HCF in preterm newborns. METHODS: Clinical data and placental pathology were obtained from singleton preterm newborns (gestational age ≤ 32.0 weeks born at Erasmus UMC Rotterdam from 2001 to 2003 (derivation cohort; n = 216 or Máxima MC Veldhoven from 2009 to 2010 (validation cohort; n = 206. HC and HCF prediction rules were developed with preference for high sensitivity using clinical variables available at birth. RESULTS: HC and HCF were present in 39% and 24% in the derivation cohort and in 44% and 22% in the validation cohort, respectively. HC was predicted with 87% accuracy, yielding an area under ROC curve of 0.95 (95%CI = 0.92-0.98, a positive predictive value of 80% (95%CI = 74-84%, and a negative predictive value of 93% (95%CI = 88-96%. Corresponding figures for HCF were: accuracy 83%, area under ROC curve 0.92 (95%CI = 0.88-0.96, positive predictive value 59% (95%CI = 52-62%, and negative predictive value 97% (95%CI = 93-99%. External validation expectedly resulted in some loss of test performance, preferentially affecting positive predictive rather than negative predictive values. CONCLUSION: Using a clinical prediction rule composed of clinical variables available at birth, HC and HCF could be predicted with good test characteristics in preterm newborns. Further studies should evaluate the clinical value of these rules to guide early treatment individualisation.

  2. Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory

    Liang, Yufeng; Vinson, John; Pemmaraju, Sri; Drisdell, Walter S.; Shirley, Eric L.; Prendergast, David

    2017-03-01

    Constrained-occupancy delta-self-consistent-field (Δ SCF ) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1 s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The Δ SCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle Δ SCF approach can be rectified by explicitly calculating many-electron transition amplitudes, producing x-ray spectra in excellent agreement with experiments. This study paves the way to accurately predict x-ray near-edge spectral fingerprints for physics and materials science beyond the Bethe-Salpether equation.

  3. Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

    An, Zhe; Rey, Daniel; Ye, Jingxin; Abarbanel, Henry D. I.

    2017-01-01

    The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of the full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. We show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.

  4. Predicting suitable optoelectronic properties of monoclinic VON semiconductor crystals for photovoltaics using accurate first-principles computations

    Harb, Moussab

    2015-08-26

    Using accurate first-principles quantum calculations based on DFT (including the perturbation theory DFPT) with the range-separated hybrid HSE06 exchange-correlation functional, we predict essential fundamental properties (such as bandgap, optical absorption coefficient, dielectric constant, charge carrier effective masses and exciton binding energy) of two stable monoclinic vanadium oxynitride (VON) semiconductor crystals for solar energy conversion applications. In addition to the predicted band gaps in the optimal range for making single-junction solar cells, both polymorphs exhibit relatively high absorption efficiencies in the visible range, high dielectric constants, high charge carrier mobilities and much lower exciton binding energies than the thermal energy at room temperature. Moreover, their optical absorption, dielectric and exciton dissociation properties are found to be better than those obtained for semiconductors frequently utilized in photovoltaic devices like Si, CdTe and GaAs. These novel results offer a great opportunity for this stoichiometric VON material to be properly synthesized and considered as a new good candidate for photovoltaic applications.

  5. Cleveland Clinic intelligent mouthguard: a new technology to accurately measure head impact in athletes and soldiers

    Bartsch, Adam; Samorezov, Sergey

    2013-05-01

    Nearly 2 million Traumatic Brain Injuries (TBI) occur in the U.S. each year, with societal costs approaching $60 billion. Including mild TBI and concussion, TBI's are prevalent in soldiers returning from Iraq and Afghanistan as well as in domestic athletes. Long-term risks of single and cumulative head impact dosage may present in the form of post traumatic stress disorder (PTSD), depression, suicide, Chronic Traumatic Encephalopathy (CTE), dementia, Alzheimer's and Parkinson's diseases. Quantifying head impact dosage and understanding associated risk factors for the development of long-term sequelae is critical toward developing guidelines for TBI exposure and post-exposure management. The current knowledge gap between head impact exposure and clinical outcomes limits the understanding of underlying TBI mechanisms, including effective treatment protocols and prevention methods for soldiers and athletes. In order to begin addressing this knowledge gap, Cleveland Clinic is developing the "Intelligent Mouthguard" head impact dosimeter. Current testing indicates the Intelligent Mouthguard can quantify linear acceleration with 3% error and angular acceleration with 17% error during impacts ranging from 10g to 174g and 850rad/s2 to 10000rad/s2, respectively. Correlation was high (R2 > 0.99, R2 = 0.98, respectively). Near-term development will be geared towards quantifying head impact dosages in vitro, longitudinally in athletes and to test new sensors for possible improved accuracy and reduced bias. Long-term, the IMG may be useful to soldiers to be paired with neurocognitive clinical data quantifying resultant TBI functional deficits.

  6. Liver iron concentration quantification by MRI: are recommended protocols accurate enough for clinical practice?

    Castiella, Agustin; Zapata, Eva M. [Mendaro Hospital, Gastroenterology Service, Mendaro (Spain); Alustiza, Jose M. [Osatek Donostia, Radiology Service, Donostia (Spain); Emparanza, Jose I. [Donostia Hospital CASPe, CIBER-ESP, Clinical Epidemiology Unit, Donostia (Spain); Costero, Belen [Principe de Asturias Hospital, Gastroenterology Service, Alcala de Henares (Spain); Diez, Maria I. [Principe de Asturias Hospital, Radiology Service, Alcala de Henares (Spain)

    2011-01-15

    To assess the accuracy of quantification of liver iron concentration (LIC) by MRI using the Rennes University (URennes) algorithm. In the overall study period 1999-2006 the LIC in 171 patients was calculated with the URennes model and the results were compared with LIC measured by liver biopsy. The biopsy showed that 107 patients had no overload, 38 moderate overload and 26 high overload. The correlation between MRI and biopsy was r = 0.86. MRI correctly classified 105 patients according to the various levels of LIC. Diagnostic accuracy was 61.4%, with a tendency to overestimate overload: 43% of patients with no overload were diagnosed as having overload, and 44.7% of patients with moderate overload were diagnosed as having high overload. The sensitivity of the URennes method for high overload was 92.3%, and the specificity for the absence of overload was 57.0%. MRI values greater than 170 {mu}mol Fe/g revealed a positive predictive value (PPV) for haemochromatosis of 100% (n = 18); concentrations below 60 {mu}mol Fe/g had a negative predictive value (NPV) of 100% for haemochromatosis (n = 101). The diagnosis in 44 patients with intermediate values remained uncertain. The assessment of LIC with the URennes method was useful in 74.3% of the patients to rule out or to diagnose high iron overload. The method has a tendency to overestimate overload, which limits its diagnostic performance. (orig.)

  7. A clinical tool to predict failed response to therapy in children with severe pneumonia.

    Mamtani, Manju; Patel, Archana; Hibberd, Patricia L; Tuan, Tran Anh; Jeena, Prakash; Chisaka, Noel; Hassan, Mumtaz; Radovan, Irene Maulen; Thea, Donald M; Qazi, Shamim; Kulkarni, Hemant

    2009-04-01

    Severe pneumonia in children under 5 years of age continues to be an important clinical entity with treatment failure rates as high as 20%. Where severe pneumonias are common, predictive tools for treatment failure like chest radiography and pulse oximetry are not available or affordable. Thus, there is a need for development of simple, accurate and inexpensive clinical tools for prediction of treatment failure. Using clinical, chest radiographic and pulse oximetry data from 1702 children recruited in the Amoxicillin Penicillin Pneumonia International Study (APPIS) trial we developed and validated a simple clinical tool. For development, a randomly derived development sample (n = 889) was used. The tool which was based on the results of multivariate logistic regression models was validated on a separate sample of 813 children. The derived clinical tool in its final form contained three clinical predictors: age of child, excess age-specific respiratory rate at baseline and at 24 hr of hospitalization. This tool had a 70% and 66% predictive accuracy in the development and validation samples, respectively. The tool is presented as an easy-to-use nomogram. It is possible to predict the likelihood of treatment failure in children with severe pneumonia based on clinical features that are simple and inexpensive to measure.

  8. Gene expression profiling predicts clinical outcome of breast cancer

    Veer, L.J. van 't; Dai, H.; Vijver, H. van de; He, Y.D.; Hart, A.A.M.; Mao, M.; Peterse, H.L.; Kooy, K. van der; Marton, M.J.; Witteveen, A.T.; Schreiber, G.J.; Kerkhoven, R.M.; Roberts, C.; Linsley, P.S.; Bernards, R.A.; Friend, S.H.

    2002-01-01

    Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour.

  9. An Optimized Method for Accurate Fetal Sex Prediction and Sex Chromosome Aneuploidy Detection in Non-Invasive Prenatal Testing.

    Wang, Ting; He, Quanze; Li, Haibo; Ding, Jie; Wen, Ping; Zhang, Qin; Xiang, Jingjing; Li, Qiong; Xuan, Liming; Kong, Lingyin; Mao, Yan; Zhu, Yijun; Shen, Jingjing; Liang, Bo; Li, Hong

    2016-01-01

    Massively parallel sequencing (MPS) combined with bioinformatic analysis has been widely applied to detect fetal chromosomal aneuploidies such as trisomy 21, 18, 13 and sex chromosome aneuploidies (SCAs) by sequencing cell-free fetal DNA (cffDNA) from maternal plasma, so-called non-invasive prenatal testing (NIPT). However, many technical challenges, such as dependency on correct fetal sex prediction, large variations of chromosome Y measurement and high sensitivity to random reads mapping, may result in higher false negative rate (FNR) and false positive rate (FPR) in fetal sex prediction as well as in SCAs detection. Here, we developed an optimized method to improve the accuracy of the current method by filtering out randomly mapped reads in six specific regions of the Y chromosome. The method reduces the FNR and FPR of fetal sex prediction from nearly 1% to 0.01% and 0.06%, respectively and works robustly under conditions of low fetal DNA concentration (1%) in testing and simulation of 92 samples. The optimized method was further confirmed by large scale testing (1590 samples), suggesting that it is reliable and robust enough for clinical testing.

  10. An Optimized Method for Accurate Fetal Sex Prediction and Sex Chromosome Aneuploidy Detection in Non-Invasive Prenatal Testing.

    Ting Wang

    Full Text Available Massively parallel sequencing (MPS combined with bioinformatic analysis has been widely applied to detect fetal chromosomal aneuploidies such as trisomy 21, 18, 13 and sex chromosome aneuploidies (SCAs by sequencing cell-free fetal DNA (cffDNA from maternal plasma, so-called non-invasive prenatal testing (NIPT. However, many technical challenges, such as dependency on correct fetal sex prediction, large variations of chromosome Y measurement and high sensitivity to random reads mapping, may result in higher false negative rate (FNR and false positive rate (FPR in fetal sex prediction as well as in SCAs detection. Here, we developed an optimized method to improve the accuracy of the current method by filtering out randomly mapped reads in six specific regions of the Y chromosome. The method reduces the FNR and FPR of fetal sex prediction from nearly 1% to 0.01% and 0.06%, respectively and works robustly under conditions of low fetal DNA concentration (1% in testing and simulation of 92 samples. The optimized method was further confirmed by large scale testing (1590 samples, suggesting that it is reliable and robust enough for clinical testing.

  11. An Optimized Method for Accurate Fetal Sex Prediction and Sex Chromosome Aneuploidy Detection in Non-Invasive Prenatal Testing

    Li, Haibo; Ding, Jie; Wen, Ping; Zhang, Qin; Xiang, Jingjing; Li, Qiong; Xuan, Liming; Kong, Lingyin; Mao, Yan; Zhu, Yijun; Shen, Jingjing; Liang, Bo; Li, Hong

    2016-01-01

    Massively parallel sequencing (MPS) combined with bioinformatic analysis has been widely applied to detect fetal chromosomal aneuploidies such as trisomy 21, 18, 13 and sex chromosome aneuploidies (SCAs) by sequencing cell-free fetal DNA (cffDNA) from maternal plasma, so-called non-invasive prenatal testing (NIPT). However, many technical challenges, such as dependency on correct fetal sex prediction, large variations of chromosome Y measurement and high sensitivity to random reads mapping, may result in higher false negative rate (FNR) and false positive rate (FPR) in fetal sex prediction as well as in SCAs detection. Here, we developed an optimized method to improve the accuracy of the current method by filtering out randomly mapped reads in six specific regions of the Y chromosome. The method reduces the FNR and FPR of fetal sex prediction from nearly 1% to 0.01% and 0.06%, respectively and works robustly under conditions of low fetal DNA concentration (1%) in testing and simulation of 92 samples. The optimized method was further confirmed by large scale testing (1590 samples), suggesting that it is reliable and robust enough for clinical testing. PMID:27441628

  12. Combining clinical variables to optimize prediction of antidepressant treatment outcomes

    Iniesta, R.; Malki, K.; Maier, W; Rietschel, M.; Mors, O; Hauser, J; Henigsberg, N.; Dernovsek, M. Z.; Souery, D.; Stahl, D.; Dobson, R.; Aitchison, K. J.; Farmer, A; Lewis, C.M.; McGuffin, P.

    2016-01-01

    The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remissio...

  13. Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance.

    Majaj, Najib J; Hong, Ha; Solomon, Ethan A; DiCarlo, James J

    2015-09-30

    database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior.

  14. A composite score combining waist circumference and body mass index more accurately predicts body fat percentage in 6-to 13-year-old children

    Aeberli, I.; Gut-Knabenhans, M.; Kusche-Ammann, R.S.; Molinari, L.; Zimmermann, M.B.

    2013-01-01

    Body mass index (BMI) and waist circumference (WC) are widely used to predict % body fat (BF) and classify degrees of pediatric adiposity. However, both measures have limitations. The aim of this study was to evaluate whether a combination of WC and BMI would more accurately predict %BF than either

  15. Brain Connectivity Predicts Placebo Response across Chronic Pain Clinical Trials

    Tétreault, Pascal; Mansour, Ali; Vachon-Presseau, Etienne; Schnitzer, Thomas J.; Apkarian, A. Vania

    2016-01-01

    Placebo response in the clinical trial setting is poorly understood and alleged to be driven by statistical confounds, and its biological underpinnings are questioned. Here we identified and validated that clinical placebo response is predictable from resting-state functional magnetic-resonance-imaging (fMRI) brain connectivity. This also led to discovering a brain region predicting active drug response and demonstrating the adverse effect of active drug interfering with placebo analgesia. Chronic knee osteoarthritis (OA) pain patients (n = 56) underwent pretreatment brain scans in two clinical trials. Study 1 (n = 17) was a 2-wk single-blinded placebo pill trial. Study 2 (n = 39) was a 3-mo double-blinded randomized trial comparing placebo pill to duloxetine. Study 3, which was conducted in additional knee OA pain patients (n = 42), was observational. fMRI-derived brain connectivity maps in study 1 were contrasted between placebo responders and nonresponders and compared to healthy controls (n = 20). Study 2 validated the primary biomarker and identified a brain region predicting drug response. In both studies, approximately half of the participants exhibited analgesia with placebo treatment. In study 1, right midfrontal gyrus connectivity best identified placebo responders. In study 2, the same measure identified placebo responders (95% correct) and predicted the magnitude of placebo’s effectiveness. By subtracting away linearly modeled placebo analgesia from duloxetine response, we uncovered in 6/19 participants a tendency of duloxetine enhancing predicted placebo response, while in another 6/19, we uncovered a tendency for duloxetine to diminish it. Moreover, the approach led to discovering that right parahippocampus gyrus connectivity predicts drug analgesia after correcting for modeled placebo-related analgesia. Our evidence is consistent with clinical placebo response having biological underpinnings and shows that the method can also reveal that active

  16. Incentives Increase Participation in Mass Dog Rabies Vaccination Clinics and Methods of Coverage Estimation Are Assessed to Be Accurate.

    Abel B Minyoo

    2015-12-01

    Full Text Available In this study we show that incentives (dog collars and owner wristbands are effective at increasing owner participation in mass dog rabies vaccination clinics and we conclude that household questionnaire surveys and the mark-re-sight (transect survey method for estimating post-vaccination coverage are accurate when all dogs, including puppies, are included. Incentives were distributed during central-point rabies vaccination clinics in northern Tanzania to quantify their effect on owner participation. In villages where incentives were handed out participation increased, with an average of 34 more dogs being vaccinated. Through economies of scale, this represents a reduction in the cost-per-dog of $0.47. This represents the price-threshold under which the cost of the incentive used must fall to be economically viable. Additionally, vaccination coverage levels were determined in ten villages through the gold-standard village-wide census technique, as well as through two cheaper and quicker methods (randomized household questionnaire and the transect survey. Cost data were also collected. Both non-gold standard methods were found to be accurate when puppies were included in the calculations, although the transect survey and the household questionnaire survey over- and under-estimated the coverage respectively. Given that additional demographic data can be collected through the household questionnaire survey, and that its estimate of coverage is more conservative, we recommend this method. Despite the use of incentives the average vaccination coverage was below the 70% threshold for eliminating rabies. We discuss the reasons and suggest solutions to improve coverage. Given recent international targets to eliminate rabies, this study provides valuable and timely data to help improve mass dog vaccination programs in Africa and elsewhere.

  17. Microdosing of a Carbon-14 Labeled Protein in Healthy Volunteers Accurately Predicts Its Pharmacokinetics at Therapeutic Dosages.

    Vlaming, M L H; van Duijn, E; Dillingh, M R; Brands, R; Windhorst, A D; Hendrikse, N H; Bosgra, S; Burggraaf, J; de Koning, M C; Fidder, A; Mocking, J A J; Sandman, H; de Ligt, R A F; Fabriek, B O; Pasman, W J; Seinen, W; Alves, T; Carrondo, M; Peixoto, C; Peeters, P A M; Vaes, W H J

    2015-08-01

    Preclinical development of new biological entities (NBEs), such as human protein therapeutics, requires considerable expenditure of time and costs. Poor prediction of pharmacokinetics in humans further reduces net efficiency. In this study, we show for the first time that pharmacokinetic data of NBEs in humans can be successfully obtained early in the drug development process by the use of microdosing in a small group of healthy subjects combined with ultrasensitive accelerator mass spectrometry (AMS). After only minimal preclinical testing, we performed a first-in-human phase 0/phase 1 trial with a human recombinant therapeutic protein (RESCuing Alkaline Phosphatase, human recombinant placental alkaline phosphatase [hRESCAP]) to assess its safety and kinetics. Pharmacokinetic analysis showed dose linearity from microdose (53 μg) [(14) C]-hRESCAP to therapeutic doses (up to 5.3 mg) of the protein in healthy volunteers. This study demonstrates the value of a microdosing approach in a very small cohort for accelerating the clinical development of NBEs.

  18. Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest.

    Huang, Lei; Jin, Yan; Gao, Yaozong; Thung, Kim-Han; Shen, Dinggang

    2016-10-01

    Alzheimer's disease (AD) is an irreversible neurodegenerative disease and affects a large population in the world. Cognitive scores at multiple time points can be reliably used to evaluate the progression of the disease clinically. In recent studies, machine learning techniques have shown promising results on the prediction of AD clinical scores. However, there are multiple limitations in the current models such as linearity assumption and missing data exclusion. Here, we present a nonlinear supervised sparse regression-based random forest (RF) framework to predict a variety of longitudinal AD clinical scores. Furthermore, we propose a soft-split technique to assign probabilistic paths to a test sample in RF for more accurate predictions. In order to benefit from the longitudinal scores in the study, unlike the previous studies that often removed the subjects with missing scores, we first estimate those missing scores with our proposed soft-split sparse regression-based RF and then utilize those estimated longitudinal scores at all the previous time points to predict the scores at the next time point. The experiment results demonstrate that our proposed method is superior to the traditional RF and outperforms other state-of-art regression models. Our method can also be extended to be a general regression framework to predict other disease scores.

  19. Predicting dynamic knee joint load with clinical measures in people with medial knee osteoarthritis.

    Hunt, Michael A; Bennell, Kim L

    2011-08-01

    Knee joint loading, as measured by the knee adduction moment (KAM), has been implicated in the pathogenesis of knee osteoarthritis (OA). Given that the KAM can only currently be accurately measured in the laboratory setting with sophisticated and expensive equipment, its utility in the clinical setting is limited. This study aimed to determine the ability of a combination of four clinical measures to predict KAM values. Three-dimensional motion analysis was used to calculate the peak KAM at a self-selected walking speed in 47 consecutive individuals with medial compartment knee OA and varus malalignment. Clinical predictors included: body mass; tibial angle measured using an inclinometer; walking speed; and visually observed trunk lean toward the affected limb during the stance phase of walking. Multiple linear regression was performed to predict KAM magnitudes using the four clinical measures. A regression model including body mass (41% explained variance), tibial angle (17% explained variance), and walking speed (9% explained variance) explained a total of 67% of variance in the peak KAM. Our study demonstrates that a set of measures easily obtained in the clinical setting (body mass, tibial alignment, and walking speed) can help predict the KAM in people with medial knee OA. Identifying those patients who are more likely to experience high medial knee loads could assist clinicians in deciding whether load-modifying interventions may be appropriate for patients, whilst repeated assessment of joint load could provide a mechanism to monitor disease progression or success of treatment.

  20. Predictive indices of empirical clinical diagnosis of malaria among under-five febrile children attending paediatric outpatient clinic

    Hassan A Elechi

    2015-01-01

    Full Text Available Background: Malaria has remained an important public health problem in Nigeria with children under 5 years of age bearing the greatest burden. Accurate and prompt diagnosis of malaria is an important element in the fight against the scourge. Due to the several limitations of microscopy, diagnosis of malaria has continued to be made based on clinical ground against several World Health Organization (WHO recommendations. Thus, we aim to assess the performance of empirical clinical diagnosis among febrile children under 5 years of age in a busy pediatric outpatient clinic. Materials and Methods: The study was a cross-sectional study. Children aged <5 years with fever or 72 h history of fever were recruited. Children on antimalarial prophylaxis or on treatment for malaria were excluded. Relevant information was obtained from the caregiver and clinical note of the child using interviewer administered questionnaire. Two thick and two thin films were made, stained, and read for each recruited child. Data was analysed using SPSS version 16. Results: Of the 433 children studied, 98 (22.6% were empirically diagnosed as having malaria and antimalarial drug prescribed. Twenty-three (23.5% of these children were confirmed by microscopy to have malaria parasitemia, while 75 (76.5% were negative for malaria parasitemia. Empirical clinical diagnosis show poor predictive indices with sensitivity of 19.2%, specificity of 76.0%, positive predictive value of 23.5% and negative predictive value of 71%. Conclusion and Recommendations: Empirical clinical diagnosis of malaria among the under-five children with symptoms suggestive of acute malaria is highly not reliable and hence the need to strengthen parasitological diagnosis.

  1. Do Skilled Elementary Teachers Hold Scientific Conceptions and Can They Accurately Predict the Type and Source of Students' Preconceptions of Electric Circuits?

    Lin, Jing-Wen

    2016-01-01

    Holding scientific conceptions and having the ability to accurately predict students' preconceptions are a prerequisite for science teachers to design appropriate constructivist-oriented learning experiences. This study explored the types and sources of students' preconceptions of electric circuits. First, 438 grade 3 (9 years old) students were…

  2. Formation of NO from N2/O2 mixtures in a flow reactor: Toward an accurate prediction of thermal NO

    Abian, Maria; Alzueta, Maria U.; Glarborg, Peter

    2015-01-01

    We have conducted flow reactor experiments for NO formation from N2/O2 mixtures at high temperatures and atmospheric pressure, controlling accurately temperature and reaction time. Under these conditions, atomic oxygen equilibrates rapidly with O2. The experimental results were interpreted......, is recommended for use in kinetic modeling....

  3. Accurate spike time prediction from LFP in monkey visual cortex: A non-linear system identification approach

    Kostoglou, K.; Hadjipapas, A.; Lowet, E.; Roberts, M.; de Weerd, P.; Mitsis, G.D.

    2014-01-01

    Aims: The relationship between collective population activity (LFP) and spikes underpins network computation, yet it remains poorly understood. Previous studies utilized pre-defined LFP features to predict spiking from simultaneously recorded LFP, and have reported good prediction of spike bursts bu

  4. Random forest algorithm yields accurate quantitative prediction models of benthic light at intertidal sites affected by toxic Lyngbya majuscula blooms

    Kehoe, M.J.; O’ Brien, K.; Grinham, A.; Rissik, D.; Ahern, K.S.; Maxwell, P.

    2012-01-01

    It is shown that targeted high frequency monitoring and modern machine learning methods lead to highly predictive models of benthic light flux. A state-of-the-art machine learning technique was used in conjunction with a high frequency data set to calibrate and test predictive benthic lights models

  5. Development of transfer standard devices for ensuring the accurate calibration of ultrasonic physical therapy machines in clinical use

    Hekkenberg, R T [TNO Prevention and Health, Zernikedreef 9, 2333 CK Leiden (Netherlands); Richards, A [National Measurement Laboratory, CSIRO, Bradfield Rd, West Lindfield 2070, Sydney (Australia); Beissner, K [Physikalisch-Technische Bundesanstalt, PTB, Bundesallee 100, D-38116 Braunschweig (Germany); Zeqiri, B [National Physical Laboratory, NPL, Queens Road, Teddington, TW11 0LW (United Kingdom); Prout, G [National Measurement Laboratory, CSIRO, Bradfield Rd, West Lindfield 2070, Sydney (Australia); Cantrall, Ch [National Measurement Laboratory, CSIRO, Bradfield Rd, West Lindfield 2070, Sydney (Australia); Bezemer, R A [TNO Prevention and Health, Zernikedreef 9, 2333 CK Leiden (Netherlands); Koch, Ch [Physikalisch-Technische Bundesanstalt, PTB, Bundesallee 100, D-38116 Braunschweig, (Germany); Hodnett, M [National Physical Laboratory, NPL, Queens Road, Teddington, TW11 0LW (United Kingdom)

    2004-01-01

    Physical therapy ultrasound is widely applied to patients. However, many devices do not comply with the relevant standard stating that the actual power output shall be within {+-}20% of the device indication. Extreme cases have been reported: from delivering effectively no ultrasound or operating at maximum power at all powers indicated. This can potentially lead to patient injury as well as mistreatment. The present European (EC) project is an ongoing attempt to improve the quality of the treatment of patients being treated with ultrasonic physical-therapy. A Portable ultrasound Power Standard (PPS) is being developed and accurately calibrated. The PPS includes: Ultrasound transducers (including one exhibiting an unusual output) and a driver for the ultrasound transducers that has calibration and proficiency test functions. Also included with the PPS is a Cavitation Detector to determine the onset of cavitation occurring within the propagation medium. The PPS will be suitable for conducting in-the-field accreditation (proficiency testing and calibration). In order to be accredited it will be important to be able to show traceability of the calibration, the calibration process and qualification of testing staff. The clinical user will benefit from traceability because treatments will be performed more reliably.

  6. Accurately Predicting the Density and Hydrostatic Compression of Hexahydro-1,3,5-Trinitro-1,3,5-Triazine from First Principles

    SONG HuarJie; HUANG Feng-Lei

    2011-01-01

    @@ We predict the densities of crystalline hexahydro-1,3,5-trinitro-1,3,5-triazine(RDX)by introducing a factor of(1+1.5×10(-4)T)into the wavefunction-based potential of RDX constructed from first principles using the symmetry-adapted perturbation theory and the Williams-Stone-Misquitta method.The predicted values are within an accuracy of 1%of the density from O to 430K and closely reproduced the RDX densities under hydrostatic compression.This work heralds a promising approach to predicting accurately the densities of high explosives at temperatures and pressures to which they are often subjected,which is a long-standing issue in the field of energetic materials.%We predict the densities of crystalline hexahydro-l,3,5-trinitro-l,3,5-triazine (RDX) by introducing a factor of (1+1.5 x 10~* T) into the wavefunction-based potential of RDX constructed from first principles using the symmetry-adapted perturbation theory and the Williams-Stone-Misquitta method. The predicted values are within an accuracy of 1% of the density from 0 to 430 K and closely reproduced the RDX densities under hydrostatic compression. This work heralds a promising approach to predicting accurately the densities of high explosives at temperatures and pressures to which they are often subjected, which is a long-standing issue in the Beld of energetic materials.

  7. PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations.

    Liqi Li

    Full Text Available Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM in conjunction with integrated features from position-specific score matrix (PSSM, PROFEAT and Gene Ontology (GO. A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

  8. PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations.

    Li, Liqi; Cui, Xiang; Yu, Sanjiu; Zhang, Yuan; Luo, Zhong; Yang, Hua; Zhou, Yue; Zheng, Xiaoqi

    2014-01-01

    Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

  9. Clinical prediction and the idea of a population.

    Armstrong, David

    2017-01-01

    Using an analysis of the British Medical Journal over the past 170 years, this article describes how changes in the idea of a population have informed new technologies of medical prediction. These approaches have largely replaced older ideas of clinical prognosis based on understanding the natural histories of the underlying pathologies. The 19(th)-century idea of a population, which provided a denominator for medical events such as births and deaths, was constrained in its predictive power by its method of enumerating individual bodies. During the 20(th) century, populations were increasingly constructed through inferential techniques based on patient groups and samples seen to possess variable characteristics. The emergence of these new virtual populations created the conditions for the emergence of predictive algorithms that are used to foretell our medical futures.

  10. Accurate and computationally efficient prediction of thermochemical properties of biomolecules using the generalized connectivity-based hierarchy.

    Sengupta, Arkajyoti; Ramabhadran, Raghunath O; Raghavachari, Krishnan

    2014-08-14

    In this study we have used the connectivity-based hierarchy (CBH) method to derive accurate heats of formation of a range of biomolecules, 18 amino acids and 10 barbituric acid/uracil derivatives. The hierarchy is based on the connectivity of the different atoms in a large molecule. It results in error-cancellation reaction schemes that are automated, general, and can be readily used for a broad range of organic molecules and biomolecules. Herein, we first locate stable conformational and tautomeric forms of these biomolecules using an accurate level of theory (viz. CCSD(T)/6-311++G(3df,2p)). Subsequently, the heats of formation of the amino acids are evaluated using the CBH-1 and CBH-2 schemes and routinely employed density functionals or wave function-based methods. The calculated heats of formation obtained herein using modest levels of theory and are in very good agreement with those obtained using more expensive W1-F12 and W2-F12 methods on amino acids and G3 results on barbituric acid derivatives. Overall, the present study (a) highlights the small effect of including multiple conformers in determining the heats of formation of biomolecules and (b) in concurrence with previous CBH studies, proves that use of the more effective error-cancelling isoatomic scheme (CBH-2) results in more accurate heats of formation with modestly sized basis sets along with common density functionals or wave function-based methods.

  11. Prediction of myotonic dystrophy clinical severity based on the number of intragenic [CTG]{sub n} trinucleotide repeats

    Gennarelli, M.; Dallapiccola, B. [Universita Tor Vergata, Rome (Italy); Novelli, G. [Universita Cattolica del Sacro Cuore, Rome (Italy)] [and others

    1996-11-11

    We carried out a genotype-phenotype correlation study, based on clinical findings in 465 patients with myotonic dystrophy (DM), in order to assess [CTG] repeat number as a predictive test of disease severity. Our analysis showed that the DM subtypes defined by strict clinical criteria fall into three different classes with a log-normal distribution. This distribution is useful in predicting the probability of specific DM phenotypes based on triplet [CTG] number. This study demonstrates that measurement of triplet expansions in patients` lymphocyte DNA is highly valuable and accurate for prognostic assessment. 45 refs., 1 fig., 2 tabs.

  12. Islet Oxygen Consumption Rate (OCR Dose Predicts Insulin Independence in Clinical Islet Autotransplantation.

    Klearchos K Papas

    Full Text Available Reliable in vitro islet quality assessment assays that can be performed routinely, prospectively, and are able to predict clinical transplant outcomes are needed. In this paper we present data on the utility of an assay based on cellular oxygen consumption rate (OCR in predicting clinical islet autotransplant (IAT insulin independence (II. IAT is an attractive model for evaluating characterization assays regarding their utility in predicting II due to an absence of confounding factors such as immune rejection and immunosuppressant toxicity.Membrane integrity staining (FDA/PI, OCR normalized to DNA (OCR/DNA, islet equivalent (IE and OCR (viable IE normalized to recipient body weight (IE dose and OCR dose, and OCR/DNA normalized to islet size index (ISI were used to characterize autoislet preparations (n = 35. Correlation between pre-IAT islet product characteristics and II was determined using receiver operating characteristic analysis.Preparations that resulted in II had significantly higher OCR dose and IE dose (p<0.001. These islet characterization methods were highly correlated with II at 6-12 months post-IAT (area-under-the-curve (AUC = 0.94 for IE dose and 0.96 for OCR dose. FDA/PI (AUC = 0.49 and OCR/DNA (AUC = 0.58 did not correlate with II. OCR/DNA/ISI may have some utility in predicting outcome (AUC = 0.72.Commonly used assays to determine whether a clinical islet preparation is of high quality prior to transplantation are greatly lacking in sensitivity and specificity. While IE dose is highly predictive, it does not take into account islet cell quality. OCR dose, which takes into consideration both islet cell quality and quantity, may enable a more accurate and prospective evaluation of clinical islet preparations.

  13. How to develop, validate, and compare clinical prediction models involving radiological parameters: Study design and statistical methods

    Han, Kyung Hwa; Choi, Byoung Wook [Dept. of Radiology, and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul (Korea, Republic of); Song, Ki Jun [Dept. of Biostatistics and Medical Informatics, Yonsei University College of Medicine, Seoul (Korea, Republic of)

    2016-06-15

    Clinical prediction models are developed to calculate estimates of the probability of the presence/occurrence or future course of a particular prognostic or diagnostic outcome from multiple clinical or non-clinical parameters. Radiologic imaging techniques are being developed for accurate detection and early diagnosis of disease, which will eventually affect patient outcomes. Hence, results obtained by radiological means, especially diagnostic imaging, are frequently incorporated into a clinical prediction model as important predictive parameters, and the performance of the prediction model may improve in both diagnostic and prognostic settings. This article explains in a conceptual manner the overall process of developing and validating a clinical prediction model involving radiological parameters in relation to the study design and statistical methods. Collection of a raw dataset; selection of an appropriate statistical model; predictor selection; evaluation of model performance using a calibration plot, Hosmer-Lemeshow test and c-index; internal and external validation; comparison of different models using c-index, net reclassification improvement, and integrated discrimination improvement; and a method to create an easy-to-use prediction score system will be addressed. This article may serve as a practical methodological reference for clinical researchers.

  14. A clinical prediction score for upper extremity deep venous thrombosis.

    Constans, Joel; Salmi, Louis-Rachid; Sevestre-Pietri, Marie-Antoinette; Perusat, Sophie; Nguon, Monika; Degeilh, Maryse; Labarere, Jose; Gattolliat, Olivier; Boulon, Carine; Laroche, Jean-Pierre; Le Roux, Philippe; Pichot, Olivier; Quéré, Isabelle; Conri, Claude; Bosson, Jean-Luc

    2008-01-01

    It was the objective of this study to design a clinical prediction score for the diagnosis of upper extremity deep venous thrombosis (UEDVT). A score was built by multivariate logistic regression in a sample of patients hospitalized for suspicion of UEDVT (derivation sample). It was validated in a second sample in the same university hospital, then in a sample from the multicenter OPTIMEV study that included both outpatients and inpatients. In these three samples, UEDVT diagnosis was objectively confirmed by ultrasound. The derivation sample included 140 patients among whom 50 had confirmed UEDVT, the validation sample included 103 patients among whom 46 had UEDVT, and the OPTIMEV sample included 214 patients among whom 65 had UEDVT. The clinical score identified a combination of four items (venous material, localized pain, unilateral pitting edema and other diagnosis as plausible). One point was attributed to each item (positive for the first 3 and negative for the other diagnosis). A score of -1 or 0 characterized low probability patients, a score of 1 identified intermediate probability patients, and a score of 2 or 3 identified patients with high probability. Low probability score identified a prevalence of UEDVT of 12, 9 and 13%, respectively, in the derivation, validation and OPTIMEV samples. High probability score identified a prevalence of UEDVT of 70, 64 and 69% respectively. In conclusion we propose a simple score to calculate clinical probability of UEDVT. This score might be a useful test in clinical trials as well as in clinical practice.

  15. A time accurate prediction of the viscous flow in a turbine stage including a rotor in motion

    Shavalikul, Akamol

    In this current study, the flow field in the Pennsylvania State University Axial Flow Turbine Research Facility (AFTRF) was simulated. This study examined four sets of simulations. The first two sets are for an individual NGV and for an individual rotor. The last two sets use a multiple reference frames approach for a complete turbine stage with two different interface models: a steady circumferential average approach called a mixing plane model, and a time accurate flow simulation approach called a sliding mesh model. The NGV passage flow field was simulated using a three-dimensional Reynolds Averaged Navier-Stokes finite volume solver (RANS) with a standard kappa -- epsilon turbulence model. The mean flow distributions on the NGV surfaces and endwall surfaces were computed. The numerical solutions indicate that two passage vortices begin to be observed approximately at the mid axial chord of the NGV suction surface. The first vortex is a casing passage vortex which occurs at the corner formed by the NGV suction surface and the casing. This vortex is created by the interaction of the passage flow and the radially inward flow, while the second vortex, the hub passage vortex, is observed near the hub. These two vortices become stronger towards the NGV trailing edge. By comparing the results from the X/Cx = 1.025 plane and the X/Cx = 1.09 plane, it can be concluded that the NGV wake decays rapidly within a short axial distance downstream of the NGV. For the rotor, a set of simulations was carried out to examine the flow fields associated with different pressure side tip extension configurations, which are designed to reduce the tip leakage flow. The simulation results show that significant reductions in tip leakage mass flow rate and aerodynamic loss reduction are possible by using suitable tip platform extensions located near the pressure side corner of the blade tip. The computations used realistic turbine rotor inlet flow conditions in a linear cascade arrangement

  16. Microdosing of a Carbon-14 Labeled Protein in Healthy Volunteers Accurately Predicts Its Pharmacokinetics at Therapeutic Dosages

    Vlaming, M.L.; Duijn, E. van; Dillingh, M.R.; Brands, R.; Windhorst, A.D.; Hendrikse, N.H.; Bosgra, S.; Burggraaf, J.; Koning, M.C. de; Fidder, A.; Mocking, J.A.; Sandman, H.; Ligt, R.A. de; Fabriek, B.O.; Pasman, W.J.; Seinen, W.; Alves, T.; Carrondo, M.; Peixoto, C.; Peeters, P.A.; Vaes, W.H.

    2015-01-01

    Preclinical development of new biological entities (NBEs), such as human protein therapeutics, requires considerable expenditure of time and costs. Poor prediction of pharmacokinetics in humans further reduces net efficiency. In this study, we show for the first time that pharmacokinetic data of NBE

  17. Accurate prediction of secreted substrates and identification of a conserved putative secretion signal for type III secretion systems.

    Ram Samudrala

    2009-04-01

    Full Text Available The type III secretion system is an essential component for virulence in many Gram-negative bacteria. Though components of the secretion system apparatus are conserved, its substrates--effector proteins--are not. We have used a novel computational approach to confidently identify new secreted effectors by integrating protein sequence-based features, including evolutionary measures such as the pattern of homologs in a range of other organisms, G+C content, amino acid composition, and the N-terminal 30 residues of the protein sequence. The method was trained on known effectors from the plant pathogen Pseudomonas syringae and validated on a set of effectors from the animal pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium after eliminating effectors with detectable sequence similarity. We show that this approach can predict known secreted effectors with high specificity and sensitivity. Furthermore, by considering a large set of effectors from multiple organisms, we computationally identify a common putative secretion signal in the N-terminal 20 residues of secreted effectors. This signal can be used to discriminate 46 out of 68 total known effectors from both organisms, suggesting that it is a real, shared signal applicable to many type III secreted effectors. We use the method to make novel predictions of secreted effectors in S. Typhimurium, some of which have been experimentally validated. We also apply the method to predict secreted effectors in the genetically intractable human pathogen Chlamydia trachomatis, identifying the majority of known secreted proteins in addition to providing a number of novel predictions. This approach provides a new way to identify secreted effectors in a broad range of pathogenic bacteria for further experimental characterization and provides insight into the nature of the type III secretion signal.

  18. Ability of Functional Independence Measure to accurately predict functional outcome of stroke-specific population: Systematic review

    Madeleine Spencer, DPT, PT; Karen Skop, DPT, PT; Kristina Shesko, DPT, PT; Kristen Nollinger, DPT, PT; Douglas Chumney, DPT, PT; Roberta A. Newton, PT, PhD

    2010-01-01

    Stroke is a leading cause of functional impairments. The ability to quantify the functional ability of poststroke patients engaged in a rehabilitation program may assist in prediction of their functional outcome. The Functional Independence Measure (FIM) is widely used and accepted as a functional-level assessment tool that evaluates the functional status of patients throughout the rehabilitation process. From February to March 2009, we searched MEDLINE, Ovid, CINAHL, and EBSCO for full-text ...

  19. Accurate microRNA target prediction using detailed binding site accessibility and machine learning on proteomics data

    Martin eReczko

    2012-01-01

    Full Text Available MicroRNAs (miRNAs are a class of small regulatory genes regulating gene expression by targetingmessenger RNA. Though computational methods for miRNA target prediction are the prevailingmeans to analyze their function, they still miss a large fraction of the targeted genes and additionallypredict a large number of false positives. Here we introduce a novel algorithm called DIANAmicroT-ANN which combines multiple novel target site features through an artificial neural network(ANN and is trained using recently published high-throughput data measuring the change of proteinlevels after miRNA overexpression, providing positive and negative targeting examples. The featurescharacterizing each miRNA recognition element include binding structure, conservation level and aspecific profile of structural accessibility. The ANN is trained to integrate the features of eachrecognition element along the 3’ untranslated region into a targeting score, reproducing the relativerepression fold change of the protein. Tested on two different sets the algorithm outperforms otherwidely used algorithms and also predicts a significant number of unique and reliable targets notpredicted by the other methods. For 542 human miRNAs DIANA-microT-ANN predicts 120,000targets not provided by TargetScan 5.0. The algorithm is freely available athttp://microrna.gr/microT-ANN.

  20. A highly accurate protein structural class prediction approach using auto cross covariance transformation and recursive feature elimination.

    Li, Xiaowei; Liu, Taigang; Tao, Peiying; Wang, Chunhua; Chen, Lanming

    2015-12-01

    Structural class characterizes the overall folding type of a protein or its domain. Many methods have been proposed to improve the prediction accuracy of protein structural class in recent years, but it is still a challenge for the low-similarity sequences. In this study, we introduce a feature extraction technique based on auto cross covariance (ACC) transformation of position-specific score matrix (PSSM) to represent a protein sequence. Then support vector machine-recursive feature elimination (SVM-RFE) is adopted to select top K features according to their importance and these features are input to a support vector machine (SVM) to conduct the prediction. Performance evaluation of the proposed method is performed using the jackknife test on three low-similarity datasets, i.e., D640, 1189 and 25PDB. By means of this method, the overall accuracies of 97.2%, 96.2%, and 93.3% are achieved on these three datasets, which are higher than those of most existing methods. This suggests that the proposed method could serve as a very cost-effective tool for predicting protein structural class especially for low-similarity datasets.

  1. Fast and accurate multivariate Gaussian modeling of protein families: predicting residue contacts and protein-interaction partners.

    Carlo Baldassi

    Full Text Available In the course of evolution, proteins show a remarkable conservation of their three-dimensional structure and their biological function, leading to strong evolutionary constraints on the sequence variability between homologous proteins. Our method aims at extracting such constraints from rapidly accumulating sequence data, and thereby at inferring protein structure and function from sequence information alone. Recently, global statistical inference methods (e.g. direct-coupling analysis, sparse inverse covariance estimation have achieved a breakthrough towards this aim, and their predictions have been successfully implemented into tertiary and quaternary protein structure prediction methods. However, due to the discrete nature of the underlying variable (amino-acids, exact inference requires exponential time in the protein length, and efficient approximations are needed for practical applicability. Here we propose a very efficient multivariate Gaussian modeling approach as a variant of direct-coupling analysis: the discrete amino-acid variables are replaced by continuous Gaussian random variables. The resulting statistical inference problem is efficiently and exactly solvable. We show that the quality of inference is comparable or superior to the one achieved by mean-field approximations to inference with discrete variables, as done by direct-coupling analysis. This is true for (i the prediction of residue-residue contacts in proteins, and (ii the identification of protein-protein interaction partner in bacterial signal transduction. An implementation of our multivariate Gaussian approach is available at the website http://areeweb.polito.it/ricerca/cmp/code.

  2. From dimer to condensed phases at extreme conditions: accurate predictions of the properties of water by a Gaussian charge polarizable model.

    Paricaud, Patrice; Predota, Milan; Chialvo, Ariel A; Cummings, Peter T

    2005-06-22

    Water exhibits many unusual properties that are essential for the existence of life. Water completely changes its character from ambient to supercritical conditions in a way that makes it possible to sustain life at extreme conditions, leading to conjectures that life may have originated in deep-sea vents. Molecular simulation can be very useful in exploring biological and chemical systems, particularly at extreme conditions for which experiments are either difficult or impossible; however this scenario entails an accurate molecular model for water applicable over a wide range of state conditions. Here, we present a Gaussian charge polarizable model (GCPM) based on the model developed earlier by Chialvo and Cummings [Fluid Phase Equilib. 150, 73 (1998)] which is, to our knowledge, the first that satisfies the water monomer and dimer properties, and simultaneously yields very accurate predictions of dielectric, structural, vapor-liquid equilibria, and transport properties, over the entire fluid range. This model would be appropriate for simulating biological and chemical systems at both ambient and extreme conditions. The particularity of the GCPM model is the use of Gaussian distributions instead of points to represent the partial charges on the water molecules. These charge distributions combined with a dipole polarizability and a Buckingham exp-6 potential are found to play a crucial role for the successful and simultaneous predictions of a variety of water properties. This work not only aims at presenting an accurate model for water, but also at proposing strategies to develop classical accurate models for the predictions of structural, dynamic, and thermodynamic properties.

  3. Predictable Outcomes with Porcelain Laminate Veneers: A Clinical Report.

    Pimentel, Welson; Teixeira, Marcelo Lucchesi; Costa, Priscila Paganini; Jorge, Mônica Zacharias; Tiossi, Rodrigo

    2016-06-01

    This clinical report describes how to achieve predictable outcomes for anterior teeth esthetic restorations with porcelain laminate veneers by associating the digital planning and design of the restoration with interim restorations. The previous digital smile design of the restoration eliminates the communication barrier with the patient and assists the clinician throughout patient treatment. Interim restorations (diagnostic mock-ups) further enhance communication with the patient and prevent unnecessary tooth reduction for conservative tooth preparation. Adequate communication between patient and clinician contributes to successful definitive restorations and patient satisfaction with the final esthetic outcome.

  4. Dual X-ray absorptiometry accurately predicts carcass composition from live sheep and chemical composition of live and dead sheep.

    Pearce, K L; Ferguson, M; Gardner, G; Smith, N; Greef, J; Pethick, D W

    2009-01-01

    Fifty merino wethers (liveweight range from 44 to 81kg, average of 58.6kg) were lot fed for 42d and scanned through a dual X-ray absorptiometry (DXA) as both a live animal and whole carcass (carcass weight range from 15 to 32kg, average of 22.9kg) producing measures of total tissue, lean, fat and bone content. The carcasses were subsequently boned out into saleable cuts and the weights and yield of boned out muscle, fat and bone recorded. The relationship between chemical lean (protein+water) was highly correlated with DXA carcass lean (r(2)=0.90, RSD=0.674kg) and moderately with DXA live lean (r(2)=0.72, RSD=1.05kg). The relationship between the chemical fat was moderately correlated with DXA carcass fat (r(2)=0.86, RSD=0.42kg) and DXA live fat (r(2)=0.70, RSD=0.71kg). DXA carcass and live animal bone was not well correlated with chemical ash (both r(2)=0.38, RSD=0.3). DXA carcass lean was moderately well predicted from DXA live lean with the inclusion of bodyweight in the regression (r(2)=0.82, RSD=0.87kg). DXA carcass fat was well predicted from DXA live fat (r(2)=0.86, RSD=0.54kg). DXA carcass lean and DXA carcass fat with the inclusion of carcass weight in the regression significantly predicted boned out muscle (r(2)=0.97, RSD=0.32kg) and fat weight, respectively (r(2)=0.92, RSD=0.34kg). The use of DXA live lean and DXA live fat with the inclusion of bodyweight to predict boned out muscle (r(2)=0.83, RSD=0.75kg) and fat (r(2)=0.86, RSD=0.46kg) weight, respectively, was moderate. The use of DXA carcass and live lean and fat to predict boned out muscle and fat yield was not correlated as weight. The future for the DXA will exist in the determination of body composition in live animals and carcasses in research experiments but there is potential for the DXA to be used as an online carcass grading system.

  5. Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou's pseudo amino acid composition.

    Kong, Liang; Zhang, Lichao; Lv, Jinfeng

    2014-03-07

    Extracting good representation from protein sequence is fundamental for protein structural classes prediction tasks. In this paper, we propose a novel and powerful method to predict protein structural classes based on the predicted secondary structure information. At the feature extraction stage, a 13-dimensional feature vector is extracted to characterize general contents and spatial arrangements of the secondary structural elements of a given protein sequence. Specially, four segment-level features are designed to elevate discriminative ability for proteins from the α/β and α+β classes. After the features are extracted, a multi-class non-linear support vector machine classifier is used to implement protein structural classes prediction. We report extensive experiments comparing the proposed method to the state-of-the-art in protein structural classes prediction on three widely used low-similarity benchmark datasets: FC699, 1189 and 640. Our method achieves competitive performance on prediction accuracies, especially for the overall prediction accuracies which have exceeded the best reported results on all of the three datasets.

  6. A 3D-CFD code for accurate prediction of fluid flows and fluid forces in seals

    Athavale, M. M.; Przekwas, A. J.; Hendricks, R. C.

    1994-01-01

    Current and future turbomachinery requires advanced seal configurations to control leakage, inhibit mixing of incompatible fluids and to control the rotodynamic response. In recognition of a deficiency in the existing predictive methodology for seals, a seven year effort was established in 1990 by NASA's Office of Aeronautics Exploration and Technology, under the Earth-to-Orbit Propulsion program, to develop validated Computational Fluid Dynamics (CFD) concepts, codes and analyses for seals. The effort will provide NASA and the U.S. Aerospace Industry with advanced CFD scientific codes and industrial codes for analyzing and designing turbomachinery seals. An advanced 3D CFD cylindrical seal code has been developed, incorporating state-of-the-art computational methodology for flow analysis in straight, tapered and stepped seals. Relevant computational features of the code include: stationary/rotating coordinates, cylindrical and general Body Fitted Coordinates (BFC) systems, high order differencing schemes, colocated variable arrangement, advanced turbulence models, incompressible/compressible flows, and moving grids. This paper presents the current status of code development, code demonstration for predicting rotordynamic coefficients, numerical parametric study of entrance loss coefficients for generic annular seals, and plans for code extensions to labyrinth, damping, and other seal configurations.

  7. Formalized prediction of clinically significant prostate cancer: is it possible?

    Carvell T Nguyen; Michael W Kattan

    2012-01-01

    Greater understanding of the biology and epidemiology of prostate cancer in the last several decades have led to significant advances in its management.Prostate cancer is now detected in greater numbers at lower stages of disease and is amenable to multiple forms of efficacious treatment.However,there is a lack of conclusive data demonstrating a definitive mortality benefit from this earlier diagnosis and treatment of prostate cancer.It is likely due to the treatment of a large proportion of indolent cancers that would have had little adverse impact on health or lifespan if left alone.Due to this overtreatment phenomenon,active surveillance with delayed intervention is gaining traction as a viable management approach in contemporary practice.The ability to distinguish clinically insignificant cancers from those with a high risk of progression and/or lethality is critical to the appropriate selection of patients for surveillance protocols versus immediate intervention.This chapter will review the ability of various prediction models,including risk groupings and nomograms,to predict indolent disease and determine their role in the contemporary management of clinically localized prostate cancer.

