Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study

Science.gov (United States)

Zhen, Xin; Chen, Jiawei; Zhong, Zichun; Hrycushko, Brian; Zhou, Linghong; Jiang, Steve; Albuquerque, Kevin; Gu, Xuejun

2017-11-01

Better understanding of the dose-toxicity relationship is critical for safe dose escalation to improve local control in late-stage cervical cancer radiotherapy. In this study, we introduced a convolutional neural network (CNN) model to analyze rectum dose distribution and predict rectum toxicity. Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively collected, including twelve toxicity patients and thirty non-toxicity patients. We adopted a transfer learning strategy to overcome the limited patient data issue. A 16-layers CNN developed by the visual geometry group (VGG-16) of the University of Oxford was pre-trained on a large-scale natural image database, ImageNet, and fine-tuned with patient rectum surface dose maps (RSDMs), which were accumulated EBRT  +  BT doses on the unfolded rectum surface. We used the adaptive synthetic sampling approach and the data augmentation method to address the two challenges, data imbalance and data scarcity. The gradient-weighted class activation maps (Grad-CAM) were also generated to highlight the discriminative regions on the RSDM along with the prediction model. We compare different CNN coefficients fine-tuning strategies, and compare the predictive performance using the traditional dose volume parameters, e.g. D 0.1/1/2cc, and the texture features extracted from the RSDM. Satisfactory prediction performance was achieved with the proposed scheme, and we found that the mean Grad-CAM over the toxicity patient group has geometric consistence of distribution with the statistical analysis result, which indicates possible rectum toxicity location. The evaluation results have demonstrated the feasibility of building a CNN-based rectum dose-toxicity prediction model with transfer learning for cervical cancer radiotherapy.

Toxicological relationships between proteins obtained from protein target predictions of large toxicity databases

International Nuclear Information System (INIS)

Nigsch, Florian; Mitchell, John B.O.

2008-01-01

The combination of models for protein target prediction with large databases containing toxicological information for individual molecules allows the derivation of 'toxiclogical' profiles, i.e., to what extent are molecules of known toxicity predicted to interact with a set of protein targets. To predict protein targets of drug-like and toxic molecules, we built a computational multiclass model using the Winnow algorithm based on a dataset of protein targets derived from the MDL Drug Data Report. A 15-fold Monte Carlo cross-validation using 50% of each class for training, and the remaining 50% for testing, provided an assessment of the accuracy of that model. We retained the 3 top-ranking predictions and found that in 82% of all cases the correct target was predicted within these three predictions. The first prediction was the correct one in almost 70% of cases. A model built on the whole protein target dataset was then used to predict the protein targets for 150 000 molecules from the MDL Toxicity Database. We analysed the frequency of the predictions across the panel of protein targets for experimentally determined toxicity classes of all molecules. This allowed us to identify clusters of proteins related by their toxicological profiles, as well as toxicities that are related. Literature-based evidence is provided for some specific clusters to show the relevance of the relationships identified

Using quantitative structure-activity relationships (QSAR) to predict toxic endpoints for polycyclic aromatic hydrocarbons (PAH).

Science.gov (United States)

Bruce, Erica D; Autenrieth, Robin L; Burghardt, Robert C; Donnelly, K C; McDonald, Thomas J

2008-01-01

Quantitative structure-activity relationships (QSAR) offer a reliable, cost-effective alternative to the time, money, and animal lives necessary to determine chemical toxicity by traditional methods. Additionally, humans are exposed to tens of thousands of chemicals in their lifetimes, necessitating the determination of chemical toxicity and screening for those posing the greatest risk to human health. This study developed models to predict toxic endpoints for three bioassays specific to several stages of carcinogenesis. The ethoxyresorufin O-deethylase assay (EROD), the Salmonella/microsome assay, and a gap junction intercellular communication (GJIC) assay were chosen for their ability to measure toxic endpoints specific to activation-, induction-, and promotion-related effects of polycyclic aromatic hydrocarbons (PAH). Shape-electronic, spatial, information content, and topological descriptors proved to be important descriptors in predicting the toxicity of PAH in these bioassays. Bioassay-based toxic equivalency factors (TEF(B)) were developed for several PAH using the quantitative structure-toxicity relationships (QSTR) developed. Predicting toxicity for a specific PAH compound, such as a bioassay-based potential potency (PP(B)) or a TEF(B), is possible by combining the predicted behavior from the QSTR models. These toxicity estimates may then be incorporated into a risk assessment for compounds that lack toxicity data. Accurate toxicity predictions are made by examining each type of endpoint important to the process of carcinogenicity, and a clearer understanding between composition and toxicity can be obtained.

Random Forests to Predict Rectal Toxicity Following Prostate Cancer Radiation Therapy

International Nuclear Information System (INIS)

Ospina, Juan D.; Zhu, Jian; Chira, Ciprian; Bossi, Alberto; Delobel, Jean B.; Beckendorf, Véronique; Dubray, Bernard; Lagrange, Jean-Léon; Correa, Juan C.

2014-01-01

Purpose: To propose a random forest normal tissue complication probability (RF-NTCP) model to predict late rectal toxicity following prostate cancer radiation therapy, and to compare its performance to that of classic NTCP models. Methods and Materials: Clinical data and dose-volume histograms (DVH) were collected from 261 patients who received 3-dimensional conformal radiation therapy for prostate cancer with at least 5 years of follow-up. The series was split 1000 times into training and validation cohorts. A RF was trained to predict the risk of 5-year overall rectal toxicity and bleeding. Parameters of the Lyman-Kutcher-Burman (LKB) model were identified and a logistic regression model was fit. The performance of all the models was assessed by computing the area under the receiving operating characteristic curve (AUC). Results: The 5-year grade ≥2 overall rectal toxicity and grade ≥1 and grade ≥2 rectal bleeding rates were 16%, 25%, and 10%, respectively. Predictive capabilities were obtained using the RF-NTCP model for all 3 toxicity endpoints, including both the training and validation cohorts. The age and use of anticoagulants were found to be predictors of rectal bleeding. The AUC for RF-NTCP ranged from 0.66 to 0.76, depending on the toxicity endpoint. The AUC values for the LKB-NTCP were statistically significantly inferior, ranging from 0.62 to 0.69. Conclusions: The RF-NTCP model may be a useful new tool in predicting late rectal toxicity, including variables other than DVH, and thus appears as a strong competitor to classic NTCP models

In silico assessment of the acute toxicity of chemicals: recent advances and new model for multitasking prediction of toxic effect.

Science.gov (United States)

Kleandrova, Valeria V; Luan, Feng; Speck-Planche, Alejandro; Cordeiro, M Natália D S

2015-01-01

The assessment of acute toxicity is one of the most important stages to ensure the safety of chemicals with potential applications in pharmaceutical sciences, biomedical research, or any other industrial branch. A huge and indiscriminate number of toxicity assays have been carried out on laboratory animals. In this sense, computational approaches involving models based on quantitative-structure activity/toxicity relationships (QSAR/QSTR) can help to rationalize time and financial costs. Here, we discuss the most significant advances in the last 6 years focused on the use of QSAR/QSTR models to predict acute toxicity of drugs/chemicals in laboratory animals, employing large and heterogeneous datasets. The advantages and drawbacks of the different QSAR/QSTR models are analyzed. As a contribution to the field, we introduce the first multitasking (mtk) QSTR model for simultaneous prediction of acute toxicity of compounds by considering different routes of administration, diverse breeds of laboratory animals, and the reliability of the experimental conditions. The mtk-QSTR model was based on artificial neural networks (ANN), allowing the classification of compounds as toxic or non-toxic. This model correctly classified more than 94% of the 1646 cases present in the whole dataset, and its applicability was demonstrated by performing predictions of different chemicals such as drugs, dietary supplements, and molecules which could serve as nanocarriers for drug delivery. The predictions given by the mtk-QSTR model are in very good agreement with the experimental results.

Prediction of human population responses to toxic compounds by a collaborative competition.

Science.gov (United States)

Eduati, Federica; Mangravite, Lara M; Wang, Tao; Tang, Hao; Bare, J Christopher; Huang, Ruili; Norman, Thea; Kellen, Mike; Menden, Michael P; Yang, Jichen; Zhan, Xiaowei; Zhong, Rui; Xiao, Guanghua; Xia, Menghang; Abdo, Nour; Kosyk, Oksana; Friend, Stephen; Dearry, Allen; Simeonov, Anton; Tice, Raymond R; Rusyn, Ivan; Wright, Fred A; Stolovitzky, Gustavo; Xie, Yang; Saez-Rodriguez, Julio

2015-09-01

The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.

Prediction of acute inhalation toxicity using in vitro lung surfactant inhibition

DEFF Research Database (Denmark)

Sørli, Jorid Birkelund; Huang, Yishi; Da Silva, Emilie

2018-01-01

impregnation products using the constant flow through set-up of the constrained drop surfactometer to determine if they inhibited LS function or not. The same products were tested in a mouse inhalation bioassay to determine their toxicity in vivo. The sensitivity was 100%, i.e. the in vitro method predicted...... the chemical composition of the products and induction of toxicity. The currently accepted method for determination of acute inhalation toxicity is based on experiments on animals; it is time-consuming, expensive and causes stress for the animals. Impregnation products are present on the market in large...... numbers and amounts and exhibit great variety. Therefore, an alternative method to screen for acute inhalation toxicity is needed. The aim of our study was to determine if inhibition of lung surfactant by impregnation products in vitro could accurately predict toxicity in vivo in mice. We tested 21...

Prediction of acute inhalation toxicity using in vitro lung surfactant inhibition.

Science.gov (United States)

Sørli, Jorid B; Huang, Yishi; Da Silva, Emilie; Hansen, Jitka S; Zuo, Yi Y; Frederiksen, Marie; Nørgaard, Asger W; Ebbehøj, Niels E; Larsen, Søren T; Hougaard, Karin S

2018-01-01

Private consumers and professionals may experience acute inhalation toxicity after inhaling aerosolized impregnation products. The distinction between toxic and non-toxic products is difficult to make for producers and product users alike, as there is no clearly described relationship between the chemical composition of the products and induction of toxicity. The currently accepted method for determination of acute inhalation toxicity is based on experiments on animals; it is time-consuming, expensive and causes stress for the animals. Impregnation products are present on the market in large numbers and amounts and exhibit great variety. Therefore, an alternative method to screen for acute inhalation toxicity is needed. The aim of our study was to determine if inhibition of lung surfactant by impregnation products in vitro could accurately predict toxicity in vivo in mice. We tested 21 impregnation products using the constant flow through set-up of the constrained drop surfactometer to determine if the products inhibited surfactant function or not. The same products were tested in a mouse inhalation bioassay to determine their toxicity in vivo. The sensitivity was 100%, i.e., the in vitro method predicted all the products that were toxic for mice to inhale. The specificity of the in vitro test was 63%, i.e., the in vitro method found three false positives in the 21 tested products. Six of the products had been involved in accidental human inhalation where they caused acute inhalation toxicity. All of these six products inhibited lung surfactant function in vitro and were toxic to mice.

Predicting human developmental toxicity of pharmaceuticals using human embryonic stem cells and metabolomics

International Nuclear Information System (INIS)

West, Paul R.; Weir, April M.; Smith, Alan M.; Donley, Elizabeth L.R.; Cezar, Gabriela G.

2010-01-01

Teratogens, substances that may cause fetal abnormalities during development, are responsible for a significant number of birth defects. Animal models used to predict teratogenicity often do not faithfully correlate to human response. Here, we seek to develop a more predictive developmental toxicity model based on an in vitro method that utilizes both human embryonic stem (hES) cells and metabolomics to discover biomarkers of developmental toxicity. We developed a method where hES cells were dosed with several drugs of known teratogenicity then LC-MS analysis was performed to measure changes in abundance levels of small molecules in response to drug dosing. Statistical analysis was employed to select for specific mass features that can provide a prediction of the developmental toxicity of a substance. These molecules can serve as biomarkers of developmental toxicity, leading to better prediction of teratogenicity. In particular, our work shows a correlation between teratogenicity and changes of greater than 10% in the ratio of arginine to asymmetric dimethylarginine levels. In addition, this study resulted in the establishment of a predictive model based on the most informative mass features. This model was subsequently tested for its predictive accuracy in two blinded studies using eight drugs of known teratogenicity, where it correctly predicted the teratogenicity for seven of the eight drugs. Thus, our initial data shows that this platform is a robust alternative to animal and other in vitro models for the prediction of the developmental toxicity of chemicals that may also provide invaluable information about the underlying biochemical pathways.

In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches

Energy Technology Data Exchange (ETDEWEB)

Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com; Gupta, Shikha

2014-03-15

Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure–toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R{sup 2}) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R{sup 2} and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals. - Graphical abstract: Importance of input variables in DTB and DTF classification models for (a) two-category, and (b) four-category toxicity intervals in T. pyriformis data. Generalization and predictive abilities of the

In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches

International Nuclear Information System (INIS)

Singh, Kunwar P.; Gupta, Shikha

2014-01-01

Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure–toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R 2 ) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R 2 and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals. - Graphical abstract: Importance of input variables in DTB and DTF classification models for (a) two-category, and (b) four-category toxicity intervals in T. pyriformis data. Generalization and predictive abilities of the

ProTox: a web server for the in silico prediction of rodent oral toxicity.

Science.gov (United States)

Drwal, Malgorzata N; Banerjee, Priyanka; Dunkel, Mathias; Wettig, Martin R; Preissner, Robert

2014-07-01

Animal trials are currently the major method for determining the possible toxic effects of drug candidates and cosmetics. In silico prediction methods represent an alternative approach and aim to rationalize the preclinical drug development, thus enabling the reduction of the associated time, costs and animal experiments. Here, we present ProTox, a web server for the prediction of rodent oral toxicity. The prediction method is based on the analysis of the similarity of compounds with known median lethal doses (LD50) and incorporates the identification of toxic fragments, therefore representing a novel approach in toxicity prediction. In addition, the web server includes an indication of possible toxicity targets which is based on an in-house collection of protein-ligand-based pharmacophore models ('toxicophores') for targets associated with adverse drug reactions. The ProTox web server is open to all users and can be accessed without registration at: http://tox.charite.de/tox. The only requirement for the prediction is the two-dimensional structure of the input compounds. All ProTox methods have been evaluated based on a diverse external validation set and displayed strong performance (sensitivity, specificity and precision of 76, 95 and 75%, respectively) and superiority over other toxicity prediction tools, indicating their possible applicability for other compound classes. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Prediction of toxicity and comparison of alternatives using WebTEST (Web-services Toxicity Estimation Software Tool)(Bled Slovenia)

Science.gov (United States)

A Java-based web service is being developed within the US EPA’s Chemistry Dashboard to provide real time estimates of toxicity values and physical properties. WebTEST can generate toxicity predictions directly from a simple URL which includes the endpoint, QSAR method, and ...

Predictive QSAR Models for the Toxicity of Disinfection Byproducts

Directory of Open Access Journals (Sweden)

Litang Qin

2017-10-01

Full Text Available Several hundred disinfection byproducts (DBPs in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure–activity relationship (QSAR models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH−, DNA+ and DNA−. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination (R2 > 0.7, explained variance in leave-one-out prediction (Q2LOO and in leave-many-out prediction (Q2LMO > 0.6, variance explained in external prediction (Q2F1, Q2F2, and Q2F3 > 0.7, and concordance correlation coefficient (CCC > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.

Predictive QSAR Models for the Toxicity of Disinfection Byproducts.

Science.gov (United States)

Qin, Litang; Zhang, Xin; Chen, Yuhan; Mo, Lingyun; Zeng, Honghu; Liang, Yanpeng

2017-10-09

Several hundred disinfection byproducts (DBPs) in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure-activity relationship (QSAR) models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH-, DNA+ and DNA-. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination ( R ²) > 0.7, explained variance in leave-one-out prediction ( Q ² LOO ) and in leave-many-out prediction ( Q ² LMO ) > 0.6, variance explained in external prediction ( Q ² F1 , Q ² F2 , and Q ² F3 ) > 0.7, and concordance correlation coefficient ( CCC ) > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.

Accurate prediction of the toxicity of benzoic acid compounds in mice via oral without using any computer codes

International Nuclear Information System (INIS)

Keshavarz, Mohammad Hossein; Gharagheizi, Farhad; Shokrolahi, Arash; Zakinejad, Sajjad

2012-01-01

Highlights: ► A novel method is introduced for desk calculation of toxicity of benzoic acid derivatives. ► There is no need to use QSAR and QSTR methods, which are based on computer codes. ► The predicted results of 58 compounds are more reliable than those predicted by QSTR method. ► The present method gives good predictions for further 324 benzoic acid compounds. - Abstract: Most of benzoic acid derivatives are toxic, which may cause serious public health and environmental problems. Two novel simple and reliable models are introduced for desk calculations of the toxicity of benzoic acid compounds in mice via oral LD 50 with more reliance on their answers as one could attach to the more complex outputs. They require only elemental composition and molecular fragments without using any computer codes. The first model is based on only the number of carbon and hydrogen atoms, which can be improved by several molecular fragments in the second model. For 57 benzoic compounds, where the computed results of quantitative structure–toxicity relationship (QSTR) were recently reported, the predicted results of two simple models of present method are more reliable than QSTR computations. The present simple method is also tested with further 324 benzoic acid compounds including complex molecular structures, which confirm good forecasting ability of the second model.

Toxicity prediction of compounds from turmeric (Curcuma longa L).

Science.gov (United States)

Balaji, S; Chempakam, B

2010-10-01

Turmeric belongs to the ginger family Zingiberaceae. Currently, cheminformatics approaches are not employed in any of the spices to study the medicinal properties traditionally attributed to them. The aim of this study is to find the most efficacious molecule which does not have any toxic effects. In the present study, toxicity of 200 chemical compounds from turmeric were predicted (includes bacterial mutagenicity, rodent carcinogenicity and human hepatotoxicity). The study shows out of 200 compounds, 184 compounds were predicted as toxigenic, 136 compounds are mutagenic, 153 compounds are carcinogenic and 64 compounds are hepatotoxic. To cross validate our results, we have chosen the popular curcumin and found that curcumin and its derivatives may cause dose dependent hepatotoxicity. The results of these studies indicate that, in contrast to curcumin, few other compounds in turmeric which are non-mutagenic, non-carcinogenic, non-hepatotoxic, and do not have any side-effects. Hence, the cost-effective approach presented in this paper could be used to filter toxic compounds from the drug discovery lifecycle. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Toxicity of ionic liquids: Database and prediction via quantitative structure–activity relationship method

International Nuclear Information System (INIS)

Zhao, Yongsheng; Zhao, Jihong; Huang, Ying; Zhou, Qing; Zhang, Xiangping; Zhang, Suojiang

2014-01-01

Highlights: • A comprehensive database on toxicity of ionic liquids (ILs) was established. • Relationship between structure and toxicity of IL has been analyzed qualitatively. • Two new QSAR models were developed for predicting toxicity of ILs to IPC-81. • Accuracy of proposed nonlinear SVM model is much higher than the linear MLR model. • The established models can be explored in designing novel green agents. - Abstract: A comprehensive database on toxicity of ionic liquids (ILs) is established. The database includes over 4000 pieces of data. Based on the database, the relationship between IL's structure and its toxicity has been analyzed qualitatively. Furthermore, Quantitative Structure–Activity relationships (QSAR) model is conducted to predict the toxicities (EC 50 values) of various ILs toward the Leukemia rat cell line IPC-81. Four parameters selected by the heuristic method (HM) are used to perform the studies of multiple linear regression (MLR) and support vector machine (SVM). The squared correlation coefficient (R 2 ) and the root mean square error (RMSE) of training sets by two QSAR models are 0.918 and 0.959, 0.258 and 0.179, respectively. The prediction R 2 and RMSE of QSAR test sets by MLR model are 0.892 and 0.329, by SVM model are 0.958 and 0.234, respectively. The nonlinear model developed by SVM algorithm is much outperformed MLR, which indicates that SVM model is more reliable in the prediction of toxicity of ILs. This study shows that increasing the relative number of O atoms of molecules leads to decrease in the toxicity of ILs

Predicting algal growth inhibition toxicity: three-step strategy using structural and physicochemical properties.

Science.gov (United States)

Furuhama, A; Hasunuma, K; Hayashi, T I; Tatarazako, N

2016-05-01

We propose a three-step strategy that uses structural and physicochemical properties of chemicals to predict their 72 h algal growth inhibition toxicities against Pseudokirchneriella subcapitata. In Step 1, using a log D-based criterion and structural alerts, we produced an interspecies QSAR between algal and acute daphnid toxicities for initial screening of chemicals. In Step 2, we categorized chemicals according to the Verhaar scheme for aquatic toxicity, and we developed QSARs for toxicities of Class 1 (non-polar narcotic) and Class 2 (polar narcotic) chemicals by means of simple regression with a hydrophobicity descriptor and multiple regression with a hydrophobicity descriptor and a quantum chemical descriptor. Using the algal toxicities of the Class 1 chemicals, we proposed a baseline QSAR for calculating their excess toxicities. In Step 3, we used structural profiles to predict toxicity either quantitatively or qualitatively and to assign chemicals to the following categories: Pesticide, Reactive, Toxic, Toxic low and Uncategorized. Although this three-step strategy cannot be used to estimate the algal toxicities of all chemicals, it is useful for chemicals within its domain. The strategy is also applicable as a component of Integrated Approaches to Testing and Assessment.

Potential carcinogenicity predicted by computational toxicity evaluation of thiophosphate pesticides using QSTR/QSCarciAR model.