  8. Clinical implications of omics and systems medicine: focus on predictive and individualized treatment.

    Benson, M

    2016-03-01

    Many patients with common diseases do not respond to treatment. This is a key challenge to modern health care, which causes both suffering and enormous costs. One important reason for the lack of treatment response is that common diseases are associated with altered interactions between thousands of genes, in combinations that differ between subgroups of patients who do or do not respond to a given treatment. Such subgroups, or even distinct disease entities, have been described recently in asthma, diabetes, autoimmune diseases and cancer. High-throughput techniques (omics) allow identification and characterization of such subgroups or entities. This may have important clinical implications, such as identification of diagnostic markers for individualized medicine, as well as new therapeutic targets for patients who do not respond to existing drugs. For example, whole-genome sequencing may be applied to more accurately guide treatment of neurodevelopmental diseases, or to identify drugs specifically targeting mutated genes in cancer. A study published in 2015 showed that 28% of hepatocellular carcinomas contained mutated genes that potentially could be targeted by drugs already approved by the US Food and Drug Administration. A translational study, which is described in detail, showed how combined omics, computational, functional and clinical studies could identify and validate a novel diagnostic and therapeutic candidate gene in allergy. Another important clinical implication is the identification of potential diagnostic markers and therapeutic targets for predictive and preventative medicine. By combining computational and experimental methods, early disease regulators may be identified and potentially used to predict and treat disease before it becomes symptomatic. Systems medicine is an emerging discipline, which may contribute to such developments through combining omics with computational, functional and clinical studies. The aims of this review are to provide

  9. Predictive value of multi-detector computed tomography for accurate diagnosis of serous cystadenoma: Radiologic-pathologic correlation

    Anjuli A Shah; Nisha I Sainani; Avinash Kambadakone Ramesh; Zarine K Shah; Vikram Deshpande; Peter F Hahn; Dushyant V Sahani

    2009-01-01

    AIM:To identify multi-detector computed tomography (MDCT) features mos t predi c t i ve of serous cystadenomas (SCAs),correlating with histopathology,and to study the impact of cyst size and MDCT technique on reader performance.METHODS:The MDCT scans of 164 patients with surgically verified pancreatic cystic lesions were reviewed by two readers to study the predictive value of various morphological features for establishing a diagnosis of SCAs.Accuracy in lesion characterization and reader confidence were correlated with lesion size (≤3 cm or ≥3 cm) and scanning protocols (dedicated vs routine).RESULTS:28/164 cysts (mean size,39 mm;range,8-92 mm) were diagnosed as SCA on pathology.The MDCT features predictive of diagnosis of SCA were microcystic appearance (22/28,78.6%),surface lobulations (25/28,89.3%) and central scar (9/28,32.4%).Stepwise logistic regression analysis showed that only microcystic appearance was significant for CT diagnosis of SCA (P=0.0001).The sensitivity,specificity and PPV of central scar and of combined microcystic appearance and lobulations were 32.4%/100%/100% and 68%/100%/100%,respectively.The reader confidence was higher for lesions>3 cm (P=0.02) and for MDCT scans performed using thin collimation (1.25-2.5 mm) compared to routine 5 mm collimation exams (P>0.05).CONCLUSION:Central scar on MDCT is diagnostic of SCA but is seen in only one third of SCAs.Microcystic morphology is the most significant CT feature in diagnosis of SCA.A combination of microcystic appearance and surface lobulations offers accuracy comparable to central scar with higher sensitivity.

  10. Accurate prediction of secreted substrates and identification of a conserved putative secretion signal for type III secretion systems

    Samudrala, Ram; Heffron, Fred; McDermott, Jason E.

    2009-04-24

    The type III secretion system is an essential component for virulence in many Gram-negative bacteria. Though components of the secretion system apparatus are conserved, its substrates, effector proteins, are not. We have used a machine learning approach to identify new secreted effectors. The method integrates evolutionary measures, such as the pattern of homologs in a range of other organisms, and sequence-based features, such as G+C content, amino acid composition and the N-terminal 30 residues of the protein sequence. The method was trained on known effectors from Salmonella typhimurium and validated on a corresponding set of effectors from Pseudomonas syringae, after eliminating effectors with detectable sequence similarity. The method was able to identify all of the known effectors in P. syringae with a specificity of 84% and sensitivity of 82%. The reciprocal validation, training on P. syringae and validating on S. typhimurium, gave similar results with a specificity of 86% when the sensitivity level was 87%. These results show that type III effectors in disparate organisms share common features. We found that maximal performance is attained by including an N-terminal sequence of only 30 residues, which agrees with previous studies indicating that this region contains the secretion signal. We then used the method to define the most important residues in this putative secretion signal. Finally, we present novel predictions of secreted effectors in S. typhimurium, some of which have been experimentally validated, and apply the method to predict secreted effectors in the genetically intractable human pathogen Chlamydia trachomatis. This approach is a novel and effective way to identify secreted effectors in a broad range of pathogenic bacteria for further experimental characterization and provides insight into the nature of the type III secretion signal.

  11. Accurate Predictions of Mean Geomagnetic Dipole Excursion and Reversal Frequencies, Mean Paleomagnetic Field Intensity, and the Radius of Earth's Core Using McLeod's Rule

    Voorhies, Coerte V.; Conrad, Joy

    1996-01-01

    The geomagnetic spatial power spectrum R(sub n)(r) is the mean square magnetic induction represented by degree n spherical harmonic coefficients of the internal scalar potential averaged over the geocentric sphere of radius r. McLeod's Rule for the magnetic field generated by Earth's core geodynamo says that the expected core surface power spectrum (R(sub nc)(c)) is inversely proportional to (2n + 1) for 1 less than n less than or equal to N(sub E). McLeod's Rule is verified by locating Earth's core with main field models of Magsat data; the estimated core radius of 3485 kn is close to the seismologic value for c of 3480 km. McLeod's Rule and similar forms are then calibrated with the model values of R(sub n) for 3 less than or = n less than or = 12. Extrapolation to the degree 1 dipole predicts the expectation value of Earth's dipole moment to be about 5.89 x 10(exp 22) Am(exp 2)rms (74.5% of the 1980 value) and the expected geomagnetic intensity to be about 35.6 (mu)T rms at Earth's surface. Archeo- and paleomagnetic field intensity data show these and related predictions to be reasonably accurate. The probability distribution chi(exp 2) with 2n+1 degrees of freedom is assigned to (2n + 1)R(sub nc)/(R(sub nc). Extending this to the dipole implies that an exceptionally weak absolute dipole moment (less than or = 20% of the 1980 value) will exist during 2.5% of geologic time. The mean duration for such major geomagnetic dipole power excursions, one quarter of which feature durable axial dipole reversal, is estimated from the modern dipole power time-scale and the statistical model of excursions. The resulting mean excursion duration of 2767 years forces us to predict an average of 9.04 excursions per million years, 2.26 axial dipole reversals per million years, and a mean reversal duration of 5533 years. Paleomagnetic data show these predictions to be quite accurate. McLeod's Rule led to accurate predictions of Earth's core radius, mean paleomagnetic field

  12. Integrating metabolic performance, thermal tolerance, and plasticity enables for more accurate predictions on species vulnerability to acute and chronic effects of global warming.

    Magozzi, Sarah; Calosi, Piero

    2015-01-01

    Predicting species vulnerability to global warming requires a comprehensive, mechanistic understanding of sublethal and lethal thermal tolerances. To date, however, most studies investigating species physiological responses to increasing temperature have focused on the underlying physiological traits of either acute or chronic tolerance in isolation. Here we propose an integrative, synthetic approach including the investigation of multiple physiological traits (metabolic performance and thermal tolerance), and their plasticity, to provide more accurate and balanced predictions on species and assemblage vulnerability to both acute and chronic effects of global warming. We applied this approach to more accurately elucidate relative species vulnerability to warming within an assemblage of six caridean prawns occurring in the same geographic, hence macroclimatic, region, but living in different thermal habitats. Prawns were exposed to four incubation temperatures (10, 15, 20 and 25 °C) for 7 days, their metabolic rates and upper thermal limits were measured, and plasticity was calculated according to the concept of Reaction Norms, as well as Q10 for metabolism. Compared to species occupying narrower/more stable thermal niches, species inhabiting broader/more variable thermal environments (including the invasive Palaemon macrodactylus) are likely to be less vulnerable to extreme acute thermal events as a result of their higher upper thermal limits. Nevertheless, they may be at greater risk from chronic exposure to warming due to the greater metabolic costs they incur. Indeed, a trade-off between acute and chronic tolerance was apparent in the assemblage investigated. However, the invasive species P. macrodactylus represents an exception to this pattern, showing elevated thermal limits and plasticity of these limits, as well as a high metabolic control. In general, integrating multiple proxies for species physiological acute and chronic responses to increasing

  13. A new accurate ground-state potential energy surface of ethylene and predictions for rotational and vibrational energy levels

    Delahaye, Thibault, E-mail: thibault.delahaye@univ-reims.fr; Rey, Michaël, E-mail: michael.rey@univ-reims.fr; Tyuterev, Vladimir G. [Groupe de Spectrométrie Moléculaire et Atmosphérique, UMR CNRS 7331, BP 1039, F-51687, Reims Cedex 2 (France); Nikitin, Andrei [Laboratory of Theoretical Spectroscopy, Institute of Atmospheric Optics, Russian Academy of Sciences, 634055 Tomsk, Russia and Quamer, State University of Tomsk (Russian Federation); Szalay, Péter G. [Institute of Chemistry, Eötvös Loránd University, P.O. Box 32, H-1518 Budapest (Hungary)

    2014-09-14

    In this paper we report a new ground state potential energy surface for ethylene (ethene) C{sub 2}H{sub 4} obtained from extended ab initio calculations. The coupled-cluster approach with the perturbative inclusion of the connected triple excitations CCSD(T) and correlation consistent polarized valence basis set cc-pVQZ was employed for computations of electronic ground state energies. The fit of the surface included 82 542 nuclear configurations using sixth order expansion in curvilinear symmetry-adapted coordinates involving 2236 parameters. A good convergence for variationally computed vibrational levels of the C{sub 2}H{sub 4} molecule was obtained with a RMS(Obs.–Calc.) deviation of 2.7 cm{sup −1} for fundamental bands centers and 5.9 cm{sup −1} for vibrational bands up to 7800 cm{sup −1}. Large scale vibrational and rotational calculations for {sup 12}C{sub 2}H{sub 4}, {sup 13}C{sub 2}H{sub 4}, and {sup 12}C{sub 2}D{sub 4} isotopologues were performed using this new surface. Energy levels for J = 20 up to 6000 cm{sup −1} are in a good agreement with observations. This represents a considerable improvement with respect to available global predictions of vibrational levels of {sup 13}C{sub 2}H{sub 4} and {sup 12}C{sub 2}D{sub 4} and rovibrational levels of {sup 12}C{sub 2}H{sub 4}.

  14. Infectious titres of sheep scrapie and bovine spongiform encephalopathy agents cannot be accurately predicted from quantitative laboratory test results.

    González, Lorenzo; Thorne, Leigh; Jeffrey, Martin; Martin, Stuart; Spiropoulos, John; Beck, Katy E; Lockey, Richard W; Vickery, Christopher M; Holder, Thomas; Terry, Linda

    2012-11-01

    It is widely accepted that abnormal forms of the prion protein (PrP) are the best surrogate marker for the infectious agent of prion diseases and, in practice, the detection of such disease-associated (PrP(d)) and/or protease-resistant (PrP(res)) forms of PrP is the cornerstone of diagnosis and surveillance of the transmissible spongiform encephalopathies (TSEs). Nevertheless, some studies question the consistent association between infectivity and abnormal PrP detection. To address this discrepancy, 11 brain samples of sheep affected with natural scrapie or experimental bovine spongiform encephalopathy were selected on the basis of the magnitude and predominant types of PrP(d) accumulation, as shown by immunohistochemical (IHC) examination; contra-lateral hemi-brain samples were inoculated at three different dilutions into transgenic mice overexpressing ovine PrP and were also subjected to quantitative analysis by three biochemical tests (BCTs). Six samples gave 'low' infectious titres (10⁶·⁵ to 10⁶·⁷ LD₅₀ g⁻¹) and five gave 'high titres' (10⁸·¹ to ≥ 10⁸·⁷ LD₅₀ g⁻¹) and, with the exception of the Western blot analysis, those two groups tended to correspond with samples with lower PrP(d)/PrP(res) results by IHC/BCTs. However, no statistical association could be confirmed due to high individual sample variability. It is concluded that although detection of abnormal forms of PrP by laboratory methods remains useful to confirm TSE infection, infectivity titres cannot be predicted from quantitative test results, at least for the TSE sources and host PRNP genotypes used in this study. Furthermore, the near inverse correlation between infectious titres and Western blot results (high protease pre-treatment) argues for a dissociation between infectivity and PrP(res).

  15. Graphics and statistics for cardiology: clinical prediction rules.

    Woodward, Mark; Tunstall-Pedoe, Hugh; Peters, Sanne Ae

    2017-04-01

    Graphs and tables are indispensable aids to quantitative research. When developing a clinical prediction rule that is based on a cardiovascular risk score, there are many visual displays that can assist in developing the underlying statistical model, testing the assumptions made in this model, evaluating and presenting the resultant score. All too often, researchers in this field follow formulaic recipes without exploring the issues of model selection and data presentation in a meaningful and thoughtful way. Some ideas on how to use visual displays to make wise decisions and present results that will both inform and attract the reader are given. Ideas are developed, and results tested, using subsets of the data that were used to develop the ASSIGN cardiovascular risk score, as used in Scotland.

  16. Clinical Exposure Boost Predictions by Integrating Cytochrome P450 3A4-Humanized Mouse Studies With PBPK Modeling.

    Zhang, Jin; Heimbach, Tycho; Scheer, Nico; Barve, Avantika; Li, Wenkui; Lin, Wen; He, Handan

    2016-04-01

    NVS123 is a poorly water-soluble protease 56 inhibitor in clinical development. Data from in vitro hepatocyte studies suggested that NVS123 is mainly metabolized by CYP3A4. As a consequence of limited solubility, NVS123 therapeutic plasma exposures could not be achieved even with high doses and optimized formulations. One approach to overcome NVS123 developability issues was to increase plasma exposure by coadministrating it with an inhibitor of CYP3A4 such as ritonavir. A clinical boost effect was predicted by using physiologically based pharmacokinetic (PBPK) modeling. However, initial boost predictions lacked sufficient confidence because a key parameter, fraction of drug metabolized by CYP3A4 (fmCYP3A4), could not be estimated with accuracy on account of disconnects between in vitro and in vivo preclinical data. To accurately estimate fmCYP3A4 in human, an in vivo boost effect study was conducted using CYP3A4-humanized mouse model which showed a 33- to 56-fold exposure boost effect. Using a top-down approach, human fmCYP3A4 for NVS123 was estimated to be very high and included in the human PBPK modeling to support subsequent clinical study design. The combined use of the in vivo boost study in CYP3A4-humanized mouse model mice along with PBPK modeling accurately predicted the clinical outcome and identified a significant NVS123 exposure boost (∼42-fold increase) with ritonavir.

  17. An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints

    Wang, Shiyao; Deng, Zhidong; Yin, Gang

    2016-01-01

    A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS–inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car. PMID:26927108

  18. An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints.

    Wang, Shiyao; Deng, Zhidong; Yin, Gang

    2016-02-24

    A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS-inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.

  19. An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints

    Shiyao Wang

    2016-02-01

    Full Text Available A high-performance differential global positioning system (GPS  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS–inertial measurement unit (IMU/dead reckoning (DR data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.

  20. Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity.

    Martin, Eric; Mukherjee, Prasenjit; Sullivan, David; Jansen, Johanna

    2011-08-22

    Profile-QSAR is a novel 2D predictive model building method for kinases. This "meta-QSAR" method models the activity of each compound against a new kinase target as a linear combination of its predicted activities against a large panel of 92 previously studied kinases comprised from 115 assays. Profile-QSAR starts with a sparse incomplete kinase by compound (KxC) activity matrix, used to generate Bayesian QSAR models for the 92 "basis-set" kinases. These Bayesian QSARs generate a complete "synthetic" KxC activity matrix of predictions. These synthetic activities are used as "chemical descriptors" to train partial-least squares (PLS) models, from modest amounts of medium-throughput screening data, for predicting activity against new kinases. The Profile-QSAR predictions for the 92 kinases (115 assays) gave a median external R²(ext) = 0.59 on 25% held-out test sets. The method has proven accurate enough to predict pairwise kinase selectivities with a median correlation of R²(ext) = 0.61 for 958 kinase pairs with at least 600 common compounds. It has been further expanded by adding a "C(k)XC" cellular activity matrix to the KxC matrix to predict cellular activity for 42 kinase driven cellular assays with median R²(ext) = 0.58 for 24 target modulation assays and R²(ext) = 0.41 for 18 cell proliferation assays. The 2D Profile-QSAR, along with the 3D Surrogate AutoShim, are the foundations of an internally developed iterative medium-throughput screening (IMTS) methodology for virtual screening (VS) of compound archives as an alternative to experimental high-throughput screening (HTS). The method has been applied to 20 actual prospective kinase projects. Biological results have so far been obtained in eight of them. Q² values ranged from 0.3 to 0.7. Hit-rates at 10 uM for experimentally tested compounds varied from 25% to 80%, except in K5, which was a special case aimed specifically at finding "type II" binders, where none of the compounds were predicted to be

  1. Multireference correlation consistent composite approach [MR-ccCA]: toward accurate prediction of the energetics of excited and transition state chemistry.

    Oyedepo, Gbenga A; Wilson, Angela K

    2010-08-26

    The correlation consistent Composite Approach, ccCA [ Deyonker , N. J. ; Cundari , T. R. ; Wilson , A. K. J. Chem. Phys. 2006 , 124 , 114104 ] has been demonstrated to predict accurate thermochemical properties of chemical species that can be described by a single configurational reference state, and at reduced computational cost, as compared with ab initio methods such as CCSD(T) used in combination with large basis sets. We have developed three variants of a multireference equivalent of this successful theoretical model. The method, called the multireference correlation consistent composite approach (MR-ccCA), is designed to predict the thermochemical properties of reactive intermediates, excited state species, and transition states to within chemical accuracy (e.g., 1 kcal/mol for enthalpies of formation) of reliable experimental values. In this study, we have demonstrated the utility of MR-ccCA: (1) in the determination of the adiabatic singlet-triplet energy separations and enthalpies of formation for the ground states for a set of diradicals and unsaturated compounds, and (2) in the prediction of energetic barriers to internal rotation, in ethylene and its heavier congener, disilene. Additionally, we have utilized MR-ccCA to predict the enthalpies of formation of the low-lying excited states of all the species considered. MR-ccCA is shown to give quantitative results without reliance upon empirically derived parameters, making it suitable for application to study novel chemical systems with significant nondynamical correlation effects.

  2. Learning a weighted sequence model of the nucleosome core and linker yields more accurate predictions in Saccharomyces cerevisiae and Homo sapiens.

    Reynolds, Sheila M; Bilmes, Jeff A; Noble, William Stafford

    2010-07-08

    DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence-301 base pairs, centered at the position to be scored-with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the

  3. Learning a weighted sequence model of the nucleosome core and linker yields more accurate predictions in Saccharomyces cerevisiae and Homo sapiens.

    Sheila M Reynolds

    Full Text Available DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence-301 base pairs, centered at the position to be scored-with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the

  4. Accurate clinical genetic testing for autoinflammatory diseases using the next-generation sequencing platform MiSeq

    Manabu Nakayama

    2017-03-01

    Full Text Available Autoinflammatory diseases occupy one of a group of primary immunodeficiency diseases that are generally thought to be caused by mutation of genes responsible for innate immunity, rather than by acquired immunity. Mutations related to autoinflammatory diseases occur in 12 genes. For example, low-level somatic mosaic NLRP3 mutations underlie chronic infantile neurologic, cutaneous, articular syndrome (CINCA, also known as neonatal-onset multisystem inflammatory disease (NOMID. In current clinical practice, clinical genetic testing plays an important role in providing patients with quick, definite diagnoses. To increase the availability of such testing, low-cost high-throughput gene-analysis systems are required, ones that not only have the sensitivity to detect even low-level somatic mosaic mutations, but also can operate simply in a clinical setting. To this end, we developed a simple method that employs two-step tailed PCR and an NGS system, MiSeq platform, to detect mutations in all coding exons of the 12 genes responsible for autoinflammatory diseases. Using this amplicon sequencing system, we amplified a total of 234 amplicons derived from the 12 genes with multiplex PCR. This was done simultaneously and in one test tube. Each sample was distinguished by an index sequence of second PCR primers following PCR amplification. With our procedure and tips for reducing PCR amplification bias, we were able to analyze 12 genes from 25 clinical samples in one MiSeq run. Moreover, with the certified primers designed by our short program—which detects and avoids common SNPs in gene-specific PCR primers—we used this system for routine genetic testing. Our optimized procedure uses a simple protocol, which can easily be followed by virtually any office medical staff. Because of the small PCR amplification bias, we can analyze simultaneously several clinical DNA samples with low cost and can obtain sufficient read numbers to detect a low level of

  5. Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'

    Snowdon Stuart

    2009-07-01

    Full Text Available Abstract Background Metabolomics experiments using Mass Spectrometry (MS technology measure the mass to charge ratio (m/z and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensional metabolite 'fingerprint' or metabolite 'profile' data. High resolution MS instruments perform routinely with a mass accuracy of Results Metabolite 'structures' harvested from publicly accessible databases were converted into a common format to generate a comprehensive archive in MZedDB. 'Rules' were derived from chemical information that allowed MZedDB to generate a list of adducts and neutral loss fragments putatively able to form for each structure and calculate, on the fly, the exact molecular weight of every potential ionisation product to provide targets for annotation searches based on accurate mass. We demonstrate that data matrices representing populations of ionisation products generated from different biological matrices contain a large proportion (sometimes > 50% of molecular isotopes, salt adducts and neutral loss fragments. Correlation analysis of ESI-MS data features confirmed the predicted relationships of m/z signals. An integrated isotope enumerator in MZedDB allowed verification of exact isotopic pattern distributions to corroborate experimental data. Conclusion We conclude that although ultra-high accurate mass instruments provide major insight into the chemical diversity of biological extracts, the facile annotation of a large proportion of signals is not possible by simple, automated query of current databases using computed molecular formulae. Parameterising MZedDB to take into account predicted ionisation behaviour and the biological source of any sample improves greatly both the frequency and accuracy of potential annotation 'hits' in ESI-MS data.

  6. PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions.

    Bendl, Jaroslav; Musil, Miloš; Štourač, Jan; Zendulka, Jaroslav; Damborský, Jiří; Brezovský, Jan

    2016-05-01

    An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors. A user-friendly web interface was developed that provides easy access to the five tools' predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations. To

  7. Stable feature selection for clinical prediction: exploiting ICD tree structure using Tree-Lasso.

    Kamkar, Iman; Gupta, Sunil Kumar; Phung, Dinh; Venkatesh, Svetha

    2015-02-01

    Modern healthcare is getting reshaped by growing Electronic Medical Records (EMR). Recently, these records have been shown of great value towards building clinical prediction models. In EMR data, patients' diseases and hospital interventions are captured through a set of diagnoses and procedures codes. These codes are usually represented in a tree form (e.g. ICD-10 tree) and the codes within a tree branch may be highly correlated. These codes can be used as features to build a prediction model and an appropriate feature selection can inform a clinician about important risk factors for a disease. Traditional feature selection methods (e.g. Information Gain, T-test, etc.) consider each variable independently and usually end up having a long feature list. Recently, Lasso and related l1-penalty based feature selection methods have become popular due to their joint feature selection property. However, Lasso is known to have problems of selecting one feature of many correlated features randomly. This hinders the clinicians to arrive at a stable feature set, which is crucial for clinical decision making process. In this paper, we solve this problem by using a recently proposed Tree-Lasso model. Since, the stability behavior of Tree-Lasso is not well understood, we study the stability behavior of Tree-Lasso and compare it with other feature selection methods. Using a synthetic and two real-world datasets (Cancer and Acute Myocardial Infarction), we show that Tree-Lasso based feature selection is significantly more stable than Lasso and comparable to other methods e.g. Information Gain, ReliefF and T-test. We further show that, using different types of classifiers such as logistic regression, naive Bayes, support vector machines, decision trees and Random Forest, the classification performance of Tree-Lasso is comparable to Lasso and better than other methods. Our result has implications in identifying stable risk factors for many healthcare problems and therefore can

  8. Discovery of a general method of solving the Schrödinger and dirac equations that opens a way to accurately predictive quantum chemistry.

    Nakatsuji, Hiroshi

    2012-09-18

    Just as Newtonian law governs classical physics, the Schrödinger equation (SE) and the relativistic Dirac equation (DE) rule the world of chemistry. So, if we can solve these equations accurately, we can use computation to predict chemistry precisely. However, for approximately 80 years after the discovery of these equations, chemists believed that they could not solve SE and DE for atoms and molecules that included many electrons. This Account reviews ideas developed over the past decade to further the goal of predictive quantum chemistry. Between 2000 and 2005, I discovered a general method of solving the SE and DE accurately. As a first inspiration, I formulated the structure of the exact wave function of the SE in a compact mathematical form. The explicit inclusion of the exact wave function's structure within the variational space allows for the calculation of the exact wave function as a solution of the variational method. Although this process sounds almost impossible, it is indeed possible, and I have published several formulations and applied them to solve the full configuration interaction (CI) with a very small number of variables. However, when I examined analytical solutions for atoms and molecules, the Hamiltonian integrals in their secular equations diverged. This singularity problem occurred in all atoms and molecules because it originates from the singularity of the Coulomb potential in their Hamiltonians. To overcome this problem, I first introduced the inverse SE and then the scaled SE. The latter simpler idea led to immediate and surprisingly accurate solution for the SEs of the hydrogen atom, helium atom, and hydrogen molecule. The free complement (FC) method, also called the free iterative CI (free ICI) method, was efficient for solving the SEs. In the FC method, the basis functions that span the exact wave function are produced by the Hamiltonian of the system and the zeroth-order wave function. These basis functions are called complement

  9. Rorschach Prediction of Success in Clinical Training: A Second Look

    Carlson, Rae

    1969-01-01

    A Rorschach Index based on ego-psychological conceptualization of an optimal personality picture predicted for 155 trainees was compared with predictions from the Miller Analogies Test (MAT) and the Strong Vocational Interest Blank (SVIB). The Index predicted success and failure more effectively. (Author)

  10. A combined clinical and biomarker approach to predict diuretic response in acute heart failure

    Ter Maaten, Jozine M; Valente, Mattia A E; Metra, Marco; Bruno, Noemi; O'Connor, Christopher M; Ponikowski, Piotr; Teerlink, John R; Cotter, Gad; Davison, Beth; Cleland, John G; Givertz, Michael M; Bloomfield, Daniel M; Dittrich, Howard C; van Veldhuisen, Dirk J; Hillege, Hans L; Damman, Kevin; Voors, Adriaan A

    2015-01-01

    BACKGROUND: Poor diuretic response in acute heart failure is related to poor clinical outcome. The underlying mechanisms and pathophysiology behind diuretic resistance are incompletely understood. We evaluated a combined approach using clinical characteristics and biomarkers to predict diuretic resp

  11. Cancer Pharmacogenomics: Integrating Discoveries in Basic, Clinical and Population Sciences to Advance Predictive Cancer Care

    Cancer Pharmacogenomics: Integrating Discoveries in Basic, Clinical and Population Sciences to Advance Predictive Cancer Care, a 2010 workshop sponsored by the Epidemiology and Genomics Research Program.

  12. ECG dispersion mapping predicts clinical deterioration, measured by increase in the Simple Clinical Score.

    Kellett, J

    2012-01-01

    Objective: ECG dispersion mapping (ECG-DM) is a novel technique that reports abnormal ECG microalternations. We report the ability of ECG-DM to predict clinical deterioration of acutely ill medical patients, as measured by an increase in the Simple Clinical Score (SCS) the day after admission to hospital. Methods: 453 acutely ill medical patients (mean age 69.7 +\\/- 14.0 years) had the SCS recorded and ECGDM performed immediately after admission to hospital. Results: 46 patients had an SCS increase 20.8 +\\/- 7.6 hours after admission. Abnormal micro-alternations during left ventricular re-polarization had the highest association with SCS increase (p=0.0005). Logistic regression showed that only nursing home residence and abnormal micro-alternations during re-polarization of the left ventricle were independent predictors of SCS increase with an odds ratio of 2.84 and 3.01, respectively. Conclusion: ECG-DM changes during left ventricular re-polarization are independent predictors of clinical deterioration the day after hospital admission.

  13. Predicting Out-of-Office Blood Pressure in the Clinic (PROOF-BP): Derivation and Validation of a Tool to Improve the Accuracy of Blood Pressure Measurement in Clinical Practice.

    Sheppard, James P; Stevens, Richard; Gill, Paramjit; Martin, Una; Godwin, Marshall; Hanley, Janet; Heneghan, Carl; Hobbs, F D Richard; Mant, Jonathan; McKinstry, Brian; Myers, Martin; Nunan, David; Ward, Alison; Williams, Bryan; McManus, Richard J

    2016-05-01

    Patients often have lower (white coat effect) or higher (masked effect) ambulatory/home blood pressure readings compared with clinic measurements, resulting in misdiagnosis of hypertension. The present study assessed whether blood pressure and patient characteristics from a single clinic visit can accurately predict the difference between ambulatory/home and clinic blood pressure readings (the home-clinic difference). A linear regression model predicting the home-clinic blood pressure difference was derived in 2 data sets measuring automated clinic and ambulatory/home blood pressure (n=991) using candidate predictors identified from a literature review. The model was validated in 4 further data sets (n=1172) using area under the receiver operator characteristic curve analysis. A masked effect was associated with male sex, a positive clinic blood pressure change (difference between consecutive measurements during a single visit), and a diagnosis of hypertension. Increasing age, clinic blood pressure level, and pulse pressure were associated with a white coat effect. The model showed good calibration across data sets (Pearson correlation, 0.48-0.80) and performed well-predicting ambulatory hypertension (area under the receiver operator characteristic curve, 0.75; 95% confidence interval, 0.72-0.79 [systolic]; 0.87; 0.85-0.89 [diastolic]). Used as a triaging tool for ambulatory monitoring, the model improved classification of a patient's blood pressure status compared with other guideline recommended approaches (93% [92% to 95%] classified correctly; United States, 73% [70% to 75%]; Canada, 74% [71% to 77%]; United Kingdom, 78% [76% to 81%]). This study demonstrates that patient characteristics from a single clinic visit can accurately predict a patient's ambulatory blood pressure. Usage of this prediction tool for triaging of ambulatory monitoring could result in more accurate diagnosis of hypertension and hence more appropriate treatment.

  14. New evidence-based adaptive clinical trial methods for optimally integrating predictive biomarkers into oncology clinical development programs

    Robert A.Beckman; Cong Chen

    2013-01-01

    Predictive biomarkers are important to the future of oncology; they can be used to identify patient populations who will benefit from therapy,increase the value of cancer medicines,and decrease the size and cost of clinical trials while increasing their chance of success.But predictive biomarkers do not always work.When unsuccessful,they add cost,complexity,and time to drug development.This perspective describes phases 2 and 3 development methods that efficiently and adaptively check the ability of a biomarker to predict clinical outcomes.In the end,the biomarker is emphasized to the extent that it can actually predict.

  15. A random forest based risk model for reliable and accurate prediction of receipt of transfusion in patients undergoing percutaneous coronary intervention.

    Hitinder S Gurm

    Full Text Available BACKGROUND: Transfusion is a common complication of Percutaneous Coronary Intervention (PCI and is associated with adverse short and long term outcomes. There is no risk model for identifying patients most likely to receive transfusion after PCI. The objective of our study was to develop and validate a tool for predicting receipt of blood transfusion in patients undergoing contemporary PCI. METHODS: Random forest models were developed utilizing 45 pre-procedural clinical and laboratory variables to estimate the receipt of transfusion in patients undergoing PCI. The most influential variables were selected for inclusion in an abbreviated model. Model performance estimating transfusion was evaluated in an independent validation dataset using area under the ROC curve (AUC, with net reclassification improvement (NRI used to compare full and reduced model prediction after grouping in low, intermediate, and high risk categories. The impact of procedural anticoagulation on observed versus predicted transfusion rates were assessed for the different risk categories. RESULTS: Our study cohort was comprised of 103,294 PCI procedures performed at 46 hospitals between July 2009 through December 2012 in Michigan of which 72,328 (70% were randomly selected for training the models, and 30,966 (30% for validation. The models demonstrated excellent calibration and discrimination (AUC: full model  = 0.888 (95% CI 0.877-0.899, reduced model AUC = 0.880 (95% CI, 0.868-0.892, p for difference 0.003, NRI = 2.77%, p = 0.007. Procedural anticoagulation and radial access significantly influenced transfusion rates in the intermediate and high risk patients but no clinically relevant impact was noted in low risk patients, who made up 70% of the total cohort. CONCLUSIONS: The risk of transfusion among patients undergoing PCI can be reliably calculated using a novel easy to use computational tool (https://bmc2.org/calculators/transfusion. This risk prediction

  16. Radiology clinical synopsis: a simple solution for obtaining an adequate clinical history for the accurate reporting of imaging studies on patients in intensive care units

    Cohen, Mervyn D. [Riley Hospital for Children, Indianapolis, IN (United States); Alam, Khurshaid [Indiana University, School of Medicine, Indianapolis, IN (United States)

    2005-09-01

    Lack of clinical history on radiology requisitions is a universal problem. We describe a simple Web-based system that readily provides radiology-relevant clinical history to the radiologist reading radiographs of intensive care unit (ICU) patients. Along with the relevant history, which includes primary and secondary diagnoses, disease progression and complications, the system provides the patient's name, record number and hospital location. This information is immediately available to reporting radiologists. New clinical information is immediately entered on-line by the radiologists as they are reviewing images. After patient discharge, the data are stored and immediately available if the patient is readmitted. The system has been in routine clinical use in our hospital for nearly 2 years. (orig.)

  17. Calibrating transition-metal energy levels and oxygen bands in first-principles calculations: Accurate prediction of redox potentials and charge transfer in lithium transition-metal oxides

    Seo, Dong-Hwa; Urban, Alexander; Ceder, Gerbrand

    2015-09-01

    Transition-metal (TM) oxides play an increasingly important role in technology today, including applications such as catalysis, solar energy harvesting, and energy storage. In many of these applications, the details of their electronic structure near the Fermi level are critically important for their properties. We propose a first-principles-based computational methodology for the accurate prediction of oxygen charge transfer in TM oxides and lithium TM (Li-TM) oxides. To obtain accurate electronic structures, the Heyd-Scuseria-Ernzerhof (HSE06) hybrid functional is adopted, and the amount of exact Hartree-Fock exchange (mixing parameter) is adjusted to reproduce reference band gaps. We show that the HSE06 functional with optimal mixing parameter yields not only improved electronic densities of states, but also better energetics (Li-intercalation voltages) for LiCo O2 and LiNi O2 as compared to the generalized gradient approximation (GGA), Hubbard U corrected GGA (GGA +U ), and standard HSE06. We find that the optimal mixing parameters for TM oxides are system specific and correlate with the covalency (ionicity) of the TM species. The strong covalent (ionic) nature of TM-O bonding leads to lower (higher) optimal mixing parameters. We find that optimized HSE06 functionals predict stronger hybridization of the Co 3 d and O 2 p orbitals as compared to GGA, resulting in a greater contribution from oxygen states to charge compensation upon delithiation in LiCo O2 . We also find that the band gaps of Li-TM oxides increase linearly with the mixing parameter, enabling the straightforward determination of optimal mixing parameters based on GGA (α =0.0 ) and HSE06 (α =0.25 ) calculations. Our results also show that G0W0@GGA +U band gaps of TM oxides (M O ,M =Mn ,Co ,Ni ) and LiCo O2 agree well with experimental references, suggesting that G0W0 calculations can be used as a reference for the calibration of the mixing parameter in cases when no experimental band gap has been

  18. Predicting Future Clinical Adjustment from Treatment Outcome and Process Variables.

    Patterson, G. R.; Forgatch, Marion S.

    1995-01-01

    Issues related to the use of outcome and process data from the treatment of antisocial children to predict future childhood adjustment were examined through a study of 69 children. Data supported the hypothesis that measures of processes thought to produce changes in child behavior would serve to predict future adjustment. (SLD)

  19. IMPre: an accurate and efficient software for prediction of T- and B-cell receptor germline genes and alleles from rearranged repertoire data

    Wei Zhang

    2016-11-01

    Full Text Available Large-scale study of the properties of T-cell receptor (TCR and B-cell receptor (BCR repertoires through next-generation sequencing is providing excellent insights into the understanding of adaptive immune responses. Variable(DiversityJoining V(DJ germline genes and alleles must be characterized in detail to facilitate repertoire analyses. However, most species do not have well-characterized TCR/BCR germline genes because of their high homology. Also, more germline alleles are required for humans and other species, which limits the capacity for studying immune repertoires. Herein, we developed Immune Germline Prediction (IMPre, a tool for predicting germline V/J genes and alleles using deep-sequencing data derived from TCR/BCR repertoires. We developed a new algorithm, Seed_Clust, for clustering, produced a multiway tree for assembly and optimized the sequence according to the characteristics of rearrangement. We trained IMPre on human samples of T-cell receptor beta (TRB and immunoglobulin heavy chain (IGH, and then tested it on additional human samples. Accuracy of 97.7%, 100%, 92.9% and 100% was obtained for TRBV, TRBJ, IGHV and IGHJ, respectively. Analyses of subsampling performance for these samples showed IMPre to be robust using different data quantities. Subsequently, IMPre was tested on samples from rhesus monkeys and human long sequences: the highly accurate results demonstrated IMPre to be stable with animal and multiple data types. With rapid accumulation of high-throughput sequence data for TCR and BCR repertoires, IMPre can be applied broadly for obtaining novel genes and a large number of novel alleles. IMPre is available at https://github.com/zhangwei2015/IMPre.

  20. Clinical diaries in COPD: compliance and utility in predicting acute exacerbations

    Walters EH

    2012-07-01

    Full Text Available E Haydn Walters,1 Julia Walters,1 Karen E Wills,1 Andrew Robinson,2 Richard Wood-Baker11Menzies Research Institute Tasmania, University of Tasmania, Hobart; 2School of Nursing and Midwifery, University of Tasmania, Hobart, AustraliaBackground: Daily diaries are often used to collect data on disease activity, but are burdensome and compliance may be poor. Their use in chronic obstructive pulmonary disease (COPD and impact on the prevention and treatment of exacerbations is poorly researched.Methods: We investigated diary-keeping in COPD and ascertained items that best predicted emergency attendances for exacerbations. Participants in the active limb of a clinical trial in COPD kept daily diaries rating breathlessness, cough, sputum, physical activity, and use of reliever medication.Results: Data on 55 participants, 67% of whom were female, showed that overall compliance with diary-keeping was 62%. Participants educated to primary school level only had lower compliance (P = 0.05. Twenty patients had at least one emergency attendance, in whom the relative risk of an acute exacerbation for an increase in item score rose from six days prior to hospitalization, most sharply in the last two days. Even for optimal combinations of items, the positive predictive value was poor, the best combination being cough, activity level, and inhaler use.Conclusion: Good compliance can be achieved using daily diaries in COPD, although this is worse in those with a poor educational level. Diary-keeping is not accurate in predicting acute exacerbations, but could be substantially simplified without loss of efficiency.Keywords: chronic obstructive pulmonary disease, daily diary, secondary prevention

  1. Predictive value of clinical history compared with urodynamic study in 1,179 women

    Jorge Milhem Haddad

    2016-02-01

    Full Text Available SUMMARY Objective: to determine the positive predictive value of clinical history in comparison with urodynamic study for the diagnosis of urinary incontinence. Methods: retrospective analysis comparing clinical history and urodynamic evaluation of 1,179 women with urinary incontinence. The urodynamic study was considered the gold standard, whereas the clinical history was the new test to be assessed. This was established after analyzing each method as the gold standard through the difference between their positive predictive values. Results: the positive predictive values of clinical history compared with urodynamic study for diagnosis of stress urinary incontinence, overactive bladder and mixed urinary incontinence were, respectively, 37% (95% CI 31-44, 40% (95% CI 33-47 and 16% (95% CI 14-19. Conclusion: we concluded that the positive predictive value of clinical history was low compared with urodynamic study for urinary incontinence diagnosis. The positive predictive value was low even among women with pure stress urinary incontinence.

  2. Highly accurate chemical formula prediction tool utilizing high-resolution mass spectra, MS/MS fragmentation, heuristic rules, and isotope pattern matching.

    Pluskal, Tomáš; Uehara, Taisuke; Yanagida, Mitsuhiro

    2012-05-15

    Mass spectrometry is commonly applied to qualitatively and quantitatively profile small molecules, such as peptides, metabolites, or lipids. Modern mass spectrometers provide accurate measurements of mass-to-charge ratios of ions, with errors as low as 1 ppm. Even such high mass accuracy, however, is not sufficient to determine the unique chemical formula of each ion, and additional algorithms are necessary. Here we present a universal software tool for predicting chemical formulas from high-resolution mass spectrometry data, developed within the MZmine 2 framework. The tool is based on the use of a combination of heuristic techniques, including MS/MS fragmentation analysis and isotope pattern matching. The performance of the tool was evaluated using a real metabolomic data set obtained with the Orbitrap MS detector. The true formula was correctly determined as the highest-ranking candidate for 79% of the tested compounds. The novel isotope pattern-scoring algorithm outperformed a previously published method in 64% of the tested Orbitrap spectra. The software described in this manuscript is freely available and its source code can be accessed within the MZmine 2 source code repository.

  3. Defining the Most Accurate Measurable Dimension(s of the Liver in Predicting Liver Volume Based on CT Volumetery and Reconstruction

    Reza Saadat Mostafavi

    2010-05-01

    Full Text Available Background/Objective: The presence of liver volume has a great effect on diagnosis and management of different diseases such as lymphoproliferative conditions. "nPatients and Methods: Abdominal CT scan of 100 patients without any findings for liver disease (in history and imaging was subjected to volumetry and reconstruction. Along with the liver volume, in axial series, the AP diameter of the left lobe (in midline and right lobe (mid-clavicular and lateral maximum diameter of the liver in the mid-axiliary line and maximum diameter to IVC were calculated. In the coronal mid-axillary and sagittal mid-clavicular plane, maximum superior-inferior dimensions were calculated with their various combinations (multiplying. Regression analysis between dimensions and volume were performed. "nResults: The most accurate combination was the superior inferior sagittal dimension multiplied by AP diameter of the right lobe (R squared 0.78, P-value<0.001 and the most solitary dimension was the lateral dimension to IVC in the axial plane (R squared 0.57, P-value<0.001 with an interval of 9-11cm for 68% of normal. "nConclusion: We recommend the lateral maximum diameter of liver from surface to IVC in the axial plane in ultrasound for liver volume prediction with an interval of 9-11cm for 68% of normal. Out of this range is regarded as abnormal.

  4. A biometric approach to predictable treatment of clinical crown discrepancies.

    Chu, Stephen J

    2007-08-01

    Dental professionals have long been guided by mathematical principles when interpreting aesthetic and tooth proportions for their patients. While many acknowledge that such principles are merely launch points for a smile design or reconstructive procedure, their existence appears to indicate practitioners' desire for predictable, objective, and reproducible means of achieving success in aesthetic dentistry. This article introduces innovative aesthetic measurement gauges as a means of objectively quantifying tooth size discrepancies and enabling the clinician to perform aesthetic restorative dentistry with success and predictability.

  5. Study of the prediction system for clinical response to M-VAC neoadjuvant chemotherapy for bladder cancer.

    Takata, R; Obara, W; Fujioka, T

    2010-01-01

    Neoadjuvant chemotherapy for invasive bladder cancer, involving a regimen of M-VAC, can manage micrometastasis and improve the prognosis. However, some patients suffer from severe adverse drug reactions without any effect, and no method yet exists for predicting the response of an individual patient to chemotherapy. Our purpose in this study is to establish a method for predicting the response to the M-VAC therapy. We analyzed gene-expression profiles of biopsy materials from 40 invasive bladder cancers using a cDNA microarray consisting of 27 648 genes, after populations of cancer cells had been purified by laser-microbeam microdissection. We identified 14 predictive genes that were expressed differently between nine responder and nine non-responder tumors and devised a prediction-scoring system that clearly separated the responder group from the non-responder group. This system accurately predicted the clinical response for 19 of the 22 additional test cases. The group of patients with positive predictive scores had significantly longer survival times than that with negative scores. As real-time RT-PCR data were highly concordant with the cDNA microarray data for those 14 genes, we developed a quantitative RT-PCR-based prediction system that could be feasible for routine clinical use. Taken together, our results suggest that the sensitivity of an invasive bladder cancer to the M-VAC neoadjuvant chemotherapy can be predicted by expression patterns in this set of genes, a step toward achievement of "personalized therapy" for treatment of this disease.

  6. Reporting and methods in clinical prediction research: a systematic review.

    Walter Bouwmeester

    Full Text Available BACKGROUND: We investigated the reporting and methods of prediction studies, focusing on aims, designs, participant selection, outcomes, predictors, statistical power, statistical methods, and predictive performance measures. METHODS AND FINDINGS: We used a full hand search to identify all prediction studies published in 2008 in six high impact general medical journals. We developed a comprehensive item list to systematically score conduct and reporting of the studies, based on recent recommendations for prediction research. Two reviewers independently scored the studies. We retrieved 71 papers for full text review: 51 were predictor finding studies, 14 were prediction model development studies, three addressed an external validation of a previously developed model, and three reported on a model's impact on participant outcome. Study design was unclear in 15% of studies, and a prospective cohort was used in most studies (60%. Descriptions of the participants and definitions of predictor and outcome were generally good. Despite many recommendations against doing so, continuous predictors were often dichotomized (32% of studies. The number of events per predictor as a measure of statistical power could not be determined in 67% of the studies; of the remainder, 53% had fewer than the commonly recommended value of ten events per predictor. Methods for a priori selection of candidate predictors were described in most studies (68%. A substantial number of studies relied on a p-value cut-off of p<0.05 to select predictors in the multivariable analyses (29%. Predictive model performance measures, i.e., calibration and discrimination, were reported in 12% and 27% of studies, respectively. CONCLUSIONS: The majority of prediction studies in high impact journals do not follow current methodological recommendations, limiting their reliability and applicability.

  7. Pressure Ulcers in Adults: Prediction and Prevention. Clinical Practice Guideline Number 3.

    Agency for Health Care Policy and Research (DHHS/PHS), Rockville, MD.

    This package includes a clinical practice guideline, quick reference guide for clinicians, and patient's guide to predicting and preventing pressure ulcers in adults. The clinical practice guideline includes the following: overview of the incidence and prevalence of pressure ulcers; clinical practice guideline (introduction, risk assessment tools…

  8. Predictive in vivo animal models and translation to clinical trials.

    Cook, Natalie; Jodrell, Duncan I; Tuveson, David A

    2012-03-01

    Vast resources are expended during the development of new cancer therapeutics, and selection of optimal in vivo models should improve this process. Genetically engineered mouse models (GEMM) of cancer have progressively improved in technical sophistication and, accurately recapitulating the human cognate condition, have had a measureable impact on our knowledge of tumourigenesis. However, the application of GEMMs to facilitate the development of innovative therapeutic and diagnostic approaches has lagged behind. GEMMs that recapitulate human cancer offer an additional opportunity to accelerate drug development, and should complement the role of the widely used engraftment tumour models.

  9. Accurate Prediction of Hyperfine Coupling Constants in Muoniated and Hydrogenated Ethyl Radicals: Ab Initio Path Integral Simulation Study with Density Functional Theory Method.

    Yamada, Kenta; Kawashima, Yukio; Tachikawa, Masanori

    2014-05-13

    We performed ab initio path integral molecular dynamics (PIMD) simulations with a density functional theory (DFT) method to accurately predict hyperfine coupling constants (HFCCs) in the ethyl radical (CβH3-CαH2) and its Mu-substituted (muoniated) compound (CβH2Mu-CαH2). The substitution of a Mu atom, an ultralight isotope of the H atom, with larger nuclear quantum effect is expected to strongly affect the nature of the ethyl radical. The static conventional DFT calculations of CβH3-CαH2 find that the elongation of one Cβ-H bond causes a change in the shape of potential energy curve along the rotational angle via the imbalance of attractive and repulsive interactions between the methyl and methylene groups. Investigation of the methyl-group behavior including the nuclear quantum and thermal effects shows that an unbalanced CβH2Mu group with the elongated Cβ-Mu bond rotates around the Cβ-Cα bond in a muoniated ethyl radical, quite differently from the CβH3 group with the three equivalent Cβ-H bonds in the ethyl radical. These rotations couple with other molecular motions such as the methylene-group rocking motion (inversion), leading to difficulties in reproducing the corresponding barrier heights. Our PIMD simulations successfully predict the barrier heights to be close to the experimental values and provide a significant improvement in muon and proton HFCCs given by the static conventional DFT method. Further investigation reveals that the Cβ-Mu/H stretching motion, methyl-group rotation, methylene-group rocking motion, and HFCC values deeply intertwine with each other. Because these motions are different between the radicals, a proper description of the structural fluctuations reflecting the nuclear quantum and thermal effects is vital to evaluate HFCC values in theory to be comparable to the experimental ones. Accordingly, a fundamental difference in HFCC between the radicals arises from their intrinsic molecular motions at a finite temperature, in

  10. Somatic cell count distributions during lactation predict clinical mastitis

    Green, M.J.; Green, L.E.; Schukken, Y.H.; Bradley, A.J.; Peeler, E.J.; Barkema, H.W.; Haas, de Y.; Collis, V.J.; Medley, G.F.

    2004-01-01

    This research investigated somatic cell count (SCC) records during lactation, with the purpose of identifying distribution characteristics (mean and measures of variation) that were most closely associated with clinical mastitis. Three separate data sets were used, one containing quarter SCC (n = 14

  11. Evaluation of the usefulness of 2 prediction models of clinical prediction models in physical therapy: a qualitative process evaluation.

    Oort, L. van; Verhagen, A.F.; Koes, B.; Vet, R. de; Anema, H.; Heymans, M.

    2014-01-01

    OBJECTIVE: The purposes of this study were to (1) evaluate the usefulness of 2 prediction models by assessing the actual use and advantages/disadvantages of application in daily clinical practice and (2) propose recommendations to enhance their implementation. METHODS: Physical therapists working in

  12. Predicting Ebola Severity: A Clinical Prioritization Score for Ebola Virus Disease

    Okoni-Williams, Harry Henry; Suma, Mohamed; Mancuso, Brooke; Al-Dikhari, Ahmed; Faouzi, Mohamed

    2017-01-01

    Background Despite the notoriety of Ebola virus disease (EVD) as one of the world’s most deadly infections, EVD has a wide range of outcomes, where asymptomatic infection may be almost as common as fatality. With increasingly sensitive EVD diagnosis, there is a need for more accurate prognostic tools that objectively stratify clinical severity to better allocate limited resources and identify those most in need of intensive treatment. Methods/Principal Findings This retrospective cohort study analyses the clinical characteristics of 158 EVD(+) patients at the GOAL-Mathaska Ebola Treatment Centre, Sierra Leone. The prognostic potential of each characteristic was assessed and incorporated into a statistically weighted disease score. The mortality rate among EVD(+) patients was 60.8% and highest in those aged 25 years (pEbola viral load (p = 0.1), potentially indicating a pathologic synergy between the infections. Similarly, referral-time interacted with viral load, and adjustment revealed referral-time as a significant determinant of mortality, thus quantifying the benefits of early reporting as a 12% mortality risk reduction per day (p = 0.012). Disorientation was the strongest unadjusted predictor of death (OR = 13.1, p = 0.014) followed by hiccups, diarrhoea, conjunctivitis, dyspnoea and myalgia. Including these characteristics in multivariate prognostic scores, we obtained a 91% and 97% ability to discriminate death at or after triage respectively (area under ROC curve). Conclusions/Significance This study proposes highly predictive and easy-to-use prognostic tools, which stratify the risk of EVD mortality at or after EVD triage. PMID:28151955

  13. Near-infrared spectroscopy in schizophrenia: A possible biomarker for predicting clinical outcome and treatment response

    Shinsuke eKoike

    2013-11-01

    Full Text Available Functional near-infrared spectroscopy (fNIRS is a relatively new technique that can measure hemoglobin changes in brain tissues, and its use in psychiatry has been progressing rapidly. Although it has several disadvantages (e.g., relatively low spatial resolution and the possibility of shallow coverage in the depth of brain regions compared with other functional neuroimaging techniques (e.g., functional magnetic resonance imaging and positron emission tomography, fNIRS may be a candidate instrument for clinical use in psychiatry, as it can measure brain activity in naturalistic position easily and noninvasively. fNIRS instruments are also small and work silently, and can be moved almost everywhere including schools and care units. Previous fNIRS studies have shown that patients with schizophrenia have impaired activity and characteristic waveform patterns in the prefrontal cortex during the letter version of the verbal fluency task, and part of these results have been approved as one of the Advanced Medical Technologies as an aid for the differential diagnosis of depressive symptoms by the Ministry of Health, Labor and Welfare of Japan in 2009, which was the first such approval in the field of psychiatry. Moreover, previous studies suggest that the activity in the frontopolar prefrontal cortex is associated with their functions in chronic schizophrenia and is its next candidate biomarker. Future studies aimed at exploring fNIRS differences in various clinical stages, longitudinal changes, drug effects, and variations during different task paradigms will be needed to develop more accurate biomarkers that can be used to aid differential diagnosis, the comprehension of the present condition, the prediction of outcome, and the decision regarding treatment options in schizophrenia. Future fNIRS researches will require standardized measurement procedures, probe settings, analytical methods and tools, manuscript description, and database systems in an

  14. Prenatal prediction of pulmonary hypoplasia: clinical, biometric, and Doppler velocity correlates

    J.A.M. Laudij (Jacqueline); D. Tibboel (Dick); S.G.F. Robben (Simon); R.R. de Krijger (Ronald); M.A.J. de Ridder (Maria); J.W. Wladimiroff (Juriy)

    2002-01-01

    textabstractOBJECTIVES: To determine the value of pulmonary artery Doppler velocimetry relative to fetal biometric indices and clinical correlates in the prenatal prediction of lethal lung hypoplasia (LH) in prolonged (>1 week) oligohydramnios. METHODS: Forty-two singleton pregnanc

  15. Locus heterogeneity for Waardenburg syndrome is predictive of clinical subtypes

    Farrer, L.A.; Hoth, C. [Boston Univ. School of Medicine, MA (United States); Arnos, K.S. [Galludet Univ., Washington, DC (United States); Asher, J.H. Jr.; Friedman, T.B. [Michigan State Univ., East Lansing, MI (United States); Grundfast, K.M.; Lalwani, A.K. [National Institute on Deafness and Other Communication Disorders, Bethesda, MD (United States); Greenberg, J. [Univ. of Cape Town (South Africa); Diehl, S.R. [and others

    1994-10-01

    Waardenburg syndrome (WS) is a dominantly inherited and clinically variable syndrome of deafness, pigmentary changes, and distinctive facial features. Clinically, WS type I (WS1) is differentiated from WS type II (WS2) by the high frequency of dystopia canthorum in the family. In some families, WS is caused by mutations in the PAX3 gene on chromosome 2q. We have typed microsatellite markers within and flanking PAX3 in 41 WS1 kindreds and 26 WS2 kindreds in order to estimate the proportion of families with probable mutations in PAX3 and to study the relationship between phenotypic and genotypic heterogeneity. Evaluation of heterogeneity in location scores obtained by multilocus analysis indicated that WS is linked to PAX3 in 60% of all WS families and in 100% of WS1 families. None of the WS2 families were linked. In those families in which equivocal lod scores (between -2 and +1) were found, PAX3 mutations have been identified in 5 of the 15 WS1 families but in none of the 4 WS2 families. Although preliminary studies do not suggest any association between the phenotype and the molecular pathology in 20 families with known PAX3 mutations and in four patients with chromosomal abnormalities in the vicinity of PAX3, the presence of dystopia in multiple family members is a reliable indicator for identifying families likely to have a defect in PAX3. 59 refs., 3 figs., 5 tabs.

  16. Planning and predictability of clinical outcomes in esthetic rehabilitation.

    Kurbad, A

    2015-01-01

    In esthetic rehabilitation, it is a challenge to meet the needs of patients with growing expectations. Creating predictable results is the key to success. This can be accomplished by performing a comprehensive esthetic diagnosis, elaborating treatment proposals that satisfy today's esthetic standards, and using modern computer-assisted methods. The diagnostic wax-up and mock-up are effective tools that allow patients to visualize treatment proposals without invasive procedures. Once the patient has approved the proposals, they provide the basis for the fabrication of the final restoration. The use of modern ceramic materials makes it possible to achieve a good esthetic outcome, even in restorations with extremely thin layer thicknesses. Esthetic cementation is the final step of restorative treatment.

  17. How electrodiagnosis predicts clinical outcome of focal peripheral nerve lesions.

    Robinson, Lawrence R

    2015-09-01

    This article reviews the electrodiagnostic (EDX) prognostic factors for focal traumatic and nontraumatic peripheral nerve injuries. Referring physicians and patients often benefit from general and nerve-specific prognostic information from the EDX consultant. Knowing the probable outcome from a nerve injury allows the referring physician to choose the best treatment options for his/her patients. Nerve injuries are variable in their mechanism, location, and pathophysiology. The general effects of the injuries on nerve and muscle are well known, but more research is needed for nerve-specific information. Several factors currently known to influence prognosis include: nature of the nerve trauma, amount of axon loss, recruitment in muscles supplied by the nerve, the extent of demyelination, and the distance to reinnervate functional muscles. This article reviews these general concepts and also nerve-specific EDX measures that predict outcome after focal neuropathies.

  18. Should I Pack My Umbrella? Clinical versus Statistical Prediction of Mental Health Decisions

    Aegisdottir, Stefania; Spengler, Paul M.; White, Michael J.

    2006-01-01

    In this rejoinder, the authors respond to the insightful commentary of Strohmer and Arm, Chwalisz, and Hilton, Harris, and Rice about the meta-analysis on statistical versus clinical prediction techniques for mental health judgments. The authors address issues including the availability of statistical prediction techniques for real-life psychology…

  19. Predicting academic performance and clinical competency for international dental students: seeking the most efficient and effective measures.

    Stacey, D Graham; Whittaker, John M

    2005-02-01

    Measures used in the selection of international dental students to a U.S. D.D.S. program were examined to identify the grouping that most effectively and efficiently predicted academic performance and clinical competency. Archival records from the International Dental Program (IDP) at Loma Linda University provided data on 171 students who had trained in countries outside the United States. The students sought admission to the D.D.S. degree program, successful completion of which qualified them to sit for U.S. licensure. As with most dental schools, competition is high for admission to the D.D.S. program. The study's goal was to identify what measures contributed to a fair and accurate selection process for dental school applicants from other nations. Multiple regression analyses identified National Board Part II and dexterity measures as significant predictors of academic performance and clinical competency. National Board Part I, TOEFL, and faculty interviews added no significant additional help in predicting eventual academic performance and clinical competency.

  20. Usefulness of Clinical Prediction Rules, D-dimer, and Arterial Blood Gas Analysis to Predict Pulmonary Embolism in Cancer Patients

    Shazia Awan

    2017-03-01

    Full Text Available Objectives: Pulmonary embolism (PE is seven times more common in cancer patients than non-cancer patients. Since the existing clinical prediction rules (CPRs were validated predominantly in a non-cancer population, we decided to look at the utility of arterial blood gas (ABG analysis and D-dimer in predicting PE in cancer patients. Methods: Electronic medical records were reviewed between December 2005 and November 2010. A total of 177 computed tomography pulmonary angiograms (CTPAs were performed. We selected 104 individuals based on completeness of laboratory and clinical data. Patients were divided into two groups, CTPA positive (patients with PE and CTPA negative (PE excluded. Wells score, Geneva score, and modified Geneva score were calculated for each patient. Primary outcomes of interest were the sensitivities, specificities, positive, and negative predictive values for all three CPRs. Results: Of the total of 104 individuals who had CTPAs, 33 (31.7% were positive for PE and 71 (68.3% were negative. There was no difference in basic demographics between the two groups. Laboratory parameters were compared and partial pressure of oxygen was significantly lower in patients with PE (68.1 mmHg vs. 71 mmHg, p = 0.030. Clinical prediction rules showed good sensitivities (88−100% and negative predictive values (93−100%. An alveolar-arterial (A-a gradient > 20 had 100% sensitivity and negative predictive values. Conclusions: CPRs and a low A-a gradient were useful in excluding PE in cancer patients. There is a need for prospective trials to validate these results.

  1. Systematic review of clinical prediction tools and prognostic factors in aneurysmal subarachnoid hemorrhage

    Benjamin W. Y. Lo

    2015-01-01

    Conclusions: Systematic reviews for clinical prognostic factors and clinical prediction tools in aneurysmal SAH face a number of methodological challenges. These include within and between study patient heterogeneity, regional variations in treatment protocols, patient referral biases, and differences in treatment, and prognosis viewpoints across different cultures.

  2. Critical appraisal of clinical prediction rules that aim to optimize treatment selection for musculoskeletal conditions

    T.R. Stanton (Tasha); M.J. Hancock (Mark J.); C. Maher (Chris); B.W. Koes (Bart)

    2010-01-01

    textabstractBackground. Clinical prediction rules (CPRs) for treatment selection in musculoskeletal conditions have become increasingly popular. Purpose. The purposes of this review are: (1) to critically appraise studies evaluating CPRs and (2) to consider the clinical utility and stage of developm

  3. Predicting pressure ulcers: cases missed using a new clinical prediction rule.

    Schoonhoven, L.; Grobbee, D.E.; Bousema, M.T.; Buskens, E.

    2005-01-01

    AIM: The aim of this paper is to report a study describing patients with pressure ulcers that were incorrectly classified as 'not at risk' by the prediction rule and comparing them with patients who were correctly classified as 'not at risk'. BACKGROUND: Patients admitted to hospital are at risk of

  4. Assessment of clinical methods and ultrasound in predicting fetal birth weight in term pregnant women

    Ruby Yadav

    2016-08-01

    Conclusions: Clinical estimation of birth weight clearly has a role in management of labour and delivery in a term pregnancy. Clinical estimation especially by SFH and times;AG method is as accurate as routine USG estimated in average birth weight. SFH and times; AG clinical formula can be of great value in developing countries like ours, where ultrasound is not available at many health care centers especially in a rural area. [Int J Reprod Contracept Obstet Gynecol 2016; 5(8.000: 2775-2779

  5. Application of a biochemical and clinical model to predict individual survival in patients with end-stage liver disease

    Eduardo Vilar Gomez; Luis Calzadilla Bertot; Bienvenido Gra Oramas; Enrique Arus Soler; Raimundo Llanio Navarro; Javier Diaz Elias; Oscar Villa Jiménez; Maria del Rosario Abreu Vazquez

    2009-01-01

    AIM:To investigate the capability of a biochemical and clinical model,BioCliM,in predicting the survival of cirrhotic patients.METHODS:We prospectively evaluated the survival of 172 cirrhotic patients.The model was constructed using clinical (ascites,encephalopathy and variceal bleeding) and biochemical (serum creatinine and serum total bilirubin) variables that were selected from a Cox proportional hazards model.It was applied to estimate 12-,52- and 104-wk survival.The model's calibration using the Hosmer-Lemeshow statistic was computed at 104 wk in a validation dataset.Finally,the model's validity was tested among an independent set of 85 patients who were stratified into 2 risk groups (low risk ≤8 and high risk>8).RESULTS:In the validation cohort,all measures of fit,discrimination and calibration were improved when the biochemical and clinical model was used.The proposed model had better predictive values (c-statistic:0.90,0.91,0.91) than the Model for End-stage Liver Disease (MELD) and Child-Pugh (CP) scores for 12-,52- and 104-wk mortality,respectively.In addition,the Hosmer-Lemeshow (H-L) statistic revealed that the biochemical and clinical model (H-L,4.69) is better calibrated than MELD (H-L,17.06) and CP (H-L,14.23).There were no significant differences between the observed and expected survival curves in the stratified risk groups (low risk,P=0.61;high risk,P=0.77).CONCLUSION:Our data suggest that the proposed model is able to accurately predict survival in cirrhotic patients.

  6. Development and clinical evaluation of a highly accurate dengue NS1 rapid test: from the preparation of a soluble NS1 antigen to the construction of an RDT.

    Lee, Jihoo; Kim, Hak-Yong; Chong, Chom-Kyu; Song, Hyun-Ok

    2015-06-01

    Early diagnosis of dengue virus (DENV) is important. There are numerous products on the market claiming to detect DENV NS1, but these are not always reliable. In this study, a highly sensitive and accurate rapid diagnostic test (RDT) was developed using anti-dengue NS1 monoclonal antibodies. A recombinant NS1 protein was produced with high antigenicity and purity. Monoclonal antibodies were raised against this purified NS1 antigen. The RDT was constructed using a capturing (4A6A10, Kd=7.512±0.419×10(-9)) and a conjugating antibody (3E12E6, Kd=7.032±0.322×10(-9)). The diagnostic performance was evaluated with NS1-positive clinical samples collected from various dengue endemic countries and compared to SD BioLine Dengue NS1 Ag kit. The constructed RDT exhibited higher sensitivity (92.9%) with more obvious diagnostic performance than the commercial kit (83.3%). The specificity of constructed RDT was 100%. The constructed RDT could offer a reliable point-of-care testing tool for the early detection of dengue infections in remote areas and contribute to the control of dengue-related diseases.

  7. Clinical and radiographic factors do not accurately diagnose smear-negative tuberculosis in HIV-infected inpatients in Uganda: a cross-sectional study.

    J Lucian Davis

    Full Text Available BACKGROUND: Although World Health Organization guidelines recommend clinical judgment and chest radiography for diagnosing tuberculosis in HIV-infected adults with unexplained cough and negative sputum smears for acid-fast bacilli, the diagnostic performance of this approach is unknown. Therefore, we sought to assess the accuracy of symptoms, physical signs, and radiographic findings for diagnosing tuberculosis in this population in a low-income country with a high incidence of tuberculosis. METHODOLOGY: We performed a cross-sectional study enrolling consecutive HIV-infected inpatients with unexplained cough and negative sputum smears for acid-fast bacilli at Mulago Hospital in Kampala, Uganda. Trained medical officers prospectively collected data on standard symptoms and signs of systemic respiratory illness, and two radiologists interpreted chest radiographs in a standardized fashion. We calculated positive- and negative-likelihood ratios of these factors for diagnosing pulmonary tuberculosis (defined when mycobacterial cultures of sputum or bronchoalveolar lavage fluid were positive. We used both conventional and novel regression techniques to develop multivariable prediction models for pulmonary tuberculosis. PRINCIPAL FINDINGS: Among 202 enrolled HIV-infected adults with negative sputum smears for acid-fast bacilli, 72 (36% had culture-positive pulmonary tuberculosis. No single factor, including respiratory symptoms, physical findings, CD4+ T-cell count, or chest radiographic abnormalities, substantially increased or decreased the likelihood of pulmonary tuberculosis. After exhaustive testing, we were also unable to identify any combination of factors which reliably predicted bacteriologically confirmed tuberculosis. CONCLUSIONS AND SIGNIFICANCE: Clinical and radiographic criteria did not help diagnose smear-negative pulmonary tuberculosis among HIV-infected patients with unexplained cough in a low-income setting. Enhanced diagnostic

  8. Clinical Dutch-English Lambert-Eaton Myasthenic Syndrome (LEMS) Tumor Association Prediction Score Accurately Predicts Small-Cell Lung Cancer in the LEMS

    Titulaer, Maarten J.; Maddison, Paul; Sont, Jacob K.; Wirtz, Paul W.; Hilton-Jones, David; Klooster, Rinse; Willcox, Nick; Potman, Marko; Smitt, Peter A. E. Sillevis; Kuks, Jan B. M.; Roep, Bart O.; Vincent, Angela; van der Maarel, Silvere M.; van Dijk, J. Gert; Lang, Bethan; Verschuuren, Jan J. G. M.

    2011-01-01

    Purpose Approximately one half of patients with Lambert-Eaton myasthenic syndrome (LEMS) have small-cell lung carcinomas (SCLC), aggressive tumors with poor prognosis. In view of its profound impact on therapy and survival, we developed and validated a score to identify the presence of SCLC early in

  9. Formation of NO from N2/O2 mixtures in a flow reactor: Toward an accurate prediction of thermal NO

    Abian, Maria; Alzueta, Maria U.; Glarborg, Peter

    2015-01-01

    We have conducted flow reactor experiments for NO formation from N2/O2 mixtures at high temperatures and atmospheric pressure, controlling accurately temperature and reaction time. Under these conditions, atomic oxygen equilibrates rapidly with O2. The experimental results were interpreted by a d...

  10. Optimal search strategies for identifying sound clinical prediction studies in EMBASE

    Haynes R Brian

    2005-04-01

    Full Text Available Abstract Background Clinical prediction guides assist clinicians by pointing to specific elements of the patient's clinical presentation that should be considered when forming a diagnosis, prognosis or judgment regarding treatment outcome. The numbers of validated clinical prediction guides are growing in the medical literature, but their retrieval from large biomedical databases remains problematic and this presents a barrier to their uptake in medical practice. We undertook the systematic development of search strategies ("hedges" for retrieval of empirically tested clinical prediction guides from EMBASE. Methods An analytic survey was conducted, testing the retrieval performance of search strategies run in EMBASE against the gold standard of hand searching, using a sample of all 27,769 articles identified in 55 journals for the 2000 publishing year. All articles were categorized as original studies, review articles, general papers, or case reports. The original and review articles were then tagged as 'pass' or 'fail' for methodologic rigor in the areas of clinical prediction guides and other clinical topics. Search terms that depicted clinical prediction guides were selected from a pool of index terms and text words gathered in house and through request to clinicians, librarians and professional searchers. A total of 36,232 search strategies composed of single and multiple term phrases were trialed for retrieval of clinical prediction studies. The sensitivity, specificity, precision, and accuracy of search strategies were calculated to identify which were the best. Results 163 clinical prediction studies were identified, of which 69 (42.3% passed criteria for scientific merit. A 3-term strategy optimized sensitivity at 91.3% and specificity at 90.2%. Higher sensitivity (97.1% was reached with a different 3-term strategy, but with a 16% drop in specificity. The best measure of specificity (98.8% was found in a 2-term strategy, but with a

  11. A nomogram to predict Gleason sum upgrading of clinically diagnosed localized prostate cancer among Chinese patients

    Jin-You Wang; Yao Zhu; Chao-Fu Wang; Shi-Lin Zhang; Bo Dai; Ding-Wei Ye

    2014-01-01

    Although several models have been developed to predict the probability of Gleason sum upgrading between biopsy and radical prostatectomy specimens, most of these models are restricted to prostate-specific antigen screening-detected prostate cancer. This study aimed to build a nomogram for the prediction of Gleason sum upgrading in clinical y diagnosed prostate cancer. The study cohort comprised 269 Chinese prostate cancer patients who underwent prostate biopsy with a minimum of 10 cores and were subsequently treated with radical prostatectomy. Of al included patients, 220 (81.8%) were referred with clinical symptoms. The prostate-specific antigen level, primary and secondary biopsy Gleason scores, and clinical T category were used in a multivariate logistic regression model to predict the probability of Gleason sum upgrading. The developed nomogram was validated internally. Gleason sum upgrading was observed in 90 (33.5%) patients. Our nomogram showed a bootstrap-corrected concordance index of 0.789 and good calibration using 4 readily available variables. The nomogram also demonstrated satisfactory statistical performance for predicting significant upgrading. External validation of the nomogram published by Chun et al. in our cohort showed a marked discordance between the observed and predicted probabilities of Gleason sum upgrading. In summary, a new nomogram to predict Gleason sum upgrading in clinically diagnosed prostate cancer was developed, and it demonstrated good statistical performance upon internal validation.

  12. Predictive capacity of risk assessment scales and clinical judgment for pressure ulcers: a meta-analysis.

    García-Fernández, Francisco Pedro; Pancorbo-Hidalgo, Pedro L; Agreda, J Javier Soldevilla

    2014-01-01

    A systematic review with meta-analysis was completed to determine the capacity of risk assessment scales and nurses' clinical judgment to predict pressure ulcer (PU) development. Electronic databases were searched for prospective studies on the validity and predictive capacity of PUs risk assessment scales published between 1962 and 2010 in English, Spanish, Portuguese, Korean, German, and Greek. We excluded gray literature sources, integrative review articles, and retrospective or cross-sectional studies. The methodological quality of the studies was assessed according to the guidelines of the Critical Appraisal Skills Program. Predictive capacity was measured as relative risk (RR) with 95% confidence intervals. When 2 or more valid original studies were found, a meta-analysis was conducted using a random-effect model and sensitivity analysis. We identified 57 studies, including 31 that included a validation study. We also retrieved 4 studies that tested clinical judgment as a risk prediction factor. Meta-analysis produced the following pooled predictive capacity indicators: Braden (RR = 4.26); Norton (RR = 3.69); Waterlow (RR = 2.66); Cubbin-Jackson (RR = 8.63); EMINA (RR = 6.17); Pressure Sore Predictor Scale (RR = 21.4); and clinical judgment (RR = 1.89). Pooled analysis of 11 studies found adequate risk prediction capacity in various clinical settings; the Braden, Norton, EMINA (mEntal state, Mobility, Incontinence, Nutrition, Activity), Waterlow, and Cubbin-Jackson scales showed the highest predictive capacity. The clinical judgment of nurses was found to achieve inadequate predictive capacity when used alone, and should be used in combination with a validated scale.

  13. Clinical presentation and outcome prediction of clinical, serological, and histopathological classification schemes in ANCA-associated vasculitis with renal involvement.

    Córdova-Sánchez, Bertha M; Mejía-Vilet, Juan M; Morales-Buenrostro, Luis E; Loyola-Rodríguez, Georgina; Uribe-Uribe, Norma O; Correa-Rotter, Ricardo

    2016-07-01

    Several classification schemes have been developed for anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), with actual debate focusing on their clinical and prognostic performance. Sixty-two patients with renal biopsy-proven AAV from a single center in Mexico City diagnosed between 2004 and 2013 were analyzed and classified under clinical (granulomatosis with polyangiitis [GPA], microscopic polyangiitis [MPA], renal limited vasculitis [RLV]), serological (proteinase 3 anti-neutrophil cytoplasmic antibodies [PR3-ANCA], myeloperoxidase anti-neutrophil cytoplasmic antibodies [MPO-ANCA], ANCA negative), and histopathological (focal, crescenteric, mixed-type, sclerosing) categories. Clinical presentation parameters were compared at baseline between classification groups, and the predictive value of different classification categories for disease and renal remission, relapse, renal, and patient survival was analyzed. Serological classification predicted relapse rate (PR3-ANCA hazard ratio for relapse 2.93, 1.20-7.17, p = 0.019). There were no differences in disease or renal remission, renal, or patient survival between clinical and serological categories. Histopathological classification predicted response to therapy, with a poorer renal remission rate for sclerosing group and those with less than 25 % normal glomeruli; in addition, it adequately delimited 24-month glomerular filtration rate (eGFR) evolution, but it did not predict renal nor patient survival. On multivariate models, renal replacement therapy (RRT) requirement (HR 8.07, CI 1.75-37.4, p = 0.008) and proteinuria (HR 1.49, CI 1.03-2.14, p = 0.034) at presentation predicted renal survival, while age (HR 1.10, CI 1.01-1.21, p = 0.041) and infective events during the induction phase (HR 4.72, 1.01-22.1, p = 0.049) negatively influenced patient survival. At present, ANCA-based serological classification may predict AAV relapses, but neither clinical nor serological

  14. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11.

    Lundegaard, Claus; Lamberth, Kasper; Harndahl, Mikkel; Buus, Søren; Lund, Ole; Nielsen, Morten

    2008-07-01

    NetMHC-3.0 is trained on a large number of quantitative peptide data using both affinity data from the Immune Epitope Database and Analysis Resource (IEDB) and elution data from SYFPEITHI. The method generates high-accuracy predictions of major histocompatibility complex (MHC): peptide binding. The predictions are based on artificial neural networks trained on data from 55 MHC alleles (43 Human and 12 non-human), and position-specific scoring matrices (PSSMs) for additional 67 HLA alleles. As only the MHC class I prediction server is available, predictions are possible for peptides of length 8-11 for all 122 alleles. artificial neural network predictions are given as actual IC(50) values whereas PSSM predictions are given as a log-odds likelihood scores. The output is optionally available as download for easy post-processing. The training method underlying the server is the best available, and has been used to predict possible MHC-binding peptides in a series of pathogen viral proteomes including SARS, Influenza and HIV, resulting in an average of 75-80% confirmed MHC binders. Here, the performance is further validated and benchmarked using a large set of newly published affinity data, non-redundant to the training set. The server is free of use and available at: http://www.cbs.dtu.dk/services/NetMHC.

  15. Is scoring system of computed tomography based metric parameters can accurately predicts shock wave lithotripsy stone-free rates and aid in the development of treatment strategies?

    Yasser ALI Badran

    2016-01-01

    Conclusion: Stone size, stone density (HU, and SSD is simple to calculate and can be reported by radiologists to applying combined score help to augment predictive power of SWL, reduce cost, and improving of treatment strategies.

  16. Rapid and Accurate Detection of Mycobacterium tuberculosis in Sputum Samples by Cepheid Xpert MTB/RIF Assay—A Clinical Validation Study

    Rachow, Andrea; Zumla, Alimuddin; Heinrich, Norbert; Rojas-Ponce, Gabriel; Mtafya, Bariki; Reither, Klaus; Ntinginya, Elias N.; O'Grady, Justin; Huggett, Jim; Dheda, Keertan; Boehme, Catharina; Perkins, Mark; Saathoff, Elmar; Hoelscher, Michael

    2011-01-01

    Background A crucial impediment to global tuberculosis control is the lack of an accurate, rapid diagnostic test for detection of patients with active TB. A new, rapid diagnostic method, (Cepheid) Xpert MTB/RIF Assay, is an automated sample preparation and real-time PCR instrument, which was shown to have good potential as an alternative to current reference standard sputum microscopy and culture. Methods We performed a clinical validation study on diagnostic accuracy of the Xpert MTB/RIF Assay in a TB and HIV endemic setting. Sputum samples from 292 consecutively enrolled adults from Mbeya, Tanzania, with suspected TB were subject to analysis by the Xpert MTB/RIF Assay. The diagnostic performance of Xpert MTB/RIF Assay was compared to standard sputum smear microscopy and culture. Confirmed Mycobacterium tuberculosis in a positive culture was used as a reference standard for TB diagnosis. Results Xpert MTB/RIF Assay achieved 88.4% (95%CI = 78.4% to 94.9%) sensitivity among patients with a positive culture and 99% (95%CI = 94.7% to 100.0%) specificity in patients who had no TB. HIV status did not affect test performance in 172 HIV-infected patients (58.9% of all participants). Seven additional cases (9.1% of 77) were detected by Xpert MTB/RIF Assay among the group of patients with clinical TB who were culture negative. Within 45 sputum samples which grew non-tuberculous mycobacteria the assay's specificity was 97.8% (95%CI = 88.2% to 99.9%). Conclusions The Xpert MTB/RIF Assay is a highly sensitive, specific and rapid method for diagnosing TB which has potential to complement the current reference standard of TB diagnostics and increase its overall sensitivity. Its usefulness in detecting sputum smear and culture negative patients needs further study. Further evaluation in high burden TB and HIV areas under programmatic health care settings to ascertain applicability, cost-effectiveness, robustness and local acceptance are required. PMID:21738575

  17. Rapid and accurate detection of Mycobacterium tuberculosis in sputum samples by Cepheid Xpert MTB/RIF assay--a clinical validation study.

    Andrea Rachow

    Full Text Available BACKGROUND: A crucial impediment to global tuberculosis control is the lack of an accurate, rapid diagnostic test for detection of patients with active TB. A new, rapid diagnostic method, (Cepheid Xpert MTB/RIF Assay, is an automated sample preparation and real-time PCR instrument, which was shown to have good potential as an alternative to current reference standard sputum microscopy and culture. METHODS: We performed a clinical validation study on diagnostic accuracy of the Xpert MTB/RIF Assay in a TB and HIV endemic setting. Sputum samples from 292 consecutively enrolled adults from Mbeya, Tanzania, with suspected TB were subject to analysis by the Xpert MTB/RIF Assay. The diagnostic performance of Xpert MTB/RIF Assay was compared to standard sputum smear microscopy and culture. Confirmed Mycobacterium tuberculosis in a positive culture was used as a reference standard for TB diagnosis. RESULTS: Xpert MTB/RIF Assay achieved 88.4% (95%CI = 78.4% to 94.9% sensitivity among patients with a positive culture and 99% (95%CI = 94.7% to 100.0% specificity in patients who had no TB. HIV status did not affect test performance in 172 HIV-infected patients (58.9% of all participants. Seven additional cases (9.1% of 77 were detected by Xpert MTB/RIF Assay among the group of patients with clinical TB who were culture negative. Within 45 sputum samples which grew non-tuberculous mycobacteria the assay's specificity was 97.8% (95%CI = 88.2% to 99.9%. CONCLUSIONS: The Xpert MTB/RIF Assay is a highly sensitive, specific and rapid method for diagnosing TB which has potential to complement the current reference standard of TB diagnostics and increase its overall sensitivity. Its usefulness in detecting sputum smear and culture negative patients needs further study. Further evaluation in high burden TB and HIV areas under programmatic health care settings to ascertain applicability, cost-effectiveness, robustness and local acceptance are required.

  18. Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets

    Swanton, C.; Larkin, J.M.; Gerlinger, M.

    2010-01-01

    RNA screens to identify and validate functionally important genomic or transcriptomic predictive biomarkers of individual drug response in patients. PREDICT's approach to predictive biomarker discovery differs from conventional associative learning approaches, which can be susceptible to the detection...... inhibitor. Through the analysis of tumour tissue derived from pre-operative renal cell carcinoma (RCC) clinical trials, the PREDICT consortium will use established and novel methods to integrate comprehensive tumour-derived genomic data with personalised tumour-derived shRNA and high throughput si......, reducing ineffective therapy in drug resistant disease, leading to improved quality of life and higher cost efficiency, which in turn should broaden patient access to beneficial therapeutics, thereby enhancing clinical outcome and cancer survival. The consortium will also establish and consolidate...

  19. Clinical algorithm for improved prediction of ambulation and patient stratification after incomplete spinal cord injury.

    Zörner, Björn; Blanckenhorn, Wolf U; Dietz, Volker; Curt, Armin

    2010-01-01

    The extent of ambulatory recovery after motor incomplete spinal cord injury (miSCI) differs considerably amongst affected persons. This makes individual outcome prediction difficult and leads to increased within-group variation in clinical trials. The aims of this study on subjects with miSCI were: (1) to rank the strongest single predictors and predictor combinations of later walking capacity; (2) to develop a reliable algorithm for clinical prediction; and (3) to identify subgroups with only limited recovery of walking function. Correlation and logistic regression analyses were performed on a dataset of 90 subjects with tetra- or paraparesis, recruited in a prospective European multicenter study. Eleven measures obtained in the subacute injury period, including clinical examination, tibial somatosensory evoked potentials (tSSEP), and demographic factors, were related to ambulatory outcome (WISCI II, 6minWT) 6 months after injury. The lower extremity motor score (LEMS) alone and in combination was identified as most predictive for later walking capacity in miSCI. Ambulatory outcome of subjects with tetraparesis was correctly predicted for 92% (WISCI II) or 100% (6minWT) of the cases when LEMS was combined with either tSSEP or the ASIA Impairment Scale, respectively. For individuals with paraparesis, prediction was less distinct, mainly due to low prediction rates for individuals with poor walking outcome. A clinical algorithm was generated that allowed for the identification of a subgroup composed of individuals with tetraparesis and poor ambulatory recovery. These data provide evidence that a combination of predictors enables a reliable prediction of walking function and early patient stratification for clinical trials in miSCI.

  20. Multiplex-PCR-Based Screening and Computational Modeling of Virulence Factors and T-Cell Mediated Immunity in Helicobacter pylori Infections for Accurate Clinical Diagnosis.

    Sinem Oktem-Okullu

    Full Text Available The outcome of H. pylori infection is closely related with bacteria's virulence factors and host immune response. The association between T cells and H. pylori infection has been identified, but the effects of the nine major H. pylori specific virulence factors; cagA, vacA, oipA, babA, hpaA, napA, dupA, ureA, ureB on T cell response in H. pylori infected patients have not been fully elucidated. We developed a multiplex- PCR assay to detect nine H. pylori virulence genes with in a three PCR reactions. Also, the expression levels of Th1, Th17 and Treg cell specific cytokines and transcription factors were detected by using qRT-PCR assays. Furthermore, a novel expert derived model is developed to identify set of factors and rules that can distinguish the ulcer patients from gastritis patients. Within all virulence factors that we tested, we identified a correlation between the presence of napA virulence gene and ulcer disease as a first data. Additionally, a positive correlation between the H. pylori dupA virulence factor and IFN-γ, and H. pylori babA virulence factor and IL-17 was detected in gastritis and ulcer patients respectively. By using computer-based models, clinical outcomes of a patients infected with H. pylori can be predicted by screening the patient's H. pylori vacA m1/m2, ureA and cagA status and IFN-γ (Th1, IL-17 (Th17, and FOXP3 (Treg expression levels. Herein, we report, for the first time, the relationship between H. pylori virulence factors and host immune responses for diagnostic prediction of gastric diseases using computer-based models.

  1. The Predictive Value of Ultrasound Learning Curves Across Simulated and Clinical Settings

    Madsen, Mette E; Nørgaard, Lone N; Tabor, Ann

    2017-01-01

    OBJECTIVES: The aim of the study was to explore whether learning curves on a virtual-reality (VR) sonographic simulator can be used to predict subsequent learning curves on a physical mannequin and learning curves during clinical training. METHODS: Twenty midwives completed a simulation-based tra......OBJECTIVES: The aim of the study was to explore whether learning curves on a virtual-reality (VR) sonographic simulator can be used to predict subsequent learning curves on a physical mannequin and learning curves during clinical training. METHODS: Twenty midwives completed a simulation...

  2. GliomaPredict: a clinically useful tool for assigning glioma patients to specific molecular subtypes

    Fine Howard A

    2010-07-01

    Full Text Available Abstract Background Advances in generating genome-wide gene expression data have accelerated the development of molecular-based tumor classification systems. Tools that allow the translation of such molecular classification schemas from research into clinical applications are still missing in the emerging era of personalized medicine. Results We developed GliomaPredict as a computational tool that allows the fast and reliable classification of glioma patients into one of six previously published stratified subtypes based on sets of extensively validated classifiers derived from hundreds of glioma transcriptomic profiles. Our tool utilizes a principle component analysis (PCA-based approach to generate a visual representation of the analyses, quantifies the confidence of the underlying subtype assessment and presents results as a printable PDF file. GliomaPredict tool is implemented as a plugin application for the widely-used GenePattern framework. Conclusions GliomaPredict provides a user-friendly, clinically applicable novel platform for instantly assigning gene expression-based subtype in patients with gliomas thereby aiding in clinical trial design and therapeutic decision-making. Implemented as a user-friendly diagnostic tool, we expect that in time GliomaPredict, and tools like it, will become routinely used in translational/clinical research and in the clinical care of patients with gliomas.

  3. Comparison of four clinical scores for the predicting lower limb deep venous thrombosis in Chinese patients

    Li Zhua; Min Liu; Xiaojuan Guo; Jianguo Wang; Youmin Guo; Chen Wang; Hongxia Ma; Yulin Guo

    2008-01-01

    To evaluate Wells, Kahn, St.Andr é and Constans scores for the prediction of deep venous thrombosis in Chinese patients.Methods:One hundred and seventy-two patients, prospectively, blinded referred for evaluation with four clinical-score systems for suspected deep venous thrombosis, were examined by ultrasonography.Sensitivity, specificity, positive predictive value, nega- tive predictive value and receiver operation curves were calculated for four clinical scores.The difference between areas of the ROC curve for each of the scores was compared with others and reference line.Results:Forty-six of 172 patients had deep venous throm- bosis proven by sonography.The sensitivity, specificity, positive predictive value and negative predictive value for Wells score was 91.3%, 27.4% and 74.2% respectively, for Constans score; 95.7%, 34.9%, 34.9% and 95.7% respectively.Area under ROV curve of Constans with the reference line.Conclusion:Based on the results of our study, the sensitivity, negative prediction value and area under ROC Considering the aim of the clinical assessment, Constans score and Wells score are more efficient for Chinese hospitalized patients.

  4. Endometrial histology and predictable clinical factors for endometrial disease in women with polycystic ovary syndrome

    Park, Joon Cheol; Lim, Su Yeon; Jang, Tae Kyu; Bae, Jin Gon; Kim, Jong In; Rhee, Jeong Ho

    2011-01-01

    Objective This study was aimed to investigate endometrial histology and to find predictable clinical factors for endometrial disease (hyperplasia or cancer) in women with polycystic ovary syndrome (PCOS). Methods We investigated the endometrial histology and analyzed the relationship between endometrial histology and clinical parameters, such as LH, FSH, estradiol, testosterone, fasting and 2 hours postprandial glucose and insulin, insulin resistance, body mass index, endometrial thickness, m...

  5. A Multi-Center Prospective Derivation and Validation of a Clinical Prediction Tool for Severe Clostridium difficile Infection.

    Na, Xi

    2015-04-23

    Prediction of severe clinical outcomes in Clostridium difficile infection (CDI) is important to inform management decisions for optimum patient care. Currently, treatment recommendations for CDI vary based on disease severity but validated methods to predict severe disease are lacking. The aim of the study was to derive and validate a clinical prediction tool for severe outcomes in CDI.

  6. Accuracy of clinical prediction rules in peptic ulcer perforation: an observational study

    Buck, David Levarett; Vester-Andersen, Morten; Møller, Morten Hylander

    2012-01-01

    and breastfeeding women, non-surgically treated patients, patients with malignant ulcers, and patients with perforation of other organs were excluded. Primary outcome measure: 30-day mortality rate. Statistical analysis: the ability of four clinical prediction rules to distinguish survivors from non......-survivors (discrimination ability) was evaluated by the area under the receiver operating characteristic curve (AUC), positive predictive values (PPVs), negative predictive values (NPVs), and adjusted relative risks. Results. Median age (range) was 70 years (25-92 years), 51% of the patients were females, and 73...

  7. The molecular genetics and morphometry-based Endometrial Intraepithelial Neoplasia classification system predicts disease progression in Endometrial hyperplasia more accurately than the 1994 World Health Organization classification system

    Baak, JP; Mutter, GL; Robboy, S; van Diest, PJ; Uyterlinde, AM; Orbo, A; Palazzo, J; Fiane, B; Lovslett, K; Burger, C; Voorhorst, F; Verheijen, RH

    2005-01-01

    BACKGROUND. The objective of this study was to compare the accuracy of disease progression prediction of the molecular genetics and morphometry-based Endometrial Intraepithelial Neoplasia (EIN) and World Health Organization 1994 (WHO94) classification systems in patients with endometrial hyperplasia

  8. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11

    Lundegaard, Claus; Lamberth, K; Harndahl, M

    2008-01-01

    been used to predict possible MHC-binding peptides in a series of pathogen viral proteomes including SARS, Influenza and HIV, resulting in an average of 75–80% confirmed MHC binders. Here, the performance is further validated and benchmarked using a large set of newly published affinity data, non...

  9. Predictor characteristics necessary for building a clinically useful risk prediction model: a simulation study

    Laura Schummers

    2016-09-01

    Full Text Available Abstract Background Compelled by the intuitive appeal of predicting each individual patient’s risk of an outcome, there is a growing interest in risk prediction models. While the statistical methods used to build prediction models are increasingly well understood, the literature offers little insight to researchers seeking to gauge a priori whether a prediction model is likely to perform well for their particular research question. The objective of this study was to inform the development of new risk prediction models by evaluating model performance under a wide range of predictor characteristics. Methods Data from all births to overweight or obese women in British Columbia, Canada from 2004 to 2012 (n = 75,225 were used to build a risk prediction model for preeclampsia. The data were then augmented with simulated predictors of the outcome with pre-set prevalence values and univariable odds ratios. We built 120 risk prediction models that included known demographic and clinical predictors, and one, three, or five of the simulated variables. Finally, we evaluated standard model performance criteria (discrimination, risk stratification capacity, calibration, and Nagelkerke’s r2 for each model. Results Findings from our models built with simulated predictors demonstrated the predictor characteristics required for a risk prediction model to adequately discriminate cases from non-cases and to adequately classify patients into clinically distinct risk groups. Several predictor characteristics can yield well performing risk prediction models; however, these characteristics are not typical of predictor-outcome relationships in many population-based or clinical data sets. Novel predictors must be both strongly associated with the outcome and prevalent in the population to be useful for clinical prediction modeling (e.g., one predictor with prevalence ≥20 % and odds ratio ≥8, or 3 predictors with prevalence ≥10 % and odds ratios ≥4. Area

  10. Predictive Validity of DSM-IV Oppositional Defiant and Conduct Disorders in Clinically Referred Preschoolers

    Keenan, Kate; Boeldt, Debra; Chen, Diane; Coyne, Claire; Donald, Radiah; Duax, Jeanne; Hart, Katherine; Perrott, Jennifer; Strickland, Jennifer; Danis, Barbara; Hill, Carri; Davis, Shante; Kampani, Smita; Humphries, Marisha

    2011-01-01

    Background: Diagnostic validity of oppositional defiant and conduct disorders (ODD and CD) for preschoolers has been questioned based on concerns regarding the ability to differentiate normative, transient disruptive behavior from clinical symptoms. Data on concurrent validity have accumulated, but predictive validity is limited. Predictive…

  11. Sensitivity, specificity and predictive value of blood cultures from cattle clinically suspected of bacterial endocarditis

    Houe, Hans; Eriksen, L.; Jungersen, Gregers;

    1993-01-01

    This study investigated the number of blood culture-positive cattle among 215 animals clinically suspected of having bacterial endocarditis. For animals that were necropsied, the sensitivity, specificity and predictive value of the diagnosis of endocarditis were calculated on the basis...

  12. Clinical picture and risk prediction of short-term mortality in cardiogenic shock

    Harjola, Veli-Pekka; Lassus, Johan; Sionis, Alessandro

    2015-01-01

    AIMS: The aim of this study was to investigate the clinical picture and outcome of cardiogenic shock and to develop a risk prediction score for short-term mortality. METHODS AND RESULTS: The CardShock study was a multicentre, prospective, observational study conducted between 2010 and 2012. Patie...

  13. Clinical prediction rules for invasive candidiasis in the ICU: ready for prime time?

    Ostrosky-Zeichner, Luis

    2011-01-01

    Invasive candidiasis is a major source of morbidity and mortality in critically ill patients. The creation and validation of clinical prediction rules to identify patients at high risk has given clinicians access to advanced management strategies, such as targeted prophylaxis, pre-emptive therapy, and protocolized empirical therapy.

  14. Clinical prediction of fall risk and white matter abnormalities: a diffusion tensor imaging study

    The Tinetti scale is a simple clinical tool designed to predict risk of falling by focusing on gait and stance impairment in elderly persons. Gait impairment is also associated with white matter (WM) abnormalities. Objective: To test the hypothesis that elderly subjects at risk for falling, as deter...

  15. Prediction of Driving Safety in Individuals with Homonymous Hemianopia and Quadrantanopia from Clinical Neuroimaging

    Michael S. Vaphiades

    2014-01-01

    Full Text Available Background. This study aimed to determine whether it is possible to predict driving safety of individuals with homonymous hemianopia or quadrantanopia based upon a clinical review of neuroimages that are routinely available in clinical practice. Methods. Two experienced neuroophthalmologists viewed a summary report of the CT/MRI scans of 16 participants with homonymous hemianopic or quadrantanopic field defects which indicated the site and extent of the lesion and they made predictions regarding whether participants would be safe/unsafe to drive. Driving safety was independently defined at the time of the study using state-recorded motor vehicle crashes (all crashes and at-fault for the previous 5 years and ratings of driving safety determined through a standardized on-road driving assessment by a certified driving rehabilitation specialist. Results. The ability to predict driving safety was highly variable regardless of the driving safety measure, ranging from 31% to 63% (kappa levels ranged from −0.29 to 0.04. The level of agreement between the neuroophthalmologists was only fair (kappa = 0.28. Conclusions. Clinical evaluation of summary reports of currently available neuroimages by neuroophthalmologists is not predictive of driving safety. Future research should be directed at identifying and/or developing alternative tests or strategies to better enable clinicians to make these predictions.

  16. Practical Guidance for Implementing Predictive Biomarkers into Early Phase Clinical Studies

    Matthew J. Marton

    2013-01-01

    Full Text Available The recent U.S. Food and Drug Administration (FDA coapprovals of several therapeutic compounds and their companion diagnostic devices (FDA News Release, 2011, 2013 to identify patients who would benefit from treatment have led to considerable interest in incorporating predictive biomarkers in clinical studies. Yet, the translation of predictive biomarkers poses unique technical, logistic, and regulatory challenges that need to be addressed by a multidisciplinary team including discovery scientists, clinicians, biomarker experts, regulatory personnel, and assay developers. These issues can be placed into four broad categories: sample collection, assay validation, sample analysis, and regulatory requirements. In this paper, we provide a primer for drug development teams who are eager to implement a predictive patient segmentation marker into an early clinical trial in a way that facilitates subsequent development of a companion diagnostic. Using examples of nucleic acid-based assays, we briefly review common issues encountered when translating a biomarker to the clinic but focus primarily on key practical issues that should be considered by clinical teams when planning to use a biomarker to balance arms of a study or to determine eligibility for a clinical study.

  17. Role of Transition Zone Index in the Prediction of Clinical Benign Prostatic Hyperplasia

    Muhammet Güzelsoy

    2016-12-01

    Full Text Available Objective The objective of this study was to determine the role of the transition zone (TZ index (TZI in the prediction of clinical benign prostatic hyperplasia (BPH in patients who underwent transurethral prostatectomy (TUR-P and to analyze the correlation between the amount of resected tissue and TZ volume (TZV. Materials and Methods Twenty-six male clinical BPH patients with obstructive complaints and 17 male benign prostate enlargement (BPE patients without any complaints were included in the study. Both the groups were over the age of 50. Clinical BPH patients underwent complete TUR-P. Statistical analysis was done with SPSS. Sensitivity, specificity, positive and negative predictive values of TZI-as a method of assessing clinical BPH-were measured. Results There was a statistically significant difference in prostate volume, uroflowmetry patterns, prostate-specific antigen (PSA, International prostate symptom score (IPSS, TZV and TZI between the two groups. There was a correlation between TZV and the amount of resected tissue (r=0.97; p0.40 has a high level of sensitivity and specificity in the prediction of clinical BPH among patients who undergo TUR-P due to obstructive symptoms and reported as BPH. There is a strong correlation between the amount of resected tissue and TZV. TZI is a valuable tool in diagnosis, and TZV gives valuable information about the patient to the surgeon.

  18. Closed-loop spontaneous baroreflex transfer function is inappropriate for system identification of neural arc but partly accurate for peripheral arc: predictability analysis.

    Kamiya, Atsunori; Kawada, Toru; Shimizu, Shuji; Sugimachi, Masaru

    2011-04-01

    Although the dynamic characteristics of the baroreflex system have been described by baroreflex transfer functions obtained from open-loop analysis, the predictability of time-series output dynamics from input signals, which should confirm the accuracy of system identification, remains to be elucidated. Moreover, despite theoretical concerns over closed-loop system identification, the accuracy and the predictability of the closed-loop spontaneous baroreflex transfer function have not been evaluated compared with the open-loop transfer function. Using urethane and α-chloralose anaesthetized, vagotomized and aortic-denervated rabbits (n = 10), we identified open-loop baroreflex transfer functions by recording renal sympathetic nerve activity (SNA) while varying the vascularly isolated intracarotid sinus pressure (CSP) according to a binary random (white-noise) sequence (operating pressure ± 20 mmHg), and using a simplified equation to calculate closed-loop-spontaneous baroreflex transfer function while matching CSP with systemic arterial pressure (AP). Our results showed that the open-loop baroreflex transfer functions for the neural and peripheral arcs predicted the time-series SNA and AP outputs from measured CSP and SNA inputs, with r2 of 0.8 ± 0.1 and 0.8 ± 0.1, respectively. In contrast, the closed-loop-spontaneous baroreflex transfer function for the neural arc was markedly different from the open-loop transfer function (enhanced gain increase and a phase lead), and did not predict the time-series SNA dynamics (r2; 0.1 ± 0.1). However, the closed-loop-spontaneous baroreflex transfer function of the peripheral arc partially matched the open-loop transfer function in gain and phase functions, and had limited but reasonable predictability of the time-series AP dynamics (r2, 0.7 ± 0.1). A numerical simulation suggested that a noise predominantly in the neural arc under resting conditions might be a possible mechanism responsible for our findings. Furthermore

  19. PredPPCrys: accurate prediction of sequence cloning, protein production, purification and crystallization propensity from protein sequences using multi-step heterogeneous feature fusion and selection.

    Huilin Wang

    Full Text Available X-ray crystallography is the primary approach to solve the three-dimensional structure of a protein. However, a major bottleneck of this method is the failure of multi-step experimental procedures to yield diffraction-quality crystals, including sequence cloning, protein material production, purification, crystallization and ultimately, structural determination. Accordingly, prediction of the propensity of a protein to successfully undergo these experimental procedures based on the protein sequence may help narrow down laborious experimental efforts and facilitate target selection. A number of bioinformatics methods based on protein sequence information have been developed for this purpose. However, our knowledge on the important determinants of propensity for a protein sequence to produce high diffraction-quality crystals remains largely incomplete. In practice, most of the existing methods display poorer performance when evaluated on larger and updated datasets. To address this problem, we constructed an up-to-date dataset as the benchmark, and subsequently developed a new approach termed 'PredPPCrys' using the support vector machine (SVM. Using a comprehensive set of multifaceted sequence-derived features in combination with a novel multi-step feature selection strategy, we identified and characterized the relative importance and contribution of each feature type to the prediction performance of five individual experimental steps required for successful crystallization. The resulting optimal candidate features were used as inputs to build the first-level SVM predictor (PredPPCrys I. Next, prediction outputs of PredPPCrys I were used as the input to build second-level SVM classifiers (PredPPCrys II, which led to significantly enhanced prediction performance. Benchmarking experiments indicated that our PredPPCrys method outperforms most existing procedures on both up-to-date and previous datasets. In addition, the predicted crystallization

  20. An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection

    Panagiotis Bountris

    2014-01-01

    Full Text Available Nowadays, there are molecular biology techniques providing information related to cervical cancer and its cause: the human Papillomavirus (HPV, including DNA microarrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS, composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement. We evaluated this method on 740 cases with complete series of cytological assessment, molecular tests, and colposcopy examination. The CDSS demonstrated high sensitivity (89.4%, high specificity (97.1%, high positive predictive value (89.4%, and high negative predictive value (97.1%, for detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+. In comparison to the tests involved in this study and their combinations, the CDSS produced the most balanced results in terms of sensitivity, specificity, PPV, and NPV. The proposed system may reduce the referral rate for colposcopy and guide personalised management and therapeutic interventions.