Science.gov (United States)

Petrescu, Alina-Maria; Ilia, Gheorghe

2017-07-01

This study presents in silico prediction of toxic activities and carcinogenicity, represented by the potential carcinogenicity DSSTox/DBS, based on vector regression with a new Kernel activity, and correlating the predicted toxicity values through a QSAR model, namely: QSTR/QSCarciAR (quantitative structure toxicity relationship/quantitative structure carcinogenicity-activity relationship) described by 2D, 3D descriptors and biological descriptors. The results showed a connection between carcinogenicity (compared to the structure of a compound) and toxicity, as a basis for future studies on this subject, but each prediction is based on structurally similar compounds and the reactivation of the substructures of these compounds.

Global concentration additivity and prediction of mixture toxicities, taking nitrobenzene derivatives as an example.

Science.gov (United States)

Li, Tong; Liu, Shu-Shen; Qu, Rui; Liu, Hai-Ling

2017-10-01

The toxicity of a mixture depends not only on the mixture concentration level but also on the mixture ratio. For a multiple-component mixture (MCM) system with a definite chemical composition, the mixture toxicity can be predicted only if the global concentration additivity (GCA) is validated. The so-called GCA means that the toxicity of any mixture in the MCM system is the concentration additive, regardless of what its mixture ratio and concentration level. However, many mixture toxicity reports have usually employed one mixture ratio (such as the EC 50 ratio), the equivalent effect concentration ratio (EECR) design, to specify several mixtures. EECR mixtures cannot simulate the concentration diversity and mixture ratio diversity of mixtures in the real environment, and it is impossible to validate the GCA. Therefore, in this paper, the uniform design ray (UD-Ray) was used to select nine mixture ratios (rays) in the mixture system of five nitrobenzene derivatives (NBDs). The representative UD-Ray mixtures can effectively and rationally describe the diversity in the NBD mixture system. The toxicities of the mixtures to Vibrio qinghaiensis sp.-Q67 were determined by the microplate toxicity analysis (MTA). For each UD-Ray mixture, the concentration addition (CA) model was used to validate whether the mixture toxicity is additive. All of the UD-Ray mixtures of five NBDs are global concentration additive. Afterwards, the CA is employed to predict the toxicities of the external mixtures from three EECR mixture rays with the NOEC, EC 30 , and EC 70 ratios. The predictive toxicities are in good agreement with the experimental toxicities, which testifies to the predictability of the mixture toxicity of the NBDs. Copyright © 2017. Published by Elsevier Inc.

A Novel Two-Step Hierarchial Quantitative Structure-Activity Relationship Modeling Workflow for Predicting Acute Toxicity of Chemicals in Rodents

Science.gov (United States)

Background: Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public–private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. Methods and results: A database co...

Predictive factors for gastroduodenal toxicity based on endoscopy following radiotherapy in patients with hepatocellular carcinoma

Energy Technology Data Exchange (ETDEWEB)

Yoon, H. [Sungkyunkwan Univ., Seoul (Korea, Republic of). Dept. of Health Sciences and Technology; Oh, D.; Park, H.C.; Han, Y.; Lim, D.H. [Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of). Dept. of Radiation Oncology; Kang, S.W. [Korea Univ., Seoul (Korea, Republic of). Dept. of Radiologic Science; Paik, S.W. [Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of). Dept. of Medicine

2013-07-15

Purpose: The aim of this work was to determine predictive factors for gastroduodenal (GD) toxicity in hepatocellular carcinoma (HCC) patients who were treated with radiotherapy (RT). Patients and methods: A total of 90 HCC patients who underwent esophagogastroduodenoscopy (EGD) before and after RT were enrolled. RT was delivered as 30-50 Gy (median 37.5 Gy) in 2-5 Gy (median 3.5 Gy) per fraction. All endoscopic findings were reviewed and GD toxicities related to RT were graded by the Common Toxicity Criteria for Adverse Events, version 3.0. The predictive factors for the {>=} grade 2 GD toxicity were investigated. Results: Endoscopic findings showed erosive gastritis in 14 patients (16 %), gastric ulcers in 8 patients (9 %), erosive duodenitis in 15 patients (17 %), and duodenal ulcers in 14 patients (16 %). Grade 2 toxicity developed in 19 patients (21 %) and grade 3 toxicity developed in 8 patients (9 %). V{sub 25} for stomach and V{sub 35} for duodenum (volume receiving a RT dose of more than x Gy) were the most predictive factors for {>=} grade 2 toxicity. The gastric toxicity rate at 6 months was 2.9 % for V{sub 25} {<=} 6.3 % and 57.1 % for V{sub 25} > 6.3 %. The duodenal toxicity rate at 6 months was 9.4 % for V{sub 35} > 5.4 % and 45.9 % for V{sub 35} > 5.4 %. By multivariate analysis including the clinical factors, V{sub 25} for stomach and V{sub 35} for duodenum were the significant factors. Conclusion: EGD revealed that GD toxicity is common following RT for HCC. V{sub 25} for the stomach and V{sub 35} for the duodenum were the significant factors to predict {>=} grade 2 GD toxicity. (orig.)

Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches.

Science.gov (United States)

Singh, Kunwar P; Gupta, Shikha; Rai, Premanjali

2013-09-01

The research aims to develop global modeling tools capable of categorizing structurally diverse chemicals in various toxicity classes according to the EEC and European Community directives, and to predict their acute toxicity in fathead minnow using set of selected molecular descriptors. Accordingly, artificial intelligence approach based classification and regression models, such as probabilistic neural networks (PNN), generalized regression neural networks (GRNN), multilayer perceptron neural network (MLPN), radial basis function neural network (RBFN), support vector machines (SVM), gene expression programming (GEP), and decision tree (DT) were constructed using the experimental toxicity data. Diversity and non-linearity in the chemicals' data were tested using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Predictive and generalization abilities of various models constructed here were compared using several statistical parameters. PNN and GRNN models performed relatively better than MLPN, RBFN, SVM, GEP, and DT. Both in two and four category classifications, PNN yielded a considerably high accuracy of classification in training (95.85 percent and 90.07 percent) and validation data (91.30 percent and 86.96 percent), respectively. GRNN rendered a high correlation between the measured and model predicted -log LC50 values both for the training (0.929) and validation (0.910) data and low prediction errors (RMSE) of 0.52 and 0.49 for two sets. Efficiency of the selected PNN and GRNN models in predicting acute toxicity of new chemicals was adequately validated using external datasets of different fish species (fathead minnow, bluegill, trout, and guppy). The PNN and GRNN models showed good predictive and generalization abilities and can be used as tools for predicting toxicities of structurally diverse chemical compounds. Copyright © 2013 Elsevier Inc. All rights reserved.

Sirc-cvs cytotoxicity test: an alternative for predicting rodent acute systemic toxicity.

Science.gov (United States)

Kitagaki, Masato; Wakuri, Shinobu; Hirota, Morihiko; Tanaka, Noriho; Itagaki, Hiroshi

2006-10-01

An in vitro crystal violet staining method using the rabbit cornea-derived cell line (SIRC-CVS) has been developed as an alternative to predict acute systemic toxicity in rodents. Seventy-nine chemicals, the in vitro cytotoxicity of which was already reported by the Multicenter Evaluation of In vitro Toxicity (MEIC) and ICCVAM/ECVAM, were selected as test compounds. The cells were incubated with the chemicals for 72 hrs and the IC(50) and IC(35) values (microg/mL) were obtained. The results were compared to the in vivo (rat or mouse) "most toxic" oral, intraperitoneal, subcutaneous and intravenous LD(50) values (mg/kg) taken from the RTECS database for each of the chemicals by using Pearson's correlation statistics. The following parameters were calculated: accuracy, sensitivity, specificity, prevalence, positive predictability, and negative predictability. Good linear correlations (Pearson's coefficient; r>0.6) were observed between either the IC(50) or the IC(35) values and all the LD(50) values. Among them, a statistically significant high correlation (r=0.8102, p50) values and the oral LD(50) values. By using the cut-off concentrations of 2,000 mg/kg (LD(50)) and 4,225 microg/mL (IC(50)), no false negatives were observed, and the accuracy was 84.8%. From this, it is concluded that this method could be used to predict the acute systemic toxicity potential of chemicals in rodents.

Use of computer-assisted prediction of toxic effects of chemical substances

International Nuclear Information System (INIS)

Simon-Hettich, Brigitte; Rothfuss, Andreas; Steger-Hartmann, Thomas

2006-01-01

The current revision of the European policy for the evaluation of chemicals (REACH) has lead to a controversy with regard to the need of additional animal safety testing. To avoid increases in animal testing but also to save time and resources, alternative in silico or in vitro tests for the assessment of toxic effects of chemicals are advocated. The draft of the original document issued in 29th October 2003 by the European Commission foresees the use of alternative methods but does not give further specification on which methods should be used. Computer-assisted prediction models, so-called predictive tools, besides in vitro models, will likely play an essential role in the proposed repertoire of 'alternative methods'. The current discussion has urged the Advisory Committee of the German Toxicology Society to present its position on the use of predictive tools in toxicology. Acceptable prediction models already exist for those toxicological endpoints which are based on well-understood mechanism, such as mutagenicity and skin sensitization, whereas mechanistically more complex endpoints such as acute, chronic or organ toxicities currently cannot be satisfactorily predicted. A potential strategy to assess such complex toxicities will lie in their dissection into models for the different steps or pathways leading to the final endpoint. Integration of these models should result in a higher predictivity. Despite these limitations, computer-assisted prediction tools already today play a complementary role for the assessment of chemicals for which no data is available or for which toxicological testing is impractical due to the lack of availability of sufficient compounds for testing. Furthermore, predictive tools offer support in the screening and the subsequent prioritization of compound for further toxicological testing, as expected within the scope of the European REACH program. This program will also lead to the collection of high-quality data which will broaden the

Identifying developmental vascular disruptor compounds using a predictive signature and alternative toxicity models

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Identifying Developmental Vascular Disruptor Compounds Using a Predictive Signature and Alternative Toxicity Models Presenting Author: Tamara Tal Affiliation: U.S. EPA/ORD/ISTD, RTP, NC, USA Chemically induced vascular toxicity during embryonic development can result in a wide...

Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis.

Science.gov (United States)

Ren, Y Y; Zhou, L C; Yang, L; Liu, P Y; Zhao, B W; Liu, H X

2016-09-01

The paper highlights the use of the logistic regression (LR) method in the construction of acceptable statistically significant, robust and predictive models for the classification of chemicals according to their aquatic toxic modes of action. Essentials accounting for a reliable model were all considered carefully. The model predictors were selected by stepwise forward discriminant analysis (LDA) from a combined pool of experimental data and chemical structure-based descriptors calculated by the CODESSA and DRAGON software packages. Model predictive ability was validated both internally and externally. The applicability domain was checked by the leverage approach to verify prediction reliability. The obtained models are simple and easy to interpret. In general, LR performs much better than LDA and seems to be more attractive for the prediction of the more toxic compounds, i.e. compounds that exhibit excess toxicity versus non-polar narcotic compounds and more reactive compounds versus less reactive compounds. In addition, model fit and regression diagnostics was done through the influence plot which reflects the hat-values, studentized residuals, and Cook's distance statistics of each sample. Overdispersion was also checked for the LR model. The relationships between the descriptors and the aquatic toxic behaviour of compounds are also discussed.

THE FUTURE OF TOXICOLOGY-PREDICTIVE TOXICOLOGY: AN EXPANDED VIEW OF CHEMICAL TOXICITY

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A chemistry approach to predictive toxicology relies on structure−activity relationship (SAR) modeling to predict biological activity from chemical structure. Such approaches have proven capabilities when applied to well-defined toxicity end points or regions of chemical space. T...

GHS additivity formula: can it predict the acute systemic toxicity of agrochemical formulations that contain acutely toxic ingredients?

Science.gov (United States)

Van Cott, Andrew; Hastings, Charles E; Landsiedel, Robert; Kolle, Susanne; Stinchcombe, Stefan

2018-02-01

In vivo acute systemic testing is a regulatory requirement for agrochemical formulations. GHS specifies an alternative computational approach (GHS additivity formula) for calculating the acute toxicity of mixtures. We collected acute systemic toxicity data from formulations that contained one of several acutely-toxic active ingredients. The resulting acute data set includes 210 formulations tested for oral toxicity, 128 formulations tested for inhalation toxicity and 31 formulations tested for dermal toxicity. The GHS additivity formula was applied to each of these formulations and compared with the experimental in vivo result. In the acute oral assay, the GHS additivity formula misclassified 110 formulations using the GHS classification criteria (48% accuracy) and 119 formulations using the USEPA classification criteria (43% accuracy). With acute inhalation, the GHS additivity formula misclassified 50 formulations using the GHS classification criteria (61% accuracy) and 34 formulations using the USEPA classification criteria (73% accuracy). For acute dermal toxicity, the GHS additivity formula misclassified 16 formulations using the GHS classification criteria (48% accuracy) and 20 formulations using the USEPA classification criteria (36% accuracy). This data indicates the acute systemic toxicity of many formulations is not the sum of the ingredients' toxicity (additivity); but rather, ingredients in a formulation can interact to result in lower or higher toxicity than predicted by the GHS additivity formula. Copyright © 2018 Elsevier Inc. All rights reserved.

From basic physics to mechanisms of toxicity: the ``liquid drop'' approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles

Science.gov (United States)

Sizochenko, Natalia; Rasulev, Bakhtiyor; Gajewicz, Agnieszka; Kuz'min, Victor; Puzyn, Tomasz; Leszczynski, Jerzy

2014-10-01

Many metal oxide nanoparticles are able to cause persistent stress to live organisms, including humans, when discharged to the environment. To understand the mechanism of metal oxide nanoparticles' toxicity and reduce the number of experiments, the development of predictive toxicity models is important. In this study, performed on a series of nanoparticles, the comparative quantitative-structure activity relationship (nano-QSAR) analyses of their toxicity towards E. coli and HaCaT cells were established. A new approach for representation of nanoparticles' structure is presented. For description of the supramolecular structure of nanoparticles the ``liquid drop'' model was applied. It is expected that a novel, proposed approach could be of general use for predictions related to nanomaterials. In addition, in our study fragmental simplex descriptors and several ligand-metal binding characteristics were calculated. The developed nano-QSAR models were validated and reliably predict the toxicity of all studied metal oxide nanoparticles. Based on the comparative analysis of contributed properties in both models the LDM-based descriptors were revealed to have an almost similar level of contribution to toxicity in both cases, while other parameters (van der Waals interactions, electronegativity and metal-ligand binding characteristics) have unequal contribution levels. In addition, the models developed here suggest different mechanisms of nanotoxicity for these two types of cells.Many metal oxide nanoparticles are able to cause persistent stress to live organisms, including humans, when discharged to the environment. To understand the mechanism of metal oxide nanoparticles' toxicity and reduce the number of experiments, the development of predictive toxicity models is important. In this study, performed on a series of nanoparticles, the comparative quantitative-structure activity relationship (nano-QSAR) analyses of their toxicity towards E. coli and HaCaT cells were

Predictive value of cell assays for developmental toxicity and embryotoxicity of conazole fungicides

DEFF Research Database (Denmark)

Sørensen, Karin Dreisig; Taxvig, Camilla; Kjærstad, Mia Birkhøj

2013-01-01

in reasonably good agreement with available in vivo effects. Ketoconazole and epoxiconazole are the most potent embryotoxic compounds, whereas prochloraz belongs to the most potent developmental toxicants. In conclusion, a rough prediction of the ranking of these conazole fungicides for in vivo toxicity data...

The current status of biomarkers for predicting toxicity

Science.gov (United States)

Campion, Sarah; Aubrecht, Jiri; Boekelheide, Kim; Brewster, David W; Vaidya, Vishal S; Anderson, Linnea; Burt, Deborah; Dere, Edward; Hwang, Kathleen; Pacheco, Sara; Saikumar, Janani; Schomaker, Shelli; Sigman, Mark; Goodsaid, Federico

2013-01-01

Introduction There are significant rates of attrition in drug development. A number of compounds fail to progress past preclinical development due to limited tools that accurately monitor toxicity in preclinical studies and in the clinic. Research has focused on improving tools for the detection of organ-specific toxicity through the identification and characterization of biomarkers of toxicity. Areas covered This article reviews what we know about emerging biomarkers in toxicology, with a focus on the 2012 Northeast Society of Toxicology meeting titled ‘Translational Biomarkers in Toxicology.’ The areas covered in this meeting are summarized and include biomarkers of testicular injury and dysfunction, emerging biomarkers of kidney injury and translation of emerging biomarkers from preclinical species to human populations. The authors also provide a discussion about the biomarker qualification process and possible improvements to this process. Expert opinion There is currently a gap between the scientific work in the development and qualification of novel biomarkers for nonclinical drug safety assessment and how these biomarkers are actually used in drug safety assessment. A clear and efficient path to regulatory acceptance is needed so that breakthroughs in the biomarker toolkit for nonclinical drug safety assessment can be utilized to aid in the drug development process. PMID:23961847

A hypothetical model for predicting the toxicity of high aspect ratio nanoparticles (HARN)

Science.gov (United States)

Tran, C. L.; Tantra, R.; Donaldson, K.; Stone, V.; Hankin, S. M.; Ross, B.; Aitken, R. J.; Jones, A. D.

2011-12-01

The ability to predict nanoparticle (dimensional structures which are less than 100 nm in size) toxicity through the use of a suitable model is an important goal if nanoparticles are to be regulated in terms of exposures and toxicological effects. Recently, a model to predict toxicity of nanoparticles with high aspect ratio has been put forward by a consortium of scientists. The High aspect ratio nanoparticles (HARN) model is a platform that relates the physical dimensions of HARN (specifically length and diameter ratio) and biopersistence to their toxicity in biological environments. Potentially, this model is of great public health and economic importance, as it can be used as a tool to not only predict toxicological activity but can be used to classify the toxicity of various fibrous nanoparticles, without the need to carry out time-consuming and expensive toxicology studies. However, this model of toxicity is currently hypothetical in nature and is based solely on drawing similarities in its dimensional geometry with that of asbestos and synthetic vitreous fibres. The aim of this review is two-fold: (a) to present findings from past literature, on the physicochemical property and pathogenicity bioassay testing of HARN (b) to identify some of the challenges and future research steps crucial before the HARN model can be accepted as a predictive model. By presenting what has been done, we are able to identify scientific challenges and research directions that are needed for the HARN model to gain public acceptance. Our recommendations for future research includes the need to: (a) accurately link physicochemical data with corresponding pathogenicity assay data, through the use of suitable reference standards and standardised protocols, (b) develop better tools/techniques for physicochemical characterisation, (c) to develop better ways of monitoring HARN in the workplace, (d) to reliably measure dose exposure levels, in order to support future epidemiological

A hypothetical model for predicting the toxicity of high aspect ratio nanoparticles (HARN)

International Nuclear Information System (INIS)

Tran, C. L.; Tantra, R.; Donaldson, K.; Stone, V.; Hankin, S. M.; Ross, B.; Aitken, R. J.; Jones, A. D.

2011-01-01

The ability to predict nanoparticle (dimensional structures which are less than 100 nm in size) toxicity through the use of a suitable model is an important goal if nanoparticles are to be regulated in terms of exposures and toxicological effects. Recently, a model to predict toxicity of nanoparticles with high aspect ratio has been put forward by a consortium of scientists. The High aspect ratio nanoparticles (HARN) model is a platform that relates the physical dimensions of HARN (specifically length and diameter ratio) and biopersistence to their toxicity in biological environments. Potentially, this model is of great public health and economic importance, as it can be used as a tool to not only predict toxicological activity but can be used to classify the toxicity of various fibrous nanoparticles, without the need to carry out time-consuming and expensive toxicology studies. However, this model of toxicity is currently hypothetical in nature and is based solely on drawing similarities in its dimensional geometry with that of asbestos and synthetic vitreous fibres. The aim of this review is two-fold: (a) to present findings from past literature, on the physicochemical property and pathogenicity bioassay testing of HARN (b) to identify some of the challenges and future research steps crucial before the HARN model can be accepted as a predictive model. By presenting what has been done, we are able to identify scientific challenges and research directions that are needed for the HARN model to gain public acceptance. Our recommendations for future research includes the need to: (a) accurately link physicochemical data with corresponding pathogenicity assay data, through the use of suitable reference standards and standardised protocols, (b) develop better tools/techniques for physicochemical characterisation, (c) to develop better ways of monitoring HARN in the workplace, (d) to reliably measure dose exposure levels, in order to support future epidemiological

NMR-based urine analysis in rats: prediction of proximal tubule kidney toxicity and phospholipidosis.

Science.gov (United States)

Lienemann, Kai; Plötz, Thomas; Pestel, Sabine

2008-01-01

The aim of safety pharmacology is early detection of compound-induced side-effects. NMR-based urine analysis followed by multivariate data analysis (metabonomics) identifies efficiently differences between toxic and non-toxic compounds; but in most cases multiple administrations of the test compound are necessary. We tested the feasibility of detecting proximal tubule kidney toxicity and phospholipidosis with metabonomics techniques after single compound administration as an early safety pharmacology approach. Rats were treated orally, intravenously, inhalatively or intraperitoneally with different test compounds. Urine was collected at 0-8 h and 8-24 h after compound administration, and (1)H NMR-patterns were recorded from the samples. Variation of post-processing and feature extraction methods led to different views on the data. Support Vector Machines were trained on these different data sets and then aggregated as experts in an Ensemble. Finally, validity was monitored with a cross-validation study using a training, validation, and test data set. Proximal tubule kidney toxicity could be predicted with reasonable total classification accuracy (85%), specificity (88%) and sensitivity (78%). In comparison to alternative histological studies, results were obtained quicker, compound need was reduced, and very importantly fewer animals were needed. In contrast, the induction of phospholipidosis by the test compounds could not be predicted using NMR-based urine analysis or the previously published biomarker PAG. NMR-based urine analysis was shown to effectively predict proximal tubule kidney toxicity after single compound administration in rats. Thus, this experimental design allows early detection of toxicity risks with relatively low amounts of compound in a reasonably short period of time.