  1. Stable, high-order SBP-SAT finite difference operators to enable accurate simulation of compressible turbulent flows on curvilinear grids, with application to predicting turbulent jet noise

    Byun, Jaeseung; Bodony, Daniel; Pantano, Carlos

    2014-11-01

    Improved order-of-accuracy discretizations often require careful consideration of their numerical stability. We report on new high-order finite difference schemes using Summation-By-Parts (SBP) operators along with the Simultaneous-Approximation-Terms (SAT) boundary condition treatment for first and second-order spatial derivatives with variable coefficients. In particular, we present a highly accurate operator for SBP-SAT-based approximations of second-order derivatives with variable coefficients for Dirichlet and Neumann boundary conditions. These terms are responsible for approximating the physical dissipation of kinetic and thermal energy in a simulation, and contain grid metrics when the grid is curvilinear. Analysis using the Laplace transform method shows that strong stability is ensured with Dirichlet boundary conditions while weaker stability is obtained for Neumann boundary conditions. Furthermore, the benefits of the scheme is shown in the direct numerical simulation (DNS) of a Mach 1.5 compressible turbulent supersonic jet using curvilinear grids and skew-symmetric discretization. Particularly, we show that the improved methods allow minimization of the numerical filter often employed in these simulations and we discuss the qualities of the simulation.

  2. A combined model for predicting CYP3A4 clinical net drug-drug interaction based on CYP3A4 inhibition, inactivation, and induction determined in vitro.

    Fahmi, Odette A; Maurer, Tristan S; Kish, Mary; Cardenas, Edwin; Boldt, Sherri; Nettleton, David

    2008-08-01

    Although approaches to the prediction of drug-drug interactions (DDIs) arising via time-dependent inactivation have recently been developed, such approaches do not account for simple competitive inhibition or induction. Accordingly, these approaches do not provide accurate predictions of DDIs arising from simple competitive inhibition (e.g., ketoconazole) or induction of cytochromes P450 (e.g., phenytoin). In addition, methods that focus upon a single interaction mechanism are likely to yield misleading predictions in the face of mixed mechanisms (e.g., ritonavir). As such, we have developed a more comprehensive mathematical model that accounts for the simultaneous influences of competitive inhibition, time-dependent inactivation, and induction of CYP3A in both the liver and intestine to provide a net drug-drug interaction prediction in terms of area under the concentration-time curve ratio. This model provides a framework by which readily obtained in vitro values for competitive inhibition, time-dependent inactivation and induction for the precipitant compound as well as literature values for f(m) and F(G) for the object drug can be used to provide quantitative predictions of DDIs. Using this model, DDIs arising via inactivation (e.g., erythromycin) continue to be well predicted, whereas those arising via competitive inhibition (e.g., ketoconazole), induction (e.g., phenytoin), and mixed mechanisms (e.g., ritonavir) are also predicted within the ranges reported in the clinic. This comprehensive model quantitatively predicts clinical observations with reasonable accuracy and can be a valuable tool to evaluate candidate drugs and rationalize clinical DDIs.

  3. Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model

    Yamamoto, Kimiyo N.; Ishii, Masatsugu; Inoue, Yoshihiro; Hirokawa, Fumitoshi; MacArthur, Ben D.; Nakamura, Akira; Haeno, Hiroshi; Uchiyama, Kazuhisa

    2016-10-01

    Although the capacity of the liver to recover its size after resection has enabled extensive liver resection, post-hepatectomy liver failure remains one of the most lethal complications of liver resection. Therefore, it is clinically important to discover reliable predictive factors after resection. In this study, we established a novel mathematical framework which described post-hepatectomy liver regeneration in each patient by incorporating quantitative clinical data. Using the model fitting to the liver volumes in series of computed tomography of 123 patients, we estimated liver regeneration rates. From the estimation, we found patients were divided into two groups: i) patients restored the liver to its original size (Group 1, n = 99) and ii) patients experienced a significant reduction in size (Group 2, n = 24). From discriminant analysis in 103 patients with full clinical variables, the prognosis of patients in terms of liver recovery was successfully predicted in 85–90% of patients. We further validated the accuracy of our model prediction using a validation cohort (prediction = 84–87%, n = 39). Our interdisciplinary approach provides qualitative and quantitative insights into the dynamics of liver regeneration. A key strength is to provide better prediction in patients who had been judged as acceptable for resection by current pragmatic criteria.

  4. Predicting counseling psychologists attitudes and clinical judgments with respect to older adults.

    Tomko, Jody K; Munley, Patrick H

    2013-01-01

    The purpose of this study was to examine age, gender, training and experience in aging issues, fear of death, and multicultural competence in predicting counseling psychologists' global attitudes toward older adults and specific clinical judgments concerning a case vignette of an older client. A national sample of 364 practicing counseling psychologists participated in the study. Participants completed a demographic measure, Polizzi's refined version of the Aging Semantic Differential (Polizzi, 2003 ), a survey of professional bias based on a clinical vignette of a 70-year-old woman (James & Haley, 1995), the Collett-Lester Fear of Death Scale 3.0 (Lester, & Abdel-Khalek, 2003), the Multicultural Counseling Knowledge and Awareness Scale (MCKAS; Ponterotto, Gretchen, Utsey, Rieger, & Austin, 2002), and a Training and Experience Questionnaire. Hierarchical multiple regression analyses were used to investigate the extent to which the selected variables predicted more favorable attitudes toward older adults and less professional bias toward an older client beyond prediction by age and gender. Results revealed that older age and higher total scores on the MCKAS predicted less professional bias in clinical judgments. Gender was a significant predictor of global attitudes toward older adults. Findings suggest that multicultural knowledge, awareness, and skills are important in working with older adults.

  5. Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model

    Yamamoto, Kimiyo N.; Ishii, Masatsugu; Inoue, Yoshihiro; Hirokawa, Fumitoshi; MacArthur, Ben D.; Nakamura, Akira; Haeno, Hiroshi; Uchiyama, Kazuhisa

    2016-01-01

    Although the capacity of the liver to recover its size after resection has enabled extensive liver resection, post-hepatectomy liver failure remains one of the most lethal complications of liver resection. Therefore, it is clinically important to discover reliable predictive factors after resection. In this study, we established a novel mathematical framework which described post-hepatectomy liver regeneration in each patient by incorporating quantitative clinical data. Using the model fitting to the liver volumes in series of computed tomography of 123 patients, we estimated liver regeneration rates. From the estimation, we found patients were divided into two groups: i) patients restored the liver to its original size (Group 1, n = 99); and ii) patients experienced a significant reduction in size (Group 2, n = 24). From discriminant analysis in 103 patients with full clinical variables, the prognosis of patients in terms of liver recovery was successfully predicted in 85–90% of patients. We further validated the accuracy of our model prediction using a validation cohort (prediction = 84–87%, n = 39). Our interdisciplinary approach provides qualitative and quantitative insights into the dynamics of liver regeneration. A key strength is to provide better prediction in patients who had been judged as acceptable for resection by current pragmatic criteria. PMID:27694914

  6. Improvement of a clinical prediction rule for clinical trials on prophylaxis for invasive candidiasis in the intensive care unit.

    Ostrosky-Zeichner, Luis; Pappas, Peter G; Shoham, Shmuel; Reboli, Annette; Barron, Michelle A; Sims, Charles; Wood, Craig; Sobel, Jack D

    2011-01-01

    We created a clinical prediction rule to identify patients at risk of invasive candidiasis (IC) in the intensive care unit (ICU) (Eur J Clin Microbiol Infect Dis 2007; 26:271). The rule applies to <10% of patients in ICUs. We sought to create a more inclusive rule for clinical trials. Retrospective review of patients admitted to ICU ≥ 4 days, collecting risk factors and outcomes. Variations of the rule based on introduction of mechanical ventilation and risk factors were assessed. We reviewed 597 patients with a mean APACHE II score of 14.4, mean ICU stay of 12.5 days and mean ventilation time of 10.7 days. A variation of the rule requiring mechanical ventilation AND central venous catheter AND broad spectrum antibiotics on days 1-3 AND an additional risk factor applied to 18% of patients, maintaining the incidence of IC at 10%. Modification of our original rule resulted in a more inclusive rule for studies.

  7. An IL28B genotype-based clinical prediction model for treatment of chronic hepatitis C.

    Thomas R O'Brien

    Full Text Available BACKGROUND: Genetic variation in IL28B and other factors are associated with sustained virological response (SVR after pegylated-interferon/ribavirin treatment for chronic hepatitis C (CHC. Using data from the HALT-C Trial, we developed a model to predict a patient's probability of SVR based on IL28B genotype and clinical variables. METHODS: HALT-C enrolled patients with advanced CHC who had failed previous interferon-based treatment. Subjects were re-treated with pegylated-interferon/ribavirin during trial lead-in. We used step-wise logistic regression to calculate adjusted odds ratios (aOR and create the predictive model. Leave-one-out cross-validation was used to predict a priori probabilities of SVR and determine area under the receiver operator characteristics curve (AUC. RESULTS: Among 646 HCV genotype 1-infected European American patients, 14.2% achieved SVR. IL28B rs12979860-CC genotype was the strongest predictor of SVR (aOR, 7.56; p10% (43.3% of subjects had an SVR rate of 27.9% and accounted for 84.8% of subjects actually achieving SVR. To verify that consideration of both IL28B genotype and clinical variables is required for treatment decisions, we calculated AUC values from published data for the IDEAL Study. CONCLUSION: A clinical prediction model based on IL28B genotype and clinical variables can yield useful individualized predictions of the probability of treatment success that could increase SVR rates and decrease the frequency of futile treatment among patients with CHC.

  8. Ability of clinicopathologic variables and clinical examination findings to predict race elimination in endurance horses.

    Fielding, C Langdon; Meier, Chloe A; Fellers, Greg K; Magdesian, K Gary

    2017-01-01

    OBJECTIVE To compare results of point-of-care laboratory testing with standard veterinary clinical examination findings at a single time point during endurance competition to identify horses at risk for elimination. ANIMALS 101 endurance horses participating in the 2013 Western States 160-km (100-mile) endurance ride. PROCEDURES At the 58-km checkpoint, blood samples were collected from all horses. Samples were analyzed for pH, Pco2, base excess, anion gap, PCV, and whole blood concentrations of sodium, potassium, chloride, total carbon dioxide, BUN, glucose, and bicarbonate. Corrected electrolyte and PCV values were calculated on the basis of plasma total protein concentration. Immediately following the blood sample collection, each horse underwent a clinical examination. In addition to standard examination variables, an adjusted heart rate was calculated on the basis of the variable interval between entry into the checkpoint and heart rate recording. A combination of stepwise logistic regression, classification and regression tree analysis, and generalized additive models was used to identify variables that were associated with overall elimination or each of 3 other elimination categories (metabolic elimination, lameness elimination, and elimination for other reasons). RESULTS Corrected whole blood potassium concentration and adjusted heart rate were predictive for overall elimination. Breed, plasma total protein concentration, and attitude were predictive for elimination due to metabolic causes. Whole blood chloride concentration and corrected PCV were predictive for elimination due to lameness. Corrected PCV was predictive for elimination due to other causes. CONCLUSIONS AND CLINICAL RELEVANCE Results indicated that for horses in endurance competition, a combination of breed and clinical examination and laboratory variables provided the best prediction of overall elimination.

  9. Integration of preclinical and clinical knowledge to predict intravenous PK in human: bilastine case study.

    Vozmediano, Valvanera; Ortega, Ignacio; Lukas, John C; Gonzalo, Ana; Rodriguez, Monica; Lucero, Maria Luisa

    2014-03-01

    Modern pharmacometrics can integrate and leverage all prior proprietary and public knowledge. Such methods can be used to scale across species or comparators, perform clinical trial simulation across alternative designs, confirm hypothesis and potentially reduce development burden, time and costs. Crucial yet typically lacking in integration is the pre-clinical stage. Prediction of PK in man, using in vitro and in vivo studies in different animal species, is increasingly well theorized but could still find wider application in drug development. The aim of the present work was to explore methods for bridging pharmacokinetic knowledge from animal species (i.v. and p.o.) and man (p.o.) into i.v. in man using the antihistamine drug bilastine as example. A model, predictive of i.v. PK in man, was developed on data from two pre-clinical species (rat and dog) and p.o. in man bilastine trials performed earlier. In the knowledge application stage, two different approaches were used to predict human plasma concentration after i.v. of bilastine: allometry (several scaling methods) and a semi-physiological method. Both approaches led to successful predictions of key i.v. PK parameters of bilastine in man. The predictive i.v. PK model was validated using later data from a clinical study of i.v. bilastine. Introduction of such knowledge in development permits proper leveraging of all emergent knowledge as well as quantification-based exploration of PK scenario, e.g. in special populations (pediatrics, renal insufficiency, comedication). In addition, the methods permit reduction or elimination and certainly optimization of learning trials, particularly those concerning alternative off-label administration routes.

  10. EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect

    Bennett, Casey; Selove, Rebecca

    2012-01-01

    Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and genetic information as well, and, as such, can be seen as repositories of increasingly valuable information about patients' health conditions and responses to treatment over time. Methods: We describe a case study of 423 patients treated by Centerstone within Tennessee and Indiana in which we utilized electronic health record data to generate predictive algorithms of individual patient treatment response. Multiple models were constructed using predictor variables derived from clinical, financial and geographic data. Results: For the 423 patients, 101 deteriorated, 223 improved and in 99 there was no change in clinical condition. Based on modeling of various clinical indicators at baseline, the high...

  11. Location of brain lesions predicts conversion of clinically isolated syndromes to multiple sclerosis

    Giorgio, Antonio; Battaglini, Marco; Rocca, Maria Assunta

    2013-01-01

    : In CIS patients with hemispheric, multifocal, and brainstem/cerebellar onset, lesion probability map clusters were seen in clinically eloquent brain regions. Significant lesion clusters were not found in CIS patients with optic nerve and spinal cord onset. At 1 year, clinically definite MS developed......OBJECTIVES: To assess in a large population of patients with clinically isolated syndrome (CIS) the relevance of brain lesion location and frequency in predicting 1-year conversion to multiple sclerosis (MS). METHODS: In this multicenter, retrospective study, clinical and MRI data at onset...... in 26% of patients. The converting group, despite a greater baseline lesion load compared with the nonconverting group (7 ± 8.1 cm(3) vs 4.6 ± 6.7 cm(3), p brain voxels occupied by lesions). High lesion frequency was found...

  12. Predictive monitoring of mobile patients by combining clinical observations with data from wearable sensors.

    Clifton, Lei; Clifton, David A; Pimentel, Marco A F; Watkinson, Peter J; Tarassenko, Lionel

    2014-05-01

    The majority of patients in the hospital are ambulatory and would benefit significantly from predictive and personalized monitoring systems. Such patients are well suited to having their physiological condition monitored using low-power, minimally intrusive wearable sensors. Despite data-collection systems now being manufactured commercially, allowing physiological data to be acquired from mobile patients, little work has been undertaken on the use of the resultant data in a principled manner for robust patient care, including predictive monitoring. Most current devices generate so many false-positive alerts that devices cannot be used for routine clinical practice. This paper explores principled machine learning approaches to interpreting large quantities of continuously acquired, multivariate physiological data, using wearable patient monitors, where the goal is to provide early warning of serious physiological determination, such that a degree of predictive care may be provided. We adopt a one-class support vector machine formulation, proposing a formulation for determining the free parameters of the model using partial area under the ROC curve, a method arising from the unique requirements of performing online analysis with data from patient-worn sensors. There are few clinical evaluations of machine learning techniques in the literature, so we present results from a study at the Oxford University Hospitals NHS Trust devised to investigate the large-scale clinical use of patient-worn sensors for predictive monitoring in a ward with a high incidence of patient mortality. We show that our system can combine routine manual observations made by clinical staff with the continuous data acquired from wearable sensors. Practical considerations and recommendations based on our experiences of this clinical study are discussed, in the context of a framework for personalized monitoring.

  13. Some uses of predictive probability of success in clinical drug development

    Mauro Gasparini

    2013-03-01

    Full Text Available Predictive probability of success is a (subjective Bayesian evaluation of the prob- ability of a future successful event in a given state of information. In the context of pharmaceutical clinical drug development, successful events relate to the accrual of positive evidence on the therapy which is being developed, like demonstration of su- perior efficacy or ascertainment of safety. Positive evidence will usually be obtained via standard frequentist tools, according to the regulations imposed in the world of pharmaceutical development.Within a single trial, predictive probability of success can be identified with expected power, i.e. the evaluation of the success probability of the trial. Success means, for example, obtaining a significant result of a standard superiority test.Across trials, predictive probability of success can be the probability of a successful completion of an entire part of clinical development, for example a successful phase III development in the presence of phase II data.Calculations of predictive probability of success in the presence of normal data with known variance will be illustrated, both for within-trial and across-trial predictions.

  14. A New Strategy for Accurately Predicting I-V Electrical Characteristics of PV Modules Using a Nonlinear Five-Point Model

    Sakaros Bogning Dongue

    2013-01-01

    Full Text Available This paper presents the modelling of electrical I-V response of illuminated photovoltaic crystalline modules. As an alternative method to the linear five-parameter model, our strategy uses advantages of a nonlinear analytical five-point model to take into account the effects of nonlinear variations of current with respect to solar irradiance and of voltage with respect to cells temperature. We succeeded in this work to predict with great accuracy the I-V characteristics of monocrystalline shell SP75 and polycrystalline GESOLAR GE-P70 photovoltaic modules. The good comparison of our calculated results to experimental data provided by the modules manufacturers makes it possible to appreciate the contribution of taking into account the nonlinear effect of operating conditions data on I-V characteristics of photovoltaic modules.

  15. Anthropometric variables accurately predict dual energy x-ray absorptiometric-derived body composition and can be used to screen for diabetes.

    Reza Yavari

    Full Text Available The current world-wide epidemic of obesity has stimulated interest in developing simple screening methods to identify individuals with undiagnosed diabetes mellitus type 2 (DM2 or metabolic syndrome (MS. Prior work utilizing body composition obtained by sophisticated technology has shown that the ratio of abdominal fat to total fat is a good predictor for DM2 or MS. The goals of this study were to determine how well simple anthropometric variables predict the fat mass distribution as determined by dual energy x-ray absorptometry (DXA, and whether these are useful to screen for DM2 or MS within a population. To accomplish this, the body composition of 341 females spanning a wide range of body mass indices and with a 23% prevalence of DM2 and MS was determined using DXA. Stepwise linear regression models incorporating age, weight, height, waistline, and hipline predicted DXA body composition (i.e., fat mass, trunk fat, fat free mass, and total mass with good accuracy. Using body composition as independent variables, nominal logistic regression was then performed to estimate the probability of DM2. The results show good discrimination with the receiver operating characteristic (ROC having an area under the curve (AUC of 0.78. The anthropometrically-derived body composition equations derived from the full DXA study group were then applied to a group of 1153 female patients selected from a general endocrinology practice. Similar to the smaller study group, the ROC from logistical regression using body composition had an AUC of 0.81 for the detection of DM2. These results are superior to screening based on questionnaires and compare favorably with published data derived from invasive testing, e.g., hemoglobin A1c. This anthropometric approach offers promise for the development of simple, inexpensive, non-invasive screening to identify individuals with metabolic dysfunction within large populations.

  16. Integration of noninvasive prenatal prediction of fetal blood group into clinical prenatal care

    Clausen, Frederik Banch

    2014-01-01

    of the fetus and newborn to fetuses of immunized women. Prediction of the fetal RhD type has been very successful and is now integrated into clinical practice to assist in the management of the pregnancies of RhD immunized women. In addition, noninvasive prediction of the fetal RhD type can be applied to guide......Incompatibility of red blood cell blood group antigens between a pregnant woman and her fetus can cause maternal immunization and, consequently, hemolytic disease of the fetus and newborn. Noninvasive prenatal testing of cell-free fetal DNA can be used to assess the risk of hemolytic disease...

  17. A novel neural-inspired learning algorithm with application to clinical risk prediction.

    Tay, Darwin; Poh, Chueh Loo; Kitney, Richard I

    2015-04-01

    Clinical risk prediction - the estimation of the likelihood an individual is at risk of a disease - is a coveted and exigent clinical task, and a cornerstone to the recommendation of life saving management strategies. This is especially important for individuals at risk of cardiovascular disease (CVD) given the fact that it is the leading causes of death in many developed counties. To this end, we introduce a novel learning algorithm - a key factor that influences the performance of machine learning-based prediction models - and utilities it to develop CVD risk prediction tool. This novel neural-inspired algorithm, called the Artificial Neural Cell System for classification (ANCSc), is inspired by mechanisms that develop the brain and empowering it with capabilities such as information processing/storage and recall, decision making and initiating actions on external environment. Specifically, we exploit on 3 natural neural mechanisms responsible for developing and enriching the brain - namely neurogenesis, neuroplasticity via nurturing and apoptosis - when implementing ANCSc algorithm. Benchmark testing was conducted using the Honolulu Heart Program (HHP) dataset and results are juxtaposed with 2 other algorithms - i.e. Support Vector Machine (SVM) and Evolutionary Data-Conscious Artificial Immune Recognition System (EDC-AIRS). Empirical experiments indicate that ANCSc algorithm (statistically) outperforms both SVM and EDC-AIRS algorithms. Key clinical markers identified by ANCSc algorithm include risk factors related to diet/lifestyle, pulmonary function, personal/family/medical history, blood data, blood pressure, and electrocardiography. These clinical markers, in general, are also found to be clinically significant - providing a promising avenue for identifying potential cardiovascular risk factors to be evaluated in clinical trials.

  18. Prediction of Clinically Relevant Safety Signals of Nephrotoxicity through Plasma Metabolite Profiling

    W. B. Mattes

    2013-01-01

    Full Text Available Addressing safety concerns such as drug-induced kidney injury (DIKI early in the drug pharmaceutical development process ensures both patient safety and efficient clinical development. We describe a unique adjunct to standard safety assessment wherein the metabolite profile of treated animals is compared with the MetaMap Tox metabolomics database in order to predict the potential for a wide variety of adverse events, including DIKI. To examine this approach, a study of five compounds (phenytoin, cyclosporin A, doxorubicin, captopril, and lisinopril was initiated by the Technology Evaluation Consortium under the auspices of the Drug Safety Executive Council (DSEC. The metabolite profiles for rats treated with these compounds matched established reference patterns in the MetaMap Tox metabolomics database indicative of each compound’s well-described clinical toxicities. For example, the DIKI associated with cyclosporine A and doxorubicin was correctly predicted by metabolite profiling, while no evidence for DIKI was found for phenytoin, consistent with its clinical picture. In some cases the clinical toxicity (hepatotoxicity, not generally seen in animal studies, was detected with MetaMap Tox. Thus metabolite profiling coupled with the MetaMap Tox metabolomics database offers a unique and powerful approach for augmenting safety assessment and avoiding clinical adverse events such as DIKI.

  19. External model validation of binary clinical risk prediction models in cardiovascular and thoracic surgery.

    Hickey, Graeme L; Blackstone, Eugene H

    2016-08-01

    Clinical risk-prediction models serve an important role in healthcare. They are used for clinical decision-making and measuring the performance of healthcare providers. To establish confidence in a model, external model validation is imperative. When designing such an external model validation study, thought must be given to patient selection, risk factor and outcome definitions, missing data, and the transparent reporting of the analysis. In addition, there are a number of statistical methods available for external model validation. Execution of a rigorous external validation study rests in proper study design, application of suitable statistical methods, and transparent reporting.

  20. Pre and per operative prediction of difficult laparoscopic cholecystectomy using clinical and ultrasonographic parameters

    Gaurav Gupta

    2015-11-01

    Methods: In 50 consecutive patients who underwent LC during 2013 to 2014 patient's characteristics, clinical history, laboratory data, ultrasonography results and intraoperative details were prospectively analyzed to determine predictors of difficult LC. Results: Of 50 patients 3 (06% required conversion to open cholecystectomy. Significant predictors of conversion were obscured anatomy of Calot's due to adhesions, sessile gall bladder, male gender and gall bladder wall thickness >3 mm. Conclusions: With preoperative clinical and ultrasonographic parameters, proper patient selection can be made to help predict difficult LC and a likelihood of conversion to open cholecystectomy. [Int J Res Med Sci 2015; 3(11.000: 3342-3346

  1. Full-Dimensional Potential Energy and Dipole Moment Surfaces of GeH4 Molecule and Accurate First-Principle Rotationally Resolved Intensity Predictions in the Infrared.

    Nikitin, A V; Rey, M; Rodina, A; Krishna, B M; Tyuterev, Vl G

    2016-11-17

    Nine-dimensional potential energy surface (PES) and dipole moment surface (DMS) of the germane molecule are constructed using extended ab initio CCSD(T) calculations at 19 882 points. PES analytical representation is determined as an expansion in nonlinear symmetry adapted products of orthogonal and internal coordinates involving 340 parameters up to eighth order. Minor empirical refinement of the equilibrium geometry and of four quadratic parameters of the PES computed at the CCSD(T)/aug-cc-pVQZ-DK level of the theory yielded the accuracy below 1 cm(-1) for all experimentally known vibrational band centers of five stable isotopologues of (70)GeH4, (72)GeH4, (73)GeH4, (74)GeH4, and (76)GeH4 up to 8300 cm(-1). The optimized equilibrium bond re = 1.517 594 Å is very close to best ab initio values. Rotational energies up to J = 15 are calculated using potential expansion in normal coordinate tensors with maximum errors of 0.004 and 0.0006 cm(-1) for (74)GeH4 and (76)GeH4. The DMS analytical representation is determined through an expansion in symmetry-adapted products of internal nonlinear coordinates involving 967 parameters up to the sixth order. Vibration-rotation line intensities of five stable germane isotopologues were calculated from purely ab initio DMS using nuclear motion variational calculations with a full account of the tetrahedral symmetry of the molecules. For the first time a good overall agreement of main absorption features with experimental rotationally resolved Pacific Northwest National Laboratory spectra was achieved in the entire range of 700-5300 cm(-1). It was found that very accurate description of state-dependent isotopic shifts is mandatory to correctly describe complex patterns of observed spectra at natural isotopic abundance resulting from the superposition of five stable isotopologues. The data obtained in this work will be made available through the TheoReTS information system.

  2. The Diagnostic Accuracy of Clinical and External Pelvimetry in Prediction of Dystocia in Nulliparous Women

    R Alijahan

    2011-08-01

    Full Text Available Introduction: Clinical pelvimetry is very uncomfortable for the patient and is associated with subjective error, while external pelvimetry is a simple and acceptable method for patients. The objective of this study was to compare the diagnostic accuracy of clinical and external pelvimetry in prediction of dystocia in nulliparous women. Methods: In this study between December 2008 and January 2009, 447 nulliparous women with a single pregnancy in vertex presentation and gestational age 38-42 weeks referring to the Ommolbanin hospital of Mashhad were included. External pelvic dimensions were assessed at the time of admission and clinical pelvimetry was performed by another examiner. These measurements were not available to the clinician in charge of the delivery. Dystocia was defined as caesarean section and vacuum or forceps delivery for abnormal progress of labor ( active uterine contractions, arrest of cervical dilatation or cervical dilatation less than 1 cm /h in the active phase for 2 hours, prolongation of second stage beyond 2 hours or fetal head descent less than 1cm/h. Statistical tests included Fisher exact test and Chi- square test. Results: The highest sensitivity obtained from clinical pelvimetry was 33.3% and related to diagonal conjugate less than 11.5 cm. The sensitivity of external pelvic dimensions was higher than clinical pelvimetry that was highest for the Michaelis transverse diameter(60.72%. Conclusion: External pelvimetry in comparison to clinical pelvimetry is a better method for identifying dystocia in nulliparous women and can replace clinical pelvimetry in antenatal care programs.

  3. Time series analysis as input for clinical predictive modeling: Modeling cardiac arrest in a pediatric ICU

    Kennedy Curtis E

    2011-10-01

    Full Text Available Abstract Background Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. Methods We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Results Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1 selecting candidate variables; 2 specifying measurement parameters; 3 defining data format; 4 defining time window duration and resolution; 5 calculating latent variables for candidate variables not directly measured; 6 calculating time series features as latent variables; 7 creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8

  4. Accurate prediction of hard-sphere virial coefficients B6 to B12 from a compressibility-based equation of state

    Hansen-Goos, Hendrik

    2016-04-01

    We derive an analytical equation of state for the hard-sphere fluid that is within 0.01% of computer simulations for the whole range of the stable fluid phase. In contrast, the commonly used Carnahan-Starling equation of state deviates by up to 0.3% from simulations. The derivation uses the functional form of the isothermal compressibility from the Percus-Yevick closure of the Ornstein-Zernike relation as a starting point. Two additional degrees of freedom are introduced, which are constrained by requiring the equation of state to (i) recover the exact fourth virial coefficient B4 and (ii) involve only integer coefficients on the level of the ideal gas, while providing best possible agreement with the numerical result for B5. Virial coefficients B6 to B10 obtained from the equation of state are within 0.5% of numerical computations, and coefficients B11 and B12 are within the error of numerical results. We conjecture that even higher virial coefficients are reliably predicted.

  5. HPA axis in major depression: cortisol, clinical symptomatology and genetic variation predict cognition.

    Keller, J; Gomez, R; Williams, G; Lembke, A; Lazzeroni, L; Murphy, G M; Schatzberg, A F

    2016-08-16

    The hypothalamic-pituitary-adrenal (HPA) axis has been implicated in the pathophysiology of a variety of mood and cognitive disorders. Neuroendocrine studies have demonstrated HPA axis overactivity in major depression, a relationship of HPA axis activity to cognitive performance and a potential role of HPA axis genetic variation in cognition. The present study investigated the simultaneous roles HPA axis activity, clinical symptomatology and HPA genetic variation play in cognitive performance. Patients with major depression with psychotic major depression (PMD) and with nonpsychotic major depression (NPMD) and healthy controls (HC) were studied. All participants underwent a diagnostic interview and psychiatric ratings, a comprehensive neuropsychological battery, overnight hourly blood sampling for cortisol and genetic assessment. Cognitive performance differed as a function of depression subtype. Across all subjects, cognitive performance was negatively correlated with higher cortisol, and PMD patients had higher cortisol than did NPMDs and HCs. Cortisol, clinical symptoms and variation in genes, NR3C1 (glucocorticoid receptor; GR) and NR3C2 (mineralocorticoid receptor; MR) that encode for GRs and MRs, predicted cognitive performance. Beyond the effects of cortisol, demographics and clinical symptoms, NR3C1 variation predicted attention and working memory, whereas NR3C2 polymorphisms predicted memory performance. These findings parallel the distribution of GR and MR in primate brain and their putative roles in specific cognitive tasks. HPA axis genetic variation and activity were important predictors of cognition across the entire sample of depressed subjects and HR. GR and MR genetic variation predicted unique cognitive functions, beyond the influence of cortisol and clinical symptoms. GR genetic variation was implicated in attention and working memory, whereas MR was implicated in verbal memory.Molecular Psychiatry advance online publication, 16 August 2016; doi

  6. Evaluating clinical abdominal scoring system in predict- ing the necessity of laparotomy in blunt abdominal trauma

    Erfantalab-Avini Peyman

    2011-06-01

    Full Text Available 【Abstract】 Objectives: Trauma is among the lead- ing causes of death. Medical management of blunt abdomi- nal trauma (BAT relies on judging patients for whom lap- arotomy is mandatory. This study aimed to determine BAT patients’ signs, as well as paraclinical data, and to clarify the accuracy, sensitivity, specificity, positive and negative predictive value of clinical abdominal scoring system (CASS, a new scoring system based on clinical signs, in predicting whether a BAT patient needs laparotomy or not. Methods: Totally 400 patients suspected of BAT that arrived at the emergency department of two university hos- pitals in Tehran from March 20, 2007 to March 19, 2009 were included in this study. They were evaluated for age, sex, type of trauma, systolic blood pressure, Glasgow coma scale (GCS, pulse rate, time of presentation after trauma, abdomi- nal clinical findings, respiratory rate, temperature, hemoglo- bin (Hb concentration, focused abdominal sonography in trauma (FAST and CASS. Results: Our measurements showed that CASS had an accuracy of 94%, sensitivity of 100%, specificity of 88%, positive predictive value of 90% and negative predictive value of 100% in determining the necessity of laparotomy in BAT patients. Moreover, in our analysis, systolic blood pressure, GCS, pulse rate, Hb concentration, time of presen- tation after trauma, abdominal clinical findings and FAST were also shown to be helpful in confirming the need for laparotomy (P<0.05. Conclusion: CASS is a promising scoring system in rapid detection of the need for laparotomy as well as in minimizing auxiliary expense for further evaluation in BAT patients, thus to promote the cost-benefit ratio and accu- racy of diagnosis. Key words: Abdominal injuries; Laparotomy; Patients; Wounds, nonpenetrating

  7. Evaluating clinical abdominal scoring system in predicting the necessity of laparotomy in blunt abdominal trauma

    Peyman Erfantalab-Avini; Nima Hafezi-Nejad; Mojtaba Chardoli; Vafa Rahimi-Movaghar

    2011-01-01

    Objectives: Trauma is among the leading causes of death. Medical management of blunt abdominal trauma (BAT) relies on judging patients for whom laparotomy is mandatory. This study aimed to determine BAT patients' signs, as well as paraclinical data, and to clarify the accuracy, sensitivity, specificity, positive and negative predictive value of clinical abdominal scoring system (CASS), a new scoring system based on clinical signs, in predicting whether a BAT patient needs laparotomy or not.Methods: Totally 400 patients suspected of BAT that arrived at the emergency department of two university hospitals in Tehran from March 20, 2007 to March 19, 2009 were included in this study. They were evaluated for age, sex,type of trauma, systolic blood pressure, Glasgow coma scale (GCS), pulse rate, time of presentation after trauma, abdominal clinical findings, respiratory rate, temperature, hemoglobin (Hb) concentration, focused abdominal sonography in trauma (FAST) and CASS.Results: Our measurements showed that CASS had an accuracy of 94%, sensitivity of 100%, specificity of 88%,positive predictive value of 90% and negative predictive value of 100% in determining the necessity of laparotomy in BAT patients. Moreover, in our analysis, systolic blood pressure, GCS, pulse rate, Hb concentration, time of presentation after trauma, abdominal clinical findings and FAST were also shown to be helpful in confirming the need for laparotomy (P<0.05).Conclusion: CASS is a promising scoring system in rapid detection of the need for laparotomy as well as in minimizing auxiliary expense for further evaluation in BAT patients, thus to promote the cost-benefit ratio and accuracy of diagnosis.

  8. Predictive Power of the Baseline QRS Complex Duration for Clinical Response to Cardiac Resynchronisation Therapy

    Ali Kazemisaeid

    2011-02-01

    Full Text Available Background: Determination of predictors of response to cardiac resynchronisation therapy (CRT in patients with moderate to severe heart failure accompanied by a ventricular dyssynchrony can play a major role in improving candidate selection for CRT.Objectives: We evaluated whether the baseline QRS duration could be used to discriminate responders from non-responders to CRT.Methods: Eighty three consecutive patients with moderate to severe heart failure and with successful implantation of a CRT device at our centre were included in the study. QRS durations were measured on 12-lead surface electrocardiogram before and 6 months after implantation of the CRT device, using the widest QRS complex in leads II, V1 and V6. Clinical response to CRT was defined as an improvement of ≥1 grade in NYHA class.Results: Optimal cut-off value to discriminate baseline QRS duration for predicting clinical response to CRT was identified at 152 ms, yielding a sensitivity of 73.3%, a specificity of 56.5% as well as positive and negative predictive values of 81.5% and 44.8%, respectively. The discriminatory pow- er of the baseline QRS duration for response to CRT assessed by the ROC curve was 0.6402 (95% CI: 0.4976 – 0.7829. Baseline QRS duration ≥ 152 ms could effectively predict clinical response to CRT after adjusting for covariates (OR = 3.743, p = 0.017.Conclusion: Baseline QRS duration can effectively predict clinical response to CRT and optimal cut-off value to discriminate baseline QRS duration for response to CRT is 152 ms.

  9. A Clinical Scoring System to Predict the Development of Bronchopulmonary Dysplasia

    Gürsoy, Tuğba; Hayran, Mutlu; Derin, Hatice; Ovalı, Fahri

    2015-01-01

    A Clinical Scoring System to Predict the Development of Bronchopulmonary Dysplasia Tugba Gursoy, MD1 Mutlu Hayran, MD2 Hatice Derin, MD3 Fahri Ovali, MD3 1Department of Neonatology, School of Medicine, KOC University, Istanbul, Turkey 2Department of Preventive Oncology, Faculty of Medicine, Hacettepe University, Ankara, Turkey 3Department of Pediatrics, Zeynep Kamil Maternity and Children’s Research and Training Hospital, Istanbul, Turkey 4Department of Neonatology,...

  10. Clinical Features That Predict the Need for Operative Intervention in Gluteus Medius Tears

    Chandrasekaran, Sivashankar; Vemula, S. Pavan; Gui, Chengcheng; Suarez-Ahedo, Carlos; Lodhia, Parth; Domb, Benjamin G.

    2015-01-01

    Background: Gluteus medius tears are a common cause of lateral hip pain. Operative intervention is usually prescribed for patients with pain despite physical therapy and/or peritrochanteric injections. Purpose: To identify clinical features that predict operative intervention in gluteus medius tears. Study Design: Case control study; Level of evidence, 3. Methods: A matched-pair controlled study was conducted on patients who underwent endoscopic gluteus medius repairs from June 2008 to August...

  11. HPA Axis in Major Depression: Cortisol, Clinical Symptomatology, and Genetic Variation Predict Cognition

    Keller, Jennifer; Gomez, Rowena; Williams, Gordon; Lembke, Anna; Lazzeroni, Laura; Murphy, Greer M.; Schatzberg, Alan F.

    2016-01-01

    The Hypothalamic Pituitary Adrenal (HPA) axis has been implicated in the pathophysiology of a variety of mood and cognitive disorders. Neuroendocrine studies have demonstrated HPA axis overactivity in major depression, a relationship of HPA axis activity to cognitive performance, and a potential role of HPA axis genetic variation in cognition. The present study investigated the simultaneous roles HPA axis activity, clinical symptomatology, and HPA genetic variation play in cognitive performance. Patients with major depression with psychosis (PMD) and without psychosis (NPMD) and healthy controls (HC) were studied. All participants underwent a diagnostic interview and psychiatric ratings, a comprehensive neuropsychological battery, overnight hourly blood sampling for cortisol, and genetic assessment. Cognitive performance differed as a function of depression subtype. Across all subjects, cognitive performance was negatively correlated with higher cortisol, and PMD patients had higher cortisol than did NPMDs and HCs. Cortisol, clinical symptoms, and variation in genes, NR3C1 (glucocorticoid receptor - GR) and NR3C2 (minercorticoid receptor – MR) that encode for glucocorticoid and mineralcorticoid receptors, predicted cognitive performance. Beyond the effects of cortisol, demographics, and clinical symptoms, NR3C1 variation predicted attention and working memory, whereas NR3C2 polymorphisms predicted memory performance. These findings parallel the distribution of GR and MR in primate brain and their putative roles in specific cognitive tasks. HPA axis genetic variation and activity were important predictors of cognition across the entire sample of depressed subjects and healthy controls. GR and MR genetic variation predicted unique cognitive functions, beyond the influence of cortisol and clinical symptoms. GR genetic variation was implicated in attention and working memory, whereas MR was implicated in verbal memory. PMID:27528460

  12. Some uses of predictive probability of success in clinical drug development

    Mauro Gasparini; Lilla Di Scala; Frank Bretz; Amy Racine-Poon

    2013-01-01

    Predictive probability of success is a (subjective) Bayesian evaluation of the prob- ability of a future successful event in a given state of information. In the context of pharmaceutical clinical drug development, successful events relate to the accrual of positive evidence on the therapy which is being developed, like demonstration of su- perior efficacy or ascertainment of safety. Positive evidence will usually be obtained via standard frequentist tools, according to the regulations impose...

  13. Endovascular Treatment of Malignant Superior Vena Cava Syndrome: Results and Predictive Factors of Clinical Efficacy

    Fagedet, Dorothee, E-mail: DFagedet@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de medecine interne, Pole Pluridisciplinaire de Medecine (France); Thony, Frederic, E-mail: FThony@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de radiologie et imagerie medicale, Pole d' Imagerie (France); Timsit, Jean-Francois, E-mail: JFTimsit@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de reanimation, Pole Medecine Aiguee Communautaire (France); Rodiere, Mathieu, E-mail: MRodiere@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de radiologie et imagerie medicale, Pole d' Imagerie (France); Monnin-Bares, Valerie, E-mail: v-monnin@chu-montpellier.fr [CHRU Arnaud de Villeneuve, Imagerie Medicale Thoracique Cardiovasculaire (France); Ferretti, Gilbert R., E-mail: GFerretti@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de radiologie et imagerie medicale, Pole d' Imagerie (France); Vesin, Aurelien; Moro-Sibilot, Denis, E-mail: DMoro.pneumo@chu-grenoble.fr [University Grenoble 1 e Albert Bonniot Institute, Inserm U823 (France)

    2013-02-15

    To demonstrate the effectiveness of endovascular treatment (EVT) with self-expandable bare stents for malignant superior vena cava syndrome (SVCS) and to analyze predictive factors of EVT efficacy. Retrospective review of the 164 patients with malignant SVCS treated with EVT in our hospital from August 1992 to December 2007 and followed until February 2009. Endovascular treatment includes angioplasty before and after stent placement. We used self-expandable bare stents. We studied results of this treatment and looked for predictive factors of clinical efficacy, recurrence, and complications by statistical analysis. Endovascular treatment was clinically successful in 95% of cases, with an acceptable rate of early mortality (2.4%). Thrombosis of the superior vena cava was the only independent factor for EVT failure. The use of stents over 16 mm in diameter was a predictive factor for complications (P = 0.008). Twenty-one complications (12.8%) occurred during the follow-up period. Relapse occurred in 36 patients (21.9%), with effective restenting in 75% of cases. Recurrence of SVCS was significantly increased in cases of occlusion (P = 0.01), initial associated thrombosis (P = 0.006), or use of steel stents (P = 0.004). Long-term anticoagulant therapy did not influence the risk of recurrence or complications. In malignancy, EVT with self-expandable bare stents is an effective SVCS therapy. These results prompt us to propose treatment with stents earlier in the clinical course of patients with SVCS and to avoid dilatation greater than 16 mm.

  14. PREDICTION OF CLINICAL EFFICIENCY OF SIMVASTATIN TREATMENT IN PATIENTS WITH RHEUMATOID ARTHRITIS

    I. V. Shirinsky

    2009-01-01

    Full Text Available Abstract. Treatment with statins results in reduction of disease activity in one-third of patients with rheumatoid arthritis (RA. The aim of this study was to assess some factors that may predict clinical response to simvastatin therapy before starting the treatment. We evaluated an association of treatment efficacy with baseline clinical and laboratory parameters including disease activity measures, cytokine profiles in sera and culture supernatants of peripheral blood mononuclear cells. Thirty-three patients with active RA were enrolled in the study. The patients were treated with simvastatin at 40 mg/day for three months. Eleven patients (33% developed a moderate response according to EULAR criteria. It was shown that serum IL-10 concentrations was higher in responders, and positively correlated with clinical response to simvastatin. We carried out a receiver operating characteristic curve (ROC analysis in order to assess the accuracy of serum IL-10 for the predicting of EULAR response development. The cut-off threshold corresponding to the highest sensitivity (89% and specificity (62% was a value of 6.5 pg/ml. In conclusion, the performance characteristics of serum IL-10 measurement proved to be good enough to predict EULAR response to simvastatin therapy in RA patients.

  15. Modeling and Predicting AD Progression by Regression Analysis of Sequential Clinical Data

    Xie, Qing

    2016-02-23

    Alzheimer\\'s Disease (AD) is currently attracting much attention in elders\\' care. As the increasing availability of massive clinical diagnosis data, especially the medical images of brain scan, it is highly significant to precisely identify and predict the potential AD\\'s progression based on the knowledge in the diagnosis data. In this paper, we follow a novel sequential learning framework to model the disease progression for AD patients\\' care. Different from the conventional approaches using only initial or static diagnosis data to model the disease progression for different durations, we design a score-involved approach and make use of the sequential diagnosis information in different disease stages to jointly simulate the disease progression. The actual clinical scores are utilized in progress to make the prediction more pertinent and reliable. We examined our approach by extensive experiments on the clinical data provided by the Alzheimer\\'s Disease Neuroimaging Initiative (ADNI). The results indicate that the proposed approach is more effective to simulate and predict the disease progression compared with the existing methods.

  16. Predictive factors of rapidly progressive-interstitial lung disease in patients with clinically amyopathic dermatomyositis.

    Xu, Y; Yang, C S; Li, Y J; Liu, X D; Wang, J N; Zhao, Q; Xiao, W G; Yang, P T

    2016-01-01

    Clinically amyopathic dermatomyositis (CADM) is a unique subset of dermatomyositis, showing a high incidence of lung involvements. The aim of this study is to identify risk factors, other than melanoma differentiation-associated protein (MDA)-5, for developing rapidly progressive-interstitial lung disease (RP-ILD) in patients with CADM. Forty CADM patients, in whom 11 patients developed RP-ILD, were enrolled. Clinical features and laboratory findings were compared between the patients with and without RP-ILD. We found that skin ulceration, CRP, serum ferritin, anti-MDA5 Ab, and lymphocytopenia were significantly associated with ILD. Multivariate logistic regression analysis indicated that anti-MDA5 Ab(+), elevated CRP, and decreased counts of lymphocyte were independent risk factors for RP-ILD, which can provide a precise predict for RP-ILD in CADM patients. When anti-MDA5 Ab(+) was removed from the multivariate regression model, using skin ulcerations, elevated serum ferritin and decreased counts of lymphocyte can also precisely predict RP-ILD. Except for MDA-5, more commonly available clinical characteristics, such as skin ulcerations, serum ferritin, and count of lymphocyte may also help to predict prognosis in CADM.

  17. Levels of uninvolved immunoglobulins predict clinical status and progression-free survival for multiple myeloma patients.

    Harutyunyan, Nika M; Vardanyan, Suzie; Ghermezi, Michael; Gottlieb, Jillian; Berenson, Ariana; Andreu-Vieyra, Claudia; Berenson, James R

    2016-07-01

    Multiple myeloma (MM) is characterized by the enhanced production of the same monoclonal immunoglobulin (M-Ig or M protein). Techniques such as serum protein electrophoresis and nephelometry are routinely used to quantify levels of this protein in the serum of MM patients. However, these methods are not without their shortcomings and problems accurately quantifying M proteins remain. Precise quantification of the types and levels of M-Ig present is critical to monitoring patient response to therapy. In this study, we investigated the ability of the HevyLite (HLC) immunoassay to correlate with clinical status based on levels of involved and uninvolved antibodies. In our cohort of MM patients, we observed that significantly higher ratios and greater differences of involved HLC levels compared to uninvolved HLC levels correlated with a worse clinical status. Similarly, higher absolute levels of involved HLC antibodies and lower levels of uninvolved HLC antibodies also correlated with a worse clinical status and a shorter progression-free survival. These findings suggest that the HLC assay is a useful and a promising tool for determining the clinical status and survival time for patients with multiple myeloma.

  18. Pharmacogenomics of Methotrexate Membrane Transport Pathway: Can Clinical Response to Methotrexate in Rheumatoid Arthritis Be Predicted?

    Aurea Lima

    2015-06-01

    Full Text Available Background: Methotrexate (MTX is widely used for rheumatoid arthritis (RA treatment. Single nucleotide polymorphisms (SNPs could be used as predictors of patients’ therapeutic outcome variability. Therefore, this study aims to evaluate the influence of SNPs in genes encoding for MTX membrane transport proteins in order to predict clinical response to MTX. Methods: Clinicopathological data from 233 RA patients treated with MTX were collected, clinical response defined, and patients genotyped for 23 SNPs. Genotype and haplotype analyses were performed using multivariate methods and a genetic risk index (GRI for non-response was created. Results: Increased risk for non-response was associated to SLC22A11 rs11231809 T carriers; ABCC1 rs246240 G carriers; ABCC1 rs3784864 G carriers; CGG haplotype for ABCC1 rs35592, rs2074087 and rs3784864; and CGG haplotype for ABCC1 rs35592, rs246240 and rs3784864. GRI demonstrated that patients with Index 3 were 16-fold more likely to be non-responders than those with Index 1. Conclusions: This study revealed that SLC22A11 and ABCC1 may be important to identify those patients who will not benefit from MTX treatment, highlighting the relevance in translating these results to clinical practice. However, further validation by independent studies is needed to develop the field of personalized medicine to predict clinical response to MTX treatment.

  19. Prediction of peak pressure from clinical and radiological measurements in patients with diabetes

    Nieman Fred HM

    2008-12-01

    Full Text Available Abstract Background Various structural and functional factors of foot function have been associated with high local plantar pressures. The therapist focuses on these features which are thought to be responsible for plantar ulceration in patients with diabetes. Risk assessment of the diabetic foot would be made easier if locally elevated plantar pressure could be indicated with a minimum set of clinical measures. Methods Ninety three patients were evaluated through vascular, orthopaedic, neurological and radiological assessment. A pressure platform was used to quantify the barefoot peak pressure for six forefoot regions: big toe (BT and metatarsals one (MT-1 to five (MT-5. Stepwise regression modelling was performed to determine which set of the clinical and radiological measures explained most variability in local barefoot plantar peak pressure in each of the six forefoot regions. Comprehensive models were computed with independent variables from the clinical and radiological measurements. The difference between the actual plantar pressure and the predicted value was examined through Bland-Altman analysis. Results Forefoot pressures were significant higher in patients with neuropathy, compared to patients without neuropathy for the whole forefoot, the MT-1 region and the MT-5 region (respectively 138 kPa, 173 kPa and 88 kPa higher: mean difference. The clinical models explained up to 39 percent of the variance in local peak pressures. Callus formation and toe deformity were identified as relevant clinical predictors for all forefoot regions. Regression models with radiological variables explained about 26 percent of the variance in local peak pressures. For most regions the combination of clinical and radiological variables resulted in a higher explained variance. The Bland and Altman analysis showed a major discrepancy between the predicted and the actual peak pressure values. Conclusion At best, clinical and radiological measurements could

  20. Clinical Application of AIMS65 Scores to Predict Outcomes in Patients with Upper Gastrointestinal Hemorrhage

    Sharma, Manik; John, Anil K; Al-Ejji, Khalid Mohsin; Wani, Hamidulla; Sultan, Khaleel; Al-Mohannadi, Muneera; Yakoob, Rafie; Derbala, Moutaz; Al-Dweik, Nazeeh; Butt, Muhammed Tariq; Al-Kaabi, Saad Rashid

    2015-01-01

    Background/Aims To evaluate the ability of the recently proposed albumin, international normalized ratio (INR), mental status, systolic blood pressure, age >65 years (AIMS65) score to predict mortality in patients with acute upper gastrointestinal bleeding (UGIB). Methods AIMS65 scores were calculated in 251 consecutive patients presenting with acute UGIB by allotting 1 point each for albumin level 1.5, alteration in mental status, systolic blood pressure ≤90 mm Hg, and age ≥65 years. Risk stratification was done during the initial 12 hours of hospital admission. Results Intensive care unit (ICU) admission, endoscopic therapy, or surgery were required in 51 patients (20.3%), 64 (25.5%), and 12 (4.8%), respectively. The predictive accuracy of AIMS65 scores ≥2 was high for blood transfusion (area under the receiver operator characteristic curve [AUROC], 0.59), ICU admission (AUROC, 0.61), and mortality (AUROC, 0.74). The overall mortality was 10.3% (n=26), and was 3%, 7.8%, 20%, 36%, and 40% for AIMS65 scores of 0, 1, 2, 3, and 4, respectively; these values were significantly higher in those with scores ≥2 (30.9%) than in those with scores <2 (4.5%, p<0.001). Conclusions AIMS65 is a simple, accurate, non-endoscopic risk score that can be applied early (within 12 hours of hospital admission) in patients with acute UGIB. AIMS65 scores ≥2 predict high in-hospital mortality. PMID:26473120

  1. Clinical signs of pneumonia in children: association with and prediction of diagnosis by fuzzy sets theory

    Pereira J.C.R.

    2004-01-01

    Full Text Available The present study compares the performance of stochastic and fuzzy models for the analysis of the relationship between clinical signs and diagnosis. Data obtained for 153 children concerning diagnosis (pneumonia, other non-pneumonia diseases, absence of disease and seven clinical signs were divided into two samples, one for analysis and other for validation. The former was used to derive relations by multi-discriminant analysis (MDA and by fuzzy max-min compositions (fuzzy, and the latter was used to assess the predictions drawn from each type of relation. MDA and fuzzy were closely similar in terms of prediction, with correct allocation of 75.7 to 78.3% of patients in the validation sample, and displaying only a single instance of disagreement: a patient with low level of toxemia was mistaken as not diseased by MDA and correctly taken as somehow ill by fuzzy. Concerning relations, each method provided different information, each revealing different aspects of the relations between clinical signs and diagnoses. Both methods agreed on pointing X-ray, dyspnea, and auscultation as better related with pneumonia, but only fuzzy was able to detect relations of heart rate, body temperature, toxemia and respiratory rate with pneumonia. Moreover, only fuzzy was able to detect a relationship between heart rate and absence of disease, which allowed the detection of six malnourished children whose diagnoses as healthy are, indeed, disputable. The conclusion is that even though fuzzy sets theory might not improve prediction, it certainly does enhance clinical knowledge since it detects relationships not visible to stochastic models.

  2. Predicting cognitive function from clinical measures of physical function and health status in older adults.

    Niousha Bolandzadeh

    Full Text Available Current research suggests that the neuropathology of dementia-including brain changes leading to memory impairment and cognitive decline-is evident years before the onset of this disease. Older adults with cognitive decline have reduced functional independence and quality of life, and are at greater risk for developing dementia. Therefore, identifying biomarkers that can be easily assessed within the clinical setting and predict cognitive decline is important. Early recognition of cognitive decline could promote timely implementation of preventive strategies.We included 89 community-dwelling adults aged 70 years and older in our study, and collected 32 measures of physical function, health status and cognitive function at baseline. We utilized an L1-L2 regularized regression model (elastic net to identify which of the 32 baseline measures were strongly predictive of cognitive function after one year. We built three linear regression models: 1 based on baseline cognitive function, 2 based on variables consistently selected in every cross-validation loop, and 3 a full model based on all the 32 variables. Each of these models was carefully tested with nested cross-validation.Our model with the six variables consistently selected in every cross-validation loop had a mean squared prediction error of 7.47. This number was smaller than that of the full model (115.33 and the model with baseline cognitive function (7.98. Our model explained 47% of the variance in cognitive function after one year.We built a parsimonious model based on a selected set of six physical function and health status measures strongly predictive of cognitive function after one year. In addition to reducing the complexity of the model without changing the model significantly, our model with the top variables improved the mean prediction error and R-squared. These six physical function and health status measures can be easily implemented in a clinical setting.

  3. Predictive Factors of Clinical Response of Infliximab Therapy in Active Nonradiographic Axial Spondyloarthritis Patients

    Lin, Zhiming; Liao, Zetao; Huang, Jianlin; Ai, Maixing; Pan, Yunfeng; Wu, Henglian; Lu, Jun; Cao, Shuangyan; Li, Li; Wei, Qiujing; Tang, Deshen; Wei, Yanlin; Li, Tianwang; Wu, Yuqiong; Xu, Manlong; Li, Qiuxia; Jin, Ou; Yu, Buyun; Gu, Jieruo

    2015-01-01

    Objectives. To evaluate the efficiency and the predictive factors of clinical response of infliximab in active nonradiographic axial spondyloarthritis patients. Methods. Active nonradiographic patients fulfilling ESSG criteria for SpA but not fulfilling modified New York criteria were included. All patients received infliximab treatment for 24 weeks. The primary endpoint was ASAS20 response at weeks 12 and 24. The abilities of baseline parameters and response at week 2 to predict ASAS20 response at weeks 12 and 24 were assessed using ROC curve and logistic regression analysis, respectively. Results. Of 70 axial SpA patients included, the proportions of patients achieving an ASAS20 response at weeks 2, 6, 12, and 24 were 85.7%, 88.6%, 87.1%, and 84.3%, respectively. Baseline MRI sacroiliitis score (AUC = 0.791; P = 0.005), CRP (AUC = 0.75; P = 0.017), and ASDAS (AUC = 0.778, P = 0.007) significantly predicted ASAS20 response at week 12. However, only ASDAS (AUC = 0.696, P = 0.040) significantly predicted ASAS20 response at week 24. Achievement of ASAS20 response after the first infliximab infusion was a significant predictor of subsequent ASAS20 response at weeks 12 and 24 (wald χ2 = 6.87, P = 0.009, and wald χ2 = 5.171, P = 0.023). Conclusions. Infliximab shows efficiency in active nonradiographic axial spondyloarthritis patients. ASDAS score and first-dose response could help predicting clinical efficacy of infliximab therapy in these patients. PMID:26273654

  4. Precision and negative predictive value of links between ClinicalTrials.gov and PubMed.

    Huser, Vojtech; Cimino, James J

    2012-01-01

    One of the goals of translational science is to shorten the time from discovery to clinical use. Clinical trial registries were established to increase transparency in completed and ongoing clinical trials, and they support linking trials with resulting publications. We set out to investigate precision and negative predictive value (NPV) of links between ClinicalTrials.gov (CT.gov) and PubMed. CT.gov has been established to increase transparency in clinical trials and the link to PubMed is crucial for supporting a number of important functions, including ascertaining publication bias. We drew a random sample of trials downloaded from CT.gov and performed manual review of retrieved publications. We characterize two types of links between trials and publications (NCT-link originating from MEDLINE and PMID-link originating from CT.gov).The link precision is different based on type (NCT-link: 100%; PMID-link: 63% to 96%). In trials with no linked publication, we were able to find publications 44% of the time (NPV=56%) by searching PubMed. This low NPV shows that there are potentially numerous publications that should have been formally linked with the trials. Our results indicate that existing trial registry and publisher policies may not be fully enforced. We suggest some automated methods for improving link quality.

  5. Are clinical features able to predict Helicobacter pylori gastritis patterns? Evidence from tertiary centers.

    Carabotti, Marilia; Lahner, Edith; Porowska, Barbara; Colacci, Enzo; Trentino, Paolo; Annibale, Bruno; Severi, Carola

    2014-12-01

    Outcome of Helicobacter pylori infection is different according to gastritis extension (i.e. antrum-restricted gastritis or pangastritis). The aim of this study is to evaluate whether different gastritis patterns are associated with specific gastrointestinal symptoms or clinical signs that could be suggestive of the topography of gastritis. 236 consecutive symptomatic outpatients were recruited in two tertiary centers. They filled in a validated and self-administered Rome III modular symptomatic questionnaire, and underwent gastroscopy with histological sampling. 154 patients with Helicobacter pylori infection were included. Clinical presentation did not differ between antrum-restricted gastritis and pangastritis, gastro-esophageal reflux disease being present in 48.2 and 54.1 % of patients and dyspepsia in 51.8 and 45.9 %, respectively. However, pangastritis statistically differed from antrum-restricted gastritis in that the presence of clinical signs (p gastritis pattern whereas their association with signs, accurately detected, is indicative for the presence of pangastritis.

  6. Development and validation of a clinical prediction rule for chest wall syndrome in primary care

    Ronga Alexandre

    2012-08-01

    Full Text Available Abstract Background Chest wall syndrome (CWS, the main cause of chest pain in primary care practice, is most often an exclusion diagnosis. We developed and evaluated a clinical prediction rule for CWS. Methods Data from a multicenter clinical cohort of consecutive primary care patients with chest pain were used (59 general practitioners, 672 patients. A final diagnosis was determined after 12 months of follow-up. We used the literature and bivariate analyses to identify candidate predictors, and multivariate logistic regression was used to develop a clinical prediction rule for CWS. We used data from a German cohort (n = 1212 for external validation. Results From bivariate analyses, we identified six variables characterizing CWS: thoracic pain (neither retrosternal nor oppressive, stabbing, well localized pain, no history of coronary heart disease, absence of general practitioner’s concern, and pain reproducible by palpation. This last variable accounted for 2 points in the clinical prediction rule, the others for 1 point each; the total score ranged from 0 to 7 points. The area under the receiver operating characteristic (ROC curve was 0.80 (95% confidence interval 0.76-0.83 in the derivation cohort (specificity: 89%; sensitivity: 45%; cut-off set at 6 points. Among all patients presenting CWS (n = 284, 71% (n = 201 had a pain reproducible by palpation and 45% (n = 127 were correctly diagnosed. For a subset (n = 43 of these correctly classified CWS patients, 65 additional investigations (30 electrocardiograms, 16 thoracic radiographies, 10 laboratory tests, eight specialist referrals, one thoracic computed tomography had been performed to achieve diagnosis. False positives (n = 41 included three patients with stable angina (1.8% of all positives. External validation revealed the ROC curve to be 0.76 (95% confidence interval 0.73-0.79 with a sensitivity of 22% and a specificity of 93%. Conclusions This CWS score offers

  7. Trabecular bone structure analysis of the spine using clinical MDCT: can it predict vertebral bone strength?

    Baum, Thomas; Gräbeldinger, Martin; Räth, Christoph; Garcia, Eduardo Grande; Burgkart, Rainer; Patsch, Janina M; Rummeny, Ernst J; Link, Thomas M; Bauer, Jan S

    2014-01-01

    Recent technical improvements have made it possible to determine trabecular bone structure parameters of the spine using clinical multi-detector computed tomography (MDCT). Therefore, the purpose of this study was to analyze trabecular bone structure parameters obtained from clinical MDCT in relation to high resolution peripheral quantitative computed tomography (HR-pQCT) as a standard of reference and to investigate whether clinical MDCT can predict vertebral bone strength. Fourteen functional spinal segment units between T7 and L3 were harvested from 14 formalin-fixed human cadavers (11 women and 3 men; age 84 ± 10 years). All functional spinal segment units were examined using HR-pQCT (isotropic voxel size of 41 μm(3)) and a clinical whole-body MDCT (interpolated voxel size of 146 × 146 × 300 μm(3)). Trabecular bone structure analyses (histomorphometric and texture measures) were performed in the HR-pQCT as well as MDCT images. Vertebral failure load (FL) of the functional spinal segment units was determined in an uniaxial biomechanical test. The HR-pQCT and MDCT derived trabecular bone structure parameters showed correlations ranging from r = 0.60 to r = 0.90 (p parameters and FL amounted up to r = 0.86 (p parameters obtained with HR-pQCT and MDCT were not significantly different (p > 0.05). In this cadaver model, the spatial resolution of clinically available whole-body MDCT scanners was suitable for trabecular bone structure analysis of the spine and to predict vertebral bone strength.

  8. Predicting clinically unrecognized coronary artery disease: use of two- dimensional echocardiography

    Nagueh Sherif F

    2009-03-01

    Full Text Available Abstract Background 2-D Echo is often performed in patients without history of coronary artery disease (CAD. We sought to determine echo features predictive of CAD. Methods 2-D Echo of 328 patients without known CAD performed within one year prior to stress myocardial SPECT and angiography were reviewed. Echo features examined were left ventricular and atrial enlargement, LV hypertrophy, wall motion abnormality (WMA, LV ejection fraction (EF 15% LV perfusion defect or multivessel distribution. Severe coronary artery stenosis (CAS was defined as left main, 3 VD or 2VD involving proximal LAD. Results The mean age was 62 ± 13 years, 59% men, 29% diabetic (DM and 148 (45% had > 2 risk factors. Pharmacologic stress was performed in 109 patients (33%. MPA was present in 200 pts (60% of which, 137 were high risk. CAS was present in 166 pts (51%, 75 were severe. Of 87 patients with WMA, 83% had MPA and 78% had CAS. Multivariate analysis identified age >65, male, inability to exercise, DM, WMA, MAC and AS as independent predictors of MPA and CAS. Independent predictors of high risk MPA and severe CAS were age, DM, inability to exercise and WMA. 2-D echo findings offered incremental value over clinical information in predicting CAD by angiography. (Chi square: 360 vs. 320 p = 0.02. Conclusion 2-D Echo was valuable in predicting presence of physiological and anatomical CAD in addition to clinical information.

  9. Predicting PTSD using the New York Risk Score with genotype data: potential clinical and research opportunities

    Boscarino JA

    2013-04-01

    Full Text Available Joseph A Boscarino,1,2 H Lester Kirchner,3,4 Stuart N Hoffman,5 Porat M Erlich1,4 1Center for Health Research, Geisinger Clinic, Danville, 2Department of Psychiatry, Temple University School of Medicine, Philadelphia, 3Division of Medicine, Geisinger Clinic, Danville, 4Department of Medicine, Temple University School of Medicine, Philadelphia, 5Department of Neurology, Geisinger Clinic, Danville, PA, USA Background: We previously developed a post-traumatic stress disorder (PTSD screening instrument, ie, the New York PTSD Risk Score (NYPRS, that was effective in predicting PTSD. In the present study, we assessed a version of this risk score that also included genetic information. Methods: Utilizing diagnostic testing methods, we hierarchically examined different prediction variables identified in previous NYPRS research, including genetic risk-allele information, to assess lifetime and current PTSD status among a population of trauma-exposed adults. Results: We found that, in predicting lifetime PTSD, the area under the receiver operating characteristic curve (AUC for the Primary Care PTSD Screen alone was 0.865. When we added psychosocial predictors from the original NYPRS to the model, including depression, sleep disturbance, and a measure of health care access, the AUC increased to 0.902, which was a significant improvement (P = 0.0021. When genetic information was added in the form of a count of PTSD risk alleles located within FKBP, COMT, CHRNA5, and CRHR1 genetic loci (coded 0–6, the AUC increased to 0.920, which was also a significant improvement (P = 0.0178. The results for current PTSD were similar. In the final model for current PTSD with the psychosocial risk factors included, genotype resulted in a prediction weight of 17 for each risk allele present, indicating that a person with six risk alleles or more would receive a PTSD risk score of 17 × 6 = 102, the highest risk score for any of the predictors studied. Conclusion: Genetic

  10. The clinical factors′ prediction of increased intradialytic qt dispersion on the electrocardiograms of chronic hemodialysis patients

    Dina Oktavia

    2013-01-01

    Full Text Available Ventricular arrhythmias and sudden death are common in patients on maintenance hemodialysis (HD. The increase in QT dispersion (QTd on the electrocardiogram (ECG reflects increased tendency for ventricular repolarization that predisposes to arrhythmias. The purpose of the study was to identify the clinical factors that may predict the increased intradialytic QTd and to assess differences in QTd before and after HD. Each of 61 chronic HD patients underwent 12-lead ECG and blood pressure (BP measurement before and every 1 h during a single HD session. The QT intervals were corrected for heart rate using Bazett′s formula. Intradialytic QTd increased in 30 (49% patients. There was no correlation between the increased QTd and the clinical factors including hypertension, pulse pressure, intradialytic hypotension, left ventricular hypertrophy, old myocardial infarct, diabetes mellitus, and nutritional status. The means of QT interval and QTd increased after HD session (from 382 ± 29 to 444 ± 26 ms, P <0.05; and from 74 ± 21 to 114 ± 53 ms, respectively, P <0.05. We conclude that the increased intradialytic QTd could not be predicted by any of the clinical factors evaluated in this study. There was significant difference in the means of QTd before and after HD session.

  11. A Panel of Cancer Testis Antigens and Clinical Risk Factors to Predict Metastasis in Colorectal Cancer

    Ramyar Molania

    2014-01-01

    Full Text Available Colorectal cancer (CRC is the third common carcinoma with a high rate of mortality worldwide and several studies have investigated some molecular and clinicopathological markers for diagnosis and prognosis of its malignant phenotypes. The aim of this study is to evaluate expression frequency of PAGE4, SCP-1, and SPANXA/D cancer testis antigen (CTA genes as well as some clinical risk markers to predict liver metastasis of colorectal cancer patients. The expression frequency of PAGE4, SCP-1, and SPANXA/D cancer/testis antigen (CTA genes was obtained using reverse transcription polymerase chain reaction (RT-PCR assay in 90 colorectal tumor samples including both negative and positive liver metastasis tumors. Statistical analysis was performed to assess the association of three studied genes and clinical risk factors with CRC liver metastasis. The frequency of PAGE4 and SCP-1 genes expression was significantly higher in the primary tumours with liver metastasis when statistically compared with primary tumors with no liver metastasis (P<0.05. Among all clinical risk factors studied, the lymph node metastasis and the depth of invasion were statistically correlated with liver metastasis of CRC patients. In addition, using multiple logistic regression, we constructed a model based on PAGE4 and lymph node metastasis to predict liver metastasis of CRC.

  12. Preoperative MRI findings predict two-year postoperative clinical outcome in lumbar spinal stenosis.

    Pekka Kuittinen

    Full Text Available To study the predictive value of preoperative magnetic resonance imaging (MRI findings for the two-year postoperative clinical outcome in lumbar spinal stenosis (LSS.84 patients (mean age 63±11 years, male 43% with symptoms severe enough to indicate LSS surgery were included in this prospective observational single-center study. Preoperative MRI of the lumbar spine was performed with a 1.5-T unit. The imaging protocol conformed to the requirements of the American College of Radiology for the performance of MRI of the adult spine. Visual and quantitative assessment of MRI was performed by one experienced neuroradiologist. At the two-year postoperative follow-up, functional ability was assessed with the Oswestry Disability Index (ODI 0-100% and treadmill test (0-1000 m, pain symptoms with the overall Visual Analogue Scale (VAS 0-100 mm, and specific low back pain (LBP and specific leg pain (LP separately with a numeric rating scale from 0-10 (NRS-11. Satisfaction with the surgical outcome was also assessed.Preoperative severe central stenosis predicted postoperatively lower LP, LBP, and VAS when compared in patients with moderate central stenosis (p<0.05. Moreover, severe stenosis predicted higher postoperative satisfaction (p = 0.029. Preoperative scoliosis predicted an impaired outcome in the ODI (p = 0.031 and lowered the walking distance in the treadmill test (p = 0.001. The preoperative finding of only one stenotic level in visual assessment predicted less postoperative LBP when compared with patients having 2 or more stenotic levels (p = 0.026. No significant differences were detected between quantitative measurements and the patient outcome.Routine preoperative lumbar spine MRI can predict the patient outcome in a two-year follow up in patients with LSS surgery. Severe central stenosis and one-level central stenosis are predictors of good outcome. Preoperative finding of scoliosis may indicate worse functional ability.

  13. Optimal marker-strategy clinical trial design to detect predictive markers for targeted therapy.

    Zang, Yong; Liu, Suyu; Yuan, Ying

    2016-07-01

    In developing targeted therapy, the marker-strategy design (MSD) provides an important approach to evaluate the predictive marker effect. This design first randomizes patients into non-marker-based or marker-based strategies. Patients allocated to the non-marker-based strategy are then further randomized to receive either the standard or targeted treatments, while patients allocated to the marker-based strategy receive treatments based on their marker statuses. Little research has been done on the statistical properties of the MSD, which has led to some widespread misconceptions and placed clinical researchers at high risk of using inefficient designs. In this article, we show that the commonly used between-strategy comparison has low power to detect the predictive effect and is valid only under a restrictive condition that the randomization ratio within the non-marker-based strategy matches the marker prevalence. We propose a Wald test that is generally valid and also uniformly more powerful than the between-strategy comparison. Based on that, we derive an optimal MSD that maximizes the power to detect the predictive marker effect by choosing the optimal randomization ratios between the two strategies and treatments. Our numerical study shows that using the proposed optimal designs can substantially improve the power of the MSD to detect the predictive marker effect. We use a lung cancer trial to illustrate the proposed optimal designs.

  14. The prognostic blood biomarker proadrenomedullin for outcome prediction in patients with chronic obstructive pulmonary disease (COPD): a qualitative clinical review.

    Schuetz, Philipp; Marlowe, Robert J; Mueller, Beat

    2015-03-01

    Plasma proadrenomedullin (ProADM) is a blood biomarker that may aid in multidimensional risk assessment of patients with chronic obstructive pulmonary disease (COPD). Co-secreted 1:1 with adrenomedullin (ADM), ProADM is a less biologically active, more chemically stable surrogate for this pluripotent regulatory peptide, which due to biological and ex vivo physical characteristics is difficult to reliably directly quantify. Upregulated by hypoxia, inflammatory cytokines, bacterial products, and shear stress and expressed widely in pulmonary cells and ubiquitously throughout the body, ADM exerts or mediates vasodilatory, natriuretic, diuretic, antioxidative, anti-inflammatory, antimicrobial, and metabolic effects. Observational data from four separate studies totaling 1366 patients suggest that as a single factor, ProADM is a significant independent, and accurate, long-term all-cause mortality predictor in COPD. This body of work also suggests that combined with different groups of demographic/clinical variables, ProADM provides significant incremental long-term mortality prediction power relative to the groups of variables alone. Additionally, the literature contains indications that ProADM may be a global cardiopulmonary stress marker, potentially supplying prognostic information when cardiopulmonary exercise testing results such as 6-min walk distance are unavailable due to time or other resource constraints or to a patient's advanced disease. Prospective, randomized, controlled interventional studies are needed to demonstrate whether ProADM use in risk-based guidance of site-of-care, monitoring, and treatment decisions improves clinical, quality-of-life, or pharmacoeconomic outcomes in patients with COPD.

  15. Development of the A-DIVA Scale: A Clinical Predictive Scale to Identify Difficult Intravenous Access in Adult Patients Based on Clinical Observations.

    van Loon, Fredericus H J; Puijn, Lisette A P M; Houterman, Saskia; Bouwman, Arthur R A

    2016-04-01

    Placement of a peripheral intravenous catheter is a routine procedure in clinical practice, but failure of intravenous cannulation regularly occurs. An accurate and reliable predictive scale for difficult venous access creates the possibility to use other techniques in an earlier time frame. We aimed to develop a predictive scale to identify adult patients with a difficult intravenous access prospectively: the A-DIVA scale. This prospective, observational, cross-sectional cohort study was conducted between January 2014 and January 2015, and performed at the department of anesthesiology of the Catharina Hospital (Eindhoven, The Netherlands). Patients 18 years or older were eligible if scheduled for any surgical procedure, regardless ASA classification, demographics, and medical history. Experienced and certified anesthesiologists and nurse anesthetists routinely obtained peripheral intravenous access. Cannulation was performed regarding standards for care. A failed peripheral intravenous cannulation on the first attempt was the outcome of interest. A population-based sample of 1063 patients was included. Failure of intravenous cannulation was observed in 182/1063 patients (17%). Five variables were associated with a failed first attempt of peripheral intravenous cannulation: palpability of the target vein (OR = 4.94, 95% CI [2.85-8.56]; P < 0.001), visibility of the target vein (OR = 3.63, 95% CI [2.09-6.32]; P < 0.001), a history of difficult peripheral intravenous cannulation (OR = 3.86, 95% CI [2.39-6.25]; P < 0.001), an unplanned indication for surgery (OR = 4.86, 95% CI [2.92-8.07]; P < 0.001), and the vein diameter of at most 2 millimeters (OR = 3.37, 95% CI [2.12-5.36]; P < 0.001). The scoring system was applied in 3 risk groups: 36/788 patients (5%) suffered from a failed first attempt in the low-risk group (A-DIVA score 0 or 1), whereas the medium (A-DIVA score 2 or 3) and high-risk group (A-DIVA score 4 plus

  16. Predictive Factors of Gastrointestinal Caustic Injury According to Clinical and Endoscopic Findings

    Cherie Quingking

    2013-03-01

    Full Text Available Background: Ingestion of caustic substances is the main reason for referral to Philippines National Poison Management and Control Center among other causes of acute poisoning. Rapid assessment of severity of injury is important for treatment and prognosis of these cases. This study was aimed to investigate the correlation of clinical factors with severity of gastrointestinal (GI mucosal injury. Methods: In this retrospective study, a total of 105 patients were included. Patients were categorized into two groups including 35 patients with low grade and 70 patients with high grade GI injury to compare the predictive value of clinical findings. Results: Mean (SD age of patients was 27 (10 and 47% of patients were male. Oral burns (P

  17. Acute axonal damage predicts clinical outcome in patients with multiple sclerosis

    Lim, E.T; Sellebjerg, F; Jensen, C.V

    2005-01-01

    The objectives of this study were (1) to determine how cerebrospinal fluid (CSF) neurofilament heavy chain (NfH(SM134) and NfH(SM135)) levels relate to clinical outcome in optic neuritis (ON) and multiple sclerosis (MS) relapse patients treated with high dose oral methylprednisolone; and (2...... in the MS attack trial were treated with oral methylprednisolone. In the MS attack trial group, CSF NfH(SM134) and NfH(SM135) measured at week 3 and deltaCSF NfH(SMI34) levels from baseline to week 3 were predictive of clinical outcome at week 8 and 52. In the ON group, no such association was seen. When...... both groups were combined, baseline CSF NfH(SHM134) and NfH(SM135) correlated positively with baseline enhancing lesion volume (ELV) (r(s) =0.50, P

  18. An Update on Crown Lengthening. Part 2: Increasing Clinical Crown Height to Facilitate Predictable Restorations.

    Kalsi, Harpoonam Jeet; Bomfim, Deborah Iola; Darbar, Ulpee

    2015-04-01

    This is the second paper in this two-part series. Paper one provided an overview of managing gingival tissue excess and paper two will focus on increasing clinical crown height to facilitate restorative treatment. Crown lengthening is a surgical procedure aimed at the removal of gingival tissue with or without adjunctive bone removal. The different types of procedure undertaken will be discussed over the two papers. In order to provide predictable restorations, care must be taken to ensure the integrity of the margins. If this is not taken into account it can lead to an impingement on the biologic width, which may in turn lead to chronic inflammation resulting in recession or the development of periodontal problems which can be hard to manage. Clinical Relevance: This paper aims to reinforce the need for thorough diagnosis and treatment planning and provides an overview of the various procedures that can be undertaken.

  19. Sputum biomarkers and the prediction of clinical outcomes in patients with cystic fibrosis.

    Theodore G Liou

    Full Text Available Lung function, acute pulmonary exacerbations (APE, and weight are the best clinical predictors of survival in cystic fibrosis (CF; however, underlying mechanisms are incompletely understood. Biomarkers of current disease state predictive of future outcomes might identify mechanisms and provide treatment targets, trial endpoints and objective clinical monitoring tools. Such CF-specific biomarkers have previously been elusive. Using observational and validation cohorts comprising 97 non-transplanted consecutively-recruited adult CF patients at the Intermountain Adult CF Center, University of Utah, we identified biomarkers informative of current disease and predictive of future clinical outcomes. Patients represented the majority of sputum producers. They were recruited March 2004-April 2007 and followed through May 2011. Sputum biomarker concentrations were measured and clinical outcomes meticulously recorded for a median 5.9 (interquartile range 5.0 to 6.6 years to study associations between biomarkers and future APE and time-to-lung transplantation or death. After multivariate modeling, only high mobility group box-1 protein (HMGB-1, mean=5.84 [log ng/ml], standard deviation [SD] =1.75 predicted time-to-first APE (hazard ratio [HR] per log-unit HMGB-1=1.56, p-value=0.005, number of future APE within 5 years (0.338 APE per log-unit HMGB-1, p<0.001 by quasi-Poisson regression and time-to-lung transplantation or death (HR=1.59, p=0.02. At APE onset, sputum granulocyte macrophage colony stimulating factor (GM-CSF, mean 4.8 [log pg/ml], SD=1.26 was significantly associated with APE-associated declines in lung function (-10.8 FEV(1% points per log-unit GM-CSF, p<0.001 by linear regression. Evaluation of validation cohorts produced similar results that passed tests of mutual consistency. In CF sputum, high HMGB-1 predicts incidence and recurrence of APE and survival, plausibly because it mediates long-term airway inflammation. High APE-associated GM

  20. Do clinical factors help to predict disease course in inflammatory bowel disease?

    Edouard; Louis; Jacques; Belaiche; Catherine; Reenaers

    2010-01-01

    While therapeutic strategies able to change the natural history of the disease are developing,it is of major importance to have available predictive factors for aggressive disease to try and target these therapeutic strategies.Clinical predictors have probably been the most broadly studied.In both Crohn's disease(CD) and ulcerative colitis(UC),age at diagnosis,disease location and smoking habit are currently the strongest predictors of disease course.A younger age at onset is associated with more aggressive...

  1. Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors

    Nelms, Benjamin E.; Zhen Heming; Tome, Wolfgang A. [Canis Lupus LLC and Department of Human Oncology, University of Wisconsin, Merrimac, Wisconsin 53561 (United States); Department of Medical Physics, University of Wisconsin, Madison, Wisconsin 53705 (United States); Departments of Human Oncology, Medical Physics, and Biomedical Engineering, University of Wisconsin, Madison, Wisconsin 53792 (United States)

    2011-02-15

    Purpose: The purpose of this work is to determine the statistical correlation between per-beam, planar IMRT QA passing rates and several clinically relevant, anatomy-based dose errors for per-patient IMRT QA. The intent is to assess the predictive power of a common conventional IMRT QA performance metric, the Gamma passing rate per beam. Methods: Ninety-six unique data sets were created by inducing four types of dose errors in 24 clinical head and neck IMRT plans, each planned with 6 MV Varian 120-leaf MLC linear accelerators using a commercial treatment planning system and step-and-shoot delivery. The error-free beams/plans were used as ''simulated measurements'' (for generating the IMRT QA dose planes and the anatomy dose metrics) to compare to the corresponding data calculated by the error-induced plans. The degree of the induced errors was tuned to mimic IMRT QA passing rates that are commonly achieved using conventional methods. Results: Analysis of clinical metrics (parotid mean doses, spinal cord max and D1cc, CTV D95, and larynx mean) vs IMRT QA Gamma analysis (3%/3 mm, 2/2, 1/1) showed that in all cases, there were only weak to moderate correlations (range of Pearson's r-values: -0.295 to 0.653). Moreover, the moderate correlations actually had positive Pearson's r-values (i.e., clinically relevant metric differences increased with increasing IMRT QA passing rate), indicating that some of the largest anatomy-based dose differences occurred in the cases of high IMRT QA passing rates, which may be called ''false negatives.'' The results also show numerous instances of false positives or cases where low IMRT QA passing rates do not imply large errors in anatomy dose metrics. In none of the cases was there correlation consistent with high predictive power of planar IMRT passing rates, i.e., in none of the cases did high IMRT QA Gamma passing rates predict low errors in anatomy dose metrics or vice versa

  2. Statistical analysis of accurate prediction of local atmospheric optical attenuation with a new model according to weather together with beam wandering compensation system: a season-wise experimental investigation

    Arockia Bazil Raj, A.; Padmavathi, S.

    2016-07-01

    Atmospheric parameters strongly affect the performance of Free Space Optical Communication (FSOC) system when the optical wave is propagating through the inhomogeneous turbulent medium. Developing a model to get an accurate prediction of optical attenuation according to meteorological parameters becomes significant to understand the behaviour of FSOC channel during different seasons. A dedicated free space optical link experimental set-up is developed for the range of 0.5 km at an altitude of 15.25 m. The diurnal profile of received power and corresponding meteorological parameters are continuously measured using the developed optoelectronic assembly and weather station, respectively, and stored in a data logging computer. Measured meteorological parameters (as input factors) and optical attenuation (as response factor) of size [177147 × 4] are used for linear regression analysis and to design the mathematical model that is more suitable to predict the atmospheric optical attenuation at our test field. A model that exhibits the R2 value of 98.76% and average percentage deviation of 1.59% is considered for practical implementation. The prediction accuracy of the proposed model is investigated along with the comparative results obtained from some of the existing models in terms of Root Mean Square Error (RMSE) during different local seasons in one-year period. The average RMSE value of 0.043-dB/km is obtained in the longer range dynamic of meteorological parameters variations.

  3. Analytical and Clinical Validation of a Digital Sequencing Panel for Quantitative, Highly Accurate Evaluation of Cell-Free Circulating Tumor DNA.

    Richard B Lanman

    Full Text Available Next-generation sequencing of cell-free circulating solid tumor DNA addresses two challenges in contemporary cancer care. First this method of massively parallel and deep sequencing enables assessment of a comprehensive panel of genomic targets from a single sample, and second, it obviates the need for repeat invasive tissue biopsies. Digital Sequencing™ is a novel method for high-quality sequencing of circulating tumor DNA simultaneously across a comprehensive panel of over 50 cancer-related genes with a simple blood test. Here we report the analytic and clinical validation of the gene panel. Analytic sensitivity down to 0.1% mutant allele fraction is demonstrated via serial dilution studies of known samples. Near-perfect analytic specificity (> 99.9999% enables complete coverage of many genes without the false positives typically seen with traditional sequencing assays at mutant allele frequencies or fractions below 5%. We compared digital sequencing of plasma-derived cell-free DNA to tissue-based sequencing on 165 consecutive matched samples from five outside centers in patients with stage III-IV solid tumor cancers. Clinical sensitivity of plasma-derived NGS was 85.0%, comparable to 80.7% sensitivity for tissue. The assay success rate on 1,000 consecutive samples in clinical practice was 99.8%. Digital sequencing of plasma-derived DNA is indicated in advanced cancer patients to prevent repeated invasive biopsies when the initial biopsy is inadequate, unobtainable for genomic testing, or uninformative, or when the patient's cancer has progressed despite treatment. Its clinical utility is derived from reduction in the costs, complications and delays associated with invasive tissue biopsies for genomic testing.

  4. Analytical and Clinical Validation of a Digital Sequencing Panel for Quantitative, Highly Accurate Evaluation of Cell-Free Circulating Tumor DNA.

    Lanman, Richard B; Mortimer, Stefanie A; Zill, Oliver A; Sebisanovic, Dragan; Lopez, Rene; Blau, Sibel; Collisson, Eric A; Divers, Stephen G; Hoon, Dave S B; Kopetz, E Scott; Lee, Jeeyun; Nikolinakos, Petros G; Baca, Arthur M; Kermani, Bahram G; Eltoukhy, Helmy; Talasaz, AmirAli

    2015-01-01

    Next-generation sequencing of cell-free circulating solid tumor DNA addresses two challenges in contemporary cancer care. First this method of massively parallel and deep sequencing enables assessment of a comprehensive panel of genomic targets from a single sample, and second, it obviates the need for repeat invasive tissue biopsies. Digital Sequencing™ is a novel method for high-quality sequencing of circulating tumor DNA simultaneously across a comprehensive panel of over 50 cancer-related genes with a simple blood test. Here we report the analytic and clinical validation of the gene panel. Analytic sensitivity down to 0.1% mutant allele fraction is demonstrated via serial dilution studies of known samples. Near-perfect analytic specificity (> 99.9999%) enables complete coverage of many genes without the false positives typically seen with traditional sequencing assays at mutant allele frequencies or fractions below 5%. We compared digital sequencing of plasma-derived cell-free DNA to tissue-based sequencing on 165 consecutive matched samples from five outside centers in patients with stage III-IV solid tumor cancers. Clinical sensitivity of plasma-derived NGS was 85.0%, comparable to 80.7% sensitivity for tissue. The assay success rate on 1,000 consecutive samples in clinical practice was 99.8%. Digital sequencing of plasma-derived DNA is indicated in advanced cancer patients to prevent repeated invasive biopsies when the initial biopsy is inadequate, unobtainable for genomic testing, or uninformative, or when the patient's cancer has progressed despite treatment. Its clinical utility is derived from reduction in the costs, complications and delays associated with invasive tissue biopsies for genomic testing.

  5. Exploiting missing clinical data in Bayesian network modeling for predicting medical problems.

    Lin, Jau-Huei; Haug, Peter J

    2008-02-01

    When machine learning algorithms are applied to data collected during the course of clinical care, it is generally accepted that the data has not been consistently collected. The absence of expected data elements is common and the mechanism through which a data element is missing often involves the clinical relevance of that data element in a specific patient. Therefore, the absence of data may have information value of its own. In the process of designing an application intended to support a medical problem list, we have studied whether the "missingness" of clinical data can provide useful information in building prediction models. In this study, we experimented with four methods of treating missing values in a clinical data set-two of them explicitly model the absence or "missingness" of data. Each of these data sets were used to build four different kinds of Bayesian classifiers-a naive Bayes structure, a human-composed network structure, and two networks based on structural learning algorithms. We compared the performance between groups with and without explicit models of missingness using the area under the ROC curve. The results showed that in most cases the classifiers trained using the explicit missing value treatments performed better. The result suggests that information may exist in "missingness" itself. Thus, when designing a decision support system, we suggest one consider explicitly representing the presence/absence of data in the underlying logic.

  6. Factors predicting suicidal ideation in the preceding 12 months among patients attending a community psychiatric outpatient clinic.

    Anyansi, Tochukwu E

    2013-06-01

    Predictive factors are used to alert the clinician to the necessity of carrying out a suicide risk assessment in those patients whose demographic and clinical characteristics suggest the possibility of suicide.

  7. Clinical prediction model to aid emergency doctors managing febrile children at risk of serious bacterial infections: Diagnostic study

    R.G. Nijman (Ruud); Y. Vergouwe (Yvonne); M.J. Thompson (Matthew); M.V. Veen (Mirjam Van); A.H.J. van Meurs (Alfred); J. van der Lei (Johan); E.W. Steyerberg (Ewout); H.A. Moll (Henriëtte); R. Oostenbrink (Rianne)

    2013-01-01

    textabstractObjective: To derive, cross validate, and externally validate a clinical prediction model that assesses the risks of different serious bacterial infections in children with fever at the emergency department. Design: Prospective observational diagnostic study. Setting: Three paediatric em

  8. Developing a clinical utility framework to evaluate prediction models in radiogenomics

    Wu, Yirong; Liu, Jie; Munoz del Rio, Alejandro; Page, David C.; Alagoz, Oguzhan; Peissig, Peggy; Onitilo, Adedayo A.; Burnside, Elizabeth S.

    2015-03-01

    Combining imaging and genetic information to predict disease presence and behavior is being codified into an emerging discipline called "radiogenomics." Optimal evaluation methodologies for radiogenomics techniques have not been established. We aim to develop a clinical decision framework based on utility analysis to assess prediction models for breast cancer. Our data comes from a retrospective case-control study, collecting Gail model risk factors, genetic variants (single nucleotide polymorphisms-SNPs), and mammographic features in Breast Imaging Reporting and Data System (BI-RADS) lexicon. We first constructed three logistic regression models built on different sets of predictive features: (1) Gail, (2) Gail+SNP, and (3) Gail+SNP+BI-RADS. Then, we generated ROC curves for three models. After we assigned utility values for each category of findings (true negative, false positive, false negative and true positive), we pursued optimal operating points on ROC curves to achieve maximum expected utility (MEU) of breast cancer diagnosis. We used McNemar's test to compare the predictive performance of the three models. We found that SNPs and BI-RADS features augmented the baseline Gail model in terms of the area under ROC curve (AUC) and MEU. SNPs improved sensitivity of the Gail model (0.276 vs. 0.147) and reduced specificity (0.855 vs. 0.912). When additional mammographic features were added, sensitivity increased to 0.457 and specificity to 0.872. SNPs and mammographic features played a significant role in breast cancer risk estimation (p-value < 0.001). Our decision framework comprising utility analysis and McNemar's test provides a novel framework to evaluate prediction models in the realm of radiogenomics.

  9. Comparison of Existing Clinical Scoring Systems in Predicting Severity and Prognoses of Hyperlipidemic Acute Pancreatitis in Chinese Patients

    Qiu, Lei; Sun, Rui Qing; Jia, Rong Rong; Ma, Xiu Ying; Cheng, Li; Tang, Mao Chun; Zhao, Yan

    2015-01-01

    Abstract It is important to identify the severity of acute pancreatitis (AP) in the early course of the disease. Clinical scoring systems may be helpful to predict the prognosis of patients with early AP; however, few analysts have forecast the accuracy of scoring systems for the prognosis in hyperlipidemic acute pancreatitis (HLAP). The purpose of this study was to summarize the clinical characteristics of HLAP and compare the accuracy of conventional scoring systems in predicting the progno...

  10. Early clinical signs in neonates with hypoxic ischemic encephalopathy predict an abnormal amplitude-integrated electroencephalogram at age 6 hours

    Horn, Alan R; Swingler, George H; Myer, Landon; Linley, Lucy L; Raban, Moegammad S; Joolay, Yaseen; Harrison, Michael C; Chandrasekaran, Manigandan; Rhoda, Natasha R; Robertson, Nicola J.

    2013-01-01

    Background An early clinical score predicting an abnormal amplitude-integrated electroencephalogram (aEEG) or moderate-severe hypoxic ischemic encephalopathy (HIE) may allow rapid triage of infants for therapeutic hypothermia. We aimed to determine if early clinical examination could predict either an abnormal aEEG at age 6 hours or moderate-severe HIE presenting within 72 hours of birth. Methods Sixty infants ≥ 36 weeks gestational age were prospectively enrolled following suspected intrapar...

  11. TH-E-BRF-05: Comparison of Survival-Time Prediction Models After Radiotherapy for High-Grade Glioma Patients Based On Clinical and DVH Features

    Magome, T; Haga, A; Igaki, H; Sekiya, N; Masutani, Y; Sakumi, A; Mukasa, A; Nakagawa, K [The University of Tokyo Hospital, Tokyo, JP (Japan)

    2014-06-15

    Purpose: Although many outcome prediction models based on dose-volume information have been proposed, it is well known that the prognosis may be affected also by multiple clinical factors. The purpose of this study is to predict the survival time after radiotherapy for high-grade glioma patients based on features including clinical and dose-volume histogram (DVH) information. Methods: A total of 35 patients with high-grade glioma (oligodendroglioma: 2, anaplastic astrocytoma: 3, glioblastoma: 30) were selected in this study. All patients were treated with prescribed dose of 30–80 Gy after surgical resection or biopsy from 2006 to 2013 at The University of Tokyo Hospital. All cases were randomly separated into training dataset (30 cases) and test dataset (5 cases). The survival time after radiotherapy was predicted based on a multiple linear regression analysis and artificial neural network (ANN) by using 204 candidate features. The candidate features included the 12 clinical features (tumor location, extent of surgical resection, treatment duration of radiotherapy, etc.), and the 192 DVH features (maximum dose, minimum dose, D95, V60, etc.). The effective features for the prediction were selected according to a step-wise method by using 30 training cases. The prediction accuracy was evaluated by a coefficient of determination (R{sup 2}) between the predicted and actual survival time for the training and test dataset. Results: In the multiple regression analysis, the value of R{sup 2} between the predicted and actual survival time was 0.460 for the training dataset and 0.375 for the test dataset. On the other hand, in the ANN analysis, the value of R{sup 2} was 0.806 for the training dataset and 0.811 for the test dataset. Conclusion: Although a large number of patients would be needed for more accurate and robust prediction, our preliminary Result showed the potential to predict the outcome in the patients with high-grade glioma. This work was partly supported by

  12. Can clinical colour vision tests be used to predict the results of the Farnsworth lantern test?

    Cole, B L; Maddocks, J D

    1998-11-01

    Clinicians usually do not have access to a lantern test when making an occupational assessment of the ability of a person with defective colour vision to recognise signal light colours: they must rely on the results of ordinary clinical tests. While all colour vision defectives fail the Holmes Wright Type B lantern test and most fail the Holmes Wright Type A lantern, 35% of colour vision defectives pass the Farnsworth lantern. Can clinical tests predict who will pass and fail the Farnsworth lantern? We find that a pass (less than two or more diametrical crossings) at the Farnsworth Panel D 15 Dichotomous test has a sensitivity of 0.67 and specificity of 0.94 in predicting a pass or fail at the Farnsworth lantern test: a Nagel range of > 10 has a sensitivity of 0.87 and a specificity of 0.57. We conclude that neither the D 15 nor the Nagel Anomaloscope matching range are satisfactory predictors of performance on the Farnsworth Lantern.

  13. Valuing structured professional judgment: predictive validity, decision-making, and the clinical-actuarial conflict.

    Falzer, Paul R

    2013-01-01

    Structured professional judgment (SPJ) has received considerable attention as an alternative to unstructured clinical judgment and actuarial assessment, and as a means of resolving their ongoing conflict. However, predictive validity studies have typically relied on receiver operating characteristic (ROC) analysis, the same technique commonly used to validate actuarial assessment tools. This paper presents SPJ as distinct from both unstructured clinical judgment and actuarial assessment. A key distinguishing feature of SPJ is the contribution of modifiable factors, either dynamic or protective, to summary risk ratings. With modifiable factors, the summary rating scheme serves as a prognostic model rather than a classification procedure. However, prognostic models require more extensive and thorough predictive validity testing than can be provided by ROC analysis. It is proposed that validation should include calibration and reclassification techniques, as well as additional measures of discrimination. Several techniques and measures are described and illustrated. The paper concludes by tracing the limitations of ROC analysis to its philosophical foundation and its origin as a statistical theory of decision-making. This foundation inhibits the performance of crucial tasks, such as determining the sufficiency of a risk assessment and examining the evidentiary value of statistical findings. The paper closes by noting a current effort to establish a viable and complementary relationship between SPJ and decision-making theory.

  14. Predicting the risk of suicide by analyzing the text of clinical notes.

    Chris Poulin

    Full Text Available We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group. From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients.

  15. Knee shape might predict clinical outcome after an anterior cruciate ligament rupture.

    Eggerding, V; van Kuijk, K S R; van Meer, B L; Bierma-Zeinstra, S M A; van Arkel, E R A; Reijman, M; Waarsing, J H; Meuffels, D E

    2014-06-01

    We have investigated whether shape of the knee can predict the clinical outcome of patients after an anterior cruciate ligament rupture. We used statistical shape modelling to measure the shape of the knee joint of 182 prospectively followed patients on lateral and Rosenberg view radiographs of the knee after a rupture of the anterior cruciate ligament. Subsequently, we associated knee shape with the International Knee Documentation Committee subjective score at two years follow-up. The mean age of patients was 31 years (21 to 51), the majority were male (n = 121) and treated operatively (n = 135). We found two modes (shape variations) that were significantly associated with the subjective score at two years: one for the operatively treated group (p = 0.002) and one for the non-operatively treated group (p = 0.003). Operatively treated patients who had higher subjective scores had a smaller intercondylar notch and a smaller width of the intercondylar eminence. Non-operatively treated patients who scored higher on the subjective score had a more pyramidal intercondylar notch as opposed to one that was more dome-shaped. We conclude that the shape of the femoral notch and the intercondylar eminence is predictive of clinical outcome two years after a rupture of the anterior cruciate ligament.

  16. Clinical manifestations that predict abnormal brain computed tomography (CT in children with minor head injury

    Nesrin Alharthy

    2015-01-01

    Full Text Available Background: Computed tomography (CT used in pediatric pediatrics brain injury (TBI to ascertain neurological manifestations. Nevertheless, this practice is associated with adverse effects. Reports in the literature suggest incidents of morbidity and mortality in children due to exposure to radiation. Hence, it is found imperative to search for a reliable alternative. Objectives: The aim of this study is to find a reliable clinical alternative to detect an intracranial injury without resorting to the CT. Materials and Methods: Retrospective cross-sectional study was undertaken in patients (1-14 years with blunt head injury and having a Glasgow Coma Scale (GCS of 13-15 who had CT performed on them. Using statistical analysis, the correlation between clinical examination and positive CT manifestation is analyzed for different age-groups and various mechanisms of injury. Results: No statistically significant association between parameteres such as Loss of Consciousness, ′fall′ as mechanism of injury, motor vehicle accidents (MVA, more than two discrete episodes of vomiting and the CT finding of intracranial injury could be noted. Analyzed data have led to believe that GCS of 13 at presentation is the only important clinical predictor of intracranial injury. Conclusion: Retrospective data, small sample size and limited number of factors for assessing clinical manifestation might present constraints on the predictive rule that was derived from this review. Such limitations notwithstanding, the decision to determine which patients should undergo neuroimaging is encouraged to be based on clinical judgments. Further analysis with higher sample sizes may be required to authenticate and validate findings.

  17. A comparative study: classification vs. user-based collaborative filtering for clinical prediction

    Fang Hao

    2016-12-01

    Full Text Available Abstract Background Recommender systems have shown tremendous value for the prediction of personalized item recommendations for individuals in a variety of settings (e.g., marketing, e-commerce, etc.. User-based collaborative filtering is a popular recommender system, which leverages an individuals’ prior satisfaction with items, as well as the satisfaction of individuals that are “similar”. Recently, there have been applications of collaborative filtering based recommender systems for clinical risk prediction. In these applications, individuals represent patients, and items represent clinical data, which includes an outcome. Methods Application of recommender systems to a problem of this type requires the recasting a supervised learning problem as unsupervised. The rationale is that patients with similar clinical features carry a similar disease risk. As the “Big Data” era progresses, it is likely that approaches of this type will be reached for as biomedical data continues to grow in both size and complexity (e.g., electronic health records. In the present study, we set out to understand and assess the performance of recommender systems in a controlled yet realistic setting. User-based collaborative filtering recommender systems are compared to logistic regression and random forests with different types of imputation and varying amounts of missingness on four different publicly available medical data sets: National Health and Nutrition Examination Survey (NHANES, 2011-2012 on Obesity, Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (SUPPORT, chronic kidney disease, and dermatology data. We also examined performance using simulated data with observations that are Missing At Random (MAR or Missing Completely At Random (MCAR under various degrees of missingness and levels of class imbalance in the response variable. Results Our results demonstrate that user-based collaborative filtering is consistently inferior

  18. Clinical utility of C-reactive protein to predict treatment response during cystic fibrosis pulmonary exacerbations

    Sharma, Ashutosh; Kirkpatrick, Gordon; Chen, Virginia; Skolnik, Kate; Hollander, Zsuzsanna; Wilcox, Pearce; Quon, Bradley S.

    2017-01-01

    Rationale C-reactive protein (CRP) is a systemic marker of inflammation that correlates with disease status in cystic fibrosis (CF). The clinical utility of CRP measurement to guide pulmonary exacerbation (PEx) treatment decisions remains uncertain. Objectives To determine whether monitoring CRP during PEx treatment can be used to predict treatment response. We hypothesized that early changes in CRP can be used to predict treatment response. Methods We reviewed all PEx events requiring hospitalization for intravenous (IV) antibiotics over 2 years at our institution. 83 PEx events met our eligibility criteria. CRP levels from admission to day 5 were evaluated to predict treatment non-response, using a modified version of a prior published composite definition. CRP was also evaluated to predict time until next exacerbation (TUNE). Measurements and main results 53% of 83 PEx events were classified as treatment non-response. Paradoxically, 24% of PEx events were characterized by a ≥ 50% increase in CRP levels within the first five days of treatment. Absolute change in CRP from admission to day 5 was not associated with treatment non-response (p = 0.58). Adjusted for FEV1% predicted, admission log10 CRP was associated with treatment non-response (OR: 2.39; 95% CI: 1.14 to 5.91; p = 0.03) and shorter TUNE (HR: 1.60; 95% CI: 1.13 to 2.27; p = 0.008). The area under the receiver operating characteristics (ROC) curve of admission CRP to predict treatment non-response was 0.72 (95% CI 0.61–0.83; p 75 mg/L with a specificity of 90% for treatment non-response. Conclusions Admission CRP predicts treatment non-response and time until next exacerbation. A very elevated admission CRP (>75mg/L) is highly specific for treatment non-response and might be used to target high-risk patients for future interventional studies aimed at improving exacerbation outcomes. PMID:28178305

  19. Can a Clinical Test of Reaction Time Predict a Functional Head-Protective Response?

    ECKNER, JAMES T.; LIPPS, DAVID B.; KIM, HOGENE; RICHARDSON, JAMES K.; ASHTON-MILLER, JAMES A.