Prediction of toxicity of nitrobenzenes using ab initio and least squares support vector machines

International Nuclear Information System (INIS)

Niazi, Ali; Jameh-Bozorghi, Saeed; Nori-Shargh, Davood

2008-01-01

A quantitative structure-property relationship (QSPR) study is suggested for the prediction of toxicity (IGC 50 ) of nitrobenzenes. Ab initio theory was used to calculate some quantum chemical descriptors including electrostatic potentials and local charges at each atom, HOMO and LUMO energies, etc. Modeling of the IGC 50 of nitrobenzenes as a function of molecular structures was established by means of the least squares support vector machines (LS-SVM). This model was applied for the prediction of the toxicity (IGC 50 ) of nitrobenzenes, which were not in the modeling procedure. The resulted model showed high prediction ability with root mean square error of prediction of 0.0049 for LS-SVM. Results have shown that the introduction of LS-SVM for quantum chemical descriptors drastically enhances the ability of prediction in QSAR studies superior to multiple linear regression and partial least squares

The utility of QSARs in predicting acute fish toxicity of pesticide metabolites: A retrospective validation approach.

Science.gov (United States)

Burden, Natalie; Maynard, Samuel K; Weltje, Lennart; Wheeler, James R

2016-10-01

The European Plant Protection Products Regulation 1107/2009 requires that registrants establish whether pesticide metabolites pose a risk to the environment. Fish acute toxicity assessments may be carried out to this end. Considering the total number of pesticide (re-) registrations, the number of metabolites can be considerable, and therefore this testing could use many vertebrates. EFSA's recent "Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge-of-field surface waters" outlines opportunities to apply non-testing methods, such as Quantitative Structure Activity Relationship (QSAR) models. However, a scientific evidence base is necessary to support the use of QSARs in predicting acute fish toxicity of pesticide metabolites. Widespread application and subsequent regulatory acceptance of such an approach would reduce the numbers of animals used. The work presented here intends to provide this evidence base, by means of retrospective data analysis. Experimental fish LC50 values for 150 metabolites were extracted from the Pesticide Properties Database (http://sitem.herts.ac.uk/aeru/ppdb/en/atoz.htm). QSAR calculations were performed to predict fish acute toxicity values for these metabolites using the US EPA's ECOSAR software. The most conservative predicted LC50 values generated by ECOSAR were compared with experimental LC50 values. There was a significant correlation between predicted and experimental fish LC50 values (Spearman rs = 0.6304, p < 0.0001). For 62% of metabolites assessed, the QSAR predicted values are equal to or lower than their respective experimental values. Refined analysis, taking into account data quality and experimental variation considerations increases the proportion of sufficiently predictive estimates to 91%. For eight of the nine outliers, there are plausible explanation(s) for the disparity between measured and predicted LC50 values. Following detailed consideration of the robustness of

Prediction of Acute Mammalian Toxicity Using QSAR Methods: A Case Study of Sulfur Mustard and Its Breakdown Products

Directory of Open Access Journals (Sweden)

John Wheeler

2012-07-01

Full Text Available Predicting toxicity quantitatively, using Quantitative Structure Activity Relationships (QSAR, has matured over recent years to the point that the predictions can be used to help identify missing comparison values in a substance’s database. In this manuscript we investigate using the lethal dose that kills fifty percent of a test population (the LD₅₀ for determining relative toxicity of a number of substances. In general, the smaller the LD₅₀ value, the more toxic the chemical, and the larger the LD₅₀ value, the lower the toxicity. When systemic toxicity and other specific toxicity data are unavailable for the chemical(s of interest, during emergency responses, LD₅₀ values may be employed to determine the relative toxicity of a series of chemicals. In the present study, a group of chemical warfare agents and their breakdown products have been evaluated using four available rat oral QSAR LD₅₀ models. The QSAR analysis shows that the breakdown products of Sulfur Mustard (HD are predicted to be less toxic than the parent compound as well as other known breakdown products that have known toxicities. The QSAR estimated break down products LD₅₀ values ranged from 299 mg/kg to 5,764 mg/kg. This evaluation allows for the ranking and toxicity estimation of compounds for which little toxicity information existed; thus leading to better risk decision making in the field.

Predicting Pulmonary O2 Toxicity: A New Look at the Unit Pulmonary Toxicity Dose

Science.gov (United States)

1985-05-01

beatl loeged# aNy ahepes in Vo was considered error. The assumptieo was shoe eapeova to a 10 below the "safe’! I02 should have produced se daeromeat...Naval Medical Research Institute SethesdolMO 20814-505b NMRI 86-52 December 1986 PREDICTING4 PULMONARY 0 2 TOXICITY: j k,, NEW LOOK AT THE UNIT...distribution is unlimited °Q0- C(-" Naval Medical Research LLj and Development Command ,, J Bethesda, Maryland 20814-5044 -4 • Department of the Navy Naval

A re-evaluation of PETROTOX for predicting acute and chronic toxicity of petroleum substances.

Science.gov (United States)

Redman, Aaron D; Parkerton, Thomas F; Leon Paumen, Miriam; Butler, Josh D; Letinski, Daniel J; den Haan, Klass

2017-08-01

The PETROTOX model was developed to perform aquatic hazard assessment of petroleum substances based on substance composition. The model relies on the hydrocarbon block method, which is widely used for conducting petroleum substance risk assessments providing further justification for evaluating model performance. Previous work described this model and provided a preliminary calibration and validation using acute toxicity data for limited petroleum substance. The objective of the present study was to re-evaluate PETROTOX using expanded data covering both acute and chronic toxicity endpoints on invertebrates, algae, and fish for a wider range of petroleum substances. The results indicated that recalibration of 2 model parameters was required, namely, the algal critical target lipid body burden and the log octanol-water partition coefficient (K OW ) limit, used to account for reduced bioavailability of hydrophobic constituents. Acute predictions from the updated model were compared with observed toxicity data and found to generally be within a factor of 3 for algae and invertebrates but overestimated fish toxicity. Chronic predictions were generally within a factor of 5 of empirical data. Furthermore, PETROTOX predicted acute and chronic hazard classifications that were consistent or conservative in 93 and 84% of comparisons, respectively. The PETROTOX model is considered suitable for the purpose of characterizing petroleum substance hazard in substance classification and risk assessments. Environ Toxicol Chem 2017;36:2245-2252. © 2017 SETAC. © 2017 SETAC.

Dosimetric factors predictive of late toxicity in prostate cancer radiotherapy; Radiotherapie prostatique: prediction de la toxicite tardive a partir des donnees dosimetriques

Energy Technology Data Exchange (ETDEWEB)

Crevoisier, R. de [Departement de radiotherapie, centre Eugene-Marquis, 35 - Rennes (France); Inserm, U 642, 35 - Rennes (France); Fiorino, C. [Medical Physics Department, San Raffaele Scientific Institute, Melghera, Milan (Italy); Dubray, B. [Departement de radiotherapie et de physique medicale, centre Henri-Becquerel, 76 - Rouen (France); EA 4108, UFR de medecine-pharmacie, QuantIF-LITIS, 76 - Rouen (France)

2010-10-15

Dose escalation in prostate cancer is made possible due to technological advances and to precise dose-volume constraints to limit normal tissue damage. This article is a literature review focusing on the correlations between exposure (doses and volumes) of organs at risk (OAR) and rectal, urinary, sexual and bone toxicity, as well as on mathematical models aiming at toxicity prediction. Dose-volume constraint recommendations are presented that have been shown to be associated with reduced rectal damage. Indeed, the clinical data is relatively strong for late rectal toxicity (bleeding), with constraints put on both the volume of the rectum receiving high doses ({>=}70 Gy) and the volume receiving intermediate doses (40 to 60 Gy). Predictive models of rectal toxicity (Normal Tissue Complication Probability) appear to accurately estimate toxicity risks. The correlations are much weaker for the bulb and the femoral heads, and nearly do not exist for the bladder. Further prospective studies are required, ideally taking into account patient-related risk factors (co-morbidities and their specific treatments), assays of normal tissue hypersensitivity to ionizing radiation and mathematical models applied on 3D images acquired under the treatment machine (e.g. Cone Beam CT). (authors)

Predicted risks of radiogenic cardiac toxicity in two pediatric patients undergoing photon or proton radiotherapy

International Nuclear Information System (INIS)

Zhang, Rui; Howell, Rebecca M; Homann, Kenneth; Giebeler, Annelise; Taddei, Phillip J; Mahajan, Anita; Newhauser, Wayne D

2013-01-01

Hodgkin disease (HD) and medulloblastoma (MB) are common malignancies found in children and young adults, and radiotherapy is part of the standard treatment. It was reported that these patients who received radiation therapy have an increased risk of cardiovascular late effects. We compared the predicted risk of developing radiogenic cardiac toxicity after photon versus proton radiotherapies for a pediatric patient with HD and a pediatric patient with MB. In the treatment plans, each patient’s heart was contoured in fine detail, including substructures of the pericardium and myocardium. Risk calculations took into account both therapeutic and stray radiation doses. We calculated the relative risk (RR) of cardiac toxicity using a linear risk model and the normal tissue complication probability (NTCP) values using relative seriality and Lyman models. Uncertainty analyses were also performed. The RR values of cardiac toxicity for the HD patient were 7.27 (proton) and 8.37 (photon), respectively; the RR values for the MB patient were 1.28 (proton) and 8.39 (photon), respectively. The predicted NTCP values for the HD patient were 2.17% (proton) and 2.67% (photon) for the myocardium, and were 2.11% (proton) and 1.92% (photon) for the whole heart. The predicted ratios of NTCP values (proton/photon) for the MB patient were much less than unity. Uncertainty analyses revealed that the predicted ratio of risk between proton and photon therapies was sensitive to uncertainties in the NTCP model parameters and the mean radiation weighting factor for neutrons, but was not sensitive to heart structure contours. The qualitative findings of the study were not sensitive to uncertainties in these factors. We conclude that proton and photon radiotherapies confer similar predicted risks of cardiac toxicity for the HD patient in this study, and that proton therapy reduced the predicted risk for the MB patient in this study

A novel two-dimensional liquid chromatographic system for the online toxicity prediction of pharmaceuticals and related substances

Energy Technology Data Exchange (ETDEWEB)

Li, Jian; Xu, Li [Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030 (China); Shi, Zhi-guo, E-mail: shizg@whu.edu.cn [Department of Chemistry, Wuhan University, Wuhan 430072 (China); Hu, Min [Hubei Instrument for Food and Drug Control, Wuhan (China)

2015-08-15

Highlights: • A novel two-dimensional liquid chromatographic system was developed. • The 1st dimension was ODS to separate components in the sample. • The 2nd dimension was biopartitioning micellar chromatography to predict toxicity. • The system was used to screen toxicity of pharmaceuticals and related substances. • It was promising for fast online toxicity screening of complex sample in one step. - Abstract: In this study, a novel two-dimensional liquid chromatographic (2D-LC) system was developed for simultaneous separation and toxicity prediction of pharmaceutical and its related substances. A conventional ODS column was used on the 1st-D to separate the sample; while, bio-partitioning micellar chromatography served as the 2nd-D to predict toxicity of the components. The established system was tested for the toxicity of ibuprofen and its impurities with known toxicity. With only one injection, ibuprofen and its impurities were separated on the 1st-D; and LC50 values of individual impurity were obtained based on the quantitative retention–activity relationships, which agreed well with the reported data. Furthermore, LC50 values of photolysis transformation products (TPs) of carprofen, ketoprofen and diclofenac acid (as unknown compounds) were screened in this 2D-LC system, which could be an indicator of the toxicity of these TPs and was meaningful for the environmental monitoring and drinking water treatment. The established 2D-LC system was cost-effective, time-saving and reliable, and was promising for fast online screening of toxicity of known and unknown analytes in the complex sample in a single step. It may find applications in environment, pharmaceutical and food, etc.

A novel two-dimensional liquid chromatographic system for the online toxicity prediction of pharmaceuticals and related substances

International Nuclear Information System (INIS)

Li, Jian; Xu, Li; Shi, Zhi-guo; Hu, Min

2015-01-01

Highlights: • A novel two-dimensional liquid chromatographic system was developed. • The 1st dimension was ODS to separate components in the sample. • The 2nd dimension was biopartitioning micellar chromatography to predict toxicity. • The system was used to screen toxicity of pharmaceuticals and related substances. • It was promising for fast online toxicity screening of complex sample in one step. - Abstract: In this study, a novel two-dimensional liquid chromatographic (2D-LC) system was developed for simultaneous separation and toxicity prediction of pharmaceutical and its related substances. A conventional ODS column was used on the 1st-D to separate the sample; while, bio-partitioning micellar chromatography served as the 2nd-D to predict toxicity of the components. The established system was tested for the toxicity of ibuprofen and its impurities with known toxicity. With only one injection, ibuprofen and its impurities were separated on the 1st-D; and LC50 values of individual impurity were obtained based on the quantitative retention–activity relationships, which agreed well with the reported data. Furthermore, LC50 values of photolysis transformation products (TPs) of carprofen, ketoprofen and diclofenac acid (as unknown compounds) were screened in this 2D-LC system, which could be an indicator of the toxicity of these TPs and was meaningful for the environmental monitoring and drinking water treatment. The established 2D-LC system was cost-effective, time-saving and reliable, and was promising for fast online screening of toxicity of known and unknown analytes in the complex sample in a single step. It may find applications in environment, pharmaceutical and food, etc

Toxicity prediction of ionic liquids based on Daphnia magna by using density functional theory

Science.gov (United States)

Nu’aim, M. N.; Bustam, M. A.

2018-04-01

By using a model called density functional theory, the toxicity of ionic liquids can be predicted and forecast. It is a theory that allowing the researcher to have a substantial tool for computation of the quantum state of atoms, molecules and solids, and molecular dynamics which also known as computer simulation method. It can be done by using structural feature based quantum chemical reactivity descriptor. The identification of ionic liquids and its Log[EC50] data are from literature data that available in Ismail Hossain thesis entitled “Synthesis, Characterization and Quantitative Structure Toxicity Relationship of Imidazolium, Pyridinium and Ammonium Based Ionic Liquids”. Each cation and anion of the ionic liquids were optimized and calculated. The geometry optimization and calculation from the software, produce the value of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO). From the value of HOMO and LUMO, the value for other toxicity descriptors were obtained according to their formulas. The toxicity descriptor that involves are electrophilicity index, HOMO, LUMO, energy gap, chemical potential, hardness and electronegativity. The interrelation between the descriptors are being determined by using a multiple linear regression (MLR). From this MLR, all descriptors being analyzed and the descriptors that are significant were chosen. In order to develop the finest model equation for toxicity prediction of ionic liquids, the selected descriptors that are significant were used. The validation of model equation was performed with the Log[EC50] data from the literature and the final model equation was developed. A bigger range of ionic liquids which nearly 108 of ionic liquids can be predicted from this model equation.

Complex versus simple models: ion-channel cardiac toxicity prediction.

Science.gov (United States)

Mistry, Hitesh B

2018-01-01

There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear model B net was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall the B net model performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models.

Complex versus simple models: ion-channel cardiac toxicity prediction

Directory of Open Access Journals (Sweden)

Hitesh B. Mistry

2018-02-01

Full Text Available There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear model Bnet was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall the Bnet model performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models.

Predicting molybdenum toxicity to higher plants: Estimation of toxicity threshold values

Energy Technology Data Exchange (ETDEWEB)

McGrath, S.P., E-mail: steve.mcgrath@bbsrc.ac.u [Soil Science Department, Centre for Soils and Ecosystems Function, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ (United Kingdom); Mico, C.; Zhao, F.J.; Stroud, J.L. [Soil Science Department, Centre for Soils and Ecosystems Function, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ (United Kingdom); Zhang, H.; Fozard, S. [Division of Environmental Science, Lancaster University, Lancaster LA1 4YQ (United Kingdom)

2010-10-15

Four plant species (oilseed rape, Brassica napus L.; red clover, Trifolium pratense L.; ryegrass, Lolium perenne L.; and tomato, Lycopersicon esculentum L.) were tested on ten soils varying widely in soil properties to assess molybdenum (Mo) toxicity. A larger range (66-fold-609-fold) of added Mo concentrations resulting in 50% inhibition of yield (ED{sub 50}) was found among soils than among plant species (2-fold-38-fold), which illustrated that the soils differed widely in the expression of Mo toxicity. Toxicity thresholds based on soil solution Mo narrowed the variation among soils compared to thresholds based on added Mo concentrations. We conclude that plant bioavailability of Mo in soil depends on Mo solubility, but this alone did not decrease the variability in observed toxicity enough to be used in risk assessment and that other soil properties influencing Mo toxicity to plants need to be considered. - Mo toxicity thresholds varied widely in different soils and therefore soil properties need to be taken into account in order to assess the risk of Mo exposure.

Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence.

Science.gov (United States)

Lee, Jia-Ying Joey; Miller, James Alastair; Basu, Sreetama; Kee, Ting-Zhen Vanessa; Loo, Lit-Hsin

2018-06-01

Human lungs are susceptible to the toxicity induced by soluble xenobiotics. However, the direct cellular effects of many pulmonotoxic chemicals are not always clear, and thus, a general in vitro assay for testing pulmonotoxicity applicable to a wide variety of chemicals is not currently available. Here, we report a study that uses high-throughput imaging and artificial intelligence to build an in vitro pulmonotoxicity assay by automatically comparing and selecting human lung-cell lines and their associated quantitative phenotypic features most predictive of in vivo pulmonotoxicity. This approach is called "High-throughput In vitro Phenotypic Profiling for Toxicity Prediction" (HIPPTox). We found that the resulting assay based on two phenotypic features of a human bronchial epithelial cell line, BEAS-2B, can accurately classify 33 reference chemicals with human pulmonotoxicity information (88.8% balance accuracy, 84.6% sensitivity, and 93.0% specificity). In comparison, the predictivity of a standard cell-viability assay on the same set of chemicals is much lower (77.1% balanced accuracy, 84.6% sensitivity, and 69.5% specificity). We also used the assay to evaluate 17 additional test chemicals with unknown/unclear human pulmonotoxicity, and experimentally confirmed that many of the pulmonotoxic reference and predicted-positive test chemicals induce DNA strand breaks and/or activation of the DNA-damage response (DDR) pathway. Therefore, HIPPTox helps us to uncover these common modes-of-action of pulmonotoxic chemicals. HIPPTox may also be applied to other cell types or models, and accelerate the development of predictive in vitro assays for other cell-type- or organ-specific toxicities.

Toxicity ratios: Their use and abuse in predicting the risk from induced cancer

International Nuclear Information System (INIS)

Mays, C.W.; Taylor, G.N.; Lloyd, R.D.

1986-01-01

The toxicity ratio concept assumes the validity of certain relationships. In some examples for bone sarcoma induction, the approximate toxicity of 239 Pu in man can be calculated algebraically from the observed toxicity in the radium-dial painters and the ratio of 239 Pu/ 226 Ra toxicities in suitable laboratory mammals. In a species highly susceptible to bone sarcoma induction, the risk coefficients for both 239 Pu and 226 Ra are elevated, but the toxicity ratio of 239 Pu to 226 Ra tends to be similar to the ratio in resistant species. Among the tested species the toxicity ratio of 239 Pu to 226 Ra ranged from 6 to 22 (a fourfold range), whereas their relative sensitivities to 239 Pu varied by a factor of 150. The toxicity ratio approach can also be used to estimate the actinide risk to man from liver cancer, by comparing to the Thorotrast patients; from lung cancer, by comparing to the uranium miners and the atomic-bomb survivors; and from neutron-induced cancers, by comparing to cancers induced by gamma rays. The toxicity ratio can be used to predict the risk to man from a specific type of cancer that has been reliably induced by a reference radiation in humans and that can be induced by both the reference and the investigated radiation in suitable laboratory animals. 26 refs., 3 figs., 1 tab

Challenges for the development of a biotic ligand model predicting copper toxicity in estuaries and seas.

Science.gov (United States)

de Polo, Anna; Scrimshaw, Mark D

2012-02-01

An effort is ongoing to develop a biotic ligand model (BLM) that predicts copper (Cu) toxicity in estuarine and marine environments. At present, the BLM accounts for the effects of water chemistry on Cu speciation, but it does not consider the influence of water chemistry on the physiology of the organisms. We discuss how chemistry affects Cu toxicity not only by controlling its speciation, but also by affecting the osmoregulatory physiology of the organism, which varies according to salinity. In an attempt to understand the mechanisms of Cu toxicity and predict its impacts, we explore the hypothesis that the common factor linking the main toxic effects of Cu is the enzyme carbonic anhydrase (CA), because it is a Cu target with multiple functions and salinity-dependent expression and activity. According to this hypothesis, the site of action of Cu in marine fish may be not only the gill, but also the intestine, because in this tissue CA plays an important role in ion transport and water adsorption. Therefore, the BLM of Cu toxicity to marine fish should also consider the intestine as a biotic ligand. Finally, we underline the need to incorporate the osmotic gradient into the BLM calculations to account for the influence of physiology on Cu toxicity. Copyright © 2011 SETAC.

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches

Directory of Open Access Journals (Sweden)

Ashok K. Sharma

2017-11-01

Full Text Available The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93% and Matthews's correlation coefficient (0.84. The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84–0.87 on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better (R2 = 0.84 than the multi-linear regression (MLR and partial least square regression (PLSR models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2 performed better (R2 = 0.68 in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity

Toxicity evaluation and prediction of toxic chemicals on activated sludge system.