    2015-01-01

    Purpose Reaction time is commonly prolonged after a sport-related concussion. Besides being a marker for injury, a rapid reaction time is necessary for protective maneuvers that can reduce the frequency and severity of additional head impacts. The purpose of this study was to determine whether a clinical test of simple visuomotor reaction time predicted the time taken to raise the hands to protect the head from a rapidly approaching ball. Methods Twenty-six healthy adult participants recruited from campus and community recreation and exercise facilities completed two experimental protocols during a single session: a manual visuomotor simple reaction time test (RTclin) and a sport-related head-protective response (RTsprt). RTclin measured the time required to catch a thin vertically oriented device on its release by the tester and was calculated from the distance the device fell before being arrested. RTsprt measured the time required to raise the hands from waist level to block a foam tennis ball fired toward the subject’s face from an air cannon and was determined using an optoelectronic camera system. A correlation coefficient was calculated between RTclin and RTsprt, with linear regression used to assess for effect modification by other covariates. Results A strong positive correlation was found between RTclin and RTsprt (r = 0.725, P < 0.001) independent of age, gender, height, or weight. Conclusions RTclin is predictive of a functional sport-related head-protective response. To our knowledge, this is the first demonstration of a clinical test predicting the ability to protect the head in a simulated sport environment. This correlation with a functional head-protective response is a relevant consideration for the potential use of RTclin as part of a multifaceted concussion assessment program. PMID:20689458

  20. Diagnosing malignant melanoma in ambulatory care: a systematic review of clinical prediction rules

    Harrington, Emma; Clyne, Barbara; Wesseling, Nieneke; Sandhu, Harkiran; Armstrong, Laura; Bennett, Holly; Fahey, Tom

    2017-01-01

    Objectives Malignant melanoma has high morbidity and mortality rates. Early diagnosis improves prognosis. Clinical prediction rules (CPRs) can be used to stratify patients with symptoms of suspected malignant melanoma to improve early diagnosis. We conducted a systematic review of CPRs for melanoma diagnosis in ambulatory care. Design Systematic review. Data sources A comprehensive search of PubMed, EMBASE, PROSPERO, CINAHL, the Cochrane Library and SCOPUS was conducted in May 2015, using combinations of keywords and medical subject headings (MeSH) terms. Study selection and data extraction Studies deriving and validating, validating or assessing the impact of a CPR for predicting melanoma diagnosis in ambulatory care were included. Data extraction and methodological quality assessment were guided by the CHARMS checklist. Results From 16 334 studies reviewed, 51 were included, validating the performance of 24 unique CPRs. Three impact analysis studies were identified. Five studies were set in primary care. The most commonly evaluated CPRs were the ABCD, more than one or uneven distribution of Colour, or a large (greater than 6 mm) Diameter (ABCD) dermoscopy rule (at a cut-point of >4.75; 8 studies; pooled sensitivity 0.85, 95% CI 0.73 to 0.93, specificity 0.72, 95% CI 0.65 to 0.78) and the 7-point dermoscopy checklist (at a cut-point of ≥1 recommending ruling in melanoma; 11 studies; pooled sensitivity 0.77, 95% CI 0.61 to 0.88, specificity 0.80, 95% CI 0.59 to 0.92). The methodological quality of studies varied. Conclusions At their recommended cut-points, the ABCD dermoscopy rule is more useful for ruling out melanoma than the 7-point dermoscopy checklist. A focus on impact analysis will help translate melanoma risk prediction rules into useful tools for clinical practice. PMID:28264830

  1. Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science.

    Montes-Torres, Julio; Subirats, José Luis; Ribelles, Nuria; Urda, Daniel; Franco, Leonardo; Alba, Emilio; Jerez, José Manuel

    2016-01-01

    One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets.

  2. Strategies to design clinical studies to identify predictive biomarkers in cancer research.

    Perez-Gracia, Jose Luis; Sanmamed, Miguel F; Bosch, Ana; Patiño-Garcia, Ana; Schalper, Kurt A; Segura, Victor; Bellmunt, Joaquim; Tabernero, Josep; Sweeney, Christopher J; Choueiri, Toni K; Martín, Miguel; Fusco, Juan Pablo; Rodriguez-Ruiz, Maria Esperanza; Calvo, Alfonso; Prior, Celia; Paz-Ares, Luis; Pio, Ruben; Gonzalez-Billalabeitia, Enrique; Gonzalez Hernandez, Alvaro; Páez, David; Piulats, Jose María; Gurpide, Alfonso; Andueza, Mapi; de Velasco, Guillermo; Pazo, Roberto; Grande, Enrique; Nicolas, Pilar; Abad-Santos, Francisco; Garcia-Donas, Jesus; Castellano, Daniel; Pajares, María J; Suarez, Cristina; Colomer, Ramon; Montuenga, Luis M; Melero, Ignacio

    2017-02-01

    The discovery of reliable biomarkers to predict efficacy and toxicity of anticancer drugs remains one of the key challenges in cancer research. Despite its relevance, no efficient study designs to identify promising candidate biomarkers have been established. This has led to the proliferation of a myriad of exploratory studies using dissimilar strategies, most of which fail to identify any promising targets and are seldom validated. The lack of a proper methodology also determines that many anti-cancer drugs are developed below their potential, due to failure to identify predictive biomarkers. While some drugs will be systematically administered to many patients who will not benefit from them, leading to unnecessary toxicities and costs, others will never reach registration due to our inability to identify the specific patient population in which they are active. Despite these drawbacks, a limited number of outstanding predictive biomarkers have been successfully identified and validated, and have changed the standard practice of oncology. In this manuscript, a multidisciplinary panel reviews how those key biomarkers were identified and, based on those experiences, proposes a methodological framework-the DESIGN guidelines-to standardize the clinical design of biomarker identification studies and to develop future research in this pivotal field.

  3. Thyroid Hormones, Autoantibodies, Ultrasonography, and Clinical Parameters for Predicting Thyroid Cancer

    He, Lin-zheng; Zeng, Tian-shu; Pu, Lin; Pan, Shi-xiu; Xia, Wen-fang; Chen, Lu-lu

    2016-01-01

    Our objective was to evaluate thyroid nodule malignancy prediction using thyroid function tests, autoantibodies, ultrasonographic imaging, and clinical data. We conducted a retrospective cohort study in 1400 patients with nodular thyroid disease (NTD). The thyroid stimulating hormone (TSH) concentration was significantly higher in patients with differentiated thyroid cancer (DTC) versus benign thyroid nodular disease (BTND) (p = 0.004). The receiver operating characteristic curve of TSH showed an AUC of 0.58 (95% CI 0.53–0.62, p = 0.001), sensitivity of 74%, and specificity of 57% at a cut-off of 1.59 mIU/L. There was an incremental increase in TSH concentration along with the increasing tumor size (p < 0.001). Thyroglobulin antibody (TgAb) concentration was associated with an increased risk of malignancy (p = 0.029), but this association was lost when the effect of TSH was taken into account (p = 0.11). Thyroid ultrasonographic characteristics, including fewer than three nodules, hypoechoic appearance, solid component, poorly defined margin, intranodular or peripheral-intranodular flow, and punctate calcification, can be used to predict the risk of thyroid cancer. In conclusion, our study suggests that preoperative serum TSH concentration, age, and ultrasonographic features can be used to predict the risk of malignancy in patients with NTD. PMID:27313612

  4. Cross-sectional imaging for diagnosis and clinical outcome prediction of acute basilar artery thrombosis

    Mortimer, A.M., E-mail: alex_mortimer@hotmail.co [Severn School of Radiology, Bristol (United Kingdom); Department of Radiology, Great Western Hospital, Swindon (United Kingdom); Saunders, T.; Cook, J.-L. [Department of Radiology, Great Western Hospital, Swindon (United Kingdom)

    2011-06-15

    Basilar artery occlusion is a potentially fatal condition and imaging findings can be subtle. Prompt diagnosis is vital, as recognition may lead to therapeutic recanalization that may improve functional outcome and survival. Furthermore, cross-sectional imaging signs may help predict eventual outcome and, therefore, guide which patients should be subjected to aggressive treatment. Computed tomography (CT) signs include a hyperdense basilar artery that has a high specificity, accuracy, positive and negative predictive value. Evidence regarding the prognostic significance of the hyperdense basilar artery sign is conflicting. Early magnetic resonance imaging (MRI) features include loss of flow void, seen as increased signal intensity within the basilar artery on T2-weigted images and identification of acute thrombus, seen as intermediate signal on T1-weighted images. MRI sequences are more sensitive for early detection of acute ischaemia or infarction, ideally with diffusion-weighted imaging (DWI). Both CT and MR angiography are sensitive for detection of acute thrombus, seen as a filling defect or occlusion. These are the non-invasive imaging techniques of choice to confirm diagnosis, with perhaps the speed and accessibility of CT angiography resulting in this technique being valuable in the acute setting. Several new scoring systems based on arterial segmentation rather than global volume assessment using CT angiography source images and DWI have shown early promise in the prediction of eventual clinical outcome in order to isolate those patients who may benefit from therapeutic recanalization.

  5. Side-effects of subthalamic stimulation in Parkinson's disease: clinical evolution and predictive factors.

    Guehl, D; Cuny, E; Benazzouz, A; Rougier, A; Tison, F; Machado, S; Grabot, D; Gross, C; Bioulac, B; Burbaud, P

    2006-09-01

    Chronic bilateral high-frequency stimulation of the subthalamic nucleus (STN) is an alternative treatment for disabling forms of Parkinson's disease when on-off fluctuations and levodopa-induced dyskinesias compromise patients' quality of life. The aim of this study was to assess the evolution of side-effects during the first year of follow-up and search for clinical predictive factors accounting for their occurrence. We compared the frequency of side-effects at 3 and 12 months after surgery in a cohort of 44 patients. The off-medication scores of Unified Parkinson's Disease Rating Scale (UPDRS) II, III, axial symptoms, disease duration and age at surgery were retained for correlation analysis. Dysarthria/hypophonia, weight gain and postural instability were the most frequent chronic side-effects. Whereas dysarthria/hypophonia remained stable over time, weight gain and postural instability increased during the first year post-op. High axial and UPDRS II scores at surgery were predictive of dysarthria/hypophonia. Age and axial score at surgery were positively correlated with postural instability. Despite the occurrence of side-effects, the benefit/side-effects ratio of STN stimulation was largely positive during the first year of follow-up. Age, intensity of axial symptoms and UDPRS II off-medication score before surgery are predictive factors of dysarthria/hypophonia and postural instability after surgery.

  6. A Clinical Indications Prediction Scale Based on TWIST1 for Human Mesenchymal Stem Cells

    Siddaraju V. Boregowda

    2016-02-01

    Full Text Available In addition to their stem/progenitor properties, mesenchymal stem cells (MSCs also exhibit potent effector (angiogenic, antiinflammatory, immuno-modulatory functions that are largely paracrine in nature. It is widely believed that effector functions underlie most of the therapeutic potential of MSCs and are independent of their stem/progenitor properties. Here we demonstrate that stem/progenitor and effector functions are coordinately regulated at the cellular level by the transcription factor Twist1 and specified within populations according to a hierarchical model. We further show that manipulation of Twist1 levels by genetic approaches or by exposure to widely used culture supplements including fibroblast growth factor 2 (Ffg2 and interferon gamma (IFN-gamma alters MSC efficacy in cell-based and in vivo assays in a predictable manner. Thus, by mechanistically linking stem/progenitor and effector functions our studies provide a unifying framework in the form of an MSC hierarchy that models the functional complexity of populations. Using this framework, we developed a CLinical Indications Prediction (CLIP scale that predicts how donor-to-donor heterogeneity and culture conditions impact the therapeutic efficacy of MSC populations for different disease indications.

  7. Predictive Value of IL-8 for Sepsis and Severe Infections After Burn Injury: A Clinical Study.

    Kraft, Robert; Herndon, David N; Finnerty, Celeste C; Cox, Robert A; Song, Juquan; Jeschke, Marc G

    2015-03-01

    The inflammatory response induced by burn injury contributes to increased incidence of infections, sepsis, organ failure, and mortality. Thus, monitoring postburn inflammation is of paramount importance but, so far, there are no reliable biomarkers available to monitor and/or predict infectious complications after burn. As interleukin 8 (IL-8) is a major mediator for inflammatory responses, the aim of our study was to determine whether IL-8 expression can be used to predict postburn sepsis, infections, and mortality. Plasma cytokines, acute-phase proteins, constitutive proteins, and hormones were analyzed during the first 60 days after injury from 468 pediatric burn patients. Demographics and clinical outcome variables (length of stay, infection, sepsis, multiorgan failure [MOF], and mortality) were recorded. A cutoff level for IL-8 was determined using receiver operating characteristic analysis. Statistical significance is set at P Patients were grouped according to their average IL-8 levels relative to this cutoff and stratified into high (H) (n = 133) and low (L) (n = 335) groups. In the L group, regression analysis revealed a significant predictive value of IL-8 to percent of total body surface area burned and incidence of MOF (P inflammatory and acute-phase responses compared with the L group (P burn patients.

  8. Outcomes of Health System Structures, Highly Pertinent Clinical Information, Idea Stimulators, Clinical Reviews, and Prediction Tools: JABFM Exemplified.

    Bowman, Marjorie A; Neale, Anne Victoria; Seehusen, Dean A

    2016-01-01

    This issue exemplifies the types of articles that JABFM publishes to advance family medicine. We have articles on the implications of health system organizational structures. Three of these are international articles at the level of the national health system (1 from China) and systematic local health interventions (1 from Canada and 1 from Netherlands). Inside the United States, where there are more family physicians, there is less obesity, and designation as a Patient Centered Medical Home is related to increased rates of colorectal cancer screening. Review articles on common clinical topics discuss treatments that are changing (acne in pregnancy) or lack consensus (distal radial fractures). We have articles on making life easier in the office, such as for predicting Vitamin D levels, osteoporosis, and pre-diabetes in normal weight adults. There are articles to raise awareness of the "newest" testing or treatments, that is, auditory brainstem implants. "Reminder" articles highlight known entities that need to be reinforced to prevent over-/underdiagnosis or treatment, for example, "cotton fever." Another article discusses the increased risk for postoperative complications with sleep apnea. We also provide "thought" pieces, in this case about the terminology we are using to extend our concept of patient-centered medical homes.

  9. Prediction of clinical factors associated with pandemic influenza A (H1N1 2009 in Pakistan.

    Nadia Nisar

    Full Text Available BACKGROUND: Influenza is a viral infection that can lead to serious complications and death(s in vulnerable groups if not diagnosed and managed in a timely manner. This study was conducted to improve the accuracy of predicting influenza through various clinical and statistical models. METHODOLOGY: A retrospective cross sectional analysis was done on demographic and epidemiological data collected from March 2009 to March 2010. Patients were classified as ILI or SARI using WHO case definitions. Respiratory specimens were tested by RT-PCR. Clinical symptoms and co-morbid conditions were analyzed using binary logistic regression models. RESULTS: In the first approach, analysis compared children (≤12 and adults (>12. Of 1,243 cases, 262 (21% tested positive for A(H1N1pdm09 and the proportion of children (≤12 and adults (>12 were 27% and 73% respectively. Four symptoms predicted influenza in children: fever (OR 2.849, 95% CI 1.931-8.722, cough (OR 1.99, 95% CI 1.512-3.643, diarrhea (OR 2.100, 95% CI 2.040-3.25 and respiratory disease (OR 3.269, 95% CI 2.128-12.624. In adults, the strongest clinical predictor was fever (OR 2.80, 95% CI 1.025-3.135 followed by cough (OR 1.431, 95% CI 1.032-2.815. In the second instance, patients were separated into two groups: SARI 326 (26% and ILI 917 (74% cases. Male to female ratio was 1.41∶1.12 for SARI and 2∶1.5 for ILI cases. Chi-square test showed that fever, cough and sore throat were significant factors for A(H1N1pdm09 infections (p = 0.008. CONCLUSION: Studies in a primary care setting should be encouraged focused on patients with influenza-like illness to develop sensitive clinical case definition that will help to improve accuracy of detecting influenza infections. Formulation of a standard "one size fits all" case definition that best correlates with influenza infections can help guide decisions for additional diagnostic testing and also discourage unjustified antibiotic prescription and usage

  10. Clinical findings just after return to play predict hamstring re-injury, but baseline MRI findings do not

    R.J. de Vos (Robert-Jan); G. Reurink (Gustaaf); G.J. Goudswaard (Gert Jan); M.H. Moen (Maaike); A. Weir (Adam); J.L. Tol (Johannes)

    2014-01-01

    markdownabstract__Abstract__ Background Acute hamstring re-injuries are common and hard to predict. The aim of this study was to investigate the association between clinical and imaging findings and the occurrence of hamstring re-injuries. Methods We obtained baseline data (clinical and MRI finding

  11. Prediction of clinical response based on pharmacokinetic/pharmacodynamic models of 5-hydroxytryptamine reuptake inhibitors in mice

    Kreilgaard, Mads; Smith, D. G.; Brennum, L. T.;

    2008-01-01

    Bridging the gap between preclinical research and clinical trials is vital for drug development. Predicting clinically relevant steady-state drug concentrations (Css) in serum from preclinical animal models may facilitate this transition. Here we used a pharmacokinetic/pharmacodynamic (PK/PD) mod...

  12. Estimating the risk of gestational diabetes mellitus : a clinical prediction model based on patient characteristics and medical history

    van Leeuwen, M.; Opmeer, B. C.; Zweers, E. J. K.; van Ballegooie, E.; ter Brugge, H. G.; de Valk, H. W.; Visser, G. H. A.; Mol, B. W. J.

    2010-01-01

    Objective To develop a clinical prediction rule that can help the clinician to identify women at high and low risk for gestational diabetes mellitus (GDM) early in pregnancy in order to improve the efficiency of GDM screening. Design We used data from a prospective cohort study to develop the clinic

  13. An Application of Quality control circle Activities in raising the Accurate rate of Clinic nurse triage%品管圈活动在提高门诊护士预检分诊准确率中的应用

    李洪艳; 李洪英

    2015-01-01

    目的:探讨开展品管圈活动在提高门诊护士预检分诊准确率中的应用效果。方法成立品管圈,选定活动主题,拟定活动计划,把握现状,设定目标,解析影响因素,拟定对策并按计划实施,比较活动前后门诊护士预检分诊准确率,从而确认活动效果。结果经品管圈活动,门诊护士预检分诊准确率由活动前92.18%提高至96.21%,超出预期目标。圈员在学习积极性、沟通能力、分析能力、团队精神、自信心五个方面均有提高。结论开展品管圈活动可提高门诊护士预检分诊准确率,通过持续性的护理质量改善活动,有效提高了护理质量及圈员的综合素质。%Objective Discusing the effect of Application Quality Control Circle Activities (QCCA) in raising the accurate rate of clinic nurse triage. Method Establish the Quality Control Circle(QCC),select the theme,make the plan,setting the goals,analysis the in-lfuence factors, plan and implement the strategies, compare the accurate rate of clinic nurse triage before and after implementing the QCCA, conifrm the effect of QCCA. Result By implementing the QCCA, accurate rate of Clinic nurse triage raised from 92.18% to 96.21%, witch Exceeded the expectation. QCC members ability of learning, communication, analyzing, collaboration and Self conif-dence are improved. Conclusion Using QCCA is helpful to raising the accurate rate of clinic nurse triage. By continue implementing QCCA in nursing, the QCC members’ comprehensive quality are improved.

  14. Predicting long-term independency in activities of daily living after middle cerebral artery stroke: does information from MRI have added predictive value compared with clinical information?

    Schiemanck, S.K.; Kwakkel, G.; Post, M.W.; Kappelle, L.J.; Prevo, A.J.

    2006-01-01

    BACKGROUND AND PURPOSE: To investigate whether neuroimaging information has added predictive value compared with clinical information for independency in activities of daily living (ADL) 1 year after stroke. METHODS: Seventy-five first-ever middle cerebral artery stroke survivors were evaluated in l

  15. Predictability of the individual clinical outcome of extracorporeal shock wave therapy for cellulite

    Schlaudraff KU

    2014-05-01

    Full Text Available Kai-Uwe Schlaudraff,1 Maren C Kiessling,2 Nikolaus BM Császár,2 Christoph Schmitz21Concept Clinic, Geneva, Switzerland; 2Department of Anatomy II, Ludwig-Maximilians-University of Munich, Munich, GermanyBackground: Extracorporeal shock wave therapy has been successfully introduced for the treatment of cellulite in recent years. However, it is still unknown whether the individual clinical outcome of cellulite treatment with extracorporeal shock wave therapy can be predicted by the patient's individual cellulite grade at baseline, individual patient age, body mass index (BMI, weight, and/or height.Methods: Fourteen Caucasian females with cellulite were enrolled in a prospective, single-center, randomized, open-label Phase II study. The mean (± standard error of the mean cellulite grade at baseline was 2.5±0.09 and mean BMI was 22.8±1.17. All patients were treated with radial extracorporeal shock waves using the Swiss DolorClast® device (Electro Medical Systems, S.A., Nyon, Switzerland. Patients were treated unilaterally with 2 weekly treatments for 4 weeks on a randomly selected side (left or right, totaling eight treatments on the selected side. Treatment was performed at 3.5–4.0 bar, with 15,000 impulses per session applied at 15 Hz. Impulses were homogeneously distributed over the posterior thigh and buttock area (resulting in 7,500 impulses per area. Treatment success was evaluated after the last treatment and 4 weeks later by clinical examination, photographic documentation, contact thermography, and patient satisfaction questionnaires.Results: The mean cellulite grade improved from 2.5±0.09 at baseline to 1.57±0.18 after the last treatment (ie, mean δ-1 was 0.93 cellulite grades and 1.68±0.16 at follow-up (ie, mean δ-2 was 0.82 cellulite grades. Compared with baseline, no patient's condition worsened, the treatment was well tolerated, and no unwanted side effects were observed. No statistically significant (ie, P<0

  16. Predictable risk factors and clinical courses for prolonged transient tachypnea of the newborn

    Ji Young Chang

    2010-03-01

    Full Text Available Purpose : Transient tachypnea of the newborn (TTN is usually benign and improves within 72 hours. However, it can also progress to prolonged tachypnea over 72 hours, profound hypoxemia, respiratory failure, and even death. The aim of this study is to find predictable risk factors and describe the clinical courses and outcomes of prolonged TTN (PTTN. Methods : The medical records of 107 newborns, &gt;35+0 weeks of gestational age with TTN, who were admitted to the NICU at Seoul Asan Medical Center from January 2001 to September 2007 were reviewed. They were divided into 2 groups based on duration of tachypnea. PTTN was defined as tachypnea ?#247;2 hours of age, and simple TTN (STTN as tachypnea &lt;72 hours of age. We randomly selected 126 healthy-term newborns as controls. We evaluated neonatal and maternal demographic findings, and various clinical factors. Results : Fifty-five infants (51% with total TTN were PTTN. PTTN infants had grunting, tachypnea &gt;90/min, FiO2 &gt;0.4, and required ventilator care more frequently than STTN infants. PTTN had lower level of serum total protein and albumin than STTN. The independent predictable risk factors for PTTN were grunting, maximal respiration rate &gt;90/min, and FiO2 &gt;0.4 within 6 hours of life. Conclusion : When a newborn has grunting, respiration rate &gt;90/min, and oxygen requirement &gt;0.4 of FiO2 within 6 hours of life, the infant is at high risk of having persistent tachypnea ?#247;2 hours. We need further study to find the way to reduce PTTN.

  17. DNA Methylation-Guided Prediction of Clinical Failure in High-Risk Prostate Cancer.

    Kirill Litovkin

    Full Text Available Prostate cancer (PCa is a very heterogeneous disease with respect to clinical outcome. This study explored differential DNA methylation in a priori selected genes to diagnose PCa and predict clinical failure (CF in high-risk patients.A quantitative multiplex, methylation-specific PCR assay was developed to assess promoter methylation of the APC, CCND2, GSTP1, PTGS2 and RARB genes in formalin-fixed, paraffin-embedded tissue samples from 42 patients with benign prostatic hyperplasia and radical prostatectomy specimens of patients with high-risk PCa, encompassing training and validation cohorts of 147 and 71 patients, respectively. Log-rank tests, univariate and multivariate Cox models were used to investigate the prognostic value of the DNA methylation.Hypermethylation of APC, CCND2, GSTP1, PTGS2 and RARB was highly cancer-specific. However, only GSTP1 methylation was significantly associated with CF in both independent high-risk PCa cohorts. Importantly, trichotomization into low, moderate and high GSTP1 methylation level subgroups was highly predictive for CF. Patients with either a low or high GSTP1 methylation level, as compared to the moderate methylation groups, were at a higher risk for CF in both the training (Hazard ratio [HR], 3.65; 95% CI, 1.65 to 8.07 and validation sets (HR, 4.27; 95% CI, 1.03 to 17.72 as well as in the combined cohort (HR, 2.74; 95% CI, 1.42 to 5.27 in multivariate analysis.Classification of primary high-risk tumors into three subtypes based on DNA methylation can be combined with clinico-pathological parameters for a more informative risk-stratification of these PCa patients.

  18. Pulmonary embolism in intensive care unit: Predictive factors, clinical manifestations and outcome

    Bahloul Mabrouk

    2010-01-01

    Full Text Available Objective : To determine predictive factors, clinical and demographics characteristics of patients with pulmonary embolism (PE in ICU, and to identify factors associated with poor outcome in the hospital and in the ICU. Methods : During a four-year prospective study, a medical committee of six ICU physicians prospectively examined all available data for each patient in order to classify patients according to the level of clinical suspicion of pulmonary thromboembolism. During the study periods, all patients admitted to our ICU were classified into four groups. The first group includes all patients with confirmed PE; the second group includes some patients without clinical manifestations of PE; the third group includes patients with suspected and not confirmed PE and the fourth group includes all patients with only deep vein thromboses (DVTs without suspicion of PE. The diagnosis of PE was confirmed either by a high-probability ventilation/perfusion (V/Q scan or by a spiral computed tomography (CT scan showing one or more filling defects in the pulmonary artery or in its branches. The diagnosis was also confirmed by echocardiography when a thrombus in the pulmonary artery was observed. Results : During the study periods, 4408 patients were admitted in our ICU. The diagnosis of PE was confirmed in 87 patients (1.9%. The mean delay of development of PE was 7.8 ± 9.5 days. On the day of PE diagnosis, clinical examination showed that 50 patients (57.5% were hypotensive, 63 (72.4% have SIRS, 15 (17.2% have clinical manifestations of DVT and 71 (81.6% have respiratory distress requiring mechanical ventilation. In our study, intravenous unfractionated heparin was used in 81 cases (93.1% and low molecular weight heparins were used in 4 cases (4.6%. The mean ICU stay was 20.2 ± 25.3 days and the mean hospital stay was 25.5 ± 25 days. The mortality rate in ICU was 47.1% and the in-hospital mortality rate was 52.9%. Multivariate analysis showed that

  19. Traditional clinical risk factors predict clopidogrel hypo-responsiveness in unselected patients undergoing non-emergent percutaneous coronary intervention

    Ratcovich, Hanna; Holmvang, Lene; Johansson, Pär Inge

    2016-01-01

    High and low platelet reactivity, HPR and LPR respectively, to clopidogrel and aspirin have previously been associated with adverse events following percutaneous coronary intervention (PCI). The aim is to test the ability of a previously developed clinical risk-score, the PREDICT score, to identify...... of PREDICT score variables and the incidence of HPR for clopidogrel (HPR (ADP)) (p ... = 0.003); 7-9 points OR 9.84 (95% CI 3.49-27.7, p clopidogrel LPR (LPR (ADP)). On the other hand, there was no clear association between PREDICT score and AA response. The PREDICT...

  20. Clinical usefulness of the clock drawing test applying rasch analysis in predicting of cognitive impairment.

    Yoo, Doo Han; Lee, Jae Shin

    2016-07-01

    [Purpose] This study examined the clinical usefulness of the clock drawing test applying Rasch analysis for predicting the level of cognitive impairment. [Subjects and Methods] A total of 187 stroke patients with cognitive impairment were enrolled in this study. The 187 patients were evaluated by the clock drawing test developed through Rasch analysis along with the mini-mental state examination of cognitive evaluation tool. An analysis of the variance was performed to examine the significance of the mini-mental state examination and the clock drawing test according to the general characteristics of the subjects. Receiver operating characteristic analysis was performed to determine the cutoff point for cognitive impairment and to calculate the sensitivity and specificity values. [Results] The results of comparison of the clock drawing test with the mini-mental state showed significant differences in according to gender, age, education, and affected side. A total CDT of 10.5, which was selected as the cutoff point to identify cognitive impairement, showed a sensitivity, specificity, Youden index, positive predictive, and negative predicive values of 86.4%, 91.5%, 0.8, 95%, and 88.2%. [Conclusion] The clock drawing test is believed to be useful in assessments and interventions based on its excellent ability to identify cognitive disorders.

  1. Multidetector-CT angiography in pulmonary embolism - can image parameters predict clinical outcome?

    Heyer, Christoph M.; Lemburg, Stefan P.; Nicolas, Volkmar; Roggenland, Daniela [Berufsgenossenschaftliches Universitaetsklinikum Bergmannsheil GmbH, Ruhr-University of Bochum, Institute of Diagnostic Radiology, Interventional Radiology and Nuclear Medicine, Bochum (Germany); Knoop, Heiko [Berufsgenossenschaftliches Universitaetsklinikum Bergmannsheil GmbH, Medical Clinic III - Pneumology, Allergology, and Sleep Medicine, Bochum (Germany); Holland-Letz, Tim [Ruhr-University of Bochum, Department of Medical Informatics, Biometry and Epidemiology, Bochum (Germany)

    2011-09-15

    To assess if pulmonary CT angiography (CTA) can predict outcome in patients with pulmonary embolism (PE). Retrospective analysis of CTA studies of patients with PE and documentation of pulmonary artery (PA)/aorta ratio, right ventricular (RV)/left ventricular (LV) ratio, superior vena cava (SVC) diameter, pulmonary obstruction index (POI), ventricular septal bowing (VSB), venous contrast reflux (VCR), pulmonary infarction and pleural effusion. Furthermore, duration of total hospital stay, necessity for/duration of ICU therapy, necessity for mechanical ventilation and mortality were recorded. Comparison was performed by logistic/linear regression analysis with significance at 5%. 152 patients were investigated. Mean duration of hospital stay was 21 {+-} 24 days. 66 patients were admitted to the ICU; 20 received mechanical ventilation. Mean duration of ICU therapy was 3 {+-} 8 days. Mortality rate was 8%. Significant positive associations of POI, VCR and pulmonary infarction with necessity for ICU therapy were shown. VCR was significantly associated with necessity for mechanical ventilation and duration of ICU treatment. Pleural effusions were significantly associated with duration of total hospital stay whereas the RV/LV ratio correlated with mortality. Selected CTA findings showed significant associations with the clinical course of PE and may thus be used as predictive parameters. (orig.)

  2. [Using CTS and PK-PD models to predict the effect of uncertainty about population parameters on clinical trial power].

    Zhu, Ling; Shi, Xinling; Liu, Yajie

    2009-02-01

    The traditional clinical trail designs always depend on expert opinions and lack statistical evaluations. In this article, we present a method and illustrate how population parameter uncertainty may be incorporated in the overall simulation model. Using the techniques of clinical trail simulation (CTS) and setting up predictions on the basis of pharmacokinetics-pharmacodynamics (PK-PD) models, we advance the modeling methods for simulation, for treatment effects, and for the clinical trail power under the given PK-PD conditions. Then we discuss the model of uncertainty, suggest an ANOVA-based method, add eta2 statistics for sensitivity analysis, and canvass the effect of uncertainty about population parameters on clinical trail power. The results from simulations and the indices derived from this type of sensitivity analysis may be used for grading the influence on the prediction quality of uncertainty about different population parameters. The experiment results are satisfactory and the approach presented has practical value in clinical trails.

  3. Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs)

    Wallace, Emma

    2011-10-14

    Abstract Clinical Prediction Rules (CPRs) are tools that quantify the contribution of symptoms, clinical signs and available diagnostic tests, and in doing so stratify patients according to the probability of having a target outcome or need for a specified treatment. Most focus on the derivation stage with only a minority progressing to validation and very few undergoing impact analysis. Impact analysis studies remain the most efficient way of assessing whether incorporating CPRs into a decision making process improves patient care. However there is a lack of clear methodology for the design of high quality impact analysis studies. We have developed a sequential four-phased framework based on the literature and the collective experience of our international working group to help researchers identify and overcome the specific challenges in designing and conducting an impact analysis of a CPR. There is a need to shift emphasis from deriving new CPRs to validating and implementing existing CPRs. The proposed framework provides a structured approach to this topical and complex area of research.

  4. Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs

    Verbakel Jan

    2011-10-01

    Full Text Available Abstract Clinical Prediction Rules (CPRs are tools that quantify the contribution of symptoms, clinical signs and available diagnostic tests, and in doing so stratify patients according to the probability of having a target outcome or need for a specified treatment. Most focus on the derivation stage with only a minority progressing to validation and very few undergoing impact analysis. Impact analysis studies remain the most efficient way of assessing whether incorporating CPRs into a decision making process improves patient care. However there is a lack of clear methodology for the design of high quality impact analysis studies. We have developed a sequential four-phased framework based on the literature and the collective experience of our international working group to help researchers identify and overcome the specific challenges in designing and conducting an impact analysis of a CPR. There is a need to shift emphasis from deriving new CPRs to validating and implementing existing CPRs. The proposed framework provides a structured approach to this topical and complex area of research.

  5. Clinical effectiveness of modified sequential organ failure assessment scoring system for predicting ICU indexing scores

    Hassan Babamohamadi

    2016-10-01

    Full Text Available Background: The ability to recognize the severity of the disease in those who their survival depend entirely on admission to the intensive care unit, is very valuable clinically. This study aimed to evaluate the clinical effectiveness of modified sequential organ failure assessment (MSOFA scale to predict mortality and length of stay in intensive care unit patients respectively. Methods: This was a retrospective cross-sectional study conducted on hospital records of patients admitted to the intensive care unit. All patients’ records who admitted to the intensive care unit of Kowsar Hospital, Semnan city (the capital of the province, Iran, in 2015 considered as the sample. Collecting data were done during 4 weeks in April and May 2016. The data collection tool was a demographic questionnaire and modified sequential organ failure assessment scale. Exclusion criteria included discharge in the first 24 hours after admission, the patient died a few hours after admission and incomplete information to complete the modified sequential organ failure assessment form. Results: The study of 105 patients' records of the intensive care unit showed that 45.7% of patients were died, 15.2% and 39% were discharged and moved to other wards respectively. The results of logistic regression analysis and receiver operating characteristic (ROC curve showed that this criterion had moderate sensitivity and specificity for prediction of mortality and length of stay in ICU patients (Area=0.635, CI= 0.527-0.743 and each unit increase in modified sequential organ failure assessment score is accompanied by increasing 32 percent chance of death (OR=1.325; 95% CI:1.129,1.555; P= 0.001(. Also each unit increase in modified sequential organ failure assessment (MSOFA score accompanied by increasing 19% length of stay in ICU (OR=1.191; 95% CI: 1.034, 1.371; P= 0.015(. Conclusion: The results of this study showed that the modified sequential organ failure assessment scale is not

  6. PREDICTING OUTCOME AND SEVERITY IN ACUTE ORGANOPHOSPHOROUS POISONING WITH CLINICAL SCORING AND SERUM CHOLINESTERASE LEVELS

    Basavaraj R

    2014-11-01

    Full Text Available BACKGROUND AND OBJECTIVES: Organophosphorus compound poisoning is the most common medico toxic emergency in India the increase in pesticide use in agriculture has paralleled the increase in the use of these products for deliberate self-warm. Respiratory failure is the most common complication of OP poisoning leading to death. Early recognition and prompt ventilator support may improve survival. Owing to limited availability of resources, all OP poisoning patients are not managed in ICUs in Indian setup. It is therefore important that clinical features and criteria to predict the need for ventilator support be identified at initial examination. Hence this study was undertaken to assess the severity of organophosphorus compound poisoning both clinically by using Peradeniya scoring and by estimating serum choline esterase levels. METHODS: Cross sectional study was done at basaveswar teaching and general hospital attached to MR Medical College. Cases with history of exposure to organophosphorus compound within previous 24 hours were chosen after applying inclusion and exclusion criteria. Patients were evaluated for Peradeniya OP poisoning scale and serum cholinesterase levels for assessment of severity of poisoning. Serum cholinesterase levels and Peradeniya OP poisoning scale were studied to predict the need for ventilator support. The results were analyzed using Chi-square test. STATISTICAL ANALYSIS: It was done using pearson’s chi square test. RESULTS: In this study requirement of ventilator support was seen in 36% of patients. Mortality in our study was 18%. Only 15.6% of patients with mild grade of poisoning according to Peradeniya OP poisoning scale required ventilator support, whereas 84.4% did not require ventilator support. Most of patients with moderate (70.6% and severe poisoning (100% according to Peradeniya OP poisoning scale required ventilator support. 93.7% of patients with serum cholinesterase levels more than 50% did not require

  7. Predictive factors of clinical response in steroid-refractory ulcerative colitis treated with granulocyte-monocyte apheresis

    Valeria D'Ovidio; Donatella Meo; Angelo Viscido; Giampaolo Bresci; Piero Vernia; Renzo Caprilli

    2011-01-01

    AIM: To identify factors predicting the clinical response of ulcerative colitis patients to granulocyte-monocyte apheresis (GMA).METHODS: Sixty-nine ulcerative colitis patients (39 F, 30 M) dependent upon/refractory to steroids were treated with GMA.Steroid dependency, clinical activity index (CAI), C reactive protein (CRP) level, erythrocyte sedimentation rate (ESR), values at baseline, use of immunosuppressant, duration of disease, and age and extent of disease were considered for statistical analysis as predictive factors of clinical response.Univariate and multivariate logistic regression models were used.RESULTS: In the univariate analysis, CAI (P = 0.039) and ESR (P = 0.017) levels at baseline were singled out as predictive of clinical remission.In the multivariate analysis steroid dependency [Odds ratio (OR) = 0.390, 95% Confidence interval (CI): 0.176-0.865, Wald 5.361, P = 0.0160] and low CAI levels at baseline (4 < CAI < 7) (OR = 0.770, 95% CI: 0.425-1.394, Wald 3.747, P = 0.028) proved to be effective as factors predicting clinical response.CONCLUSION: GMA may be a valid therapeutic option for steroid-dependent ulcerative colitis patients with mild-moderate disease and its clinical efficacy seems to persist for 12 mo.

  8. Evaluation of clinical and immunological markers for predicting virological failure in a HIV/AIDS treatment cohort in Busia, Kenya.

    Cecilia Ferreyra

    Full Text Available BACKGROUND: In resource-limited settings where viral load (VL monitoring is scarce or unavailable, clinicians must use immunological and clinical criteria to define HIV virological treatment failure. This study examined the performance of World Health Organization (WHO clinical and immunological failure criteria in predicting virological failure in HIV patients receiving antiretroviral therapy (ART. METHODS: In a HIV/AIDS program in Busia District Hospital, Kenya, a retrospective, cross-sectional cohort analysis was performed in April 2008 for all adult patients (>18 years old on ART for ≥12 months, treatment-naive at ART start, attending the clinic at least once in last 6 months, and who had given informed consent. Treatment failure was assessed per WHO clinical (disease stage 3 or 4 and immunological (CD4 cell count criteria, and compared with virological failure (VL >5,000 copies/mL. RESULTS: Of 926 patients, 123 (13.3% had clinically defined treatment failure, 53 (5.7% immunologically defined failure, and 55 (6.0% virological failure. Sensitivity, specificity, positive predictive value, and negative predictive value of both clinical and immunological criteria (combined in predicting virological failure were 36.4%, 83.5%, 12.3%, and 95.4%, respectively. CONCLUSIONS: In this analysis, clinical and immunological criteria were found to perform relatively poorly in predicting virological failure of ART. VL monitoring and new algorithms for assessing clinical or immunological treatment failure, as well as improved adherence strategies, are required in ART programs in resource-limited settings.

  9. Autoreactive T Cells in Human Smokers Is Predictive of Clinical Outcome

    Chuang eXu

    2012-08-01

    Full Text Available Cross-sectional studies have suggested a role for activation of adaptive immunity in smokers with emphysema, but the clinical application of these findings has not been explored. Here we examined the utility of detecting autoreactive T cells as a screening tool for emphysema in an at-risk population of smokers. We followed 156 former and current (ever-smokers for two years to assess whether peripheral blood CD4 T cell cytokine responses to lung elastin fragments (EFs could discriminate between those with and without emphysema, and to evaluate the relevance of autoreactive T cells to predict changes during follow-up in lung physiological parameters. Volunteers underwent baseline complete phenotypic assessment with pulmonary function tests, quantitative chest CT, yearly six minutes walk distance (6MWD testing, and annual measurement of CD4 T cell cytokine responses to EFs. The areas under the receiver operating characteristic curve to predict emphysema for interferon gamma (IFN-γ, and interleukin 6 (IL-6 responses to EFs were 0.81 (95% CI of 0.74 to 0.88 and 0.79 (95% CI of 0.72 to 0.86 respectively. We developed a dual cytokine enzyme-linked immunocell spot assay, the γ-6 Spot, using CD4 T cell IFN-γ and IL-6 responses and found that it discriminated emphysema with 90% sensitivity. After adjusting for potential confounders, the presence of autoreactive T cells was predictive of a decrease in 6MWD over two years (decline in 6MWD, -19 meters (m per fold change in IFN-γ; P=0.026, and -26 m per fold change in IL-6; P=0.003. These findings collectively suggest that the EF specific autoreactive CD4 T cell assay, γ-6 Spot, could provide a non-invasive diagnostic tool with potential application to large-scale screening to discriminate emphysema in ever-smokers, and predict early relevant physiological outcomes in those at risk.

  10. Prediction consistency and clinical presentations of breast cancer molecular subtypes for Han Chinese population

    Huang Chi-Cheng

    2012-09-01

    Full Text Available Abstract Background Breast cancer is a heterogeneous disease in terms of transcriptional aberrations; moreover, microarray gene expression profiles had defined 5 molecular subtypes based on certain intrinsic genes. This study aimed to evaluate the prediction consistency of breast cancer molecular subtypes from 3 distinct intrinsic gene sets (Sørlie 500, Hu 306 and PAM50 as well as clinical presentations of each molecualr subtype in Han Chinese population. Methods In all, 169 breast cancer samples (44 from Taiwan and 125 from China of Han Chinese population were gathered, and the gene expression features corresponding to 3 distinct intrinsic gene sets (Sørlie 500, Hu 306 and PAM50 were retrieved for molecular subtype prediction. Results For Sørlie 500 and Hu 306 intrinsic gene set, mean-centring of genes and distance-weighted discrimination (DWD remarkably reduced the number of unclassified cases. Regarding pairwise agreement, the highest predictive consistency was found between Hu 306 and PAM50. In all, 150 and 126 samples were assigned into identical subtypes by both Hu 306 and PAM50 genes, under mean-centring and DWD. Luminal B tended to show a higher nuclear grade and have more HER2 over-expression status than luminal A did. No basal-like breast tumours were ER positive, and most HER2-enriched breast tumours showed HER2 over-expression, whereas, only two-thirds of ER negativity/HER2 over-expression tumros were predicted as HER2-enriched molecular subtype. For 44 Taiwanese breast cancers with survival data, a better prognosis of luminal A than luminal B subtype in ER-postive breast cancers and a better prognosis of basal-like than HER2-enriched subtype in ER-negative breast cancers was observed. Conclusions We suggest that the intrinsic signature Hu 306 or PAM50 be used for breast cancers in the Han Chinese population during molecular subtyping. For the prognostic value and decision making based on intrinsic subtypes, further prospective

  11. Comparison of Glasgow-Blatchford score and full Rockall score systems to predict clinical outcomes in patients with upper gastrointestinal bleeding

    Mokhtare M

    2016-10-01

    Full Text Available Marjan Mokhtare, Vida Bozorgi, Shahram Agah, Mehdi Nikkhah, Amirhossein Faghihi, Amirhossein Boghratian, Neda Shalbaf, Abbas Khanlari, Hamidreza Seifmanesh Colorectal Research Center, Rasoul Akram Hospital, Tehran, Iran Background: Various risk scoring systems have been recently developed to predict clinical outcomes in patients with upper gastrointestinal bleeding (UGIB. The two commonly used scoring systems include full Rockall score (RS and the Glasgow-Blatchford score (GBS. Bleeding scores were assessed in terms of prediction of clinical outcomes in patients with UGIB. Patients and methods: Two hundred patients (age >18 years with obvious symptoms of UGIB in the emergency department of Rasoul Akram Hospital were enrolled. Full RS and GBS were calculated. We followed the patients for records of rebleeding and 1-month mortality. A receiver operating characteristic curve by using areas under the curve (AUCs was used to statistically identify the best cutoff point. Results: Eighteen patients were excluded from the study due to failure to follow-up. Rebleeding and mortality rate were 9.34% (n=17 and 11.53% (n=21, respectively. Regarding 1-month mortality, full RS was better than GBS (AUC, 0.648 versus 0.582; P=0.021. GBS was more accurate in terms of detecting transfusion need (AUC, 0.757 versus 0.528; P=0.001, rebleeding rate (AUC, 0.722 versus 0.520; P=0.002, intensive care unit admission rate (AUC, 0.648 versus 0.582; P=0.021, and endoscopic intervention rate (AUC, 0.771 versus 0.650; P<0.001. Conclusion: We found the full RS system is better for 1-month mortality prediction while GBS system is better for prediction of other outcomes. Keywords: full Rockall score, Glasgow-Blatchford score, gastrointestinal bleeding, mortality, prognosis

  12. Predictive validity of measurements of clinical competence using the team objective structured bedside assessment (TOSBA): assessing the clinical competence of final year medical students.

    Meagher, Frances M

    2009-11-01

    The importance of valid and reliable assessment of student competence and performance is gaining increased recognition. Provision of valid patient-based formative assessment is an increasing challenge for clinical teachers in a busy hospital setting. A formative assessment tool that reliably predicts performance in the summative setting would be of value to both students and teachers.

  13. Predicting asthma in preschool children at high risk presenting in primary care: Development of a clinical asthma prediction score

    L.B. Van Der Mark (Lonneke); K.E. Wonderen (Karina); J. Mohrs (Jacob); W.M.C. van Aalderen (Willem); G. ter Riet; P.J.E. Bindels (Patrick)

    2014-01-01

    textabstractBackground: A setting-specific asthma prediction score for preschool children with wheezing and/or dyspnoea presenting in primary healthcare is needed since existing indices are mainly based on general populations. Aims: To find an optimally informative yet practical set of predictors fo

  14. Pre-clinical grades predict clinical performance in the MBBS stage II examination at the University of the West Indies, Mona Campus.

    Pepple, Dagogo J; Young, Lauriann E; Gordon-Strachan, Georgiana M; Carroll, Robert G

    2013-12-20

    In the preclinical sciences, statistically significant predictive values have been reported between the performances in one discipline and the others, supporting the hypothesis that students who perform well in one discipline were likely to perform well in the other disciplines. We therefore decided to conduct a retrospective study to investigate the predictive effects of preclinical subjects on clinical subjects from 87 students of The University of the West Indies (UWI), Mona Campus who took the MBBS Stage II examination at various times between May 2000 and May 2002. The grade in Pathology was significantly predicted by scores in Anatomy and Pharmacology; Medicine by Physiology and Pharmacology scores; Surgery by Anatomy and Social and Preventive Medicine scores; while, the Obstetrics and Gynecology grade was predicted by the Anatomy score. The results support the hypothesis that the scores in some preclinical subjects can predict the performance in specific clinical subjects, which could be interpreted to suggest that poor performance in specific preclinical disciplines could be a warning sign of future poor performance in the related clinical disciplines.

  15. Relative power of clinical, exercise test, and angiographic variables in predicting clinical outcome after myocardial infarction: the Newham and Tower Hamlets study.

    de Belder, M A; Pumphrey, C W; Skehan, J D; Rimington, H; al Wakeel, B; Evans, S J; Rothman, M; Mills, P G

    1988-11-01

    The interrelations of clinical, exercise test, and angiographic variables and their relative values in predicting specific clinical outcomes after myocardial infarction have not been fully established. Of 302 consecutive stable survivors of infarction, 262 performed a predischarge submaximal exercise test. In the first year after infarction patients with a "positive" exercise test were 13 times more likely to die, 2.8 times more likely to have an ischaemic event, and 2.3 times more likely to develop left ventricular failure than patients with negative tests. Patients with positive exercise tests underwent cardiac catheterization. Features of the history, 12 lead electrocardiogram, in-hospital clinical course, exercise test, and left ventricular and coronary angiograms that predicted these clinical end points were identified by univariate analysis. Then multivariable analysis was used to assess the relative powers of all variables in predicting end points. Certain features of the exercise test remained independent predictors of future ischaemic events and the development of overt left ventricular failure, but clinical and angiographic variables were more powerful predictors of mortality. Because the exercise test is also used to select patients for angiography, however, the results of this study strongly support the use of early submaximal exercise testing after infarction.

  16. Predictive value of clinical risk indicators in child development: final results of a study based on psychoanalytic theory

    Maria Cristina Machado Kupfer; Alfredo Nestor Jerusalinsky; Leda Mariza Fischer Bernardino; Daniele Wanderley; Paulina Schmidtbauer Barbosa Rocha; Silvia Eugenia Molina; Léa Martins Sales; Regina Stellin; M. Eugênia Pesaro; Rogerio Lerner

    2010-01-01

    We present the final results of a study using the IRDI (Clinical Risk Indicators in Child Development). Based on a psychoanalytic approach, 31 risk signs for child development were constructed and applied to 726 children between the ages of 0 and 18 months. One sub-sample was evaluated at the age of three. The results showed a predictive capacity of IRDIs to indicate developmental problems; 15 indicators for the IRDI were also highlighted that predict psychic risk for the constitution of the ...

  17. Usefulness of fecal lactoferrin in predicting and monitoring the clinical severity of infectious diarrhea

    Chien-Chang Chen; Chee-Jen Chang; Tzou-Yien Lin; Ming-Wei Lai; Hsun-Chin Chao; Man-Shan Kong

    2011-01-01

    AIM: To explore the value of fecal lactoferrin in predicting and monitoring the clinical severity of infectious diarrhea.``METHODS: Patients with acute infectious diarrhea ranging from 3 mo to 10 years in age were enrolled, and one to three stool samples from each subject were collected. Certain parameters, including white blood cells/differential count, C-reactive protein, fecal mucus, fecal pus cells, duration of fever, vomiting, diarrhea and severity (indicated by Clark and Vesikari scores), were recorded and analyzed. Fecal lactoferrin was determined by enzyme-linked immunosorbent assay and compared in different pathogen and disease activity. Generalized estimating equations (GEE) were also used for analysis.``RESULTS: Data included 226 evaluations for 117 individuals across three differenttime points. Fecal lactoferrin was higher in patients with Salmonella (11.17 )j,g/g ± 2.73 μg/g) or Campyhbacter (10.32 μg/g ± 2.94 μig/g) infections and lower in patients with rotavirus (2.82 μg/g ± 1.27 μg/g) or norovirus (3.16 μg/g ± 1.18 |ag/g) infections. Concentrations of fecal lactoferrin were significantly elevated in patients with severe (11.32 μg/g ± 3.29 μag/g) or moderate (3.77 μg/g ± 2.08 μg/g) disease activity compared with subjects with mild (1.51 yig/g ± 1.36 μg/g) disease activity (P < 0.05). GEE analysis suggests that this marker could be used to monitor the severity and course of gastrointestinal infections and may provide information for disease management.``CONCLUSION: Fecal lactoferrin increased during bacterial infection and with greater disease severity and may be a good marker for predicting and monitoring intestinal inflammation in children with infectious diarrhea.