Science.gov (United States)

Cai, Bijing; Xie, Li; Yang, Dianhai; Arcangeli, Jean-Pierre

2010-05-15

The gaps of data for evaluating toxicity of new or overloaded organic chemicals on activated sludge system resulted in the requirements for methodology of toxicity estimation. In this study, 24 aromatic chemicals typically existed in the industrial wastewater were selected and classified into three groups of benzenes, phenols and anilines. Their toxicity on activated sludge was then investigated. Two indexes of IC(50-M) and IC(50-S) were determined respectively from the respiration rates of activated sludge with different toxicant concentration at mid-term (24h) and short-term (30min) time intervals. Experimental results showed that the group of benzenes was the most toxic, followed by the groups of phenols and anilines. The values of IC(50-M) of the tested chemicals were higher than those of IC(50-S). In addition, quantitative structure-activity relationships (QSARs) models developed from IC(50-M) were more stable and accurate than those of IC(50-S). The multiple linear models based on molecular descriptors and K(ow) presented better reliability than single linear models based on K(ow). Among these molecular descriptors, E(lumo) was the most important impact factor for evaluation of mid-term toxicity. Copyright (c) 2009 Elsevier B.V. All rights reserved.

In silico prediction of toxicity of phenols to Tetrahymena pyriformis by using genetic algorithm and decision tree-based modeling approach.

Science.gov (United States)

Abbasitabar, Fatemeh; Zare-Shahabadi, Vahid

2017-04-01

Risk assessment of chemicals is an important issue in environmental protection; however, there is a huge lack of experimental data for a large number of end-points. The experimental determination of toxicity of chemicals involves high costs and time-consuming process. In silico tools such as quantitative structure-toxicity relationship (QSTR) models, which are constructed on the basis of computational molecular descriptors, can predict missing data for toxic end-points for existing or even not yet synthesized chemicals. Phenol derivatives are known to be aquatic pollutants. With this background, we aimed to develop an accurate and reliable QSTR model for the prediction of toxicity of 206 phenols to Tetrahymena pyriformis. A multiple linear regression (MLR)-based QSTR was obtained using a powerful descriptor selection tool named Memorized_ACO algorithm. Statistical parameters of the model were 0.72 and 0.68 for R training 2 and R test 2 , respectively. To develop a high-quality QSTR model, classification and regression tree (CART) was employed. Two approaches were considered: (1) phenols were classified into different modes of action using CART and (2) the phenols in the training set were partitioned to several subsets by a tree in such a manner that in each subset, a high-quality MLR could be developed. For the first approach, the statistical parameters of the resultant QSTR model were improved to 0.83 and 0.75 for R training 2 and R test 2 , respectively. Genetic algorithm was employed in the second approach to obtain an optimal tree, and it was shown that the final QSTR model provided excellent prediction accuracy for the training and test sets (R training 2 and R test 2 were 0.91 and 0.93, respectively). The mean absolute error for the test set was computed as 0.1615. Copyright © 2016 Elsevier Ltd. All rights reserved.

Discriminating modes of toxic action in mice using toxicity in BALB/c mouse fibroblast (3T3) cells.

Science.gov (United States)

Huang, Tao; Yan, Lichen; Zheng, Shanshan; Wang, Yue; Wang, Xiaohong; Fan, Lingyun; Li, Chao; Zhao, Yuanhui; Martyniuk, Christopher J

2017-12-01

The objective of this study was to determine whether toxicity in mouse fibroblast cells (3T3 cells) could predict toxicity in mice. Synthesized data on toxicity was subjected to regression analysis and it was observed that relationship of toxicities between mice and 3T3 cells was not strong (R 2  = 0.41). Inclusion of molecular descriptors (e.g. ionization, pKa) improved the regression to R 2  = 0.56, indicating that this relationship is influenced by kinetic processes of chemicals or specific toxic mechanisms associated to the compounds. However, to determine if we were able to discriminate modes of action (MOAs) in mice using the toxicities generated from 3T3 cells, compounds were first classified into "baseline" and "reactive" guided by the toxic ratio (TR) for each compound in mice. Sequence, binomial and recursive partitioning analyses provided strong predictions of MOAs in mice based upon toxicities in 3T3 cells. The correct classification of MOAs based on these methods was 86%. Nearly all the baseline compounds predicted from toxicities in 3T3 cells were identified as baseline compounds from the TR in mice. The incorrect assignment of MOAs for some compounds is hypothesized to be due to experimental uncertainty that exists in toxicity assays for both mice and 3T3 cells. Conversely, lack of assignment can also arise because some reactive compounds have MOAs that are different in mice compared to 3T3 cells. The methods developed here are novel and contribute to efforts to reduce animal numbers in toxicity tests that are used to evaluate risks associated with organic pollutants in the environment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Improving reptile ecological risk assessment: oral and dermal toxicity of pesticides to a common lizard species (Sceloporus occidentalis).

Science.gov (United States)

Weir, Scott M; Yu, Shuangying; Talent, Larry G; Maul, Jonathan D; Anderson, Todd A; Salice, Christopher J

2015-08-01

Reptiles have been understudied in ecotoxicology, which limits consideration in ecological risk assessments. The goals of the present study were 3-fold: to improve oral and dermal dosing methodologies for reptiles, to generate reptile toxicity data for pesticides, and to correlate reptile and avian toxicity. The authors first assessed the toxicity of different dosing vehicles: 100 μL of water, propylene glycol, and acetone were not toxic. The authors then assessed the oral and dermal toxicity of 4 pesticides following the up-and-down procedure. Neither brodifacoum nor chlorothalonil caused mortality at doses ≤ 1750 μg/g. Under the "neat pesticide" oral exposure, endosulfan (median lethal dose [LD50] = 9.8 μg/g) was more toxic than λ-cyhalothrin (LD50 = 916.5 μg/g). Neither chemical was toxic via dermal exposure. An acetone dosing vehicle increased λ-cyhalothrin toxicity (oral LD50 = 9.8 μg/g; dermal LD50 = 17.5 μg/g), but not endosulfan. Finally, changes in dosing method and husbandry significantly increased dermal λ-cyhalothrin LD50s, which highlights the importance of standardized methods. The authors combined data from the present study with other reptile LD50s to correlate with available avian data. When only definitive LD50s were used in the analysis, a strong correlation was found between avian and reptile toxicity. The results suggest it is possible to build predictive relationships between avian and reptile LD50s. More research is needed, however, to understand trends associated with chemical classes and modes of action. © 2015 SETAC.

Predicting refinery effluent toxicity on the basis of hydrocarbon composition determined by GCxGC analysis

Energy Technology Data Exchange (ETDEWEB)

Whale, G. [and others

2013-04-15

A high resolution analytical method for determining hydrocarbon blocks in petroleum products by comprehensive two-dimensional gas chromatography (GCxGC) was used for the analysis of petroleum hydrocarbons extracted from refinery effluents. From 105 CONCAWE refineries in Europe 111 refinery effluents were collected in the period June 2008 to March 2009 (CONCAWE, 2010). The effluents were analysed for metals, standard effluent parameters (including Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), oil in water (OiW), GCxGC speciated hydrocarbons, BTEX (Benzene, Toluene, Ethylbenzene, and Xylenes) and volatile organic compounds. This report describes the subsequent analysis of the GCxGC data, as described in hydrocarbon blocks, and uses the PETROTOX model, to predict the environmental toxicity (i.e. ecotoxicity) of the discharged effluents. A further analysis was undertaken to address the potential environmental impact of these predicted effects initially using default dilution factors and then,when necessary site specific factors. The report describes all the methods used to arrive at the predictions, and shows that for the majority of refinery effluents direct toxicity effects in the effluents are not anticipated. Furthermore, when applying either the EU Risk Assessment Technical Guidance Document (TGD) default dilution factors or site specific dilution factors, none of the refineries are predicted to exerting either acute or chronic toxicity to organisms in the receiving aquatic environment, based on their hydrocarbon composition present in the effluent samples.

Using machine learning and quantum chemistry descriptors to predict the toxicity of ionic liquids.

Science.gov (United States)

Cao, Lingdi; Zhu, Peng; Zhao, Yongsheng; Zhao, Jihong

2018-06-15

Large-scale application of ionic liquids (ILs) hinges on the advancement of designable and eco-friendly nature. Research of the potential toxicity of ILs towards different organisms and trophic levels is insufficient. Quantitative structure-activity relationships (QSAR) model is applied to evaluate the toxicity of ILs towards the leukemia rat cell line (ICP-81). The structures of 57 cations and 21 anions were optimized by quantum chemistry. The electrostatic potential surface area (S EP ) and charge distribution area (S σ-profile ) descriptors are calculated and used to predict the toxicity of ILs. The performance and predictive aptitude of extreme learning machine (ELM) model are analyzed and compared with those of multiple linear regression (MLR) and support vector machine (SVM) models. The highest R 2 and the lowest AARD% and RMSE of the training set, test set and total set for the ELM are observed, which validates the superior performance of the ELM than that of obtained by the MLR and SVM. The applicability domain of the model is assessed by the Williams plot. Copyright © 2018 Elsevier B.V. All rights reserved.

A novel approach for predicting the uptake and toxicity of metallic and metalloid ions

Science.gov (United States)

Wang, Peng

2011-01-01

Electrostatic nature of plant plasma membrane (PM) plays significant roles in the ion uptake and toxicity. Electrical potential at the PM exterior surface (ψ0o) influences ion distribution at the PM exterior surface, and the depolarization of ψ0o negativity increases the electrical driving force for cation transport, but decreases the driving force for anion transport across the PMs. Assessing environmental risks of toxic ions has been a difficult task because the ion concentration (activity) in medium is not directly corrected to its potential effects. Medium characteristics like the content of major cations have important influences on the bioavailability and toxicity of ions in natural waters and soils. Models such as the Free Ion Activity Model (FIAM) and the Biotic Ligand Model (BLM), as usually employed, neglect the ψ0o and hence often lead to false conclusions about interaction mechanisms between toxic ions and major cations for biology. The neglect of ψ0o is not inconsistent with its importance, and possibly reflects the difficulty in the measurement of ψ0o. Based on the dual effects of the ψ0o, electrostatic models were developed to better predict the uptake and toxicity of metallic and metalloid ions. These results suggest that the electrostatic models provides a more robust mechanistic framework to assess metal(loid) ecotoxicity and predict critical metal(loid) concentrations linked to a biological effect, indicating its potential utility in risk assessment of metal(loid)s in water and terrestrial ecosystems. PMID:21386661

Plasma citrulline levels predict intestinal toxicity in patients treated with pelvic radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Onal, Cem; Kotek, Ayse; Arslan, Gungor; Topkan, Erkan (Dept. of Radiation Oncology, Baskent Univ. Faculty of Medicine, Adana (Turkey)), E-mail: hcemonal@hotmail.com; Unal, Birsel (Dept. of Biochemistry, Baskent Univ. Faculty of Medicine, Ankara (Turkey)); Yavuz, Aydin; Yavuz, Melek (Dept. of Radiation Oncology, Akdeniz Univ. Faculty of Medicine, Antalya (Turkey))

2011-11-15

Background. Radiotherapy (RT) for abdominal and pelvic malignancies often causes severe small bowel toxicity. Citrulline concentrations are known to decrease with intestinal failure. We thus evaluated the feasibility of plasma citrulline levels in predicting radiation-induced intestinal toxicity. Material and methods. Fifty-three patients (36 prostate cancer, 17 endometrial cancer) who received 45 Gy pelvic RT using conventional fractionation were prospectively evaluated. Patients with prostate cancer received an additional 25-30.6 Gy conformal boost. Plasma citrulline levels were assessed on day 0, mid- (week 3) and post-RT (week 8), and four months post-RT. Dose-volume histogram, citrulline concentration changes, and weekly intestinal toxicity scores were analyzed. Results. Mean age was 63 years (range: 43-81 years) and mean baseline citrulline concentration was 38.0 +- 10.1 mumol/l. Citrulline concentrations were significantly reduced at week 3 (27.4 +- 5.9 mumol/l; p < 0.0001), treatment end (29.9 +- 8.8 mumol/l; p < 0.0001), and four months post-treatment (34.3 +- 12.1; p 0.01). The following factor pairs were significantly positively correlated: Citrulline concentration/mean bowel dose during, end of treatment, and four months post-RT; dose-volume parameters/citrulline change groups; cumulative mean radiation dose/intestinal toxicity at end and four months post-RT; citrulline changes/intestinal toxicity during and end of RT. Citrulline concentration changes significantly differed during treatment according to RTOG intestinal toxicity grades (p < 0.0001). Although the citrulline changes differed significantly within RTOG intestinal toxicity grades (p = 0.003), the difference between Grade 0 and Grade 1 did not differ significantly at the end of the treatment. At four months after RT, no significant differences were apparent. Conclusion. Citrulline-based assessment scores are objective and should be considered in measuring radiation-induced intestinal toxicity

Plasma citrulline levels predict intestinal toxicity in patients treated with pelvic radiotherapy

International Nuclear Information System (INIS)

Onal, Cem; Kotek, Ayse; Arslan, Gungor; Topkan, Erkan; Unal, Birsel; Yavuz, Aydin; Yavuz, Melek

2011-01-01

Background. Radiotherapy (RT) for abdominal and pelvic malignancies often causes severe small bowel toxicity. Citrulline concentrations are known to decrease with intestinal failure. We thus evaluated the feasibility of plasma citrulline levels in predicting radiation-induced intestinal toxicity. Material and methods. Fifty-three patients (36 prostate cancer, 17 endometrial cancer) who received 45 Gy pelvic RT using conventional fractionation were prospectively evaluated. Patients with prostate cancer received an additional 25-30.6 Gy conformal boost. Plasma citrulline levels were assessed on day 0, mid- (week 3) and post-RT (week 8), and four months post-RT. Dose-volume histogram, citrulline concentration changes, and weekly intestinal toxicity scores were analyzed. Results. Mean age was 63 years (range: 43-81 years) and mean baseline citrulline concentration was 38.0 ± 10.1 μmol/l. Citrulline concentrations were significantly reduced at week 3 (27.4 ± 5.9 μmol/l; p < 0.0001), treatment end (29.9 ± 8.8 μmol/l; p < 0.0001), and four months post-treatment (34.3 ± 12.1; p 0.01). The following factor pairs were significantly positively correlated: Citrulline concentration/mean bowel dose during, end of treatment, and four months post-RT; dose-volume parameters/citrulline change groups; cumulative mean radiation dose/intestinal toxicity at end and four months post-RT; citrulline changes/intestinal toxicity during and end of RT. Citrulline concentration changes significantly differed during treatment according to RTOG intestinal toxicity grades (p < 0.0001). Although the citrulline changes differed significantly within RTOG intestinal toxicity grades (p = 0.003), the difference between Grade 0 and Grade 1 did not differ significantly at the end of the treatment. At four months after RT, no significant differences were apparent. Conclusion. Citrulline-based assessment scores are objective and should be considered in measuring radiation-induced intestinal toxicity

Work-principle model for predicting toxic fumes of nonideal explosives

Energy Technology Data Exchange (ETDEWEB)

Wieland, Michael S. [National Institute of Occupational Safety and Health, Pittsburgh Research Center, P.O. Box 18070, Pittsburgh, PA 15236-0070 (United States)

2004-08-01

The work-principle from thermodynamics was used to formulate a model for predicting toxic fumes from mining explosives in underground chamber tests, where rapid turbulent combustion within the surrounding air noticeably changes the resulting concentrations. Two model constants were required to help characterize the reaction zone undergoing rapid chemical transformations in conjunction with heat transfer and work output: a stoichiometry mixing fraction and a reaction-quenching temperature. Rudimentary theory with an unsteady uniform concentration gradient was taken to characterize the combustion zone, yielding 75% for the mixing fraction. Four quenching temperature trends were resolved and compared to test results of ammonium nitrate compositions with different fuel-oil percentages (ANFO). The quenching temperature 2345 K was the optimum choice for fitting the two major components of fume toxicity: carbon monoxide (CO) and total nitrogen oxides (NO{sub X}). The resulting two-constant model was used to generate comparisons for test results of ANFO compositions with additives. Though respectable fits were usually found, charge formulations which reacted weakly could not be resolved numerically. The work-principle model yields toxic concentrations for a range of charge formulations, making it a useful tool for investigating the potential hazard of released fumes and reducing the risk of unwanted incidents. (Abstract Copyright [2004], Wiley Periodicals, Inc.)

A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: A preliminary study

Science.gov (United States)

Kim, Kwang Hyeon; Lee, Suk; Shim, Jang Bo; Chang, Kyung Hwan; Yang, Dae Sik; Yoon, Won Sup; Park, Young Je; Kim, Chul Yong; Cao, Yuan Jie

2017-08-01

The aim of this study is an integrated research for text-based data mining and toxicity prediction modeling system for clinical decision support system based on big data in radiation oncology as a preliminary research. The structured and unstructured data were prepared by treatment plans and the unstructured data were extracted by dose-volume data image pattern recognition of prostate cancer for research articles crawling through the internet. We modeled an artificial neural network to build a predictor model system for toxicity prediction of organs at risk. We used a text-based data mining approach to build the artificial neural network model for bladder and rectum complication predictions. The pattern recognition method was used to mine the unstructured toxicity data for dose-volume at the detection accuracy of 97.9%. The confusion matrix and training model of the neural network were achieved with 50 modeled plans (n = 50) for validation. The toxicity level was analyzed and the risk factors for 25% bladder, 50% bladder, 20% rectum, and 50% rectum were calculated by the artificial neural network algorithm. As a result, 32 plans could cause complication but 18 plans were designed as non-complication among 50 modeled plans. We integrated data mining and a toxicity modeling method for toxicity prediction using prostate cancer cases. It is shown that a preprocessing analysis using text-based data mining and prediction modeling can be expanded to personalized patient treatment decision support based on big data.

An integrated multi-label classifier with chemical-chemical interactions for prediction of chemical toxicity effects.

Science.gov (United States)

Liu, Tao; Chen, Lei; Pan, Xiaoyong

2018-05-31

Chemical toxicity effect is one of the major reasons for declining candidate drugs. Detecting the toxicity effects of all chemicals can accelerate the procedures of drug discovery. However, it is time-consuming and expensive to identify the toxicity effects of a given chemical through traditional experiments. Designing quick, reliable and non-animal-involved computational methods is an alternative way. In this study, a novel integrated multi-label classifier was proposed. First, based on five types of chemical-chemical interactions retrieved from STITCH, each of which is derived from one aspect of chemicals, five individual classifiers were built. Then, several integrated classifiers were built by integrating some or all individual classifiers. By testing the integrated classifiers on a dataset with chemicals and their toxicity effects in Accelrys Toxicity database and non-toxic chemicals with their performance evaluated by jackknife test, an optimal integrated classifier was selected as the proposed classifier, which provided quite high prediction accuracies and wide applications. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Improvement in Shrimp Hatchery Procedures for Toxicity Tests

International Nuclear Information System (INIS)

Nor Azizah Marsiddi; Fazliana Mohd Saaya; Anee Suryani Sued

2015-01-01

Toxicity testing of brine shrimp Artemia salina Brine shrimp lethality assay is a screening test to determine half the dose mortality (LC50) for its shrimp given certain herbal extract at a concentration tested. The shrimp child mortality half a dose indicator to determine level of toxicity before further testing done on animal cell culture and animal experiments also on the mouse. The use of new hardware, namely Artemio 1 has increased its shrimp production at a rate that more and faster than the use of the black box hatching previously taken from the method by Solis, 1993. brine shrimp eggs from Artemio mix also easier to use because it contains egg and sea salt have been ready mixed for use in experiments. In conclusion, this method improvements help increase the number of offspring produced shrimp and produce experimental method easier than previous methods. (author)

Mixture toxicity of wood preservative products in the fish embryo toxicity test.

Science.gov (United States)

Coors, Anja; Dobrick, Jan; Möder, Monika; Kehrer, Anja

2012-06-01

Wood preservative products are used globally to protect wood from fungal decay and insects. We investigated the aquatic toxicity of five commercial wood preservative products, the biocidal active substances and some formulation additives contained therein, as well as six generic binary mixtures of the active substances in the fish embryo toxicity test (FET). Median lethal concentrations (LC50) of the single substances, the mixtures, and the products were estimated from concentration-response curves and corrected for concentrations measured in the test medium. The comparison of the experimentally observed mixture toxicity with the toxicity predicted by the concept of concentration addition (CA) showed less than twofold deviation for all binary mixtures of the active substances and for three of the biocidal products. A more than 60-fold underestimation of the toxicity of the fourth product by the CA prediction was detected and could be explained fully by the toxicity of one formulation additive, which had been labeled as a hazardous substance. The reason for the 4.6-fold underestimation of toxicity of the fifth product could not be explained unambiguously. Overall, the FET was found to be a suitable screening tool to verify whether the toxicity of formulated wood preservatives can reliably be predicted by CA. Applied as a quick and simple nonanimal screening test, the FET may support approaches of applying component-based mixture toxicity predictions within the environmental risk assessment of biocidal products, which is required according to European regulations. Copyright © 2012 SETAC.

Audiovisual biofeedback improves motion prediction accuracy.

Science.gov (United States)

Pollock, Sean; Lee, Danny; Keall, Paul; Kim, Taeho

2013-04-01

The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients' respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this study was to test the hypothesis that AV biofeedback improves the accuracy of motion prediction. An AV biofeedback system combined with real-time respiratory data acquisition and MR images were implemented in this project. One-dimensional respiratory data from (1) the abdominal wall (30 Hz) and (2) the thoracic diaphragm (5 Hz) were obtained from 15 healthy human subjects across 30 studies. The subjects were required to breathe with and without the guidance of AV biofeedback during each study. The obtained respiratory signals were then implemented in a kernel density estimation prediction algorithm. For each of the 30 studies, five different prediction times ranging from 50 to 1400 ms were tested (150 predictions performed). Prediction error was quantified as the root mean square error (RMSE); the RMSE was calculated from the difference between the real and predicted respiratory data. The statistical significance of the prediction results was determined by the Student's t-test. Prediction accuracy was considerably improved by the implementation of AV biofeedback. Of the 150 respiratory predictions performed, prediction accuracy was improved 69% (103/150) of the time for abdominal wall data, and 78% (117/150) of the time for diaphragm data. The average reduction in RMSE due to AV biofeedback over unguided respiration was 26% (p biofeedback improves prediction accuracy. This would result in increased efficiency of motion management techniques affected by system latencies used in radiotherapy.