  18. READMIT: a clinical risk index to predict 30-day readmission after discharge from acute psychiatric units.

    Vigod, Simone N; Kurdyak, Paul A; Seitz, Dallas; Herrmann, Nathan; Fung, Kinwah; Lin, Elizabeth; Perlman, Christopher; Taylor, Valerie H; Rochon, Paula A; Gruneir, Andrea

    2015-02-01

    Our aim was to create a clinically useful risk index, administered prior to discharge, for determining the probability of psychiatric readmission within 30 days of hospital discharge for general psychiatric inpatients. We used population-level sociodemographic and health administrative data to develop a predictive model for 30-day readmission among adults discharged from an acute psychiatric unit in Ontario, Canada (2008-2011), and converted the final model into a risk index system. We derived the predictive model in one-half of the sample (n = 32,749) and validated it in the other half of the sample (n = 32,750). Variables independently associated with 30-day readmission (forming the mnemonic READMIT) were: (R) Repeat admissions; (E) Emergent admissions (i.e. harm to self/others); (D) Diagnoses (psychosis, bipolar and/or personality disorder), and unplanned Discharge; (M) Medical comorbidity; (I) prior service use Intensity; and (T) Time in hospital. Each 1-point increase in READMIT score (range 0-41) increased the odds of 30-day readmission by 11% (odds ratio 1.11, 95% CI 1.10-1.12). The index had moderate discriminative capacity in both derivation (C-statistic = 0.631) and validation (C-statistic = 0.630) datasets. Determining risk of psychiatric readmission for individual patients is a critical step in efforts to address the potentially avoidable high rate of this negative outcome. The READMIT index provides a framework for identifying patients at high risk of 30-day readmission prior to discharge, and for the development, evaluation and delivery of interventions that can assist with optimizing the transition to community care for patients following psychiatric discharge.

  19. Ruling out coronary heart disease in primary care patients with chest pain: a clinical prediction score

    Burnand Bernard

    2010-01-01

    Full Text Available Abstract Background Chest pain raises concern for the possibility of coronary heart disease. Scoring methods have been developed to identify coronary heart disease in emergency settings, but not in primary care. Methods Data were collected from a multicenter Swiss clinical cohort study including 672 consecutive patients with chest pain, who had visited one of 59 family practitioners' offices. Using delayed diagnosis we derived a prediction rule to rule out coronary heart disease by means of a logistic regression model. Known cardiovascular risk factors, pain characteristics, and physical signs associated with coronary heart disease were explored to develop a clinical score. Patients diagnosed with angina or acute myocardial infarction within the year following their initial visit comprised the coronary heart disease group. Results The coronary heart disease score was derived from eight variables: age, gender, duration of chest pain from 1 to 60 minutes, substernal chest pain location, pain increasing with exertion, absence of tenderness point at palpation, cardiovascular risks factors, and personal history of cardiovascular disease. Area under the receiver operating characteristics curve was of 0.95 with a 95% confidence interval of 0.92; 0.97. From this score, 413 patients were considered as low risk for values of percentile 5 of the coronary heart disease patients. Internal validity was confirmed by bootstrapping. External validation using data from a German cohort (Marburg, n = 774 revealed a receiver operating characteristics curve of 0.75 (95% confidence interval, 0.72; 0.81 with a sensitivity of 85.6% and a specificity of 47.2%. Conclusions This score, based only on history and physical examination, is a complementary tool for ruling out coronary heart disease in primary care patients complaining of chest pain.

  20. Preoperative neutrophil response as a predictive marker of clinical outcome following open heart surgery and the impact of leukocyte filtration.

    Soo, Alan W

    2010-11-01

    Open heart surgery is associated with a massive systemic inflammatory response. Neutrophils, are the main mediator of this response. We hypothesised that the degree of neutrophil activation and inflammatory response to open heart surgery varies individually and correlates with clinical outcome. The aim of this study was to determine if individual clinical outcome can be predicted preoperatively through assessment of in-vitro stimulated neutrophil responses. Following that, the effects of neutrophil depletion through leukocyte filters are examined.

  1. Innovative drugs to treat depression: did animal models fail to be predictive or did clinical trials fail to detect effects?

    Belzung, Catherine

    2014-04-01

    Over recent decades, encouraging preclinical evidence using rodent models pointed to innovative pharmacological targets to treat major depressive disorder. However, subsequent clinical trials have failed to show convincing results. Two explanations for these rather disappointing results can be put forward, either animal models of psychiatric disorders have failed to predict the clinical effectiveness of treatments or clinical trials have failed to detect the effects of these new drugs. A careful analysis of the literature reveals that both statements are true. Indeed, in some cases, clinical efficacy has been predicted on the basis of inappropriate animal models, although the contrary is also true, as some clinical trials have not targeted the appropriate dose or clinical population. On the one hand, refinement of animal models requires using species that have better homological validity, designing models that rely on experimental manipulations inducing pathological features, and trying to model subtypes of depression. On the other hand, clinical research should consider carefully the results from preclinical studies, in order to study these compounds at the correct dose, in the appropriate psychiatric nosological entity or symptomatology, in relevant subpopulations of patients characterized by specific biomarkers. To achieve these goals, translational research has to strengthen the dialogue between basic and clinical science.

  2. Prospective assessment of a gene signature potentially predictive of clinical benefit in metastatic melanoma patients following MAGE-A3 immunotherapeutic (PREDICT)

    Saiag, P.; Gutzmer, R.; Ascierto, P. A.; Maio, M.; Grob, J.-J.; Murawa, P.; Dreno, B.; Ross, M.; Weber, J.; Hauschild, A.; Rutkowski, P.; Testori, A.; Levchenko, E.; Enk, A.; Misery, L.; Vanden Abeele, C.; Vojtek, I.; Peeters, O.; Brichard, V. G.; Therasse, P.

    2016-01-01

    Background Genomic profiling of tumor tissue may aid in identifying predictive or prognostic gene signatures (GS) in some cancers. Retrospective gene expression profiling of melanoma and non-small-cell lung cancer led to the characterization of a GS associated with clinical benefit, including improved overall survival (OS), following immunization with the MAGE-A3 immunotherapeutic. The goal of the present study was to prospectively evaluate the predictive value of the previously characterized GS. Patients and methods An open-label prospective phase II trial (‘PREDICT’) in patients with MAGE-A3-positive unresectable stage IIIB-C/IV-M1a melanoma. Results Of 123 subjects who received the MAGE-A3 immunotherapeutic, 71 (58.7%) displayed the predictive GS (GS+). The 1-year OS rate was 83.1%/83.3% in the GS+/GS− populations. The rate of progression-free survival at 12 months was 5.8%/4.1% in GS+/GS− patients. The median time-to-treatment failure was 2.7/2.4 months (GS+/GS−). There was one complete response (GS−) and two partial responses (GS+). The MAGE-A3 immunotherapeutic was similarly immunogenic in both populations and had a clinically acceptable safety profile. Conclusion Treatment of patients with MAGE-A3-positive unresectable stage IIIB-C/IV-M1a melanoma with the MAGE-A3 immunotherapeutic demonstrated an overall 1-year OS rate of 83.5%. GS− and GS+ patients had similar 1-year OS rates, indicating that in this study, GS was not predictive of outcome. Unexpectedly, the objective response rate was lower in this study than in other studies carried out in the same setting with the MAGE-A3 immunotherapeutic. Investigation of a GS to predict clinical benefit to adjuvant MAGE-A3 immunotherapeutic treatment is ongoing in another melanoma study. This study is registered at www.clinicatrials.gov NCT00942162. PMID:27502712

  3. Development of a Simple Clinical Risk Score for Early Prediction of Severe Dengue in Adult Patients.

    Ing-Kit Lee

    , irrespective of the day of illness onset, suggesting that our simple risk score can be easily implemented in resource-limited countries for early prediction of dengue patients at risk of SD provided that they have rapid dengue confirmed tests. For patients with other acute febrile illnesses or bacterial infections usually have SD risk score of >1. Thus, these scoring algorithms cannot totally replace good clinical judgement of the physician, and most importantly, early differentiating dengue from other febrile illnesses is critical for appropriate monitoring and management.

  4. Angiographically Negative Acute Arterial Upper and Lower Gastrointestinal Bleeding: Incidence, Predictive Factors, and Clinical Outcomes

    Kim, Jin Hyoung; Shin, Ji Hoon; Yoon, Hyun Ki; Chae, Eun Young; Myung, Seung Jae; Ko, Gi Young; Gwon, Dong Il; Sung, Kyu Bo [Asan Medical Center, Seoul (Korea, Republic of)

    2009-08-15

    To evaluate the incidence, predictive factors, and clinical outcomes of angiographically negative acute arterial upper and lower gastrointestinal (GI) bleeding. From 2001 to 2008, 143 consecutive patients who underwent an angiography for acute arterial upper or lower GI bleeding were examined. The angiographies revealed a negative bleeding focus in 75 of 143 (52%) patients. The incidence of an angiographically negative outcome was significantly higher in patients with a stable hemodynamic status (p < 0.001), or in patients with lower GI bleeding (p = 0.032). A follow-up of the 75 patients (range: 0-72 months, mean: 8 {+-} 14 months) revealed that 60 of the 75 (80%) patients with a negative bleeding focus underwent conservative management only, and acute bleeding was controlled without rebleeding. Three of the 75 (4%) patients underwent exploratory surgery due to prolonged bleeding; however, no bleeding focus was detected. Rebleeding occurred in 12 of 75 (16%) patients. Of these, six patients experienced massive rebleeding and died of disseminated intravascular coagulation within four to nine hours after the rebleeding episode. Four of the 16 patients underwent a repeat angiography and the two remaining patients underwent a surgical intervention to control the bleeding. Angiographically negative results are relatively common in patients with acute GI bleeding, especially in patients with a stable hemodynamic status or lower GI bleeding. Most patients with a negative bleeding focus have experienced spontaneous resolution of their condition.

  5. Primary gastric mucosa associated lymphoid tissue lymphoma: Clinical data predicted treatment outcome

    Milena Todorovic; Miodrag Krstic; Bela Balint; Miodrag Jevtic; Nada Suvajdzic; Amela Ceric; Dragana Stamatovic; Olivera Markovic; Maja Perunicic; Slobodan Marjanovic

    2008-01-01

    AIM: To determine clinical characteristics and treatmentoutcome of gastric lymphoma after chemotherapy and immuno-chemotherapy.METHODS: Thirty four patients with primary gastric mucosa associated lymphoid tissue (MALT) lymphoma (Ann Arbor stages I to IV) were enrolled. All had upper gastric endoscopy, abdominal ultrasonography, CT and H py/or/status assessment (histology and serology).After anti-/-/py/or/treatment and initial chemotherapy,patients were re-examined every 4 mo.RESULTS: Histological regression of the lymphoma wascomplete in 22/34 (64.7%) and partial in 9 (26.5%)patients. Median follow up time for these 31 responders was 60 mo (range 48-120). No regression was noted in 3 patients. Among the 25 (73.5%) H py/or/positive patients, the eradication rate was 100%.CONCLUSION: Using univariate analysis, predictive factors for overall survival were international prognostic index (IPI) score, hemoglobin level, erythrocyte sedimentation rate (ESR), and platelet numbers (P < 0.005). In addition to this, Cox proportion hazard model differentiate IPI score, ESR, and platelets as predictors of survival.

  6. Modeling using clinical examination indicators predicts interstitial lung disease among patients with rheumatoid arthritis

    Wang, Yao; Song, Wuqi; Wu, Jing; Li, Zhangming; Mu, Fengyun; Li, Yang; Huang, He; Zhu, Wenliang

    2017-01-01

    Interstitial lung disease (ILD) is a severe extra-articular manifestation of rheumatoid arthritis (RA) that is well-defined as a chronic systemic autoimmune disease. A proportion of patients with RA-associated ILD (RA-ILD) develop pulmonary fibrosis (PF), resulting in poor prognosis and increased lifetime risk. We investigated whether routine clinical examination indicators (CEIs) could be used to identify RA patients with high PF risk. A total of 533 patients with established RA were recruited in this study for model building and 32 CEIs were measured for each of them. To identify PF risk, a new artificial neural network (ANN) was built, in which inputs were generated by calculating Euclidean distance of CEIs between patients. Receiver operating characteristic curve analysis indicated that the ANN performed well in predicting the PF risk (Youden index = 0.436) by only incorporating four CEIs including age, eosinophil count, platelet count, and white blood cell count. A set of 218 RA patients with healthy lungs or suffering from ILD and a set of 87 RA patients suffering from PF were used for independent validation. Results showed that the model successfully identified ILD and PF with a true positive rate of 84.9% and 82.8%, respectively. The present study suggests that model integration of multiple routine CEIs contributes to identification of potential PF risk among patients with RA.

  7. Prediction of the potential clinical outcomes for post-resuscitated patients after cardiac arrest

    Hong, Sungmin; Kwon, Bojun; Yun, Il Dong; Lee, Sang Uk; Kim, Kyuseok; Kim, Joonghee

    2013-02-01

    Cerebral injuries after cardiac arrest are serious causes for morbidity. Many previous researches in the medical society have been proposed to prognosticate the functional recoveries of post-resuscitated patients after cardiac arrest, but the validity of suggested features and the automation of prognostication have not been made yet. This paper presents the automatic classification method which predicts the potential clinical outcomes of post-resuscitated patients who suffered from cardiac arrest. The global features and the local features are adapted from the researches from the medical society. The global features, which are consisted of the percentage of the partial volume under the uniformly increasing thresholds, represent the global tendency of apparent diffusion coefficient value in a DWI. The local features are localized and measured on the refined local apparent diffusion coefficient minimal points. The local features represent the ischemic change of small areas in a brain. The features are trained and classified by the random forest method, which have been widely used in the machine learning society for classification. The validity of features is automatically evaluated during the classification process. The proposed method achieved the 0.129 false-positive rate while maintaining the perfect true-positive rate. The area-under-curve of the proposed method was 0.9516, which showed the feasibility and the robustness of the proposed method.

  8. Speaking Fluently And Accurately

    JosephDeVeto

    2004-01-01

    Even after many years of study,students make frequent mistakes in English. In addition, many students still need a long time to think of what they want to say. For some reason, in spite of all the studying, students are still not quite fluent.When I teach, I use one technique that helps students not only speak more accurately, but also more fluently. That technique is dictations.

  9. Predicting Calcium Values for Gastrointestinal Bleeding Patients in Intensive Care Unit Using Clinical Variables and Fuzzy Modeling

    G Khalili-Zadeh-Mahani

    2016-07-01

    Full Text Available Introduction: Reducing unnecessary laboratory tests is an essential issue in the Intensive Care Unit. One solution for this issue is to predict the value of a laboratory test to specify the necessity of ordering the tests. The aim of this paper was to propose a clinical decision support system for predicting laboratory tests values. Calcium laboratory tests of three categories of patients, including upper and lower gastrointestinal bleeding, and unspecified hemorrhage of gastrointestinal tract, have been selected as the case studies for this research. Method: In this research, the data have been collected from MIMIC-II database. For predicting calcium laboratory values, a Fuzzy Takagi-Sugeno model is used and the input variables of the model are heart rate and previous value of calcium laboratory test. Results: The results showed that the values of calcium laboratory test for the understudy patients were predictable with an acceptable accuracy. In average, the mean absolute errors of the system for the three categories of the patients are 0.27, 0.29, and 0.28, respectively. Conclusion: In this research, using fuzzy modeling and two variables of heart rate and previous calcium laboratory values, a clinical decision support system was proposed for predicting laboratory values of three categories of patients with gastrointestinal bleeding. Using these two clinical values as input variables, the obtained results were acceptable and showed the capability of the proposed system in predicting calcium laboratory values. For achieving better results, the impact of more input variables should be studied. Since, the proposed system predicts the laboratory values instead of just predicting the necessity of the laboratory tests; it was more generalized than previous studies. So, the proposed method let the specialists make the decision depending on the condition of each patient.

  10. Prediction of Deep Neck Abscesses by Contrast-Enhanced Computerized Tomography in 76 Clinically Suspect Consecutive Patients

    Freling, Nicole; Roele, Elise; Schaefer-Prokop, Cornelia; Fokkens, Wytske

    2009-01-01

    Objectives/Hypothesis: Contrast-enhanced computerized tomography (CECT) has become the imaging method of choice in patients with clinical suspicion of a deep neck abscess. The purpose of this retrospective study was to assess the predictive value of the diagnosis of deep neck abscess using CECT. Stu

  11. Prediction of the Clinical Course of COPD using the new GOLD Classification A Study of the General Population

    Lange, Peter; Marott, Jacob Louis; Vestbo, Jørgen;

    2012-01-01

    RATIONALE: The new Global initiative for obstructive lung disease (GOLD) stratification of COPD into categories A, B, C and D is based on symptoms, level of lung function, and history of exacerbations. OBJECTIVE: To investigate the abilities of this stratification to predict clinical course of COPD...

  12. Immunohistochemical profiling of caspase signaling pathways predicts clinical response to chemotherapy in primary nodal diffuse large B-cell lymphomas.

    Muris, J.J.; Cillessen, S.A.; Vos, W.; Houdt, I.S. van; Kummer, J.A.; Krieken, J.H.J.M. van; Jiwa, N.M.; Jansen, P.A.M.; Kluin-Nelemans, H.C.; Ossenkoppele, G.J.; Gundy, C.; Meijer, C.J.M.; Oudejans, J.J.

    2005-01-01

    We used biopsy specimens of primary nodal diffuse large B-cell lymphoma (DLBCL) to investigate whether the inhibition of caspase 8 and/or 9 apoptosis signaling pathways predicts clinical outcome. Expression levels of cellular FLICE inhibitory protein (c-Flip) and numbers of active caspase 3-positive

  13. Immunohistochemical profiling of caspase signaling pathways predicts clinical response to chemotherapy in primary nodal diffuse large B-cell lymphomas

    Muris, JJF; Cillessen, SAGM; Vos, W; van Houdt, IS; Kummer, JA; van Krieken, JHJM; Jiwa, NM; Jansen, PM; Kluin-Nelemans, HC; Ossenkoppele, GJ; Gundy, C; Meijer, CJLM; Oudejans, JJ

    2005-01-01

    We used biopsy specimens of primary nodal diffuse large B-cell lymphoma (DLBCL) to investigate whether the inhibition of caspase 8 and/or 9 apoplosis signaling pathways predicts clinical outcome. Expression levels of cellular FLICE inhibitory protein (c-Flip) and numbers of active caspase 3-positive

  14. Prediction

    Sornette, Didier

    2010-01-01

    This chapter first presents a rather personal view of some different aspects of predictability, going in crescendo from simple linear systems to high-dimensional nonlinear systems with stochastic forcing, which exhibit emergent properties such as phase transitions and regime shifts. Then, a detailed correspondence between the phenomenology of earthquakes, financial crashes and epileptic seizures is offered. The presented statistical evidence provides the substance of a general phase diagram for understanding the many facets of the spatio-temporal organization of these systems. A key insight is to organize the evidence and mechanisms in terms of two summarizing measures: (i) amplitude of disorder or heterogeneity in the system and (ii) level of coupling or interaction strength among the system's components. On the basis of the recently identified remarkable correspondence between earthquakes and seizures, we present detailed information on a class of stochastic point processes that has been found to be particu...

  15. Spontaneous intracerebral hemorrhage: Clinical and computed tomography findings in predicting in-hospital mortality in Central Africans

    Michel Lelo Tshikwela

    2012-01-01

    Full Text Available Background and Purpose: Intracerebral hemorrhage (ICH constitutes now 52% of all strokes. Despite of its deadly pattern, locally there is no clinical grading scale for ICH-related mortality prediction. The first objective of this study was to develop a risk stratification scale (Kinshasa ICH score by assessing the strength of independent predictors and their association with in-hospital 30-day mortality. The second objective of the study was to create a specific local and African model for ICH prognosis. Materials and Methods: Age, sex, hypertension, type 2 diabetes mellitus (T2DM, smoking, alcohol intake, and neuroimaging data from CT scan (ICH volume, Midline shift of patients admitted with primary ICH and follow-upped in 33 hospitals of Kinshasa, DR Congo, from 2005 to 2008, were analyzed using logistic regression models. Results: A total of 185 adults and known hypertensive patients (140 men and 45 women were examined. 30-day mortality rate was 35% (n=65. ICH volume>25 mL (OR=8 95% CI: 3.1-20.2; P 7 mm, a consequence of ICH volume, was also a significant predictor of mortality. The Kinshasa ICH score was the sum of individual points assigned as follows: Presence of coma coded 2 (2 × 2 = 4, absence of coma coded 1 (1 × 2 = 2, ICH volume>25 mL coded 2 (2 × 2=4, ICH volume of ≤25 mL coded 1(1 × 2=2, left hemispheric site of ICH coded 2 (2 × 1=2, and right hemispheric site of hemorrhage coded 1(1 × 1 = 1. All patients with Kinshasa ICH score ≤7 survived and the patients with a score >7 died. In considering sex influence (Model 3, points were allowed as follows: Presence of coma (2 × 3 = 6, absence of coma (1 × 3 = 3, men (2 × 2 = 4, women (1 × 2 = 2, midline shift ≤7 mm (1 × 3 = 3, and midline shift >7 mm (2 × 3 = 6. Patients who died had the Kinshasa ICH score ≥16. Conclusion: In this study, the Kinshasa ICH score seems to be an accurate method for distinguishing those ICH patients who need continuous and special management

  16. Coronary bifurcation angle from 3-D predicts clinical outcomes after stenting bifurcation lesions

    CHEN Shao-liang; DING Shi-qing; Tak W Kwan; Teguh Santoso; ZHANG Jun-jie; YE Fei; XU Ya-wei; FU Qiang; KAN Jing; Chitprapai Paiboon; ZHOU Yong

    2012-01-01

    Background The predictive value of bifurcation angle (BA) for worse events after stenting bifurcation lesions remains to be unknown.The present study was to investigate the dynamic change of BA and clinical relevance for patients with coronary bifurcation lesions treated by drug-eluting stent (DES).Methods BA was calculated by 3-D quantitative coronary analysis from 347 patients in DKCRUSH-Ⅱ study.Primary endpoint was the occurrence of composite major adverse cardiac events (MACE) at 12-month,including cardiac death,myocardial infarction (MI) and target vessel revascularization (TVR).Secondary end points were the rate of binary restenosis and stent thrombosis at 12-month.Results Stenting was associated with the reduction of distal BA.The cut-off value of distal BAfor predicting MACE was 60° Distal BA in <60° group had less reduction after stenting ((-1.96±13.58)° vs.(-12.12±23.58)°,P <0.001 ); two-stent technique was associated with significant reduction of distal BA (△(-4.05±14.20)°),compared to single stent group (△+1.55±11.73,P=0.003); the target lesion revascularization (TLR),TVR and MACE rate was higher in one-stent group (16.5%,19.0% and 21.5%),compared to two-stent group (3.8%,P=0.002; 7.5%,P=0.016; and 9.8%,P=0.024),respectively.Among patients in ≥60° group,there were no significant differences in distal BA,stent thrombosis (ST),MI,MACE,death,TLR,TVR between one- and two-stent groups; after stenting procedure,there was only slight change of distal BA in left anterior descending (LAD)-Ieft circumflex (LCX) subgroup (from (88.54±21.33)° at baseline to (82.44±31.72)° post-stenting),compared to either LAD-diagonal branch (Di),or LCX-obtuse marginal branch (OM),or RCA distal (RCAd) (all P <0.001 ).Conclusion Two-stent technique was associated with significant reduction of distal BA.DK crush stenting had reduced rate of MACE in patients in <60° group,compared to one-stent technique.

  17. A rat retinal damage model predicts for potential clinical visual disturbances induced by Hsp90 inhibitors

    Zhou, Dan, E-mail: DZhou@syntapharma.com [Synta Pharmaceuticals Corp., 45 Hartwell Avenue, Lexington, MA 02421 (United States); Liu, Yuan; Ye, Josephine; Ying, Weiwen; Ogawa, Luisa Shin; Inoue, Takayo; Tatsuta, Noriaki; Wada, Yumiko; Koya, Keizo [Synta Pharmaceuticals Corp., 45 Hartwell Avenue, Lexington, MA 02421 (United States); Huang, Qin [Department of Pathology and Laboratory Medicine, Veterans Affairs Boston Healthcare System, 1400 VFW Parkway, West Roxbury, MA 02132 (United States); Bates, Richard C.; Sonderfan, Andrew J. [Synta Pharmaceuticals Corp., 45 Hartwell Avenue, Lexington, MA 02421 (United States)

    2013-12-01

    In human trials certain heat shock protein 90 (Hsp90) inhibitors, including 17-DMAG and NVP-AUY922, have caused visual disorders indicative of retinal dysfunction; others such as 17-AAG and ganetespib have not. To understand these safety profile differences we evaluated histopathological changes and exposure profiles of four Hsp90 inhibitors, with or without clinical reports of adverse ocular effects, using a rat retinal model. Retinal morphology, Hsp70 expression (a surrogate marker of Hsp90 inhibition), apoptotic induction and pharmacokinetic drug exposure analysis were examined in rats treated with the ansamycins 17-DMAG and 17-AAG, or with the second-generation compounds NVP-AUY922 and ganetespib. Both 17-DMAG and NVP-AUY922 induced strong yet restricted retinal Hsp70 up-regulation and promoted marked photoreceptor cell death 24 h after the final dose. In contrast, neither 17-AAG nor ganetespib elicited photoreceptor injury. When the relationship between drug distribution and photoreceptor degeneration was examined, 17-DMAG and NVP-AUY922 showed substantial retinal accumulation, with high retina/plasma (R/P) ratios and slow elimination rates, such that 51% of 17-DMAG and 65% of NVP-AUY922 present at 30 min post-injection were retained in the retina 6 h post-dose. For 17-AAG and ganetespib, retinal elimination was rapid (90% and 70% of drugs eliminated from the retina at 6 h, respectively) which correlated with lower R/P ratios. These findings indicate that prolonged inhibition of Hsp90 activity in the eye results in photoreceptor cell death. Moreover, the results suggest that the retina/plasma exposure ratio and retinal elimination rate profiles of Hsp90 inhibitors, irrespective of their chemical class, may predict for ocular toxicity potential. - Highlights: • In human trials some Hsp90 inhibitors cause visual disorders, others do not. • Prolonged inhibition of Hsp90 in the rat eye results in photoreceptor cell death. • Retina/plasma ratio and retinal

  18. Positive Predictive Value of the WHO Clinical and Immunologic Criteria to Predict Viral Load Failure among Adults on First, or Second-Line Antiretroviral Therapy in Kenya.

    Anthony Waruru

    Full Text Available Routine HIV viral load (VL monitoring is the standard of care for persons receiving antiretroviral therapy (ART in developed countries. Although the World Health Organization recommends annual VL monitoring of patients on ART, recognizing difficulties in conducting routine VL testing, the WHO continues to recommend targeted VL testing to confirm treatment failure for persons who meet selected immunologic and clinical criteria. Studies have measured positive predictive value (PPV, negative predictive value, sensitivity and specificity of these criteria among patients receiving first-line ART but not specifically among those on second-line or subsequent regimens. Between 2008 and 2011, adult ART patients in Nyanza, Kenya who met national clinical or immunologic criteria for treatment failure received targeted VL testing. We calculated PPV and 95% confidence intervals (CI of these criteria to detect virologic treatment failure among patients receiving a first-line ART, b second/subsequent ART, and c any regimen. Of 12,134 patient specimens tested, 2,874 (23.7% were virologically confirmed as treatment failures. The PPV for 2,834 first-line ART patients who met either the clinical or immunologic criteria for treatment failure was 34.4% (95% CI 33.2-35.7, 33.1% (95% CI 24.7-42.3 for the 40 patients on second-line/subsequent regimens, and 33.4% (95% CI 33.1-35.6 for any ART. PPV, regardless of criteria, for first-line ART patients was lowest among patients over 44 years old and highest for patients aged 15 to 34 years. PPV of immunological and clinical criteria for correctly identifying treatment failure was similarly low for adult patients receiving either first-line or second-line/subsequent ART regimens. Our data confirm the inadequacy of clinical and immunologic criteria to correctly identify treatment failure and support the implementation of routine VL testing.

  19. Positive Predictive Value of the WHO Clinical and Immunologic Criteria to Predict Viral Load Failure among Adults on First, or Second-Line Antiretroviral Therapy in Kenya.

    Waruru, Anthony; Muttai, Hellen; Ng'ang'a, Lucy; Ackers, Marta; Kim, Andrea; Miruka, Fredrick; Erick, Opiyo; Okonji, Julie; Ayuaya, Tolbert; Schwarcz, Sandra

    2016-01-01

    Routine HIV viral load (VL) monitoring is the standard of care for persons receiving antiretroviral therapy (ART) in developed countries. Although the World Health Organization recommends annual VL monitoring of patients on ART, recognizing difficulties in conducting routine VL testing, the WHO continues to recommend targeted VL testing to confirm treatment failure for persons who meet selected immunologic and clinical criteria. Studies have measured positive predictive value (PPV), negative predictive value, sensitivity and specificity of these criteria among patients receiving first-line ART but not specifically among those on second-line or subsequent regimens. Between 2008 and 2011, adult ART patients in Nyanza, Kenya who met national clinical or immunologic criteria for treatment failure received targeted VL testing. We calculated PPV and 95% confidence intervals (CI) of these criteria to detect virologic treatment failure among patients receiving a) first-line ART, b) second/subsequent ART, and c) any regimen. Of 12,134 patient specimens tested, 2,874 (23.7%) were virologically confirmed as treatment failures. The PPV for 2,834 first-line ART patients who met either the clinical or immunologic criteria for treatment failure was 34.4% (95% CI 33.2-35.7), 33.1% (95% CI 24.7-42.3) for the 40 patients on second-line/subsequent regimens, and 33.4% (95% CI 33.1-35.6) for any ART. PPV, regardless of criteria, for first-line ART patients was lowest among patients over 44 years old and highest for patients aged 15 to 34 years. PPV of immunological and clinical criteria for correctly identifying treatment failure was similarly low for adult patients receiving either first-line or second-line/subsequent ART regimens. Our data confirm the inadequacy of clinical and immunologic criteria to correctly identify treatment failure and support the implementation of routine VL testing.

  20. The Implications of Endoscopic Ulcer in Early Gastric Cancer: Can We Predict Clinical Behaviors from Endoscopy?

    Lee, Yoo Jin; Kim, Jie-Hyun; Park, Jae Jun; Youn, Young Hoon; Park, Hyojin; Kim, Jong Won; Choi, Seung Ho; Noh, Sung Hoon

    2016-01-01

    Background The presence of ulcer in early gastric cancer (EGC) is important for the feasibility of endoscopic resection, only a few studies have examined the clinicopathological implications of endoscopic ulcer in EGC. Objectives To determine the role of endoscopic ulcer as a predictor of clinical behaviors in EGC. Methods Data of 3,270 patients with EGC who underwent surgery between January 2005 and December 2012 were reviewed. Clinicopathological characteristics were analyzed in relation to the presence and stage of ulcer in EGC. Based on endoscopic findings, the stage of ulcer was categorized as active, healing, or scar. Logistic regression analysis was performed to analyze factors associated with lymph node metastasis (LNM). Results 2,343 (71.7%) patients had endoscopic findings of ulceration in EGC. Submucosal (SM) invasion, LNM, lymphovascular invasion (LVI), perineural invasion, and undifferentiated-type histology were significantly higher in ulcerative than non-ulcerative EGC. Comparison across different stages of ulcer revealed that SM invasion, LNM, and LVI were significantly associated with the active stage, and that these features exhibited significant stage-based differences, being most common at the active stage, and least common at the scar stage. The presence of endoscopic ulcer and active status of the ulcer were identified as independent risk factors for LNM. Conclusions Ulcerative EGC detected by endoscopy exhibited more aggressive behaviors than non-ulcerative EGC. Additionally, the endoscopic stage of ulcer may predict the clinicopathological behaviors of EGC. Therefore, the appearance of ulcers should be carefully evaluated to determine an adequate treatment strategy for EGC. PMID:27741275

  1. Clinical prediction and diagnosis of neurosyphilis in HIV-infected patients with early Syphilis.

    Dumaresq, Jeannot; Langevin, Stéphanie; Gagnon, Simon; Serhir, Bouchra; Deligne, Benoît; Tremblay, Cécile; Tsang, Raymond S W; Fortin, Claude; Coutlée, François; Roger, Michel

    2013-12-01

    The diagnosis of neurosyphilis (NS) is a challenge, especially in HIV-infected patients, and the criteria for deciding when to perform a lumbar puncture (LP) in HIV-infected patients with syphilis are controversial. We retrospectively reviewed demographic, clinical, and laboratory data from 122 cases of HIV-infected patients with documented early syphilis who underwent an LP to rule out NS, and we evaluated 3 laboratory-developed validated real-time PCR assays, the Treponema pallidum particle agglutination (TPPA) assay, the fluorescent treponemal antibody absorption (FTA-ABS) assay, and the line immunoassay INNO-LIA Syphilis, for the diagnosis of NS from cerebrospinal fluid (CSF) samples of these patients. NS was defined by a reactive CSF-VDRL test result and/or a CSF white blood cell (WBC) count of >20 cells/μl. Thirty of the 122 patients (24.6%) had early NS. Headache, visual symptoms, a CD4 cell count of HIV-1 RNA count of ≥50 copies/ml, were associated with NS in multivariate analysis (P = diagnosis of NS, the PCR, FTA-ABS, TPPA, and INNO-LIA assays had sensitivities of 58%, 100%, 68%, and 100%, specificities of 67%, 12%, 49%, and 13%, and negative predictive values of 85%, 100%, 84%, and 100%, respectively. Visual disturbances, headache, uncontrolled HIV-1 viremia, and a CD4 cell count of HIV-infected patients with early syphilis, while blood serum RPR titers were not; therefore, RPR titers should not be used as the sole criterion for deciding whether to perform an LP in early syphilis. When applied to CSF samples, the INNO-LIA Syphilis assay easily helped rule out NS.

  2. More Accurate Definition of Clinical Target Volume Based on the Measurement of Microscopic Extensions of the Primary Tumor Toward the Uterus Body in International Federation of Gynecology and Obstetrics Ib-IIa Squamous Cell Carcinoma of the Cervix

    Xie, Wen-Jia [Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong Province (China); Wu, Xiao [Department of Pathology, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong Province (China); Xue, Ren-Liang; Lin, Xiang-Ying [Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong Province (China); Kidd, Elizabeth A. [Department of Radiation Oncology, Stanford University, Stanford, California (United States); Yan, Shu-Mei [Department of Pathology, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong Province (China); Zhang, Yao-Hong [Department of Radiation Oncology, Chaozhou Hospital of Chaozhou City, Guangdong Province (China); Zhai, Tian-Tian; Lu, Jia-Yang; Wu, Li-Li; Zhang, Hao [Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong Province (China); Huang, Hai-Hua [Department of Pathology, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong Province (China); Chen, Zhi-Jian; Li, De-Rui [Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong Province (China); Xie, Liang-Xi, E-mail: xieliangxi1@qq.com [Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong Province (China)

    2015-01-01

    Purpose: To more accurately define clinical target volume for cervical cancer radiation treatment planning by evaluating tumor microscopic extension toward the uterus body (METU) in International Federation of Gynecology and Obstetrics stage Ib-IIa squamous cell carcinoma of the cervix (SCCC). Patients and Methods: In this multicenter study, surgical resection specimens from 318 cases of stage Ib-IIa SCCC that underwent radical hysterectomy were included. Patients who had undergone preoperative chemotherapy, radiation, or both were excluded from this study. Microscopic extension of primary tumor toward the uterus body was measured. The association between other pathologic factors and METU was analyzed. Results: Microscopic extension toward the uterus body was not common, with only 12.3% of patients (39 of 318) demonstrating METU. The mean (±SD) distance of METU was 0.32 ± 1.079 mm (range, 0-10 mm). Lymphovascular space invasion was associated with METU distance and occurrence rate. A margin of 5 mm added to gross tumor would adequately cover 99.4% and 99% of the METU in the whole group and in patients with lymphovascular space invasion, respectively. Conclusion: According to our analysis of 318 SCCC specimens for METU, using a 5-mm gross tumor volume to clinical target volume margin in the direction of the uterus should be adequate for International Federation of Gynecology and Obstetrics stage Ib-IIa SCCC. Considering the discrepancy between imaging and pathologic methods in determining gross tumor volume extent, we recommend a safer 10-mm margin in the uterine direction as the standard for clinical practice when using MRI for contouring tumor volume.

  3. Validation of World Health Organisation HIV/AIDS clinical staging in predicting initiation of antiretroviral therapy and clinical predictors of low CD4 cell count in Uganda.

    Steven Baveewo

    Full Text Available INTRODUCTION: The WHO clinical guidelines for HIV/AIDS are widely used in resource limited settings to represent the gold standard of CD4 counts for antiviral therapy initiation. The utility of the WHO-defined stage 1 and 2 clinical factors used in WHO HIV/AIDS clinical staging in predicting low CD4 cell count has not been established in Uganda. Although the WHO staging has shown low sensitivity for predicting CD4<200 cells/mm(3, it has not been evaluated at for CD4 cut-offs of <250 cells/mm(3 or <350 cells/mm(3. OBJECTIVE: To validate the World Health Organisation HIV/AIDS clinical staging in predicting initiation of antiretroviral therapy in a low-resource setting and to determine the clinical predictors of low CD4 cell count in Uganda. RESULTS: Data was collected on 395 participants from the Joint Clinical Research Centre, of whom 242 (61.3% were classified as in stages 1 and 2 and 262 (68% were females. Participants had a mean age of 36.8 years (SD 8.5. We found a significant inverse correlation between the CD4 lymphocyte count and WHO clinical stages. The sensitivity the WHO clinical staging at CD4 cell count of 250 cells/mm(3 and 350 cells/mm(3 was 53.5% and 49.1% respectively. Angular cheilitis, papular pruritic eruptions and recurrent upper respiratory tract infections were found to be significant predictors of low CD4 cell count among participants in WHO stage 1 and 2. CONCLUSION: The WHO HIV/AIDS clinical staging guidelines have a low sensitivity and about half of the participants in stages 1 and 2 would be eligible for ART initiation if they had been tested for CD4 count. Angular cheilitis and papular pruritic eruptions and recurrent upper respiratory tract infections may be used, in addition to the WHO staging, to improve sensitivity in the interim, as access to CD4 machines increases in Uganda.

  4. Prediction of Metastasis and Recurrence in Colorectal Cancer Based on Gene Expression Analysis: Ready for the Clinic?

    Shibayama, Masaki [Sysmex Corporation, Central Research Laboratories, Kobe 651-2271 (Japan); Maak, Matthias; Nitsche, Ulrich [Chirurgische Klinik, Klinikum Rechts der Isar der TUM, München 81657 (Germany); Gotoh, Kengo [Sysmex Corporation, Central Research Laboratories, Kobe 651-2271 (Japan); Rosenberg, Robert; Janssen, Klaus-Peter, E-mail: klaus-peter.janssen@lrz.tum.de [Chirurgische Klinik, Klinikum Rechts der Isar der TUM, München 81657 (Germany)

    2011-07-07

    Cancers of the colon and rectum, which rank among the most frequent human tumors, are currently treated by surgical resection in locally restricted tumor stages. However, disease recurrence and formation of local and distant metastasis frequently occur even in cases with successful curative resection of the primary tumor (R0). Recent technological advances in molecular diagnostic analysis have led to a wealth of knowledge about the changes in gene transcription in all stages of colorectal tumors. Differential gene expression, or transcriptome analysis, has been proposed by many groups to predict disease recurrence, clinical outcome, and also response to therapy, in addition to the well-established clinico-pathological factors. However, the clinical usability of gene expression profiling as a reliable and robust prognostic tool that allows evidence-based clinical decisions is currently under debate. In this review, we will discuss the most recent data on the prognostic significance and potential clinical application of genome wide expression analysis in colorectal cancer.

  5. Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders

    Place, Skyler; Rubin, Channah; Gorrostieta, Cristina; Mead, Caroline; Kane, John; Marx, Brian P; Feast, Joshua; Deckersbach, Thilo; Pentland, Alex “Sandy”; Nierenberg, Andrew; Azarbayejani, Ali

    2017-01-01

    Background There is a critical need for real-time tracking of behavioral indicators of mental disorders. Mobile sensing platforms that objectively and noninvasively collect, store, and analyze behavioral indicators have not yet been clinically validated or scalable. Objective The aim of our study was to report on models of clinical symptoms for post-traumatic stress disorder (PTSD) and depression derived from a scalable mobile sensing platform. Methods A total of 73 participants (67% [49/73] male, 48% [35/73] non-Hispanic white, 33% [24/73] veteran status) who reported at least one symptom of PTSD or depression completed a 12-week field trial. Behavioral indicators were collected through the noninvasive mobile sensing platform on participants’ mobile phones. Clinical symptoms were measured through validated clinical interviews with a licensed clinical social worker. A combination hypothesis and data-driven approach was used to derive key features for modeling symptoms, including the sum of outgoing calls, count of unique numbers texted, absolute distance traveled, dynamic variation of the voice, speaking rate, and voice quality. Participants also reported ease of use and data sharing concerns. Results Behavioral indicators predicted clinically assessed symptoms of depression and PTSD (cross-validated area under the curve [AUC] for depressed mood=.74, fatigue=.56, interest in activities=.75, and social connectedness=.83). Participants reported comfort sharing individual data with physicians (Mean 3.08, SD 1.22), mental health providers (Mean 3.25, SD 1.39), and medical researchers (Mean 3.03, SD 1.36). Conclusions Behavioral indicators passively collected through a mobile sensing platform predicted symptoms of depression and PTSD. The use of mobile sensing platforms can provide clinically validated behavioral indicators in real time; however, further validation of these models and this platform in large clinical samples is needed. PMID:28302595

  6. The usefulness of holotranscobalamin in predicting vitamin B12 status in different clinical settings.

    Herrmann, Wolfgang; Obeid, Rima; Schorr, Heike; Geisel, Jürgen

    2005-02-01

    Serum concentrations of homocysteine (Hcy) and methylmalonic acid (MMA) become increased in B12-deficient subjects and are therefore, considered specific markers of B12 deficiency. Serum level of holotranscobalamin (holoTC) becomes decreased before the development of the metabolic dysfunction. We investigated the usefulness of holoTC in diagnosing B12 deficiency in some clinical settings. We measured serum concentrations of holoTC, MMA, Hcy and total B12 in omnivores, vegetarians, elderly people and haemodialysis patients. Our results indicated that the incidence of holoTC vegans (76%). Low holoTC and elevated MMA were detected in 64% of the vegans and 43% of the lacto- and lacto-ovovegetarians. An elevated MMA and a low holoTC were found in subjects with total serum B12 as high as 300 pmol/L. The distribution of holoTC in elderly people was similar to that in younger adults (median holoTC 55 pmol/L in both groups). A low holoTC and an elevated MMA were found in 16% of the elderly group. An elevated MMA and a normal holoTC were found in 20% of the elderly group who had a relatively high median serum concentration of creatinine (106.1 micromol/L). Serum concentrations of holoTC in dialysis patients were considerably higher than all other groups (median 100 pmol/L). This was also associated with severely increased serum levels of MMA (median 987 nmol/L). From these results it can be concluded that serum concentration of holoTC is a much better predictor of B12 status than total B12. This was particularly evident in case of dietary B12 deficiency. Serum concentrations of holoTC as well as MMA can be affected by renal dysfunction. Elevated MMA and normal holoTC in patients with renal insufficiency may not exclude vitamin B12 deficiency. HoloTC seems not to be a promising marker in predicting B12 status in renal patients.

  7. Efficient and accurate fragmentation methods.

    Pruitt, Spencer R; Bertoni, Colleen; Brorsen, Kurt R; Gordon, Mark S

    2014-09-16

    mechanical calculations on fragment-fragment (dimer) interactions. The EFMO method is both more accurate and more computationally efficient than the most commonly used FMO implementation (FMO2), in which all dimers are explicitly included in the calculation. While the FMO2 method itself does not incorporate three-body interactions, such interactions are included in the EFMO method via the EFP self-consistent induction term. Several applications (ranging from clusters to proteins) of the three methods are discussed to demonstrate their efficacy. The EFMO method will be especially exciting once the analytic gradients have been completed, because this will allow geometry optimizations, the prediction of vibrational spectra, reaction path following, and molecular dynamics simulations using the method.

  8. Comparison and clinical suitability of eight prediction models for cardiac surgery-related acute kidney injury

    Kiers, H.D.; Boogaard, M.H.W.A. van den; Schoenmakers, M.C.J.; Hoeven, J.G. van der; Swieten, H.A. van; Heemskerk, S.; Pickkers, P.

    2013-01-01

    BACKGROUND: Cardiac surgery-related acute kidney injury (CS-AKI) results in increased morbidity and mortality. Different models have been developed to identify patients at risk of CS-AKI. While models that predict dialysis and CS-AKI defined by the RIFLE criteria are available, their predictive powe

  9. Social Anxiety Predicts Aggression in Children with ASD: Clinical Comparisons with Socially Anxious and Oppositional Youth

    Pugliese, Cara E.; White, Bradley A.; White, Susan W.; Ollendick, Thomas H.

    2013-01-01

    The present study examined the degree to which social anxiety predicts aggression in children with high functioning autism spectrum disorders (HFASD, n = 20) compared to children with Social Anxiety Disorder (SAD, n = 20) or with Oppositional Defiant Disorder or Conduct Disorder (ODD/CD, n = 20). As predicted, children with HFASD reported levels…

  10. Clinical versus Actuarial Predictions of Violence in Patients with Mental Illness.

    Gardner, William; And Others

    1996-01-01

    Compared accuracy of an actuarial procedure for the prediction of community violence by patients with mental illnesses to accuracy of clinicians' concern ratings of patient violence. Data came from a study of 357 pairs of patients seen in a psychiatric emergency room. Actuarial predictions based only on patients' histories of violence were more…

  11. A clinical prediction rule for ambulation outcomes after traumatic spinal cord injury: a longitudinal cohort study

    Middendorp, J.J. van; Hosman, A.J.F.; Donders, A.R.T.; Pouw, M.H.; Ditunno Jr., J.F.; Curt, A.; Geurts, A.C.H.; Meent, H. van de

    2011-01-01

    BACKGROUND: Traumatic spinal cord injury is a serious disorder in which early prediction of ambulation is important to counsel patients and to plan rehabilitation. We developed a reliable, validated prediction rule to assess a patient's chances of walking independently after such injury. METHODS: We

  12. Selecting new health technologies for evaluation:Can clinical experts predict which new anticancer drugswill impact Danish health care?

    Douw, Karla; Vondeling, Hindrik

    2007-01-01

    source of information on new health technologies, but research on the relevance of their input is scarce. In 2000, we asked six Danish expert oncologists to predict whether a sample of 19 new anticancer drugs would impact Danish health care over the next 5 years. In 2005, we assessed the accuracy...... that clinical experts have the ability to predict which new anticancer drugs are unlikely to have an impact. This information can be used to increase the efficiency of selecting new technologies for evaluation. As the experts missed 37% of drugs that are in need of guidance, they should not be relied upon......Several countries have systems in place to support the managed entry of new health technologies. The big challenge for these so-called horizon-scanning systems is to select those technologies that require decision support by means of an early evaluation. Clinical experts are considered a valuable...

  13. Predictive value of clinical evaluation in the follow-up of children with a brain tumor.

    Graaf, N. de; Hew, J.M.; Fock, J.M.; Kamps, W.A.; Graaf, S.S.N. de

    2002-01-01

    BACKGROUND: During follow-up of children with a brain tumor, traditionally surveillance-imaging studies are done in addition to clinical evaluations. The purpose of this study was to determine the role of clinical evaluations by a multidisciplinary team for the detection of recurrent tumor. PROCEDUR

  14. Predictive value of clinical evaluation in the follow-up of children with a brain tumor

    Hew, JM; Fock, JM; Kamps, WA

    2002-01-01

    Background. During follow-up of children with a brain tumor, traditionally surveillance-imaging studies are done in addition to clinical evaluations, The purpose of this study was to determine the role of clinical evaluations by a multidisciplinary team for the detection of recurrent tumor. Procedur

  15. Common clinical practice versus new PRIM score in predicting coronary heart disease risk

    Frikke-Schmidt, Ruth; Tybjærg-Hansen, Anne; Schnohr, Peter;

    2010-01-01

    To compare the new Patient Rule Induction Method (PRIM) Score and common clinical practice with the Framingham Point Score for classification of individuals with respect to coronary heart disease (CHD) risk.......To compare the new Patient Rule Induction Method (PRIM) Score and common clinical practice with the Framingham Point Score for classification of individuals with respect to coronary heart disease (CHD) risk....

  16. Is magnetic resonance imaging reliable in predicting clinical outcome after articular cartilage repair of the knee?

    Windt, de T.S.; Welsch, G.H.; Brittberg, M.; Vonk, L.A.; Marlovits, S.; Trattnig, S.; Saris, D.B.F.

    2013-01-01

    Background: While MRI can provide a detailed morphological evaluation after articular cartilage repair, its additional value in determining clinical outcome has yet to be determined. Purpose: To evaluate the correlation between MRI and clinical outcome after cartilage repair and to identify parame

  17. Empirically and Clinically Useful Decision Making in Psychotherapy: Differential Predictions with Treatment Response Models

    Lutz, Wolfgang; Saunders, Stephen M.; Leon, Scott C.; Martinovich, Zoran; Kosfelder, Joachim; Schulte, Dietmar; Grawe, Klaus; Tholen, Sven

    2006-01-01

    In the delivery of clinical services, outcomes monitoring (i.e., repeated assessments of a patient's response to treatment) can be used to support clinical decision making (i.e., recurrent revisions of outcome expectations on the basis of that response). Outcomes monitoring can be particularly useful in the context of established practice research…

  18. Evaluation of the Cerebral State Index in Cats under Isoflurane Anaesthesia: Dose-Effect Relationship and Prediction of Clinical Signs

    Joana R. Sousa

    2014-01-01

    Full Text Available The performance of the cerebral state index (CSI in reflecting different levels of isoflurane anaesthesia was evaluated in ten cats subjected to four end-tidal isoflurane concentrations (EtIso, each maintained for 15 minutes (0.8%, 1.2%, 1.6%, or 2.0% EtIso. The CSI, hemodynamic data, ocular reflexes, and eye position were recorded for each EtIso concentration. Pharmacodynamic analysis of CSI with EtIso was performed, as well as prediction probability analysis with a clinical scale based on the eye reflexes. The CSI values showed great variability. Between all parameters, burst suppression ratio showed the better fitting with the sigmoidal concentration-effect model (R2=0.93 followed by CSI (R2=0.82 and electromyographic activity (R2=0.79. EtIso was the variable with better prediction of the clinical scale of anaesthesia (prediction probability value of 0.94. Although the CSI values decrease with increasing isoflurane concentrations, the huge variability in CSI values may be a strong limitation for its use in cats and it seems to be no better than EtIso as a predictor of clinical signs.

  19. DEVELOPMENT OF THE NOMOGRAM THAT PREDICTS PATHOLOGICAL LYMPH NODE INVOLVEMENT IN BLADDER CANCER PATIENTS BASED ON CLINICAL VARIABLES

    L. V. Mirylenko

    2012-01-01

    Full Text Available Objective: to develop nomogram based on clinical variables, that predicts pathological lymph node involvement (рN+ in bladder cancer patients.Material and methods: We used data of 511 patients with bladder cancer, that have undergone radical cystectomy between 1999 and 2008 at N.N. Alexandrov National Cancer Centre. Mono- and multivariate logistic regression analyses were used for pN+ prediction on preoperative data. Coefficients from logistic regression equation were used to construct the nomogram. Nomogram accuracy was evaluated with concordance index and construction of the calibration plot. Internal validation by bootstrap method with 200 variants of dataset was performed.Results: We developed nomogram, that includes: clinical stage сТ, tumor grade, tumor macroscopic appearance, and creatinine level. Bootstrap-corrected prognostic accuracy of nomogram was 71,6%, that was 9,4% better than clinical stage accuracy.Conclusion: utilization of developed nomogram can significantly improve pathologic tumor stage prediction accuracy that may be used to select patients for neoadjuvant chemotherapy.