Early hematologic changes during prostate cancer radiotherapy predictive for late urinary and bowel toxicity

Energy Technology Data Exchange (ETDEWEB)

Pinkawa, Michael; Djukic, Victoria; Klotz, Jens; Holy, Richard; Eble, Michael J. [RWTH Aachen University, Department of Radiation Oncology, Aachen (Germany); Ribbing, Carolina [RWTH Aachen University, Department of Diagnostic and Interventional Radiology, Aachen (Germany)

2015-10-15

The primary objective of the study was to identify early hematologic changes predictive for radiotherapy (RT)-associated genitourinary and gastrointestinal toxicity. In a group of 91 prostate cancer patients presenting for primary (n = 51) or postoperative (n = 40) curative RT, blood samples (blood count, acute phase proteins, and cytokines) were analyzed before (T1), three times during (T2-T4), and 6-8 weeks after (T5) radiotherapy. Before RT (baseline), on the last day (acute toxicity), a median of 2 months and 16 months (late toxicity) after RT, patients responded to a validated questionnaire (Expanded Prostate Cancer Index Composite). Acute score changes > 20 points and late changes > 10 points were considered clinically relevant. Radiotherapy resulted in significant changes of hematologic parameters, with the largest effect on lymphocytes (mean decrease of 31-45 %) and significant dependence on target volume. C-reactive protein (CRP) elevation > 5 mg/l and hemoglobin level decrease ≥ 5 G/1 at T2 were found to be independently predictive for acute urinary toxicity (p < 0.01, respectively). CRP elevation was predominantly detected in primary prostate RT (p = 0.02). Early lymphocyte level elevation ≥ 0.3G/l at T2 was protective against late urinary and bowel toxicity (p = 0.02, respectively). Other significant predictive factors for late bowel toxicity were decreasing hemoglobin levels (cut-off ≥ 5 G/l) at T2 (p = 0.04); changes of TNF-α (tumor necrosis factor; p = 0.03) and ferritin levels (p = 0.02) at T5. All patients with late bowel toxicity had interleukin (IL)-6 levels < 1.5 ng/l at T2 (63 % without; p = 0.01). Early hematologic changes during prostate cancer radiotherapy are predictive for late urinary and bowel toxicity. (orig.) [German] Das primaere Ziel der Studie war die Identifikation von fruehen haematologischen Veraenderungen mit praediktiver Bedeutung fuer radiotherapieassoziierte genitourinale und gastrointestinale Toxizitaet. In einer

Prediction of Chest Wall Toxicity From Lung Stereotactic Body Radiotherapy (SBRT)

Energy Technology Data Exchange (ETDEWEB)

Stephans, Kevin L., E-mail: stephak@ccf.org [Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH (United States); Djemil, Toufik; Tendulkar, Rahul D. [Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH (United States); Robinson, Cliff G. [Department of Radiation Oncology, Siteman Cancer Center, Washington University, St Louis, MO (United States); Reddy, Chandana A.; Videtic, Gregory M.M. [Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH (United States)

2012-02-01

Purpose: To determine patient, tumor, and treatment factors related to the development of late chest wall toxicity after lung stereotactic body radiotherapy (SBRT). Methods and Materials: We reviewed a registry of 134 patients treated with lung SBRT to 60 Gy in 3 fractions who had greater than 1 year of clinical follow-up and no history of multiple treatments to the same lobe (n = 48). Patients were treated as per Radiation Therapy Oncology Group Protocol 0236 without specific chest wall avoidance criteria. The chest wall was retrospectively contoured. Thirty-two lesions measured less than 3 cm, and sixteen measured 3 to 5 cm. The median planning target volume was 29 cm{sup 3}. Results: With a median follow-up of 18.8 months, 10 patients had late symptomatic chest wall toxicity (4 Grade 1 and 6 Grade 2) at a median of 8.8 months after SBRT. No patient characteristics (age, diabetes, hypertension, peripheral vascular disease, or body mass index) were predictive for toxicity, whereas there was a trend for continued smoking (p = 0.066; odds ratio [OR], 4.4). Greatest single tumor dimension (p = 0.047; OR, 2.63) and planning target volume (p = 0.040; OR, 1.04) were correlated with toxicity, whereas distance from tumor edge to chest wall and gross tumor volume did not reach statistical significance. Volumes of chest wall receiving 30 Gy (V30) through 70 Gy (V70) were all highly significant, although this correlation weakened for V65 and V70 and maximum chest wall point dose only trended to significance (p = 0.06). On multivariate analysis, tumor volume was no longer correlated with toxicity and only V30 through V60 remained statistically significant. Conclusions: Tumor size and chest wall dosimetry are correlated to late chest wall toxicity. Only chest wall V30 through V60 remained significant on multivariate analysis. Restricting V30 to 30 cm{sup 3} or less and V60 to 3 cm{sup 3} or less should result in a 10% to 15% risk of late chest wall toxicity or lower.

In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods.

Science.gov (United States)

Cheng, Feixiong; Shen, Jie; Yu, Yue; Li, Weihua; Liu, Guixia; Lee, Philip W; Tang, Yun

2011-03-01

There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods. Copyright © 2010 Elsevier Ltd. All rights reserved.

Predictive Systems Toxicology

KAUST Repository

Kiani, Narsis A.; Shang, Ming-Mei; Zenil, Hector; Tegner, Jesper

2018-01-01

In this review we address to what extent computational techniques can augment our ability to predict toxicity. The first section provides a brief history of empirical observations on toxicity dating back to the dawn of Sumerian civilization. Interestingly, the concept of dose emerged very early on, leading up to the modern emphasis on kinetic properties, which in turn encodes the insight that toxicity is not solely a property of a compound but instead depends on the interaction with the host organism. The next logical step is the current conception of evaluating drugs from a personalized medicine point-of-view. We review recent work on integrating what could be referred to as classical pharmacokinetic analysis with emerging systems biology approaches incorporating multiple omics data. These systems approaches employ advanced statistical analytical data processing complemented with machine learning techniques and use both pharmacokinetic and omics data. We find that such integrated approaches not only provide improved predictions of toxicity but also enable mechanistic interpretations of the molecular mechanisms underpinning toxicity and drug resistance. We conclude the chapter by discussing some of the main challenges, such as how to balance the inherent tension between the predictive capacity of models, which in practice amounts to constraining the number of features in the models versus allowing for rich mechanistic interpretability, i.e. equipping models with numerous molecular features. This challenge also requires patient-specific predictions on toxicity, which in turn requires proper stratification of patients as regards how they respond, with or without adverse toxic effects. In summary, the transformation of the ancient concept of dose is currently successfully operationalized using rich integrative data encoded in patient-specific models.

Predictive Systems Toxicology

KAUST Repository

Kiani, Narsis A.

2018-01-15

In this review we address to what extent computational techniques can augment our ability to predict toxicity. The first section provides a brief history of empirical observations on toxicity dating back to the dawn of Sumerian civilization. Interestingly, the concept of dose emerged very early on, leading up to the modern emphasis on kinetic properties, which in turn encodes the insight that toxicity is not solely a property of a compound but instead depends on the interaction with the host organism. The next logical step is the current conception of evaluating drugs from a personalized medicine point-of-view. We review recent work on integrating what could be referred to as classical pharmacokinetic analysis with emerging systems biology approaches incorporating multiple omics data. These systems approaches employ advanced statistical analytical data processing complemented with machine learning techniques and use both pharmacokinetic and omics data. We find that such integrated approaches not only provide improved predictions of toxicity but also enable mechanistic interpretations of the molecular mechanisms underpinning toxicity and drug resistance. We conclude the chapter by discussing some of the main challenges, such as how to balance the inherent tension between the predictive capacity of models, which in practice amounts to constraining the number of features in the models versus allowing for rich mechanistic interpretability, i.e. equipping models with numerous molecular features. This challenge also requires patient-specific predictions on toxicity, which in turn requires proper stratification of patients as regards how they respond, with or without adverse toxic effects. In summary, the transformation of the ancient concept of dose is currently successfully operationalized using rich integrative data encoded in patient-specific models.

Is the Factor-of-2 Rule Broadly Applicable for Evaluating the Prediction Accuracy of Metal-Toxicity Models?

Science.gov (United States)

Meyer, Joseph S; Traudt, Elizabeth M; Ranville, James F

2018-01-01

In aquatic toxicology, a toxicity-prediction model is generally deemed acceptable if its predicted median lethal concentrations (LC50 values) or median effect concentrations (EC50 values) are within a factor of 2 of their paired, observed LC50 or EC50 values. However, that rule of thumb is based on results from only two studies: multiple LC50 values for the fathead minnow (Pimephales promelas) exposed to Cu in one type of exposure water, and multiple EC50 values for Daphnia magna exposed to Zn in another type of exposure water. We tested whether the factor-of-2 rule of thumb also is supported in a different dataset in which D. magna were exposed separately to Cd, Cu, Ni, or Zn. Overall, the factor-of-2 rule of thumb appeared to be a good guide to evaluating the acceptability of a toxicity model's underprediction or overprediction of observed LC50 or EC50 values in these acute toxicity tests.

Lipoprotein metabolism indicators improve cardiovascular risk prediction.

Directory of Open Access Journals (Sweden)

Daniël B van Schalkwijk

Full Text Available BACKGROUND: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to investigate whether lipoprotein metabolism indicators can improve cardiovascular risk prediction and therapy management. METHODS AND RESULTS: We calculated lipoprotein metabolism indicators for 1981 subjects (145 cases, 1836 controls from the Framingham Heart Study offspring cohort in which NMR lipoprotein profiles were measured. We applied a statistical learning algorithm using a support vector machine to select conventional risk factors and lipoprotein metabolism indicators that contributed to predicting risk for general cardiovascular disease. Risk prediction was quantified by the change in the Area-Under-the-ROC-Curve (ΔAUC and by risk reclassification (Net Reclassification Improvement (NRI and Integrated Discrimination Improvement (IDI. Two VLDL lipoprotein metabolism indicators (VLDLE and VLDLH improved cardiovascular risk prediction. We added these indicators to a multivariate model with the best performing conventional risk markers. Our method significantly improved both CVD prediction and risk reclassification. CONCLUSIONS: Two calculated VLDL metabolism indicators significantly improved cardiovascular risk prediction. These indicators may help to reduce prescription of unnecessary cholesterol-lowering medication, reducing costs and possible side-effects. For clinical application, further validation is required.

SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients

Energy Technology Data Exchange (ETDEWEB)

Cho, Daniel D; Wernicke, A Gabriella; Nori, Dattatreyudu; Chao, KSC; Parashar, Bhupesh; Chang, Jenghwa [Weill Cornell Medical College, NY, NY (United States)

2014-06-01

Purpose/Objective(s): The aim of this study is to build the estimator of toxicity using artificial neural network (ANN) for head and neck cancer patients Materials/Methods: An ANN can combine variables into a predictive model during training and considered all possible correlations of variables. We constructed an ANN based on the data from 73 patients with advanced H and N cancer treated with external beam radiotherapy and/or chemotherapy at our institution. For the toxicity estimator we defined input data including age, sex, site, stage, pathology, status of chemo, technique of external beam radiation therapy (EBRT), length of treatment, dose of EBRT, status of post operation, length of follow-up, the status of local recurrences and distant metastasis. These data were digitized based on the significance and fed to the ANN as input nodes. We used 20 hidden nodes (for the 13 input nodes) to take care of the correlations of input nodes. For training ANN, we divided data into three subsets such as training set, validation set and test set. Finally, we built the estimator for the toxicity from ANN output. Results: We used 13 input variables including the status of local recurrences and distant metastasis and 20 hidden nodes for correlations. 59 patients for training set, 7 patients for validation set and 7 patients for test set and fed the inputs to Matlab neural network fitting tool. We trained the data within 15% of errors of outcome. In the end we have the toxicity estimation with 74% of accuracy. Conclusion: We proved in principle that ANN can be a very useful tool for predicting the RT outcomes for high risk H and N patients. Currently we are improving the results using cross validation.

SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients

International Nuclear Information System (INIS)

Cho, Daniel D; Wernicke, A Gabriella; Nori, Dattatreyudu; Chao, KSC; Parashar, Bhupesh; Chang, Jenghwa

2014-01-01

Purpose/Objective(s): The aim of this study is to build the estimator of toxicity using artificial neural network (ANN) for head and neck cancer patients Materials/Methods: An ANN can combine variables into a predictive model during training and considered all possible correlations of variables. We constructed an ANN based on the data from 73 patients with advanced H and N cancer treated with external beam radiotherapy and/or chemotherapy at our institution. For the toxicity estimator we defined input data including age, sex, site, stage, pathology, status of chemo, technique of external beam radiation therapy (EBRT), length of treatment, dose of EBRT, status of post operation, length of follow-up, the status of local recurrences and distant metastasis. These data were digitized based on the significance and fed to the ANN as input nodes. We used 20 hidden nodes (for the 13 input nodes) to take care of the correlations of input nodes. For training ANN, we divided data into three subsets such as training set, validation set and test set. Finally, we built the estimator for the toxicity from ANN output. Results: We used 13 input variables including the status of local recurrences and distant metastasis and 20 hidden nodes for correlations. 59 patients for training set, 7 patients for validation set and 7 patients for test set and fed the inputs to Matlab neural network fitting tool. We trained the data within 15% of errors of outcome. In the end we have the toxicity estimation with 74% of accuracy. Conclusion: We proved in principle that ANN can be a very useful tool for predicting the RT outcomes for high risk H and N patients. Currently we are improving the results using cross validation

Critical analysis of 3-D organoid in vitro cell culture models for high-throughput drug candidate toxicity assessments.

Science.gov (United States)

Astashkina, Anna; Grainger, David W

2014-04-01

Drug failure due to toxicity indicators remains among the primary reasons for staggering drug attrition rates during clinical studies and post-marketing surveillance. Broader validation and use of next-generation 3-D improved cell culture models are expected to improve predictive power and effectiveness of drug toxicological predictions. However, after decades of promising research significant gaps remain in our collective ability to extract quality human toxicity information from in vitro data using 3-D cell and tissue models. Issues, challenges and future directions for the field to improve drug assay predictive power and reliability of 3-D models are reviewed. Copyright © 2014 Elsevier B.V. All rights reserved.

Predictive factors for acute and late urinary toxicity after permanent interstitial brachytherapy in Japanese patients

International Nuclear Information System (INIS)

Tanimoto, Ryuta; Bekku, Kensuke; Katayama, Norihisa

2013-01-01

The objectives of this study were to describe the frequency of and to determine predictive factors associated with Radiation Therapy Oncology Group urinary toxicity in prostate brachytherapy patients. From January 2004 to April 2011, 466 consecutive Japanese patients underwent permanent iodine-125-seed brachytherapy (median follow up 48 months). International Prostate Symptom Score and Radiation Therapy Oncology Group toxicity data were prospectively collected. Prostate volume, International Prostate Symptom Score before and after brachytherapy, and postimplant analysis were examined for an association with urinary toxicity, defined as Radiation Therapy Oncology Group urinary toxicity of Grade 1 or higher. Logistic regression analysis was used to examine the factors associated with urinary toxicity. The rate of Radiation Therapy Oncology Group urinary toxicity grade 1 or higher at 1, 6, 12, 24, 36 and 48 months was 67%, 40%, 21%, 31%, 27% and 28%, respectively. Grade 2 or higher urinary toxicity was less than 1% at each time-point. International Prostate Symptom Score was highest at 3 months and returned to normal 12 months after brachytherapy. On multivariate analysis, patients with a larger prostate size, greater baseline International Prostate Symptom Score, higher prostate V100, higher prostate V150, higher prostate D90 and a greater number of seeds had more acute urinary toxicities at 1 month and 12 months after brachytherapy. On multivariate analysis, significant predictors for urinary toxicity at 1 month and 12 months were a greater baseline International Prostate Symptom Score and prostate V100. Most urinary symptoms are tolerated and resolved within 12 months after prostate brachytherapy. Acute and late urinary toxicity after brachytherapy is strongly related to the baseline International Prostate Symptom Score and prostate V100. (author)

Comparative analysis of pharmaceuticals versus industrial chemicals acute aquatic toxicity classification according to the United Nations classification system for chemicals. Assessment of the (Q)SAR predictability of pharmaceuticals acute aquatic toxicity and their predominant acute toxic mode-of-action

DEFF Research Database (Denmark)

Sanderson, Hans; Thomsen, Marianne

2009-01-01

data. Pharmaceuticals were found to be more frequent than industrial chemicals in GHS category III. Acute toxicity was predictable (>92%) using a generic (Q)SAR ((Quantitative) Structure Activity Relationship) suggesting a narcotic MOA. Analysis of model prediction error suggests that 68...

Multiple linear regression models for predicting chronic aluminum toxicity to freshwater aquatic organisms and developing water quality guidelines.

Science.gov (United States)

DeForest, David K; Brix, Kevin V; Tear, Lucinda M; Adams, William J

2018-01-01

The bioavailability of aluminum (Al) to freshwater aquatic organisms varies as a function of several water chemistry parameters, including pH, dissolved organic carbon (DOC), and water hardness. We evaluated the ability of multiple linear regression (MLR) models to predict chronic Al toxicity to a green alga (Pseudokirchneriella subcapitata), a cladoceran (Ceriodaphnia dubia), and a fish (Pimephales promelas) as a function of varying DOC, pH, and hardness conditions. The MLR models predicted toxicity values that were within a factor of 2 of observed values in 100% of the cases for P. subcapitata (10 and 20% effective concentrations [EC10s and EC20s]), 91% of the cases for C. dubia (EC10s and EC20s), and 95% (EC10s) and 91% (EC20s) of the cases for P. promelas. The MLR models were then applied to all species with Al toxicity data to derive species and genus sensitivity distributions that could be adjusted as a function of varying DOC, pH, and hardness conditions (the P. subcapitata model was applied to algae and macrophytes, the C. dubia model was applied to invertebrates, and the P. promelas model was applied to fish). Hazardous concentrations to 5% of the species or genera were then derived in 2 ways: 1) fitting a log-normal distribution to species-mean EC10s for all species (following the European Union methodology), and 2) fitting a triangular distribution to genus-mean EC20s for animals only (following the US Environmental Protection Agency methodology). Overall, MLR-based models provide a viable approach for deriving Al water quality guidelines that vary as a function of DOC, pH, and hardness conditions and are a significant improvement over bioavailability corrections based on single parameters. Environ Toxicol Chem 2018;37:80-90. © 2017 SETAC. © 2017 SETAC.

Postimplantation Analysis Enables Improvement of Dose-Volume Histograms and Reduction of Toxicity for Permanent Seed Implantation

International Nuclear Information System (INIS)

Wust, Peter; Postrach, Johanna; Kahmann, Frank; Henkel, Thomas; Graf, Reinhold; Cho, Chie Hee; Budach, Volker; Boehmer, Dirk

2008-01-01

Purpose: To demonstrate how postimplantation analysis is useful for improving permanent seed implantation and reducing toxicity. Patients and Methods: We evaluated 197 questionnaires completed by patients after permanent seed implantation (monotherapy between 1999 and 2003). For 70% of these patients, a computed tomography was available to perform postimplantation analysis. The index doses and volumes of the dose-volume histograms (DVHs) were determined and categorized with respect to the date of implantation. Differences in symptom scores relative to pretherapeutic status were analyzed with regard to follow-up times and DVH descriptors. Acute and subacute toxicities in a control group of 117 patients from an earlier study (June 1999 to September 2001) by Wust et al. (2004) were compared with a matched subgroup from this study equaling 110 patients treated between October 2001 and August 2003. Results: Improved performance, identifying a characteristic time dependency of DVH parameters (after implantation) and toxicity scores, was demonstrated. Although coverage (volume covered by 100% of the prescription dose of the prostate) increased slightly, high-dose regions decreased with the growing experience of the users. Improvement in the DVH and a reduction of toxicities were found in the patient group implanted in the later period. A decline in symptoms with follow-up time counteracts this gain of experience and must be considered. Urinary and sexual discomfort was enhanced by dose heterogeneities (e.g., dose covering 10% of the prostate volume, volume covered by 200% of prescription dose). In contrast, rectal toxicities correlated with exposed rectal volumes, especially the rectal volume covered by 100% of the prescription dose. Conclusion: The typical side effects occurring after permanent seed implantation can be reduced by improving the dose distributions. An improvement in dose distributions and a reduction of toxicities were identified with elapsed time between

Challenges for the development of a biotic ligand model predicting copper toxicity in estuaries and seas

OpenAIRE

de Polo, A; Scrimshaw, MD

2012-01-01

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2011 SETAC. An effort is ongoing to develop a biotic ligand model (BLM) that predicts copper (Cu) toxicity in estuarine and marine environments. At present, the BLM accounts for the effects of water chemistry on Cu speciation, but it does not consider the influence of water chemistry on the physiology of the organisms. We discuss how chemistry affects Cu toxicity not only by ...

COMPUTER-BASED PREDICTION OF TOXICITY USING THE ELECTRON-CONFORMATIONAL METHOD. APPLICATION TO FRAGRANCE ALLERGENS AND OTHER ENVIRONMENTAL POLLUTANTS

Directory of Open Access Journals (Sweden)

Natalia N. Gorinchoy

2012-06-01

Full Text Available The electron-conformational (EC method is employed for the toxicophore (Tph identification and quantitative prediction of toxicity using the training set of 24 compounds that are considered as fragrance allergens. The values of a=LD50 in oral exposure of rats were chosen as a measure of toxicity. EC parameters are evaluated on the base of conformational analysis and ab initio electronic structure calculations (including solvent influence. The Tph consists of four sites which in this series of compounds are represented by three carbon and one oxygen atoms, but may be any other atoms that have the same electronic and geometric features within the tolerance limits. The regression model taking into consideration the Tph flexibility, anti-Tph shielding, and influence of out-of-Tph functional groups predicts well the experimental values of toxicity (R2 = 0.93 with a reasonable leaveone- out cross-validation.

Prediction of acute toxicity of cadmium and lead to zebrafish larvae by using a refined toxicokinetic-toxicodynamic model

International Nuclear Information System (INIS)

Gao, Yongfei; Feng, Jianfeng; Zhu, Lin

2015-01-01

Highlights: • We developed a BLM-aided TK-TD model that considers the effects of H"+. • The time-course metal concentration in larvae was well described by the TK model. • The time-course survival of zebrafish larvae was well simulated by the TD model. - Abstract: The biotic ligand model (BLM) and the toxicokinetic-toxicodynamic (TK-TD) model are essential in predicting the acute toxicity of metals in various species and exposure conditions; however, these models are usually separately utilized. In this study, a mechanistic TK-TD model was developed to predict the acute toxicity of 10"−"6 M Cd and 10"−"6 M Pb to zebrafish (Danio rerio) larvae. The novel approach links the BLM with relevant TK processes to simulate the bioaccumulation processes of Cd or Pb as a function of the maximum uptake rate of each metal, the affinity constants, and the concentrations of free metal ions and H"+ in test solutions. Results showed that the refined TK-TD model can accurately predict the accumulation and acute toxicity of Cd and Pb to zebrafish larvae at pH 5.5, 6.5, and 7.0.

Prediction of acute toxicity of cadmium and lead to zebrafish larvae by using a refined toxicokinetic-toxicodynamic model

Energy Technology Data Exchange (ETDEWEB)

Gao, Yongfei; Feng, Jianfeng, E-mail: fengjf@nankai.edu.cn; Zhu, Lin, E-mail: zhulin@nankai.edu.cn

2015-12-15

Highlights: • We developed a BLM-aided TK-TD model that considers the effects of H{sup +}. • The time-course metal concentration in larvae was well described by the TK model. • The time-course survival of zebrafish larvae was well simulated by the TD model. - Abstract: The biotic ligand model (BLM) and the toxicokinetic-toxicodynamic (TK-TD) model are essential in predicting the acute toxicity of metals in various species and exposure conditions; however, these models are usually separately utilized. In this study, a mechanistic TK-TD model was developed to predict the acute toxicity of 10{sup −6} M Cd and 10{sup −6} M Pb to zebrafish (Danio rerio) larvae. The novel approach links the BLM with relevant TK processes to simulate the bioaccumulation processes of Cd or Pb as a function of the maximum uptake rate of each metal, the affinity constants, and the concentrations of free metal ions and H{sup +} in test solutions. Results showed that the refined TK-TD model can accurately predict the accumulation and acute toxicity of Cd and Pb to zebrafish larvae at pH 5.5, 6.5, and 7.0.

Serum Creatinine Versus Plasma Methotrexate Levels to Predict Toxicities in Children Receiving High-dose Methotrexate.

Science.gov (United States)

Tiwari, Priya; Thomas, M K; Pathania, Subha; Dhawan, Deepa; Gupta, Y K; Vishnubhatla, Sreenivas; Bakhshi, Sameer

2015-01-01

Facilities for measuring methotrexate (MTX) levels are not available everywhere, potentially limiting administration of high-dose methotrexate (HDMTX). We hypothesized that serum creatinine alteration after HDMTX administration predicts MTX clearance. Overall, 122 cycles in 50 patients of non-Hodgkin lymphoma or acute lymphoblastic leukemia aged ≤18 years receiving HDMTX were enrolled prospectively. Plasma MTX levels were measured at 12, 24, 36, 48, 60, and 72 hours; serum creatinine was measured at baseline, 24, 48, and 72 hours. Correlation of plasma MTX levels with creatinine levels and changes in creatinine from baseline (Δ creatinine) were evaluated. Plasma MTX levels at 72 hours showed positive correlation with serum creatinine at 48 hours (P = .011) and 72 hours (P = .013) as also Δ creatinine at 48 hours (P = .042) and 72 hours (P = .045). However, cut-off value of either creatinine or Δ creatinine could not be established to reliably predict delayed MTX clearance. Greater than 50% Δ creatinine at 48 and 72 hours significantly predicted grade 3/4 leucopenia (P = .036 and P = .001, respectively) and thrombocytopenia (P = .012 and P = .009, respectively) but not mucositis (P = .827 and P = .910, respectively). Delayed MTX elimination did not predict any grade 3/4 toxicity. In spite of demonstration of significant correlation between serum creatinine and Δ creatinine with plasma MTX levels at 72 hours, cut-off value of either variable to predict MTX delay could not be established. Thus, either of these cannot be used as a surrogate for plasma MTX estimation. Interestingly, Δ creatinine effectively predicted hematological toxicities, which were not predicted by delayed MTX clearance.

Prediction of clinical toxicity in locally advanced head and neck cancer patients by radio-induced apoptosis in peripheral blood lymphocytes (PBLs)

International Nuclear Information System (INIS)

Bordón, Elisa; Henríquez-Hernández, Luis Alberto; Lara, Pedro C; Ruíz, Ana; Pinar, Beatriz; Rodríguez-Gallego, Carlos; Lloret, Marta

2010-01-01

Head and neck cancer is treated mainly by surgery and radiotherapy. Normal tissue toxicity due to x-ray exposure is a limiting factor for treatment success. Many efforts have been employed to develop predictive tests applied to clinical practice. Determination of lymphocyte radio-sensitivity by radio-induced apoptosis arises as a possible method to predict tissue toxicity due to radiotherapy. The aim of the present study was to analyze radio-induced apoptosis of peripheral blood lymphocytes in head and neck cancer patients and to explore their role in predicting radiation induced toxicity. Seventy nine consecutive patients suffering from head and neck cancer, diagnosed and treated in our institution, were included in the study. Toxicity was evaluated using the Radiation Therapy Oncology Group scale. Peripheral blood lymphocytes were isolated and irradiated at 0, 1, 2 and 8 Gy during 24 hours. Apoptosis was measured by flow cytometry using annexin V/propidium iodide. Lymphocytes were marked with CD45 APC-conjugated monoclonal antibody. Radiation-induced apoptosis increased in order to radiation dose and fitted to a semi logarithmic model defined by two constants: α and β. α, as the origin of the curve in the Y axis determining the percentage of spontaneous cell death, and β, as the slope of the curve determining the percentage of cell death induced at a determined radiation dose, were obtained. β value was statistically associated to normal tissue toxicity in terms of severe xerostomia, as higher levels of apoptosis were observed in patients with low toxicity (p = 0.035; Exp(B) 0.224, I.C.95% (0.060-0.904)). These data agree with our previous results and suggest that it is possible to estimate the radiosensitivity of peripheral blood lymphocytes from patients determining the radiation induced apoptosis with annexin V/propidium iodide staining. β values observed define an individual radiosensitivity profile that could predict late toxicity due to radiotherapy

Developing predictions of in vivo developmental toxicity of ToxCast chemicals using mouse embryonic stem cells.

Science.gov (United States)

Developing predictions of in vivo developmental toxicity of ToxCast chemicals using mouse embryonic stem cells S. Hunter, M. Rosen, M. Hoopes, H. Nichols, S. Jeffay, K. Chandler1, Integrated Systems Toxicology Division, National Health and Environmental Effects Research Labor...

Genomics and the prediction of xenobiotic toxicity

International Nuclear Information System (INIS)

Meyer, Urs-A.; Gut, Josef

2002-01-01

The systematic identification and functional analysis of human genes is revolutionizing the study of disease processes and the development and rational use of drugs. It increasingly enables medicine to make reliable assessments of the individual risk to acquire a particular disease, raises the number and specificity of drug targets and explains interindividual variation of the effectiveness and toxicity of drugs. Mutant alleles at a single gene locus for more than 20 drug metabolizing enzymes are some of the best studied individual risk factors for adverse drug reactions and xenobiotic toxicity. Increasingly, genetic polymorphisms of transporter and receptor systems are also recognized as causing interindividual variation in drug response and drug toxicity. However, pharmacogenetic and toxicogenetic factors rarely act alone; they produce a phenotype in concert with other variant genes and with environmental factors. Environmental factors may affect gene expression in many ways. For instance, numerous drugs induce their own and the metabolism of other xenobiotics by interacting with nuclear receptors such as AhR, PPAR, PXR and CAR. Genomics is providing the information and technology to analyze these complex situations to obtain individual genotypic and gene expression information to assess the risk of toxicity

In silico toxicology: comprehensive benchmarking of multi-label classification methods applied to chemical toxicity data

KAUST Repository

Raies, Arwa B.

2017-12-05

One goal of toxicity testing, among others, is identifying harmful effects of chemicals. Given the high demand for toxicity tests, it is necessary to conduct these tests for multiple toxicity endpoints for the same compound. Current computational toxicology methods aim at developing models mainly to predict a single toxicity endpoint. When chemicals cause several toxicity effects, one model is generated to predict toxicity for each endpoint, which can be labor and computationally intensive when the number of toxicity endpoints is large. Additionally, this approach does not take into consideration possible correlation between the endpoints. Therefore, there has been a recent shift in computational toxicity studies toward generating predictive models able to predict several toxicity endpoints by utilizing correlations between these endpoints. Applying such correlations jointly with compounds\\' features may improve model\\'s performance and reduce the number of required models. This can be achieved through multi-label classification methods. These methods have not undergone comprehensive benchmarking in the domain of predictive toxicology. Therefore, we performed extensive benchmarking and analysis of over 19,000 multi-label classification models generated using combinations of the state-of-the-art methods. The methods have been evaluated from different perspectives using various metrics to assess their effectiveness. We were able to illustrate variability in the performance of the methods under several conditions. This review will help researchers to select the most suitable method for the problem at hand and provide a baseline for evaluating new approaches. Based on this analysis, we provided recommendations for potential future directions in this area.

In silico toxicology: comprehensive benchmarking of multi-label classification methods applied to chemical toxicity data

KAUST Repository

Raies, Arwa B.; Bajic, Vladimir B.

2017-01-01

One goal of toxicity testing, among others, is identifying harmful effects of chemicals. Given the high demand for toxicity tests, it is necessary to conduct these tests for multiple toxicity endpoints for the same compound. Current computational toxicology methods aim at developing models mainly to predict a single toxicity endpoint. When chemicals cause several toxicity effects, one model is generated to predict toxicity for each endpoint, which can be labor and computationally intensive when the number of toxicity endpoints is large. Additionally, this approach does not take into consideration possible correlation between the endpoints. Therefore, there has been a recent shift in computational toxicity studies toward generating predictive models able to predict several toxicity endpoints by utilizing correlations between these endpoints. Applying such correlations jointly with compounds' features may improve model's performance and reduce the number of required models. This can be achieved through multi-label classification methods. These methods have not undergone comprehensive benchmarking in the domain of predictive toxicology. Therefore, we performed extensive benchmarking and analysis of over 19,000 multi-label classification models generated using combinations of the state-of-the-art methods. The methods have been evaluated from different perspectives using various metrics to assess their effectiveness. We were able to illustrate variability in the performance of the methods under several conditions. This review will help researchers to select the most suitable method for the problem at hand and provide a baseline for evaluating new approaches. Based on this analysis, we provided recommendations for potential future directions in this area.

Concentration addition and independent action model: Which is better in predicting the toxicity for metal mixtures on zebrafish larvae.

Science.gov (United States)

Gao, Yongfei; Feng, Jianfeng; Kang, Lili; Xu, Xin; Zhu, Lin

2018-01-01

The joint toxicity of chemical mixtures has emerged as a popular topic, particularly on the additive and potential synergistic actions of environmental mixtures. We investigated the 24h toxicity of Cu-Zn, Cu-Cd, and Cu-Pb and 96h toxicity of Cd-Pb binary mixtures on the survival of zebrafish larvae. Joint toxicity was predicted and compared using the concentration addition (CA) and independent action (IA) models with different assumptions in the toxic action mode in toxicodynamic processes through single and binary metal mixture tests. Results showed that the CA and IA models presented varying predictive abilities for different metal combinations. For the Cu-Cd and Cd-Pb mixtures, the CA model simulated the observed survival rates better than the IA model. By contrast, the IA model simulated the observed survival rates better than the CA model for the Cu-Zn and Cu-Pb mixtures. These findings revealed that the toxic action mode may depend on the combinations and concentrations of tested metal mixtures. Statistical analysis of the antagonistic or synergistic interactions indicated that synergistic interactions were observed for the Cu-Cd and Cu-Pb mixtures, non-interactions were observed for the Cd-Pb mixtures, and slight antagonistic interactions for the Cu-Zn mixtures. These results illustrated that the CA and IA models are consistent in specifying the interaction patterns of binary metal mixtures. Copyright © 2017 Elsevier B.V. All rights reserved.

Predictive factors of gastroduodenal toxicity in cirrhotic patients after three-dimensional conformal radiotherapy for hepatocellular carcinoma

International Nuclear Information System (INIS)

Kim, Haeyoung; Lim, Do Hoon; Paik, Seung Woon; Yoo, Byung Chul; Koh, Kwang Gheol; Lee, Joon Hyoek; Choi, Moon Seok; Park, Won; Park, Hee Chul; Huh, Seung Jae; Choi, Doo Ho; Ahn, Yong Chan

2009-01-01

Background and purpose: To identify predictive factors for the development of gastroduodenal toxicity (GDT) in cirrhotic patients treated with three-dimensional conformal radiotherapy (3D-CRT) for hepatocellular carcinoma (HCC). Materials and methods: We retrospectively analyzed dose-volume histograms (DVHs) and clinical records of 73 cirrhotic patients treated with 3D-CRT for HCC. The median radiation dose was 36 Gy (range, 30-54 Gy) with a daily dose of 3 Gy. The grade of GDT was defined by the Common Toxicity Criteria Version 2. The predictive factors of grade 3 GDT were identified. Results: Grade 3 GDT was found in 9 patients. Patient's age and the percentage of gastroduodenal volume receiving more than 35 Gy (V 35 ) significantly affected the development of grade 3 GDT. Patients over 50 years of age developed grade 3 GDT more frequently than patients under 50 years of age. The risk of grade 3 GDT grew exponentially as V 35 increased. The 1-year actuarial rate of grade 3 GDT in patients with V 35 35 ≥5% (4% vs. 48%, p 35 were the most predictive factors for the development of grade 3 GDT in patients treated with RT.

Comparison of the capacity of two biotic ligand models to predict chronic copper toxicity to two Daphnia magna clones and formulation of a generalized bioavailability model.

Science.gov (United States)

Van Regenmortel, Tina; Janssen, Colin R; De Schamphelaere, Karel A C

2015-07-01

Although it is increasingly recognized that biotic ligand models (BLMs) are valuable in the risk assessment of metals in aquatic systems, the use of 2 differently structured and parameterized BLMs (1 in the United States and another in the European Union) to obtain bioavailability-based chronic water quality criteria for copper is worthy of further investigation. In the present study, the authors evaluated the predictive capacity of these 2 BLMs for a large dataset of chronic copper toxicity data with 2 Daphnia magna clones, termed K6 and ARO. One BLM performed best with clone K6 data, whereas the other performed best with clone ARO data. In addition, there was an important difference between the 2 BLMs in how they predicted the bioavailability of copper as a function of pH. These modeling results suggested that the effect of pH on chronic copper toxicity is different between the 2 clones considered, which was confirmed with additional chronic toxicity experiments. Finally, because fundamental differences in model structure between the 2 BLMs made it impossible to create an average BLM, a generalized bioavailability model (gBAM) was developed. Of the 3 gBAMs developed, the authors recommend the use of model gBAM-C(uni), which combines a log-linear relation between the 21-d median effective concentration (expressed as free Cu(2+) ion activity) and pH, with more conventional BLM-type competition constants for sodium, calcium, and magnesium. This model can be considered a first step in further improving the accuracy of chronic toxicity predictions of copper as a function of water chemistry (for a variety of Daphnia magna clones), even beyond the robustness of the current BLMs used in regulatory applications. © 2015 SETAC.

Improved distribution and reduced toxicity of adriamycin bound to albumin-heparin microspheres

NARCIS (Netherlands)

Cremers, Harry; Cremers, H.F.M.; Bayon, L.G.; Verrijk, R.; Wesseling, M.M.; Wondergem, J.; Heuff, G.; Kwon, G.S.; Bae, Y.H.; Feijen, Jan; Kim, S.W.

1995-01-01

Adriamycin (ADR) was formulated in albumin-heparin conjugate microspheres (AHCMS) to improve site-specific delivery and to reduce the toxicity of the drug. The effect of formulating ADR in AHCMS was investigated upon intrahepatic administration to male Wag/Rij rats. After intraveno-portal (i.v.p.)

Human Pluripotent Stem Cell-Based Assay Predicts Developmental Toxicity Potential of ToxCast Chemicals (ACT meeting)

Science.gov (United States)

Worldwide initiatives to screen for toxicity potential among the thousands of chemicals currently in use require inexpensive and high-throughput in vitro models to meet their goals. The devTOX quickPredict platform is an in vitro human pluripotent stem cell-based assay used to as...

Addition of contaminant bioavailability and species susceptibility to a sediment toxicity assessment: Application in an urban stream in China

International Nuclear Information System (INIS)

Li, Huizhen; Sun, Baoquan; Chen, Xin; Lydy, Michael J.; You, Jing

2013-01-01

Sediments collected from an urban creek in China exhibited high acute toxicity to Hyalella azteca with 81.3% of sediments being toxic. A toxic unit (TU) estimation demonstrated that the pyrethroid, cypermethrin, was the major contributor to toxicity. The traditional TU approach, however, overestimated the toxicity. Reduced bioavailability of sediment-associated cypermethrin due to sequestration explained the overestimation. Additionally, antagonism among multiple contaminants and species susceptibility to various contaminants also contributed to the unexpectedly low toxicity to H. azteca. Bioavailable TUs derived from the bioavailability-based approaches, Tenax extraction and matrix-solid phase microextraction (matrix-SPME), showed better correlations with the noted toxicity compared to traditional TUs. As the first successful attempt to use matrix-SPME for estimating toxicity caused by emerging insecticides in field sediment, the present study found freely dissolved cypermethrin concentrations significantly improved the prediction of sediment toxicity to H. azteca compared to organic carbon normalized and Tenax extractable concentrations. Highlights: •Over 80% sediments from an urban stream in China were acutely toxic to H. azteca. •Toxic unit analysis showed cypermethrin was the major contributor to toxicity. •The traditional toxic unit approach overestimated sediment toxicity. •Reduced bioavailability was the reason for overestimating sediment toxicity. •Freely dissolved cypermethrin concentrations greatly improved toxicity prediction. -- Field sediment toxicity caused by current-use pesticides could be more accurately evaluated by incorporating bioavailability measurements into the toxic unit analysis

Large-scale assessment of the zebrafish embryo as a possible predictive model in toxicity testing.

Directory of Open Access Journals (Sweden)

Shaukat Ali

Full Text Available BACKGROUND: In the drug discovery pipeline, safety pharmacology is a major issue. The zebrafish has been proposed as a model that can bridge the gap in this field between cell assays (which are cost-effective, but low in data content and rodent assays (which are high in data content, but less cost-efficient. However, zebrafish assays are only likely to be useful if they can be shown to have high predictive power. We examined this issue by assaying 60 water-soluble compounds representing a range of chemical classes and toxicological mechanisms. METHODOLOGY/PRINCIPAL FINDINGS: Over 20,000 wild-type zebrafish embryos (including controls were cultured individually in defined buffer in 96-well plates. Embryos were exposed for a 96 hour period starting at 24 hours post fertilization. A logarithmic concentration series was used for range-finding, followed by a narrower geometric series for LC(50 determination. Zebrafish embryo LC(50 (log mmol/L, and published data on rodent LD(50 (log mmol/kg, were found to be strongly correlated (using Kendall's rank correlation tau and Pearson's product-moment correlation. The slope of the regression line for the full set of compounds was 0.73403. However, we found that the slope was strongly influenced by compound class. Thus, while most compounds had a similar toxicity level in both species, some compounds were markedly more toxic in zebrafish than in rodents, or vice versa. CONCLUSIONS: For the substances examined here, in aggregate, the zebrafish embryo model has good predictivity for toxicity in rodents. However, the correlation between zebrafish and rodent toxicity varies considerably between individual compounds and compound class. We discuss the strengths and limitations of the zebrafish model in light of these findings.

Non toxic additives for improved fabric filter performance

Energy Technology Data Exchange (ETDEWEB)

Bustard, C.J.; Baldrey, K.E.; Ebner, T.G. [ADA Technologies, Inc., Englewood, CO (United States)] [and others

1995-11-01

The overall objective of this three-phase Small Business innovative Research (SBIR) program funded by the Department of Energy pittsburgh Energy Technology Center (PETC) is to commercialize a technology based upon the use of non-toxic, novel flue gas conditioning agents to improve particulate air toxic control and overall fabric filter performance. The ultimate objective of the Phase II program currently in progress is to demonstrate that the candidate additives are successful at full-scale on flue gas from a coal-fired utility boiler. This paper covers bench-scale field tests conducted during the period February through May, 1995. The bench-scale additives testing was conducted on a flue gas slipstream taken upstream of the existing particulate control device at a utility power plant firing a Texas lignite coal. These tests were preceded by extensive testing with additives in the laboratory using a simulated flue gas stream and re-dispersed flyash from the same power plant. The bench-scale field testing was undertaken to demonstrate the performance with actual flue gas of the bet candidate additives previously identified in the laboratory. Results from the bench-scale tests will be used to establish operating parameters for a larger-scale demonstration on either a single baghouse compartment or a full baghouse at the same site.