  20. DEVELOPMENT OF THE NOMOGRAM THAT PREDICTS PATHOLOGICAL LYMPH NODE INVOLVEMENT IN BLADDER CANCER PATIENTS BASED ON CLINICAL VARIABLES

    L. V. Mirylenko

    2014-07-01

    Full Text Available Objective: to develop nomogram based on clinical variables, that predicts pathological lymph node involvement (рN+ in bladder cancer patients.Material and methods: We used data of 511 patients with bladder cancer, that have undergone radical cystectomy between 1999 and 2008 at N.N. Alexandrov National Cancer Centre. Mono- and multivariate logistic regression analyses were used for pN+ prediction on preoperative data. Coefficients from logistic regression equation were used to construct the nomogram. Nomogram accuracy was evaluated with concordance index and construction of the calibration plot. Internal validation by bootstrap method with 200 variants of dataset was performed.Results: We developed nomogram, that includes: clinical stage сТ, tumor grade, tumor macroscopic appearance, and creatinine level. Bootstrap-corrected prognostic accuracy of nomogram was 71,6%, that was 9,4% better than clinical stage accuracy.Conclusion: utilization of developed nomogram can significantly improve pathologic tumor stage prediction accuracy that may be used to select patients for neoadjuvant chemotherapy.

  1. CREATION OF THE NOMOGRAM THAT PREDICTS PATHOLOGICAL LOCAL EXTENT OF THE BLADDER CANCER BASED ON CLINICAL VARIABLES

    L. V. Mirylenka

    2014-08-01

    Full Text Available Objective: to develop nomogram based on clinical variables, that predicts pathological local extent of the bladder cancer рТ3-рТ4 (рТ3+.Material and methods: We used data of 511 patients with bladder cancer, that have undergone radical cystectomy between 1999 and 2008 at N.N. Alexandrov National Cancer Centre. For prediction of pT3+ on preoperative data were used mono- and multivariate logistic regression analysis. Coefficients from logistic regression equalization were used to construct nomogram. Nomogram accuracy was evaluated with concordance index (с-index and by building the calibration plot. Internal validation by bootstrap method with 200 variants of dataset was performed.Results: We developed nomogram, that include: clinical stage сТ, tumor grade, tumor macroscopic appearance, presence of upper tract dilatation, prostatic urethra and/or prostatic lobe(s involvement, 3 or more bladder walls involvement, ESR and creatinine level. Bootstrapcorrected prognostic accuracy of nomogram was 81,4%, that 12,6% better than clinical stage accuracy.Conclusion: developed nomogram can significantly improve pathologic tumor stage prediction accuracy that may be used to select patients for neoadjuvant chemotherapy.

  2. CREATION OF THE NOMOGRAM THAT PREDICTS PATHOLOGICAL LOCAL EXTENT OF THE BLADDER CANCER BASED ON CLINICAL VARIABLES

    L. V. Mirylenka

    2012-01-01

    Full Text Available Objective: to develop nomogram based on clinical variables, that predicts pathological local extent of the bladder cancer рТ3-рТ4 (рТ3+.Material and methods: We used data of 511 patients with bladder cancer, that have undergone radical cystectomy between 1999 and 2008 at N.N. Alexandrov National Cancer Centre. For prediction of pT3+ on preoperative data were used mono- and multivariate logistic regression analysis. Coefficients from logistic regression equalization were used to construct nomogram. Nomogram accuracy was evaluated with concordance index (с-index and by building the calibration plot. Internal validation by bootstrap method with 200 variants of dataset was performed.Results: We developed nomogram, that include: clinical stage сТ, tumor grade, tumor macroscopic appearance, presence of upper tract dilatation, prostatic urethra and/or prostatic lobe(s involvement, 3 or more bladder walls involvement, ESR and creatinine level. Bootstrapcorrected prognostic accuracy of nomogram was 81,4%, that 12,6% better than clinical stage accuracy.Conclusion: developed nomogram can significantly improve pathologic tumor stage prediction accuracy that may be used to select patients for neoadjuvant chemotherapy.

  3. A prospective, clinical study on asymptomatic sensitisation and development of allergic rhinitis: high negative predictive value of allergological testing

    Bodtger, Uffe; Assing, Kristian; Poulsen, Lars K

    2011-01-01

    populations, conferring bias towards higher incidence rates. Objective: The aim was to determine the incidence of onset of symptoms among clinically well-characterised asymptomatic, sensitised subjects compared with controls, and to evaluate the predictive values of common allergological tests. Methods: We...... an annual incidence rate of 5% for the onset of symptoms in the AS group (healthy control group 0%). At baseline, the AS group displayed intermediate experimental allergen susceptibility. Subjects developing symptoms had higher levels of specific IgE and larger late-phase reaction than those persistently...... asymptomatic. However, the positive predictive values were low (14-27%) in contrast to the negative predictive values (95-100%). Conclusion: In a well-characterised young population, asymptomatic aeroallergen sensitisation conferred a low risk for onset of symptoms during the 2-year follow-up. Persistent...

  4. Facets of psychopathy among mentally disordered offenders: clinical comorbidity patterns and prediction of violent and criminal behavior.

    Wallinius, Märta; Nilsson, Thomas; Hofvander, Björn; Anckarsäter, Henrik; Stålenheim, Gunilla

    2012-07-30

    The complexity and consequences of psychopathy are still debated, and its relation to other mental disorders, pathological personality traits, and criminality needs to be further investigated by clinical, longitudinal studies using structured diagnostic instruments. The present study used two groups of mentally disordered offenders (N=153) investigated with in-depth clinical assessments and prospective long-term follow-up to identify the convergence between 1) the four facets of psychopathy defined by the Psychopathy Checklist-Revised (PCL-R; Interpersonal, Affective, Lifestyle, and Antisocial), 2) mental disorders according to SCID I and II interviews, 3) personality traits as measured by the Karolinska Scales of Personality, and 4) criminal recidivism. The Interpersonal facet differed substantially from the other three facets by not being significantly associated with substance use disorders, antisocial personality disorder (the other facets at P≤0.001 level), or personality traits involving impulsive and aggressive antisocial behaviors (the other facets at Pfacet could not predict violent recidivism better than random. The Antisocial facet outperformed not only the other facets but also the total PCL-R score in the prediction of violent recidivism, P<0.001.The findings confirm psychopathy as a heterogeneous phenomenon and have clinical implications for assessments of psychopathy and violence risk assessments in clinical and forensic contexts.

  5. Accurate cloud-based smart IMT measurement, its validation and stroke risk stratification in carotid ultrasound: A web-based point-of-care tool for multicenter clinical trial.

    Saba, Luca; Banchhor, Sumit K; Suri, Harman S; Londhe, Narendra D; Araki, Tadashi; Ikeda, Nobutaka; Viskovic, Klaudija; Shafique, Shoaib; Laird, John R; Gupta, Ajay; Nicolaides, Andrew; Suri, Jasjit S

    2016-08-01

    . Statistical tests were performed to demonstrate consistency, reliability and accuracy of the results. The proposed AtheroCloud™ system is completely reliable, automated, fast (3-5 seconds depending upon the image size having an internet speed of 180Mbps), accurate, and an intelligent, web-based clinical tool for multi-center clinical trials and routine telemedicine clinical care.

  6. MRI characteristics are predictive for CDMS in monofocal, but not in multifocal patients with a clinically isolated syndrome

    Nielsen Jessica M

    2009-05-01

    Full Text Available Abstract Background To diagnose multiple sclerosis (MS, evidence for dissemination in space and time is required. There is no clear definition on how symptoms and signs of a patient indicate clinical dissemination in space. To provide a uniform approach on this subject, a clinical classification system was described recently differentiating patients with mono- and multifocal clinical presentation. Here we assess the predictive value of clinically defined dissemination in space at first presentation for time to clinically definite MS (CDMS. Methods Four hundred and sixty-eight patients with a first episode suggestive of MS were classified as clinically mono- or multifocal by two neurologists blinded to magnetic resonance imaging (MRI results. These patients were part of the BENEFIT study in which 292 patients were randomized to interferon beta-1b (IFNB-1b and 176 to placebo. By using Kaplan-Meier statistics the risk for CDMS was studied in mono- and multifocal patients of the placebo group, both with and without taking into account MRI measures of potential prognostic relevance. Results Time to CDMS was similar in monofocal and multifocal patients. In monofocal patients, the risk for CDMS over 2 years was significantly higher when ≥ 9 T2 lesions or at least one Gd-enhancing lesion were present at the first event or 3 or 6 months after the first event. In patients with multifocal presentation, these MRI measures had no significant added value in predicting time to CDMS. Conclusion These data indicate that a carefully performed neurological assessment of symptoms and signs, combined with lesions on MRI, is important for defining the risk of conversion to CDMS. Trial Registration The Benefit trial has been registered under NCT00185211 http://www.clinicaltrials.gov

  7. Comparison of the effectiveness of three manual physical therapy techniques in a subgroup of patients with low back pain who satisfy a clinical prediction rule: Study protocol of a randomized clinical trial [NCT00257998

    Childs John D

    2006-02-01

    Full Text Available Abstract Background Recently a clinical prediction rule (CPR has been developed and validated that accurately identifies patients with low back pain (LBP that are likely to benefit from a lumbo-pelvic thrust manipulation. The studies that developed and validated the rule used the identical manipulation procedure. However, recent evidence suggests that different manual therapy techniques may result similar outcomes. The purpose of this study is to investigate the effectiveness of three different manual therapy techniques in a subgroup of patient with low back pain that satisfy the CPR. Methods/Design Consecutive patients with LBP referred to physical therapy clinics in one of four geographical locations who satisfy the CPR will be invited to participate in this randomized clinical trial. Subjects who agree to participate will undergo a standard evaluation and complete a number of patient self-report questionnaires including the Oswestry Disability Index (OSW, which will serve as the primary outcome measure. Following the baseline examination patients will be randomly assigned to receive the lumbopelvic manipulation used in the development of the CPR, an alternative lumbar manipulation technique, or non-thrust lumbar mobilization technique for the first 2 visits. Beginning on visit 3, all 3 groups will receive an identical standard exercise program for 3 visits (visits 3,4,5. Outcomes of interest will be captured by a therapist blind to group assignment at 1 week (3rd visit, 4 weeks (6th visit and at a 6-month follow-up. The primary aim of the study will be tested with analysis of variance (ANOVA using the change in OSW score from baseline to 4-weeks (OSWBaseline – OSW4-weeks as the dependent variable. The independent variable will be treatment with three levels (lumbo-pelvic manipulation, alternative lumbar manipulation, lumbar mobilization. Discussion This trial will be the first to investigate the effectiveness of various manual therapy

  8. Burnout and Work Demands Predict Reduced Job Satisfaction in Health Professionals Working In a Surgery Clinic

    Dragan Mijakoski

    2015-03-01

    CONCLUSIONS: Adequate management of work demands, particularly excessive workload, time pressure, and lack of staff can lead to prevention of burnout and reduced job satisfaction in surgery clinic HPs, and contribute to better quality of patient care.

  9. Can Psychological, Social and Demographical Factors Predict Clinical Characteristics Symptomatology of Bipolar Affective Disorder and Schizophrenia?

    Maciukiewicz, Malgorzata; Pawlak, Joanna; Kapelski, Pawel; Łabędzka, Magdalena; Skibinska, Maria; Zaremba, Dorota; Leszczynska-Rodziewicz, Anna; Dmitrzak-Weglarz, Monika; Hauser, Joanna

    2016-09-01

    Schizophrenia (SCH) is a complex, psychiatric disorder affecting 1 % of population. Its clinical phenotype is heterogeneous with delusions, hallucinations, depression, disorganized behaviour and negative symptoms. Bipolar affective disorder (BD) refers to periodic changes in mood and activity from depression to mania. It affects 0.5-1.5 % of population. Two types of disorder (type I and type II) are distinguished by severity of mania episodes. In our analysis, we aimed to check if clinical and demographical characteristics of the sample are predictors of symptom dimensions occurrence in BD and SCH cases. We included total sample of 443 bipolar and 439 schizophrenia patients. Diagnosis was based on DSM-IV criteria using Structured Clinical Interview for DSM-IV. We applied regression models to analyse associations between clinical and demographical traits from OPCRIT and symptom dimensions. We used previously computed dimensions of schizophrenia and bipolar affective disorder as quantitative traits for regression models. Male gender seemed protective factor for depression dimension in schizophrenia and bipolar disorder sample. Presence of definite psychosocial stressor prior disease seemed risk factor for depressive and suicidal domain in BD and SCH. OPCRIT items describing premorbid functioning seemed related with depression, positive and disorganised dimensions in schizophrenia and psychotic in BD. We proved clinical and demographical characteristics of the sample are predictors of symptom dimensions of schizophrenia and bipolar disorder. We also saw relation between clinical dimensions and course of disorder and impairment during disorder.

  10. Protein-Based Classifier to Predict Conversion from Clinically Isolated Syndrome to Multiple Sclerosis.

    Borràs, Eva; Cantó, Ester; Choi, Meena; Maria Villar, Luisa; Álvarez-Cermeño, José Carlos; Chiva, Cristina; Montalban, Xavier; Vitek, Olga; Comabella, Manuel; Sabidó, Eduard

    2016-01-01

    Multiple sclerosis is an inflammatory, demyelinating, and neurodegenerative disease of the central nervous system. In most patients, the disease initiates with an episode of neurological disturbance referred to as clinically isolated syndrome, but not all patients with this syndrome develop multiple sclerosis over time, and currently, there is no clinical test that can conclusively establish whether a patient with a clinically isolated syndrome will eventually develop clinically defined multiple sclerosis. Here, we took advantage of the capabilities of targeted mass spectrometry to establish a diagnostic molecular classifier with high sensitivity and specificity able to differentiate between clinically isolated syndrome patients with a high and a low risk of developing multiple sclerosis. Based on the combination of abundances of proteins chitinase 3-like 1 and ala-β-his-dipeptidase in cerebrospinal fluid, we built a statistical model able to assign to each patient a precise probability of conversion to clinically defined multiple sclerosis. Our results are of special relevance for patients affected by multiple sclerosis as early treatment can prevent brain damage and slow down the disease progression.

  11. Predicting Anxiety Among Patients In LPU Clinical Dispensary During Dental Treatment: Towards Student’s Clinical Performance Enhancement

    Maribel D. Mayuga-Barrion

    2013-12-01

    Full Text Available The study aimed to determine the respondents’ profile in terms of age, gender, frequency of dental visit, and type of patient whether dental phobic or not; to determine the dental anxiety of patients in LPU dental dispensary; to identify the causes and severity of anxiety of the patients in LPU dental dispensary; to determine if there is a significant difference between the respondents’ demographic profile and their level of anxiety; and lastly, to propose a program that will help the patients cope with dental anxiety and a program that will enhance the students’ clinical performance. The study used the descriptive research design with the combination of content analysis of documents and related materials. Results showed that majority of the respondents belonged to age range of 14-18 years old range whereas for gender or sex, majority who avail of the clinic’s services are males. This is because women are more afraid than men in terms of dental problems. Further, younger people are more afraid than older ones. The weighted mean distribution of the level of anxiety showed that the level of anxiety of patients varies on moderately to not anxious. Feeling or experiencing pain during dental treatment ranked first followed by the fear or worry of not working the proposed treatment and thirdly, the dentist is in a hurry while treating also made the patients moderately anxious. Overall, the level of anxiety of patients is moderately anxious. Probing to asses gum disease, dislike the numb feeling and injection were the top three causes of dental anxiety. Only type of patient shows significant difference, thus the null hypothesis of no significant difference on the level of anxiety when grouped according to profile variables is rejected. This means that the level of anxiety of both phobic and not phobic differs.

  12. Comparison of Existing Clinical Scoring Systems in Predicting Severity and Prognoses of Hyperlipidemic Acute Pancreatitis in Chinese Patients

    Qiu, Lei; Sun, Rui Qing; Jia, Rong Rong; Ma, Xiu Ying; Cheng, Li; Tang, Mao Chun; Zhao, Yan

    2015-01-01

    Abstract It is important to identify the severity of acute pancreatitis (AP) in the early course of the disease. Clinical scoring systems may be helpful to predict the prognosis of patients with early AP; however, few analysts have forecast the accuracy of scoring systems for the prognosis in hyperlipidemic acute pancreatitis (HLAP). The purpose of this study was to summarize the clinical characteristics of HLAP and compare the accuracy of conventional scoring systems in predicting the prognosis of HLAP. This study retrospectively analyzed all consecutively diagnosed AP patients between September 2008 and March 2014. We compared the clinical characteristics between HLAP and nonhyperlipidemic acute pancreatitis. The bedside index for severity of acute pancreatitis (BISAP), Ranson, computed tomography severity index (CTSI), and systemic inflammatory response syndrome (SIRS) scores were applied within 48 hours following admission. Of 909 AP patients, 129 (14.2%) had HLAP, 20 were classified as severe acute pancreatitis (SAP), 8 had pseudocysts, 9 had pancreatic necrosis, 30 had pleural effusions, 33 had SIRS, 14 had persistent organ failure, and there was 1 death. Among the HLAP patients, the area under curves for BISAP, Ranson, SIRS, and CTSI in predicting SAP were 0.905, 0.938, 0.812, and 0.834, 0.874, 0.726, 0.668, and 0.848 for local complications, and 0.904, 0.917, 0.758, and 0.849 for organ failure, respectively. HLAP patients were characterized by younger age at onset, higher recurrence rate, and being more prone to pancreatic necrosis, organ failure, and SAP. BISAP, Ranson, SIRS, and CTSI all have accuracy in predicting the prognosis of HLAP patients, but each has different strengths and weaknesses. PMID:26061329

  13. A Prediction Algorithm for Drug Response in Patients with Mesial Temporal Lobe Epilepsy Based on Clinical and Genetic Information

    Carvalho, Benilton S.; Bilevicius, Elizabeth; Alvim, Marina K. M.; Lopes-Cendes, Iscia

    2017-01-01

    Mesial temporal lobe epilepsy is the most common form of adult epilepsy in surgical series. Currently, the only characteristic used to predict poor response to clinical treatment in this syndrome is the presence of hippocampal sclerosis. Single nucleotide polymorphisms (SNPs) located in genes encoding drug transporter and metabolism proteins could influence response to therapy. Therefore, we aimed to evaluate whether combining information from clinical variables as well as SNPs in candidate genes could improve the accuracy of predicting response to drug therapy in patients with mesial temporal lobe epilepsy. For this, we divided 237 patients into two groups: 75 responsive and 162 refractory to antiepileptic drug therapy. We genotyped 119 SNPs in ABCB1, ABCC2, CYP1A1, CYP1A2, CYP1B1, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4, and CYP3A5 genes. We used 98 additional SNPs to evaluate population stratification. We assessed a first scenario using only clinical variables and a second one including SNP information. The random forests algorithm combined with leave-one-out cross-validation was used to identify the best predictive model in each scenario and compared their accuracies using the area under the curve statistic. Additionally, we built a variable importance plot to present the set of most relevant predictors on the best model. The selected best model included the presence of hippocampal sclerosis and 56 SNPs. Furthermore, including SNPs in the model improved accuracy from 0.4568 to 0.8177. Our findings suggest that adding genetic information provided by SNPs, located on drug transport and metabolism genes, can improve the accuracy for predicting which patients with mesial temporal lobe epilepsy are likely to be refractory to drug treatment, making it possible to identify patients who may benefit from epilepsy surgery sooner. PMID:28052106

  14. Predicting radiotherapy-related clinical toxicities in cancer: a literature review.

    O'Gorman, Claire; Sasiadek, Wojciech; Denieffe, Suzanne; Gooney, Martina

    2014-06-01

    Assessment of patients receiving radiotherapy for cancer is essential, with the ability to identify those who may be more likely to experience radiotherapy-related side effects noted as an important issue for nurses. Body mass, age, and radiation dose may be predictive factors for the development of such side effects. This review considers these factors and how nurses can use this evidence to inform their care, with results indicating that the dose of radiation, the site treated, and body mass index are predictive of toxicities that may develop. Increased awareness of these predictive factors will aid nurses in identifying patients at greater risk of developing radiation-related side effects. This will assist in guiding nursing interventions, as well as enabling the individualization of patient education, by placing greater emphasis on preventive measures for patients who are more vulnerable to the development of radiation-related toxicities.

  15. Predictive factors for a severe clinical course in ulcerative colitis: Results from population-based studies

    Magnus Hofrenning Wander?s; Bj?rn A Moum; Marte Lie H?ivik; ?istein Hovde

    2016-01-01

    Ulcerative colitis(UC)is characterized by chronic inflammation of the large bowel in genetically suscep-tible individuals exposed to environmental risk factors.The disease course can be difficult to predict,with symptoms ranging from mild to severe.There is no generally accepted definition of severe UC,and no single outcome is sufficient to classify a disease course as severe.There are several outcomes indicating a severe disease course,including progression of the disease’s extension,a high relapse rate,the development of acute severe colitis,colectomy,the occurrence of colorectal cancer and UC-related mortality.When evaluating a patient’s prognosis,it is helpful to do so in relation to these outcomes.Using these outcomes also makes it easier to isolate factors predictive of severe disease.The aims of this article are to evaluate different disease outcomes and to present predictive factors for these outcomes.

  16. A dynamic model of once-daily 5-aminosalicylic acid predicts clinical efficacy

    Deepak; Parakkal; Eli; D; Ehrenpreis; Matthew; P; Thorpe; Karson; S; Putt; Bruce; Hannon

    2010-01-01

    New once daily mesalamine formulations may improve adherence to medication usage.Response to Asacol and other forms of 5-aminosalicyclic acid(5-ASA)is better correlated with tissue concentrations and best predicted by concentrations of the drug within the lumen of the colon.Our group used computer simulation to predict colonic 5-ASA levels after Asacol administration.In our study,the model simulated Asacol distribution in the healthy colon,and during quiescent and active ulcerative colitis.An Asacol dosage ...

  17. Added value of CT perfusion compared to CT angiography in predicting clinical outcomes of stroke patients treated with mechanical thrombectomy

    Tsogkas, Ioannis; Knauth, Michael; Schregel, Katharina; Behme, Daniel; Psychogios, Marios Nikos [University Medicine Goettingen, Department of Neuroradiology, Goettingen (Germany); Wasser, Katrin; Maier, Ilko; Liman, Jan [University Medicine Goettingen, Department of Neurology, Goettingen (Germany)

    2016-11-15

    CTP images analyzed with the Alberta stroke program early CT scale (ASPECTS) have been shown to be optimal predictors of clinical outcome. In this study we compared two biomarkers, the cerebral blood volume (CBV)-ASPECTS and the CTA-ASPECTS as predictors of clinical outcome after thrombectomy. Stroke patients with thrombosis of the M1 segment of the middle cerebral artery were included in our study. All patients underwent initial multimodal CT with CTP and CTA on a modern CT scanner. Treatment consisted of full dose intravenous tissue plasminogen activator, when applicable, and mechanical thrombectomy. Three neuroradiologists separately scored CTP and CTA images with the ASPECTS score. Sixty-five patients were included. Median baseline CBV-ASPECTS and CTA-ASPECTS for patients with favourable clinical outcome at follow-up were 8 [interquartile range (IQR) 8-9 and 7-9 respectively]. Patients with poor clinical outcome showed a median baseline CBV-ASPECTS of 6 (IQR 5-8, P < 0.0001) and a median baseline CTA-ASPECTS of 7 (IQR 7-8, P = 0.18). Using CBV-ASPECTS and CTA-ASPECTS raters predicted futile reperfusions in 96 % and 56 % of the cases, respectively. CBV-ASPECTS is a significant predictor of clinical outcome in patients with acute ischemic stroke treated with mechanical thrombectomy. (orig.)

  18. Presence of Systemic Inflammatory Response Syndrome Predicts a Poor Clinical Outcome in Dogs with a Primary Hepatitis.

    Kilpatrick, Scott; Dreistadt, Margaret; Frowde, Polly; Powell, Roger; Milne, Elspeth; Smith, Sionagh; Morrison, Linda; Gow, Adam G; Handel, Ian; Mellanby, Richard J

    2016-01-01

    Primary hepatopathies are a common cause of morbidity and mortality in dogs. The underlying aetiology of most cases of canine hepatitis is unknown. Consequently, treatments are typically palliative and it is difficult to provide accurate prognostic information to owners. In human hepatology there is accumulating data which indicates that the presence of systemic inflammatory response syndrome (SIRS) is a common and debilitating event in patients with liver diseases. For example, the presence of SIRS has been linked to the development of complications such as hepatic encephalopathy (HE) and is associated with a poor clinical outcome in humans with liver diseases. In contrast, the relationship between SIRS and clinical outcome in dogs with a primary hepatitis is unknown. Seventy dogs with histologically confirmed primary hepatitis were enrolled into the study. Additional clinical and clinicopathological information including respiratory rate, heart rate, temperature, white blood cell count, sodium, potassium, sex, presence of ascites, HE score, alanine aminotransferase (ALT), alkaline phosphatase (ALP), bilirubin and red blood cell concentration were available in all cases. The median survival of dogs with a SIRS score of 0 or 1 (SIRS low) was 231 days compared to a median survival of 7 days for dogs with a SIRS score of 2, 3 or 4 (SIRS high) (pdogs with a primary hepatitis is deserving of further study.

  19. SU-E-T-629: Prediction of the ViewRay Radiotherapy Treatment Time for Clinical Logistics

    Liu, S; Wooten, H; Wu, Y; Yang, D [Washington University in St Louis, St Louis, MO (United States)

    2015-06-15

    Purpose: An algorithm is developed in our clinic, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance-image guided radiation therapy (MR-IGRT) delivery system. This algorithm is necessary for managing patient treatment appointments, and is useful as an indicator to assess the treatment plan complexity. Methods: A patient’s total treatment delivery time, not including time required for localization, may be described as the sum of four components: (1) the treatment initialization time; (2) the total beam-on time; (3) the gantry rotation time; and (4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam-on time can be calculated using both the planned beam-on time and the decay-corrected delivery dose rate. To predict the remaining components, we quantitatively analyze the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle and MLC leaf positions of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, and between the furthest MLC leaf moving distance and the corresponding MLC motion time, the total delivery time is predicted using linear regression. Results: The proposed algorithm has demonstrated the feasibility of predicting the ViewRay treatment delivery time for any treatment plan of any patient. The average prediction error is 0.89 minutes or 5.34%, and the maximal prediction error is 2.09 minutes or 13.87%. Conclusion: We have developed a treatment delivery time prediction algorithm based on the analysis of previous patients’ treatment delivery records. The accuracy of our prediction is sufficient for guiding and arranging patient treatment appointments on a daily basis. The predicted delivery time could also be used as an indicator to assess the

  20. A clinical diagnostic model for predicting influenza among young adult military personnel with febrile respiratory illness in Singapore.

    Vernon J Lee

    Full Text Available INTRODUCTION: Influenza infections present with wide-ranging clinical features. We aim to compare the differences in presentation between influenza and non-influenza cases among those with febrile respiratory illness (FRI to determine predictors of influenza infection. METHODS: Personnel with FRI (defined as fever ≥ 37.5 °C, with cough or sore throat were recruited from the sentinel surveillance system in the Singapore military. Nasal washes were collected, and tested using the Resplex II and additional PCR assays for etiological determination. Interviewer-administered questionnaires collected information on patient demographics and clinical features. Univariate comparison of the various parameters was conducted, with statistically significant parameters entered into a multivariate logistic regression model. The final multivariate model for influenza versus non-influenza cases was used to build a predictive probability clinical diagnostic model. RESULTS: 821 out of 2858 subjects recruited from 11 May 2009 to 25 Jun 2010 had influenza, of which 434 (52.9% had 2009 influenza A (H1N1, 58 (7.1% seasonal influenza A (H3N2 and 269 (32.8% influenza B. Influenza-positive cases were significantly more likely to present with running nose, chills and rigors, ocular symptoms and higher temperature, and less likely with sore throat, photophobia, injected pharynx, and nausea/vomiting. Our clinical diagnostic model had a sensitivity of 65% (95% CI: 58%, 72%, specificity of 69% (95% CI: 62%, 75%, and overall accuracy of 68% (95% CI: 64%, 71%, performing significantly better than conventional influenza-like illness (ILI criteria. CONCLUSIONS: Use of a clinical diagnostic model may help predict influenza better than the conventional ILI definition among young adults with FRI.

  1. Admission to intensive care can be reliably predicted using only clinical judgment

    Brabrand, M.

    2015-01-01

    staffwere able to identify patients in need of critical care using only clinical judgment and to compare this with the National Early Warning Score (NEWS). Methods This was a prospective cohort study of all adult patients with a first-time admission to a medical admission unit at a 450-bed regional teaching......Introduction Not all patients in need of critical care arrive in clinical distress and some deteriorate after arrival. Identifying these patients early in their clinical course could potentially improve outcome. The present study was performed with the aim of assessing whether nursing and physician...... hospital over a 3-month period in 2010. All subspecialties of internal medicine are present as well as a level 2 ICU. Upon first contact with the patient after arrival, nursing staffand physicians were asked to report their estimation of the probability of ICU admission (0 to 100%). Survival status...

  2. Assessment of dual tasking has no clinical value for fall prediction in Parkinson's disease

    Smulders, K.; Esselink, R.A.J.; Weiss, A.; Kessels, R.P.C.; Geurts, A.C.H.; Bloem, B.R.

    2012-01-01

    The objective of this study is to investigate the value of dual-task performance for the prediction of falls inpatients with Parkinson's disease (PD). Two hundred sixty three patients with PD (H&Y 1-3, 65.2 +/- 7.9 years)walked two times along a 10-m trajectory, both under single-task and dual-task

  3. Predictive Validity of the MMPI-2 Clinical, PSY-5, and RC Scales for Therapy Disruptive Behavior

    Scholte, W.; Tiemens, B.G.; Verheul, R.; Meerman, A.; Egger, J.; Hutschemaekers, G.

    2012-01-01

    Background. Impulsive acts, parasuicidal behavior, and other therapy disruptive incidents occur frequently in the treatment of patients with personality disorders and increase the risk that patients will drop out of treatment. Objective. This study examined the predictive validity of the Minnesota M

  4. CONFIRMATION OF CLINICAL-DIAGNOSIS IN REQUESTS FOR PRENATAL PREDICTION OF SMA TYPE-I

    COBBEN, JM; DEVISSER, M; SCHEFFER, H; OSINGA, J; VANDERSTEEGE, G; BUYS, CHCM; VANOMMEN, GJ; TENKATE, LP

    1993-01-01

    The recent discovery of a major SMA-locus in the chromosomal region 5q makes it possible to carry out prenatal DNA studies in families in which a child with SMA type I has been born. Since direct mutation analysis is not yet possible, the reliability of prenatal prediction of SMA type I usually depe

  5. Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy

    Simon Fong

    2013-01-01

    Full Text Available Earlier on, a conceptual design on the real-time clinical decision support system (rt-CDSS with data stream mining was proposed and published. The new system is introduced that can analyze medical data streams and can make real-time prediction. This system is based on a stream mining algorithm called VFDT. The VFDT is extended with the capability of using pointers to allow the decision tree to remember the mapping relationship between leaf nodes and the history records. In this paper, which is a sequel to the rt-CDSS design, several popular machine learning algorithms are investigated for their suitability to be a candidate in the implementation of classifier at the rt-CDSS. A classifier essentially needs to accurately map the events inputted to the system into one of the several predefined classes of assessments, such that the rt-CDSS can follow up with the prescribed remedies being recommended to the clinicians. For a real-time system like rt-CDSS, the major technological challenges lie in the capability of the classifier to process, analyze and classify the dynamic input data, quickly and upmost reliably. An experimental comparison is conducted. This paper contributes to the insight of choosing and embedding a stream mining classifier into rt-CDSS with a case study of diabetes therapy.

  6. Quantifying risk of early relapse in patients with first demyelinating events: Prediction in clinical practice

    Spelman, Tim; Meyniel, Claire; Rojas, Juan Ignacio

    2016-01-01

    BACKGROUND: Characteristics at clinically isolated syndrome (CIS) examination assist in identification of patient at highest risk of early second attack and could benefit the most from early disease-modifying drugs (DMDs). OBJECTIVE: To examine determinants of second attack and validate a prognos......BACKGROUND: Characteristics at clinically isolated syndrome (CIS) examination assist in identification of patient at highest risk of early second attack and could benefit the most from early disease-modifying drugs (DMDs). OBJECTIVE: To examine determinants of second attack and validate...

  7. Clinical and biomarker changes in premanifest Huntington disease show trial feasibility: a decade of the PREDICT-HD study

    Jane S Paulsen

    2014-04-01

    Full Text Available There is growing consensus that intervention and treatment of Huntington disease (HD should occur at the earliest stage possible. Various early-intervention methods for this fatal neurodegenerative disease have been identified, but preventive clinical trials for HD are limited by a lack of knowledge of the natural history of the disease and a dearth of appropriate outcome measures. Objectives of the current study are to document the natural history of premanifest HD progression in the largest cohort ever studied and to develop a battery of imaging and clinical markers of premanifest HD progression that can be used as outcome measures in preventive clinical trials. PREDICT-HD is a 32-site, international, observational study of premanifest HD, with annual examination of 1013 participants with premanifest HD and 301 gene-expansion negative controls between 2001 and 2012. Findings document 39 variables representing imaging, motor, cognitive, functional, and psychiatric domains, showing different rates of decline between premanifest Huntington disease and controls. Required sample size and models of premanifest HD are presented to inform future design of clinical and preclinical research. Preventive clinical trials in premanifest HD with participants who have a medium or high probability of motor onset are calculated to be as resource-effective as those conducted in diagnosed HD and could interrupt disease seven to twelve years earlier. Methods and measures for preventive clinical trials in premanifest HD more than a dozen years from motor onset are also feasible. These findings represent the most thorough documentation of a clinical battery for experimental therapeutics in stages of premanifest HD, the time period for which effective intervention may provide the most positive possible outcome for patients and their families affected by this devastating disease.

  8. Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning.

    Wu, Mon-Ju; Mwangi, Benson; Bauer, Isabelle E; Passos, Ives C; Sanches, Marsal; Zunta-Soares, Giovana B; Meyer, Thomas D; Hasan, Khader M; Soares, Jair C

    2017-01-15

    Diagnosis, clinical management and research of psychiatric disorders remain subjective - largely guided by historically developed categories which may not effectively capture underlying pathophysiological mechanisms of dysfunction. Here, we report a novel approach of identifying and validating distinct and biologically meaningful clinical phenotypes of bipolar disorders using both unsupervised and supervised machine learning techniques. First, neurocognitive data were analyzed using an unsupervised machine learning approach and two distinct clinical phenotypes identified namely; phenotype I and phenotype II. Second, diffusion weighted imaging scans were pre-processed using the tract-based spatial statistics (TBSS) method and 'skeletonized' white matter fractional anisotropy (FA) and mean diffusivity (MD) maps extracted. The 'skeletonized' white matter FA and MD maps were entered into the Elastic Net machine learning algorithm to distinguish individual subjects' phenotypic labels (e.g. phenotype I vs. phenotype II). This calculation was performed to ascertain whether the identified clinical phenotypes were biologically distinct. Original neurocognitive measurements distinguished individual subjects' phenotypic labels with 94% accuracy (sensitivity=92%, specificity=97%). TBSS derived FA and MD measurements predicted individual subjects' phenotypic labels with 76% and 65% accuracy respectively. In addition, individual subjects belonging to phenotypes I and II were distinguished from healthy controls with 57% and 92% accuracy respectively. Neurocognitive task variables identified as most relevant in distinguishing phenotypic labels included; Affective Go/No-Go (AGN), Cambridge Gambling Task (CGT) coupled with inferior fronto-occipital fasciculus and callosal white matter pathways. These results suggest that there may exist two biologically distinct clinical phenotypes in bipolar disorders which can be identified from healthy controls with high accuracy and at an

  9. Do medical students’ scores using different assessment instruments predict their scores in clinical reasoning using a computer-based simulation?

    Fida M

    2015-02-01

    Full Text Available Mariam Fida,1 Salah Eldin Kassab2 1Department of Molecular Medicine, College of Medicine and Medical Sciences, Arabian Gulf University, Manama, Bahrain; 2Department of Medical Education, Faculty of Medicine, Suez Canal University, Ismailia, Egypt Purpose: The development of clinical problem-solving skills evolves over time and requires structured training and background knowledge. Computer-based case simulations (CCS have been used for teaching and assessment of clinical reasoning skills. However, previous studies examining the psychometric properties of CCS as an assessment tool have been controversial. Furthermore, studies reporting the integration of CCS into problem-based medical curricula have been limited. Methods: This study examined the psychometric properties of using CCS software (DxR Clinician for assessment of medical students (n=130 studying in a problem-based, integrated multisystem module (Unit IX during the academic year 2011–2012. Internal consistency reliability of CCS scores was calculated using Cronbach's alpha statistics. The relationships between students' scores in CCS components (clinical reasoning, diagnostic performance, and patient management and their scores in other examination tools at the end of the unit including multiple-choice questions, short-answer questions, objective structured clinical examination (OSCE, and real patient encounters were analyzed using stepwise hierarchical linear regression. Results: Internal consistency reliability of CCS scores was high (α=0.862. Inter-item correlations between students' scores in different CCS components and their scores in CCS and other test items were statistically significant. Regression analysis indicated that OSCE scores predicted 32.7% and 35.1% of the variance in clinical reasoning and patient management scores, respectively (P<0.01. Multiple-choice question scores, however, predicted only 15.4% of the variance in diagnostic performance scores (P<0.01, while

  10. Development of predictive models for estimating warfarin maintenance dose based on genetic and clinical factors.

    Yang, Lu; Linder, Mark W

    2013-01-01

    In this chapter, we use calculation of estimated warfarin maintenance dosage as an example to illustrate how to develop a multiple linear regression model to quantify the relationship between several independent variables (e.g., patients' genotype information) and a dependent variable (e.g., measureable clinical outcome).

  11. Undergraduate Nurse Variables that Predict Academic Achievement and Clinical Competence in Nursing

    Blackman, Ian; Hall, Margaret; Darmawan, I Gusti Ngurah.

    2007-01-01

    A hypothetical model was formulated to explore factors that influenced academic and clinical achievement for undergraduate nursing students. Sixteen latent variables were considered including the students' background, gender, type of first language, age, their previous successes with their undergraduate nursing studies and status given for…

  12. Factors Predicting Oncology Care Providers' Behavioral Intention to Adopt Clinical Decision Support Systems

    Wolfenden, Andrew

    2012-01-01

    The purpose of this quantitative correlation study was to examine the predictors of user behavioral intention on the decision of oncology care providers to adopt or reject the clinical decision support system. The Unified Theory of Acceptance and Use of Technology (UTAUT) formed the foundation of the research model and survey instrument. The…

  13. Cultural Responsivity in Clinical Psychology Graduate Students: A Developmental Approach to the Prediction of Learning

    Berrin, Sebastian Everett

    2010-01-01

    This study used a mixed-method approach to examine students' experiences in multicultural training and their opinions about various aspects of their course(s). A developmental model of learning was employed to analyze results. More specifically, this study explored the relationship between clinical psychology doctoral students' self-reported…

  14. Early prediction of a benign course of multiple sclerosis on clinical grounds : a systematic review

    Ramsaransing, G; Maurits, N; Zwanikken, C; De Keyser, J

    2001-01-01

    Background. There is growing consensus that neurologists should consider disease-modifying therapies early in multiple sclerosis (MS). However there is a subgroup with a natural benign course, in which treatment could be postponed. We sought to determine the frequency of benign MS and early clinical

  15. An Endotoxin Tolerance Signature Predicts Sepsis and Organ Dysfunction at Initial Clinical Presentation

    Olga M. Pena

    2014-11-01

    Interpretation: Our data support an updated model of sepsis pathogenesis in which endotoxin tolerance-mediated immune dysfunction (cellular reprogramming is present throughout the clinical course of disease and related to disease severity. Thus endotoxin tolerance might offer new insights guiding the development of new therapies and diagnostics for early sepsis.

  16. Relationship-Specific Condom Attitudes Predict Condom Use among STD Clinic Patients with both Primary and Non-Primary Partners

    Senn, Theresa E.; Scott-Sheldon, Lori A. J.; Carey, Michael P.

    2014-01-01

    Although condom use differs by partner type (i.e., primary vs. non-primary partner), attitudes towards condom use are typically measured without consideration of partner type. This study investigated the predictive utility of condom attitudes measured separately by partner type. Participants were 270 patients (37% women, 72% Black) recruited from a publicly-funded STD clinic who reported having both primary and non-primary partners. They completed a computerized survey assessing relationship-specific condom attitudes by partner type, condom attitudes related to pleasure and respect, and condom use with primary and non-primary partners. Participants reported more positive relationship-specific condom attitudes with a non-primary vs. primary partner. When considering pleasure-related, respect-related, and relationship-specific condom attitudes simultaneously, only relationship-specific condom attitudes predicted unprotected sex, with both primary and non-primary partners. In general, pleasure and respect-related condom attitudes did not predict unprotected sex; however, pleasure-related attitudes predicted unprotected sex with a non-primary partner for men. Future research should assess relationship-specific condom attitudes. Sexual risk reduction interventions that address interpersonal consequences of condom use in both primary and non-primary relationships should be a priority. PMID:24567031

  17. Presence of Systemic Inflammatory Response Syndrome Predicts a Poor Clinical Outcome in Dogs with a Primary Hepatitis.

    Scott Kilpatrick

    Full Text Available Primary hepatopathies are a common cause of morbidity and mortality in dogs. The underlying aetiology of most cases of canine hepatitis is unknown. Consequently, treatments are typically palliative and it is difficult to provide accurate prognostic information to owners. In human hepatology there is accumulating data which indicates that the presence of systemic inflammatory response syndrome (SIRS is a common and debilitating event in patients with liver diseases. For example, the presence of SIRS has been linked to the development of complications such as hepatic encephalopathy (HE and is associated with a poor clinical outcome in humans with liver diseases. In contrast, the relationship between SIRS and clinical outcome in dogs with a primary hepatitis is unknown. Seventy dogs with histologically confirmed primary hepatitis were enrolled into the study. Additional clinical and clinicopathological information including respiratory rate, heart rate, temperature, white blood cell count, sodium, potassium, sex, presence of ascites, HE score, alanine aminotransferase (ALT, alkaline phosphatase (ALP, bilirubin and red blood cell concentration were available in all cases. The median survival of dogs with a SIRS score of 0 or 1 (SIRS low was 231 days compared to a median survival of 7 days for dogs with a SIRS score of 2, 3 or 4 (SIRS high (p<0.001. A Cox proportional hazard model, which included all other co-variables, revealed that a SIRS high score was an independent predictor of a poor clinical outcome. The effect of modulating inflammation on treatment outcomes in dogs with a primary hepatitis is deserving of further study.

  18. Prediction of new drug indications based on clinical data and network modularity.

    Yu, Liang; Ma, Xiaoke; Zhang, Long; Zhang, Jing; Gao, Lin

    2016-09-28

    Drug repositioning is commonly done within the drug discovery process in order to adjust or expand the application line of an active molecule. Previous computational methods in this domain mainly focused on shared genes or correlations between genes to construct new drug-disease associations. We propose a method that can not only handle drugs or diseases with or without related genes but consider the network modularity. Our method firstly constructs a drug network and a disease network based on side effects and symptoms respectively. Because similar drugs imply similar diseases, we then cluster the two networks to identify drug and disease modules, and connect all possible drug-disease module pairs. Further, based on known drug-disease associations in CTD and using local connectivity of modules, we predict potential drug-disease associations. Our predictions are validated by testing their overlaps with drug indications reported in published literatures and CTD, and KEGG enrichment analysis are also made on their related genes. The experimental results demonstrate that our approach can complement the current computational approaches and its predictions can provide new clues for the candidate discovery of drug repositioning.

  19. Prediction of new drug indications based on clinical data and network modularity

    Yu, Liang; Ma, Xiaoke; Zhang, Long; Zhang, Jing; Gao, Lin

    2016-01-01

    Drug repositioning is commonly done within the drug discovery process in order to adjust or expand the application line of an active molecule. Previous computational methods in this domain mainly focused on shared genes or correlations between genes to construct new drug-disease associations. We propose a method that can not only handle drugs or diseases with or without related genes but consider the network modularity. Our method firstly constructs a drug network and a disease network based on side effects and symptoms respectively. Because similar drugs imply similar diseases, we then cluster the two networks to identify drug and disease modules, and connect all possible drug-disease module pairs. Further, based on known drug-disease associations in CTD and using local connectivity of modules, we predict potential drug-disease associations. Our predictions are validated by testing their overlaps with drug indications reported in published literatures and CTD, and KEGG enrichment analysis are also made on their related genes. The experimental results demonstrate that our approach can complement the current computational approaches and its predictions can provide new clues for the candidate discovery of drug repositioning. PMID:27678071

  20. Applying psychological theories to evidence-based clinical practice: Identifying factors predictive of managing upper respiratory tract infections without antibiotics

    Glidewell Elizabeth

    2007-08-01

    Full Text Available Abstract Background Psychological models can be used to understand and predict behaviour in a wide range of settings. However, they have not been consistently applied to health professional behaviours, and the contribution of differing theories is not clear. The aim of this study was to explore the usefulness of a range of psychological theories to predict health professional behaviour relating to management of upper respiratory tract infections (URTIs without antibiotics. Methods Psychological measures were collected by postal questionnaire survey from a random sample of general practitioners (GPs in Scotland. The outcome measures were clinical behaviour (using antibiotic prescription rates as a proxy indicator, behavioural simulation (scenario-based decisions to managing URTI with or without antibiotics and behavioural intention (general intention to managing URTI without antibiotics. Explanatory variables were the constructs within the following theories: Theory of Planned Behaviour (TPB, Social Cognitive Theory (SCT, Common Sense Self-Regulation Model (CS-SRM, Operant Learning Theory (OLT, Implementation Intention (II, Stage Model (SM, and knowledge (a non-theoretical construct. For each outcome measure, multiple regression analysis was used to examine the predictive value of each theoretical model individually. Following this 'theory level' analysis, a 'cross theory' analysis was conducted to investigate the combined predictive value of all significant individual constructs across theories. Results All theories were tested, but only significant results are presented. When predicting behaviour, at the theory level, OLT explained 6% of the variance and, in a cross theory analysis, OLT 'evidence of habitual behaviour' also explained 6%. When predicting behavioural simulation, at the theory level, the proportion of variance explained was: TPB, 31%; SCT, 26%; II, 6%; OLT, 24%. GPs who reported having already decided to change their management to

  1. Three-tiered risk stratification model to predict progression in Barrett's esophagus using epigenetic and clinical features.

    Fumiaki Sato

    Full Text Available Barrett's esophagus predisposes to esophageal adenocarcinoma. However, the value of endoscopic surveillance in Barrett's esophagus has been debated because of the low incidence of esophageal adenocarcinoma in Barrett's esophagus. Moreover, high inter-observer and sampling-dependent variation in the histologic staging of dysplasia make clinical risk assessment problematic. In this study, we developed a 3-tiered risk stratification strategy, based on systematically selected epigenetic and clinical parameters, to improve Barrett's esophagus surveillance efficiency.We defined high-grade dysplasia as endpoint of progression, and Barrett's esophagus progressor patients as Barrett's esophagus patients with either no dysplasia or low-grade dysplasia who later developed high-grade dysplasia or esophageal adenocarcinoma. We analyzed 4 epigenetic and 3 clinical parameters in 118 Barrett's esophagus tissues obtained from 35 progressor and 27 non-progressor Barrett's esophagus patients from Baltimore Veterans Affairs Maryland Health Care Systems and Mayo Clinic. Based on 2-year and 4-year prediction models using linear discriminant analysis (area under the receiver-operator characteristic (ROC curve: 0.8386 and 0.7910, respectively, Barrett's esophagus specimens were stratified into high-risk (HR, intermediate-risk (IR, or low-risk (LR groups. This 3-tiered stratification method retained both the high specificity of the 2-year model and the high sensitivity of the 4-year model. Progression-free survivals differed significantly among the 3 risk groups, with p = 0.0022 (HR vs. IR and p<0.0001 (HR or IR vs. LR. Incremental value analyses demonstrated that the number of methylated genes contributed most influentially to prediction accuracy.This 3-tiered risk stratification strategy has the potential to exert a profound impact on Barrett's esophagus surveillance accuracy and efficiency.

  2. A novel model to combine clinical and pathway-based transcriptomic information for the prognosis prediction of breast cancer.

    Sijia Huang

    2014-09-01

    Full Text Available Breast cancer is the most common malignancy in women worldwide. With the increasing awareness of heterogeneity in breast cancers, better prediction of breast cancer prognosis is much needed for more personalized treatment and disease management. Towards this goal, we have developed a novel computational model for breast cancer prognosis by combining the Pathway Deregulation Score (PDS based pathifier algorithm, Cox regression and L1-LASSO penalization method. We trained the model on a set of 236 patients with gene expression data and clinical information, and validated the performance on three diversified testing data sets of 606 patients. To evaluate the performance of the model, we conducted survival analysis of the dichotomized groups, and compared the areas under the curve based on the binary classification. The resulting prognosis genomic model is composed of fifteen pathways (e.g., P53 pathway that had previously reported cancer relevance, and it successfully differentiated relapse in the training set (log rank p-value = 6.25e-12 and three testing data sets (log rank p-value < 0.0005. Moreover, the pathway-based genomic models consistently performed better than gene-based models on all four data sets. We also find strong evidence that combining genomic information with clinical information improved the p-values of prognosis prediction by at least three orders of magnitude in comparison to using either genomic or clinical information alone. In summary, we propose a novel prognosis model that harnesses the pathway-based dysregulation as well as valuable clinical information. The selected pathways in our prognosis model are promising targets for therapeutic intervention.

  3. De novo sequencing of circulating miRNAs identifies novel markers predicting clinical outcome of locally advanced breast cancer

    Wu Xiwei

    2012-03-01

    Full Text Available Abstract Background MicroRNAs (miRNAs have been recently detected in the circulation of cancer patients, where they are associated with clinical parameters. Discovery profiling of circulating small RNAs has not been reported in breast cancer (BC, and was carried out in this study to identify blood-based small RNA markers of BC clinical outcome. Methods The pre-treatment sera of 42 stage II-III locally advanced and inflammatory BC patients who received neoadjuvant chemotherapy (NCT followed by surgical tumor resection were analyzed for marker identification by deep sequencing all circulating small RNAs. An independent validation cohort of 26 stage II-III BC patients was used to assess the power of identified miRNA markers. Results More than 800 miRNA species were detected in the circulation, and observed patterns showed association with histopathological profiles of BC. Groups of circulating miRNAs differentially associated with ER/PR/HER2 status and inflammatory BC were identified. The relative levels of selected miRNAs measured by PCR showed consistency with their abundance determined by deep sequencing. Two circulating miRNAs, miR-375 and miR-122, exhibited strong correlations with clinical outcomes, including NCT response and relapse with metastatic disease. In the validation cohort, higher levels of circulating miR-122 specifically predicted metastatic recurrence in stage II-III BC patients. Conclusions Our study indicates that certain miRNAs can serve as potential blood-based biomarkers for NCT response, and that miR-122 prevalence in the circulation predicts BC metastasis in early-stage patients. These results may allow optimized chemotherapy treatments and preventive anti-metastasis interventions in future clinical applications.

  4. Translating biology into clinic: new insights on prognostic and predictive biomarkers for urothelial bladder carcinoma

    2013-01-01

    Tese de doutoramento em Ciências da Saúde Urothelial bladder carcinoma (UBC) represents a significant health problem, as a consequence of its heterogeneous natural history and clinical behavior. Most morbidity and mortality associated with UBC is caused by the muscle-invasive (MI) form of the disease, which represents about 20-30% of all newly diagnosed cases. Moreover, an important proportion of high risk non-muscle invasive (NMI) tumours relapse after transurethral resection and progress...

  5. Predictive value of combined clinically diagnosed bruxism and occlusal features for TMJ pain.</