Toxicity Estimation Software Tool (TEST)

Science.gov (United States)

The Toxicity Estimation Software Tool (TEST) was developed to allow users to easily estimate the toxicity of chemicals using Quantitative Structure Activity Relationships (QSARs) methodologies. QSARs are mathematical models used to predict measures of toxicity from the physical c...

Baltimore Air Toxics Study (BATS)

Energy Technology Data Exchange (ETDEWEB)

Sullivan, D.A. [Sullivan Environmental Consulting, Inc., Alexandria, VA (United States)

1996-12-31

The Baltimore Air Toxics Study is one of the three urban air toxics initiatives funded by EPA to support the development of the national air toxics strategy. As part of this project, the Air Quality Integrated Management System (AIMS) is under development. AIMS is designed to bring together the key components of urban air quality management into an integrated system, including emissions assessment, air quality modeling, and air quality monitoring. Urban area source emissions are computed for a wide range of pollutants and source categories, and are joined with existing point source emissions data. Measured air quality data are used to evaluate the adequacy of the emissions data and model treatments as a function of season, meteorological parameters, and daytime/nighttime conditions. Based on tested model performance, AIMS provides the potential to improve the ability to predict air quality benefits of alternative control options for criteria and toxic air pollutants. This paper describes the methods used to develop AIMS, and provides examples from its application in the Baltimore metropolitan area. The use of AIMS in the future to enhance environmental management of major industrial facilities also will be addressed in the paper.

Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis.

Science.gov (United States)

Zhu, Hao; Tropsha, Alexander; Fourches, Denis; Varnek, Alexandre; Papa, Ester; Gramatica, Paola; Oberg, Tomas; Dao, Phuong; Cherkasov, Artem; Tetko, Igor V

2008-04-01

Selecting most rigorous quantitative structure-activity relationship (QSAR) approaches is of great importance in the development of robust and predictive models of chemical toxicity. To address this issue in a systematic way, we have formed an international virtual collaboratory consisting of six independent groups with shared interests in computational chemical toxicology. We have compiled an aqueous toxicity data set containing 983 unique compounds tested in the same laboratory over a decade against Tetrahymena pyriformis. A modeling set including 644 compounds was selected randomly from the original set and distributed to all groups that used their own QSAR tools for model development. The remaining 339 compounds in the original set (external set I) as well as 110 additional compounds (external set II) published recently by the same laboratory (after this computational study was already in progress) were used as two independent validation sets to assess the external predictive power of individual models. In total, our virtual collaboratory has developed 15 different types of QSAR models of aquatic toxicity for the training set. The internal prediction accuracy for the modeling set ranged from 0.76 to 0.93 as measured by the leave-one-out cross-validation correlation coefficient ( Q abs2). The prediction accuracy for the external validation sets I and II ranged from 0.71 to 0.85 (linear regression coefficient R absI2) and from 0.38 to 0.83 (linear regression coefficient R absII2), respectively. The use of an applicability domain threshold implemented in most models generally improved the external prediction accuracy but at the same time led to a decrease in chemical space coverage. Finally, several consensus models were developed by averaging the predicted aquatic toxicity for every compound using all 15 models, with or without taking into account their respective applicability domains. We find that consensus models afford higher prediction accuracy for the

Combined Adjuvant Radiochemotherapy With IMRT/XELOX Improves Outcome With Low Renal Toxicity in Gastric Cancer

International Nuclear Information System (INIS)

Boda-Heggemann, Judit; Hofheinz, Ralf-Dieter; Weiss, Christel; Mennemeyer, Philipp; Mai, Sabine K.; Hermes, Petra; Wertz, Hansjoerg; Post, Stefan; Massner, Bernd; Hieber, Udo; Hochhaus, Andreas; Wenz, Frederik; Lohr, Frank

2009-01-01

Objectives: Adjuvant radiochemotherapy improves survival of patients with advanced gastric cancer. We assessed in two sequential cohorts whether improved radiotherapy technique (IMRT) together with intensified chemotherapy improves outcome vs. conventional three-dimensional conformal radiotherapy (3D-CRT) and standard chemotherapy in these patients while maintaining or reducing renal toxicity. Materials and Methods: Sixty consecutive patients treated for gastric cancer either with 3D-CRT (n = 27) and IMRT (n = 33) were evaluated. More than 70% had undergone D2 resection. Although there was a slight imbalance in R0 status between cohorts, N+ status was balanced. Chemotherapy consisted predominantly of 5-fluorouracil/folinic acid (n = 36) in the earlier cohort and mostly of oxaliplatin/capecitabine (XELOX, n = 24) in the later cohort. Primary end points were overall survival (OS), disease-free survival (DFS), and renal toxicity based on creatinine levels. Results: Median follow-up (FU) of all patients in the 3D-CRT group was 18 months and in the IMRT group 22 months (median FU of surviving patients 67 months in the 3D-CRT group and 25 months in the IMRT group). Overall median survival (and DFS) were 18 (13) months in the 3D-CRT group and both not reached in the IMRT group (p = 0.0492 and 0.0216). Actuarial 2-year survival was 37% and 67% in the 3D-CRT and IMRT groups, respectively. No late renal toxicity >Grade 2 (LENT-SOMA scale) was observed in either cohort. Conclusion: When comparing sequentially treated patient cohorts with similar characteristics, OS and DFS improved with the use of IMRT and intensified chemotherapy without signs of increased renal toxicity.

Rectal toxicity profile after transperineal interstitial permanent prostate brachytherapy: Use of a comprehensive toxicity scoring system and identification of rectal dosimetric toxicity predictors

International Nuclear Information System (INIS)

Shah, Jinesh N.; Ennis, Ronald D.

2006-01-01

Purpose: To better understand rectal toxicity after prostate brachytherapy, we employed the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE version 3.0), a comprehensive system with distinct and separately reported gastrointestinal adverse event items (unlike Radiation Therapy Oncology Group morbidity scoring), to evaluate item-specific postimplant rectal toxicities. Methods and Materials: We analyzed 135 patients treated with brachytherapy ± hormonal therapy, using CTCAE v3.0 to score acute/late rectal toxicities (median follow-up, 41 months). Dosimetric parameters were evaluated for ability to predict toxicities. Results: Use of CTCAE yielded a novel rectal toxicity profile consisting of diarrhea, incontinence, urgency, proctitis, pain, spasms, and hemorrhage event rates. No item had a 25 (percent of rectal volume receiving 25% of prescribed prostate dose) ≤ 25% vs. 60% for %V 25 > 25% (p 1 ≤ 40% vs. 44% for %V 1 > 40% (p = 0.007). Conclusions: A comprehensive understanding of item-specific postimplant rectal toxicities was obtained using CTCAE. Rectal %V 25 > 25% and %V 1 > 40% predicted worse late diarrhea and maximum toxicity, respectively

Clinical Factors Predicting Late Severe Urinary Toxicity After Postoperative Radiotherapy for Prostate Carcinoma: A Single-Institute Analysis of 742 Patients

Energy Technology Data Exchange (ETDEWEB)

Cozzarini, Cesare, E-mail: cozzarini.cesare@hsr.it [Department of Radiotherapy, San Raffaele Scientific Institute, Milan (Italy); Fiorino, Claudio [Department of Medical Physics, San Raffaele Scientific Institute, Milan (Italy); Da Pozzo, Luigi Filippo [Department of Urology, San Raffaele Scientific Institute, Milan (Italy); Alongi, Filippo; Berardi, Genoveffa; Bolognesi, Angelo [Department of Radiotherapy, San Raffaele Scientific Institute, Milan (Italy); Briganti, Alberto [Department of Urology, San Raffaele Scientific Institute, Milan (Italy); Broggi, Sara [Department of Medical Physics, San Raffaele Scientific Institute, Milan (Italy); Deli, Aniko [Department of Radiotherapy, San Raffaele Scientific Institute, Milan (Italy); Guazzoni, Giorgio [Department of Urology, San Raffaele Scientific Institute, Milan (Italy); Perna, Lucia [Department of Medical Physics, San Raffaele Scientific Institute, Milan (Italy); Pasetti, Marcella; Salvadori, Giovannella [Department of Radiotherapy, San Raffaele Scientific Institute, Milan (Italy); Montorsi, Francesco; Rigatti, Patrizio [Department of Urology, San Raffaele Scientific Institute, Milan (Italy); Di Muzio, Nadia [Department of Radiotherapy, San Raffaele Scientific Institute, Milan (Italy)

2012-01-01

Purpose: To investigate the clinical factors independently predictive of long-term severe urinary sequelae after postprostatectomy radiotherapy. Patients and Methods: Between 1993 and 2005, 742 consecutive patients underwent postoperative radiotherapy with either adjuvant (n = 556; median radiation dose, 70.2 Gy) or salvage (n = 186; median radiation dose, 72 Gy) intent. Results: After a median follow-up of 99 months, the 8-year risk of Grade 2 or greater and Grade 3 late urinary toxicity was almost identical (23.9% vs. 23.7% and 12% vs. 10%) in the adjuvant and salvage cohorts, respectively. On univariate analysis, acute toxicity was significantly predictive of late Grade 2 or greater sequelae in both subgroups (p <.0001 in both cases), and hypertension (p = .02) and whole-pelvis radiotherapy (p = .02) correlated significantly in the adjuvant cohort only. The variables predictive of late Grade 3 sequelae were acute Grade 2 or greater toxicity in both groups and whole-pelvis radiotherapy (8-year risk of Grade 3 events, 21% vs. 11%, p = .007), hypertension (8-year risk, 18% vs. 10%, p = .005), age {<=} 62 years at RT (8-year risk, 16% vs. 11%, p = .04) in the adjuvant subset, and radiation dose >72 Gy (8-year risk, 19% vs. 6%, p = .007) and age >71 years (8-year risk, 16% vs. 6%, p = .006) in the salvage subgroup. Multivariate analysis confirmed the independent predictive role of all the covariates indicated as statistically significant on univariate analysis. Conclusions: The risk of late Grade 2 or greater and Grade 3 urinary toxicity was almost identical, regardless of the RT intent. In the salvage cohort, older age and greater radiation doses resulted in a worse toxicity profile, and younger, hypertensive patients experienced a greater rate of severe late sequelae in the adjuvant setting. The causes of this latter correlation and apparently different etiopathogenesis of chronic damage in the two subgroups were unclear and deserve additional investigation.

A Bayesian network model for predicting aquatic toxicity mode of action using two dimensional theoretical molecular descriptors

Energy Technology Data Exchange (ETDEWEB)

Carriger, John F. [U.S. Environmental Protection Agency, Office of Research and Development, Gulf Ecology Division, Gulf Breeze, FL, 32561 (United States); Martin, Todd M. [U.S. Environmental Protection Agency, Office of Research and Development, Sustainable Technology Division, Cincinnati, OH, 45220 (United States); Barron, Mace G., E-mail: barron.mace@epa.gov [U.S. Environmental Protection Agency, Office of Research and Development, Gulf Ecology Division, Gulf Breeze, FL, 32561 (United States)

2016-11-15

Highlights: • A Bayesian network was developed to classify chemical mode of action (MoA). • The network was based on the aquatic toxicity MoA for over 1000 chemicals. • A Markov blanket algorithm selected a subset of theoretical molecular descriptors. • Sensitivity analyses found influential descriptors for classifying the MoAs. • Overall precision of the Bayesian MoA classification model was 80%. - Abstract: The mode of toxic action (MoA) has been recognized as a key determinant of chemical toxicity, but development of predictive MoA classification models in aquatic toxicology has been limited. We developed a Bayesian network model to classify aquatic toxicity MoA using a recently published dataset containing over one thousand chemicals with MoA assignments for aquatic animal toxicity. Two dimensional theoretical chemical descriptors were generated for each chemical using the Toxicity Estimation Software Tool. The model was developed through augmented Markov blanket discovery from the dataset of 1098 chemicals with the MoA broad classifications as a target node. From cross validation, the overall precision for the model was 80.2%. The best precision was for the AChEI MoA (93.5%) where 257 chemicals out of 275 were correctly classified. Model precision was poorest for the reactivity MoA (48.5%) where 48 out of 99 reactive chemicals were correctly classified. Narcosis represented the largest class within the MoA dataset and had a precision and reliability of 80.0%, reflecting the global precision across all of the MoAs. False negatives for narcosis most often fell into electron transport inhibition, neurotoxicity or reactivity MoAs. False negatives for all other MoAs were most often narcosis. A probabilistic sensitivity analysis was undertaken for each MoA to examine the sensitivity to individual and multiple descriptor findings. The results show that the Markov blanket of a structurally complex dataset can simplify analysis and interpretation by

Toxicity challenges in environmental chemicals: Prediction of ...

Science.gov (United States)

Physiologically based pharmacokinetic (PBPK) models bridge the gap between in vitro assays and in vivo effects by accounting for the adsorption, distribution, metabolism, and excretion of xenobiotics, which is especially useful in the assessment of human toxicity. Quantitative structure-activity relationships (QSAR) serve as a vital tool for the high-throughput prediction of chemical-specific PBPK parameters, such as the fraction of a chemical unbound by plasma protein (Fub). The presented work explores the merit of utilizing experimental pharmaceutical Fub data for the construction of a universal QSAR model, in order to compensate for the limited range of high-quality experimental Fub data for environmentally relevant chemicals, such as pollutants, pesticides, and consumer products. Independent QSAR models were constructed with three machine-learning algorithms, k nearest neighbors (kNN), random forest (RF), and support vector machine (SVM) regression, from a large pharmaceutical training set (~1000) and assessed with independent test sets of pharmaceuticals (~200) and environmentally relevant chemicals in the ToxCast program (~400). Small descriptor sets yielded the optimal balance of model complexity and performance, providing insight into the biochemical factors of plasma protein binding, while preventing over fitting to the training set. Overlaps in chemical space between pharmaceutical and environmental compounds were considered through applicability of do

Current and future perspectives on the development, evaluation and application of in silico approaches for predicting toxicity

Science.gov (United States)

Safety-related problems continue to be one of the major reasons of attrition in drug development. Non-testing approaches to predict toxicity could form part of the solution. This review provides a perspective of current status of non-testing approaches available for the predictio...

Transcriptomics-based identification of developmental toxicants through their interference with cardiomyocyte differentiation of embryonic stem cells

International Nuclear Information System (INIS)

Dartel, Dorien A.M. van; Pennings, Jeroen L.A.; Schooten, Frederik J. van; Piersma, Aldert H.

2010-01-01

The embryonic stem cell test (EST) predicts developmental toxicity based on the inhibition of cardiomyocyte differentiation of embryonic stem cells (ESC). The subjective endpoint, the long culture duration together with the undefined applicability domain and related predictivity need further improvement to facilitate implementation of the EST into regulatory strategies. These aspects may be improved by studying gene expression changes in the ESC differentiation cultures and their modulation by compound exposure using transcriptomics. Here, we tested the developmental toxicants monobutyl phthalate and 6-aminonicotinamide. ESC were allowed to differentiated, and cardiomyocyte differentiation was assessed after 10 days of culture. RNA of solvent controls was collected after 0, 24, 48, 72 and 96 h of exposure, and RNA of developmental-toxicant-exposed cultures was collected after 24 and 96 h. Samples were hybridized to DNA microarrays, and 1355 genes were found differentially expressed among the unexposed experimental groups. These regulated genes were involved in differentiation-related processes, and Principal Component Analysis (PCA) based on these genes showed that the unexposed experimental groups appeared in chronological order in the PCA, which can therefore be regarded as a continuous representation of the differentiation track. The developmental-toxicant-exposed cultures appeared to deviate significantly from this differentiation track, which confirms the compound-modulating effects on the differentiation process. The incorporation of transcriptomics in the EST is expected to provide a more informative and improved endpoint in the EST as compared with morphology, allowing early detection of differentiation modulation. Furthermore, this approach may improve the definition of the applicability domain and predictivity of the EST.

Cerebral CT appearances of toxic encephalopathy of tetramine

International Nuclear Information System (INIS)

Zheng Wenlong; Wu Aiqin; Xu Chongyong; Ying Binyu; Hong Ruizhen

2003-01-01

Objective: To investigate the cerebral CT appearances of toxic encephalopathy of tetramine and improve the recognition on this disease. Methods: Four cases of toxic encephalopathy of tetramine were collected and their cerebral CT appearances were retrospectively analyzed. Results: Cerebral CT appearances in acute phase (within 8 days): (1) cerebral edema in different degree. CT abnormalities consisted of cortical hypodensities and complete loss of gray-white matter differentiation. The CT value were in 11-13 HU, and to be watery density in serious case, (2) subarachnoid hemorrhage. It demonstrated the signs of poisoning hypoxic ischemic encephalopathy in chronic phase. Conclusion: The cerebral CT appearances of toxic encephalopathy of tetramine had some character in acute phase and it can predict the serious degree of intoxication, but there was no characteristic findings in chronic phase

A long-term three dimensional liver co-culture system for improved prediction of clinically relevant drug-induced hepatotoxicity

International Nuclear Information System (INIS)

Kostadinova, Radina; Boess, Franziska; Applegate, Dawn; Suter, Laura; Weiser, Thomas; Singer, Thomas; Naughton, Brian; Roth, Adrian

2013-01-01

Drug-induced liver injury (DILI) is the major cause for liver failure and post-marketing drug withdrawals. Due to species-specific differences in hepatocellular function, animal experiments to assess potential liabilities of drug candidates can predict hepatotoxicity in humans only to a certain extent. In addition to animal experimentation, primary hepatocytes from rat or human are widely used for pre-clinical safety assessment. However, as many toxic responses in vivo are mediated by a complex interplay among different cell types and often require chronic drug exposures, the predictive performance of hepatocytes is very limited. Here, we established and characterized human and rat in vitro three-dimensional (3D) liver co-culture systems containing primary parenchymal and non-parenchymal hepatic cells. Our data demonstrate that cells cultured on a 3D scaffold have a preserved composition of hepatocytes, stellate, Kupffer and endothelial cells and maintain liver function for up to 3 months, as measured by the production of albumin, fibrinogen, transferrin and urea. Additionally, 3D liver co-cultures maintain cytochrome P450 inducibility, form bile canaliculi-like structures and respond to inflammatory stimuli. Upon incubation with selected hepatotoxicants including drugs which have been shown to induce idiosyncratic toxicity, we demonstrated that this model better detected in vivo drug-induced toxicity, including species-specific drug effects, when compared to monolayer hepatocyte cultures. In conclusion, our results underline the importance of more complex and long lasting in vitro cell culture models that contain all liver cell types and allow repeated drug-treatments for detection of in vivo-relevant adverse drug effects. - Highlights: ► 3D liver co-cultures maintain liver specific functions for up to three months. ► Activities of Cytochrome P450s remain drug- inducible accross three months. ► 3D liver co-cultures recapitulate drug-induced liver toxicity

A long-term three dimensional liver co-culture system for improved prediction of clinically relevant drug-induced hepatotoxicity

Energy Technology Data Exchange (ETDEWEB)

Kostadinova, Radina; Boess, Franziska [Non-Clinical Safety, Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Building 73 / Room 117b, 4070 Basel (Switzerland); Applegate, Dawn [RegeneMed, 9855 Towne Centre Drive Suite 200, San Diego, CA 92121 (United States); Suter, Laura; Weiser, Thomas; Singer, Thomas [Non-Clinical Safety, Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Building 73 / Room 117b, 4070 Basel (Switzerland); Naughton, Brian [RegeneMed, 9855 Towne Centre Drive Suite 200, San Diego, CA 92121 (United States); Roth, Adrian, E-mail: adrian_b.roth@roche.com [Non-Clinical Safety, Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Building 73 / Room 117b, 4070 Basel (Switzerland)

2013-04-01

Drug-induced liver injury (DILI) is the major cause for liver failure and post-marketing drug withdrawals. Due to species-specific differences in hepatocellular function, animal experiments to assess potential liabilities of drug candidates can predict hepatotoxicity in humans only to a certain extent. In addition to animal experimentation, primary hepatocytes from rat or human are widely used for pre-clinical safety assessment. However, as many toxic responses in vivo are mediated by a complex interplay among different cell types and often require chronic drug exposures, the predictive performance of hepatocytes is very limited. Here, we established and characterized human and rat in vitro three-dimensional (3D) liver co-culture systems containing primary parenchymal and non-parenchymal hepatic cells. Our data demonstrate that cells cultured on a 3D scaffold have a preserved composition of hepatocytes, stellate, Kupffer and endothelial cells and maintain liver function for up to 3 months, as measured by the production of albumin, fibrinogen, transferrin and urea. Additionally, 3D liver co-cultures maintain cytochrome P450 inducibility, form bile canaliculi-like structures and respond to inflammatory stimuli. Upon incubation with selected hepatotoxicants including drugs which have been shown to induce idiosyncratic toxicity, we demonstrated that this model better detected in vivo drug-induced toxicity, including species-specific drug effects, when compared to monolayer hepatocyte cultures. In conclusion, our results underline the importance of more complex and long lasting in vitro cell culture models that contain all liver cell types and allow repeated drug-treatments for detection of in vivo-relevant adverse drug effects. - Highlights: ► 3D liver co-cultures maintain liver specific functions for up to three months. ► Activities of Cytochrome P450s remain drug- inducible accross three months. ► 3D liver co-cultures recapitulate drug-induced liver toxicity

Verification and improvement of a predictive model for radionuclide migration

International Nuclear Information System (INIS)

Miller, C.W.; Benson, L.V.; Carnahan, C.L.

1982-01-01

Prediction of the rates of migration of contaminant chemical species in groundwater flowing through toxic waste repositories is essential to the assessment of a repository's capability of meeting standards for release rates. A large number of chemical transport models, of varying degrees of complexity, have been devised for the purpose of providing this predictive capability. In general, the transport of dissolved chemical species through a water-saturated porous medium is influenced by convection, diffusion/dispersion, sorption, formation of complexes in the aqueous phase, and chemical precipitation. The reliability of predictions made with the models which omit certain of these processes is difficult to assess. A numerical model, CHEMTRN, has been developed to determine which chemical processes govern radionuclide migration. CHEMTRN builds on a model called MCCTM developed previously by Lichtner and Benson

Development of a Set of Nomograms to Predict Acute Lower Gastrointestinal Toxicity for Prostate Cancer 3D-CRT

International Nuclear Information System (INIS)

Valdagni, Riccardo; Rancati, Tiziana; Fiorino, Claudio; Fellin, Gianni; Magli, Alessandro; Baccolini, Michela; Bianchi, Carla; Cagna, Emanuela; Greco, Carlo; Mauro, Flora A.; Monti, Angelo F.; Munoz, Fernando; Stasi, Michele; Franzone, Paola; Vavassori, Vittorio

2008-01-01

Purpose: To predict acute Radiation Therapy Oncology Group (RTOG)/European Organization for Research and Treatment of Cancer (EORTC) and Subjective Objective Signs Management and Analysis/Late Effect of Normal Tissue (SOMA/LENT) toxicities of the lower gastrointestinal (LGI) syndrome in patients with prostate cancer undergoing three-dimensional conformal radiotherapy using a tool (nomogram) that takes into account clinical and dosimetric variables that proved to be significant in the Italian Association for Radiation Oncology (AIRO) Group on Prostate Cancer (AIROPROS) 0102 trial. Methods and Materials: Acute rectal toxicity was scored in 1,132 patients by using both the RTOG/EORTC scoring system and a 10-item self-assessed questionnaire. Correlation between clinical variables/dose-volume histogram constraints and rectal toxicity was investigated by means of multivariate logistic analyses. Multivariate logistic analyses results were used to create nomograms predicting the symptoms of acute LGI syndrome. Results: Mean rectal dose was a strong predictor of Grade 2-3 RTOG/EORTC acute LGI toxicity (p 0.0004; odds ratio (OR) = 1.035), together with hemorrhoids (p = 0.02; OR 1.51), use of anticoagulants/antiaggregants (p = 0.02; OR = 0.63), and androgen deprivation (AD) (p = 0.04; OR = 0.65). Diabetes (p = 0.34; OR 1.28) and pelvic node irradiation (p = 0.11; OR = 1.56) were significant variables to adjust toxicity prediction. Bleeding was related to hemorrhoids (p = 0.02; OR = 173), AD (p = 0.17; OR = 0.67), and mean rectal dose (p 0.009; OR = 1.024). Stool frequency was related to seminal vesicle irradiation (p = 0.07; OR = 6.46), AD administered for more than 3 months (p = 0.002; OR = 0.32), and the percent volume of rectum receiving more than 60 Gy (V60Gy) V60 (p = 0.02; OR = 1.02). Severe fecal incontinence depended on seminal vesicle irradiation (p = 0.14; OR = 4.5) and V70 (p = 0.033; OR 1.029). Conclusions: To the best of our knowledge, this work presents the

Toxicity assessment of silica coated iron oxide nanoparticles and biocompatibility improvement by surface engineering.

Directory of Open Access Journals (Sweden)

Maria Ada Malvindi

Full Text Available We have studied in vitro toxicity of iron oxide nanoparticles (NPs coated with a thin silica shell (Fe3O4/SiO2 NPs on A549 and HeLa cells. We compared bare and surface passivated Fe3O4/SiO2 NPs to evaluate the effects of the coating on the particle stability and toxicity. NPs cytotoxicity was investigated by cell viability, membrane integrity, mitochondrial membrane potential (MMP, reactive oxygen species (ROS assays, and their genotoxicity by comet assay. Our results show that NPs surface passivation reduces the oxidative stress and alteration of iron homeostasis and, consequently, the overall toxicity, despite bare and passivated NPs show similar cell internalization efficiency. We found that the higher toxicity of bare NPs is due to their stronger in-situ degradation, with larger intracellular release of iron ions, as compared to surface passivated NPs. Our results indicate that surface engineering of Fe3O4/SiO2 NPs plays a key role in improving particles stability in biological environments reducing both cytotoxic and genotoxic effects.

Computer-assisted engineering of the synthetic pathway for biodegradation of a toxic persistent pollutant.

Science.gov (United States)

Kurumbang, Nagendra Prasad; Dvorak, Pavel; Bendl, Jaroslav; Brezovsky, Jan; Prokop, Zbynek; Damborsky, Jiri

2014-03-21

Anthropogenic halogenated compounds were unknown to nature until the industrial revolution, and microorganisms have not had sufficient time to evolve enzymes for their degradation. The lack of efficient enzymes and natural pathways can be addressed through a combination of protein and metabolic engineering. We have assembled a synthetic route for conversion of the highly toxic and recalcitrant 1,2,3-trichloropropane to glycerol in Escherichia coli, and used it for a systematic study of pathway bottlenecks. Optimal ratios of enzymes for the maximal production of glycerol, and minimal toxicity of metabolites were predicted using a mathematical model. The strains containing the expected optimal ratios of enzymes were constructed and characterized for their viability and degradation efficiency. Excellent agreement between predicted and experimental data was observed. The validated model was used to quantitatively describe the kinetic limitations of currently available enzyme variants and predict improvements required for further pathway optimization. This highlights the potential of forward engineering of microorganisms for the degradation of toxic anthropogenic compounds.

Improving Earth/Prediction Models to Improve Network Processing

Science.gov (United States)

Wagner, G. S.

2017-12-01

The United States Atomic Energy Detection System (USAEDS) primaryseismic network consists of a relatively small number of arrays andthree-component stations. The relatively small number of stationsin the USAEDS primary network make it both necessary and feasibleto optimize both station and network processing.Station processing improvements include detector tuning effortsthat use Receiver Operator Characteristic (ROC) curves to helpjudiciously set acceptable Type 1 (false) vs. Type 2 (miss) errorrates. Other station processing improvements include the use ofempirical/historical observations and continuous background noisemeasurements to compute time-varying, maximum likelihood probabilityof detection thresholds.The USAEDS network processing software makes extensive use of theazimuth and slowness information provided by frequency-wavenumberanalysis at array sites, and polarization analysis at three-componentsites. Most of the improvements in USAEDS network processing aredue to improvements in the models used to predict azimuth, slowness,and probability of detection. Kriged travel-time, azimuth andslowness corrections-and associated uncertainties-are computedusing a ground truth database. Improvements in station processingand the use of improved models for azimuth, slowness, and probabilityof detection have led to significant improvements in USADES networkprocessing.

Comparative Analysis of Predictive Models for Liver Toxicity Using ToxCast Assays and Quantitative Structure-Activity Relationships (MCBIOS)

Science.gov (United States)

Comparative Analysis of Predictive Models for Liver Toxicity Using ToxCast Assays and Quantitative Structure-Activity Relationships Jie Liu1,2, Richard Judson1, Matthew T. Martin1, Huixiao Hong3, Imran Shah1 1National Center for Computational Toxicology (NCCT), US EPA, RTP, NC...

Predicting toxic heavy metal movements in upper Sanyati catchment ...

African Journals Online (AJOL)

Water samples from boreholes located in areas where mining, mineral processing and agricultural activities were dominant, yielded the highest values of toxic heavy metals. Dilution Attenuation Factor (DAF) for each toxic heavy metal was calculated to observe metal behaviour along the contaminant path for each season.

A Novel Method for Predicting Late Genitourinary Toxicity After Prostate Radiation Therapy and the Need for Age-Based Risk-Adapted Dose Constraints

International Nuclear Information System (INIS)

Ahmed, Awad A.; Egleston, Brian; Alcantara, Pino; Li, Linna; Pollack, Alan; Horwitz, Eric M.; Buyyounouski, Mark K.

2013-01-01

Background: There are no well-established normal tissue sparing dose–volume histogram (DVH) criteria that limit the risk of urinary toxicity from prostate radiation therapy (RT). The aim of this study was to determine which criteria predict late toxicity among various DVH parameters when contouring the entire solid bladder and its contents versus the bladder wall. The area under the histogram curve (AUHC) was also analyzed. Methods and Materials: From 1993 to 2000, 503 men with prostate cancer received 3-dimensional conformal RT (median follow-up time, 71 months). The whole bladder and the bladder wall were contoured in all patients. The primary endpoint was grade ≥2 genitourinary (GU) toxicity occurring ≥3 months after completion of RT. Cox regressions of time to grade ≥2 toxicity were estimated separately for the entire bladder and bladder wall. Concordance probability estimates (CPE) assessed model discriminative ability. Before training the models, an external random test group of 100 men was set aside for testing. Separate analyses were performed based on the mean age (≤ 68 vs >68 years). Results: Age, pretreatment urinary symptoms, mean dose (entire bladder and bladder wall), and AUHC (entire bladder and bladder wall) were significant (P 68 years. Conclusion: The AUHC method based on bladder wall volumes was superior for predicting late GU toxicity. Age >68 years was associated with late grade ≥2 GU toxicity, which suggests that risk-adapted dose constraints based on age should be explored

Decadal climate predictions improved by ocean ensemble dispersion filtering

Science.gov (United States)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-06-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.Plain Language SummaryDecadal predictions aim to predict the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. The ocean memory due to its heat capacity holds big potential skill. In recent years, more precise initialization techniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions. Ensembles are another important aspect. Applying slightly perturbed predictions to trigger the famous butterfly effect results in an ensemble. Instead of evaluating one prediction, but the whole ensemble with its

Assessing the toxic effects of ethylene glycol ethers using Quantitative Structure Toxicity Relationship models

International Nuclear Information System (INIS)

Ruiz, Patricia; Mumtaz, Moiz; Gombar, Vijay

2011-01-01

Experimental determination of toxicity profiles consumes a great deal of time, money, and other resources. Consequently, businesses, societies, and regulators strive for reliable alternatives such as Quantitative Structure Toxicity Relationship (QSTR) models to fill gaps in toxicity profiles of compounds of concern to human health. The use of glycol ethers and their health effects have recently attracted the attention of international organizations such as the World Health Organization (WHO). The board members of Concise International Chemical Assessment Documents (CICAD) recently identified inadequate testing as well as gaps in toxicity profiles of ethylene glycol mono-n-alkyl ethers (EGEs). The CICAD board requested the ATSDR Computational Toxicology and Methods Development Laboratory to conduct QSTR assessments of certain specific toxicity endpoints for these chemicals. In order to evaluate the potential health effects of EGEs, CICAD proposed a critical QSTR analysis of the mutagenicity, carcinogenicity, and developmental effects of EGEs and other selected chemicals. We report here results of the application of QSTRs to assess rodent carcinogenicity, mutagenicity, and developmental toxicity of four EGEs: 2-methoxyethanol, 2-ethoxyethanol, 2-propoxyethanol, and 2-butoxyethanol and their metabolites. Neither mutagenicity nor carcinogenicity is indicated for the parent compounds, but these compounds are predicted to be developmental toxicants. The predicted toxicity effects were subjected to reverse QSTR (rQSTR) analysis to identify structural attributes that may be the main drivers of the developmental toxicity potential of these compounds.

[Source identification of toxic wastewaters in a petrochemical industrial park].

Science.gov (United States)

Yang, Qian; Yu, Yin; Zhou, Yue-Xi; Chen, Xue-Min; Fu, Xiao-Yong; Wang, Miao

2014-12-01

Petrochemical wastewaters have toxic impacts on the microorganisms in biotreatment processes, which are prone to cause deterioration of effluent quality of the wastewater treatment plants. In this study, the inhibition effects of activated sludge's oxygen consumption were tested to evaluate the toxicity of production wastewaters in a petrochemical industrial park. The evaluation covered the wastewaters from not only different production units in the park, but also different production nodes in each unit. No direct correlation was observed between the toxicity effects and the organic contents, suggesting that the toxic properties of the effluents could not be predicted by the organic contents. In view of the variation of activated sludge sensitivity among different tests, the toxicity data were standardized according to the concentration-effect relationships of the standard toxic substance 3, 5-dichlorophenol on each day, in order to improve the comparability among the toxicity data. Furthermore, the Quality Emission Load (QEL) of corresponding standard toxic substance was calculated by multiplying the corresponding 3, 5-dichlorophenol concentration and the wastewater flow quantity, to indicate the toxicity emission contribution of each wastewater to the wastewater treatment plant. According to the rank list of the toxicity contribution of wastewater from different units and nodes, the sources of toxic wastewater in the petrochemical industrial park were clearly identified. This study provides effective guidance for source control of wastewater toxicity in the large industrial park.

A toxicity reduction evaluation for an oily waste treatment plant exhibiting episodic effluent toxicity.

Science.gov (United States)

Erten-Unal, M; Gelderloos, A B; Hughes, J S

1998-07-30

A Toxicity Reduction Evaluation (TRE) was conducted on the oily wastewater treatment plant (Plant) at a Naval Fuel Depot. The Plant treats ship and ballast wastes, berm water from fuel storage areas and wastes generated in the fuel reclamation plant utilizing physical/chemical treatment processes. In the first period of the project (Period I), the TRE included chemical characterization of the plant wastewaters, monitoring the final effluent for acute toxicity and a thorough evaluation of each treatment process and Plant operating procedures. Toxicity Identification Evaluation (TIE) procedures were performed as part of the overall TRE to characterize and identify possible sources of toxicity. Several difficulties were encountered because the effluent was saline, test organisms were marine species and toxicity was sporadic and unpredictable. The treatability approach utilizing enhancements, improved housekeeping, and operational changes produced substantial reductions in the acute toxicity of the final effluent. In the second period (Period II), additional acute toxicity testing and chemical characterization were performed through the Plant to assess the long-term effects of major unit process improvements for the removal of toxicity. The TIE procedures were also modified for saline wastewaters to focus on suspected class of toxicants such as surfactants. The TRE was successful in reducing acute toxicity of the final effluent through process improvements and operational modifications. The results indicated that the cause of toxicity was most likely due to combination of pollutants (matrix effect) rather than a single pollutant.

Fluorine-18-fluorocholine PET/CT parameters predictive for hematological toxicity to radium-223 therapy in castrate-resistant prostate cancer patients with bone metastases: a pilot study.

Science.gov (United States)

Vija Racaru, Lavinia; Sinigaglia, Mathieu; Kanoun, Salim; Ben Bouallègue, Fayçal; Tal, Ilan; Brillouet, Sévérine; Bauriaud-Mallet, Mathilde; Zerdoud, Slimane; Dierickx, Lawrence; Vallot, Delphine; Caselles, Olivier; Gabiache, Erwan; Pascal, Pierre; Courbon, Frederic

2018-05-21

This study aims to predict hematological toxicity induced by Ra therapy. We investigated the value of metabolically active bone tumor volume (MBTV) and total bone lesion activity (TLA) calculated on pretreatment fluorine-18-fluorocholine (F-FCH) PET/CT in castrate-resistant prostate cancer (CRPC) patients with bone metastases treated with Ra radionuclide therapy. F-FCH PET/CT imaging was performed in 15 patients with CRPC before treatment with Ra. Bone metastatic disease was quantified on the basis of the maximum standardized uptake value (SUV), total lesion activity (TLA=MBTV×SUVmean), or MBTV/height (MBTV/H) and TLA/H. F-FCH PET/CT bone tumor burden and activity were analyzed to identify which parameters could predict hematological toxicity [on hemoglobin (Hb), platelets (PLTs), and lymphocytes] while on Ra therapy. Pearson's correlation was used to identify the correlations between age, prostate-specific antigen, and F-FCH PET parameters. MBTV ranged from 75 to 1259 cm (median: 392 cm). TLA ranged from 342 to 7198 cm (median: 1853 cm). Patients benefited from two to six cycles of Ra (n=56 cycles in total). At the end of Ra therapy, five of the 15 (33%) patients presented grade 2/3 toxicity on Hb and lymphocytes, whereas three of the 15 (20%) patients presented grade 2/3 PLT toxicity.Age was correlated negatively with both MBTV (r=-0.612, P=0.015) and TLA (r=-0.596, P=0.018). TLA, TLA/H, and MBTV/H predicted hematological toxicity on Hb, whereas TLA/H and MBTV/H predicted toxicity on PLTs at the end of Ra cycles. Receiver operating characteristic curve analysis allowed to define the cutoffs for MBTV (915 cm) and TLA (4198 cm) predictive for PLT toxicity, with an accuracy of 0.92 and 0.99. Tumor bone burden calculation is feasible with F-FCH PET/CT with freely available open-source software. In this pilot study, baseline F-FCH PET/CT markers (TLA, MBTV) have shown abilities to predict Hb and PLT toxicity after Ra therapy and could be explored for

High-Density Real-Time PCR-Based in Vivo Toxicogenomic Screen to Predict Organ-Specific Toxicity

Directory of Open Access Journals (Sweden)

Laszlo G. Puskas

2011-09-01

Full Text Available Toxicogenomics, based on the temporal effects of drugs on gene expression, is able to predict toxic effects earlier than traditional technologies by analyzing changes in genomic biomarkers that could precede subsequent protein translation and initiation of histological organ damage. In the present study our objective was to extend in vivo toxicogenomic screening from analyzing one or a few tissues to multiple organs, including heart, kidney, brain, liver and spleen. Nanocapillary quantitative real-time PCR (QRT-PCR was used in the study, due to its higher throughput, sensitivity and reproducibility, and larger dynamic range compared to DNA microarray technologies. Based on previous data, 56 gene markers were selected coding for proteins with different functions, such as proteins for acute phase response, inflammation, oxidative stress, metabolic processes, heat-shock response, cell cycle/apoptosis regulation and enzymes which are involved in detoxification. Some of the marker genes are specific to certain organs, and some of them are general indicators of toxicity in multiple organs. Utility of the nanocapillary QRT-PCR platform was demonstrated by screening different references, as well as discovery of drug-like compounds for their gene expression profiles in different organs of treated mice in an acute experiment. For each compound, 896 QRT-PCR were done: four organs were used from each of the treated four animals to monitor the relative expression of 56 genes. Based on expression data of the discovery gene set of toxicology biomarkers the cardio- and nephrotoxicity of doxorubicin and sulfasalazin, the hepato- and nephrotoxicity of rotenone, dihydrocoumarin and aniline, and the liver toxicity of 2,4-diaminotoluene could be confirmed. The acute heart and kidney toxicity of the active metabolite SN-38 from its less toxic prodrug, irinotecan could be differentiated, and two novel gene markers for hormone replacement therapy were identified

Adding propensity scores to pure prediction models fails to improve predictive performance

Directory of Open Access Journals (Sweden)

Amy S. Nowacki

2013-08-01

Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.

Improved Modeling and Prediction of Surface Wave Amplitudes

Science.gov (United States)

2017-05-31

AFRL-RV-PS- AFRL-RV-PS- TR-2017-0162 TR-2017-0162 IMPROVED MODELING AND PREDICTION OF SURFACE WAVE AMPLITUDES Jeffry L. Stevens, et al. Leidos...data does not license the holder or any other person or corporation; or convey any rights or permission to manufacture, use, or sell any patented...SUBTITLE Improved Modeling and Prediction of Surface Wave Amplitudes 5a. CONTRACT NUMBER FA9453-14-C-0225 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER

Improving contact prediction along three dimensions.

Directory of Open Access Journals (Sweden)

Christoph Feinauer

2014-10-01

Full Text Available Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their members. The typical pipeline to address this task, which we in this paper refer to as the three dimensions of contact prediction, is to (i filter and align the raw sequence data representing the evolutionarily related proteins; (ii choose a predictive model to describe a sequence alignment; (iii infer the model parameters and interpret them in terms of structural properties, such as an accurate contact map. We show here that all three dimensions are important for overall prediction success. In particular, we show that it is possible to improve significantly along the second dimension by going beyond the pair-wise Potts models from statistical physics, which have hitherto been the focus of the field. These (simple extensions are motivated by multiple sequence alignments often containing long stretches of gaps which, as a data feature, would be rather untypical for independent samples drawn from a Potts model. Using a large test set of proteins we show that the combined improvements along the three dimensions are as large as any reported to date.

Text mining improves prediction of protein functional sites.

Directory of Open Access Journals (Sweden)

Karin M Verspoor

Full Text Available We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites. The structure analysis was carried out using Dynamics Perturbation Analysis (DPA, which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.

Text Mining Improves Prediction of Protein Functional Sites

Science.gov (United States)

Cohn, Judith D.; Ravikumar, Komandur E.

2012-01-01

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions. PMID:22393388

Antimony Toxicity

Directory of Open Access Journals (Sweden)

Shyam Sundar

2010-12-01

Full Text Available Antimony toxicity occurs either due to occupational exposure or during therapy. Occupational exposure may cause respiratory irritation, pneumoconiosis, antimony spots on the skin and gastrointestinal symptoms. In addition antimony trioxide is possibly carcinogenic to humans. Improvements in working conditions have remarkably decreased the incidence of antimony toxicity in the workplace. As a therapeutic, antimony has been mostly used for the treatment of leishmaniasis and schistosomiasis. The major toxic side-effects of antimonials as a result of therapy are cardiotoxicity (~9% of patients and pancreatitis, which is seen commonly in HIV and visceral leishmaniasis co-infections. Quality control of each batch of drugs produced and regular monitoring for toxicity is required when antimonials are used therapeutically.

Characterizing toxicity of metal-contaminated sediments from mining areas

International Nuclear Information System (INIS)

Besser, John M.; Brumbaugh, William G.; Ingersoll, Christopher G.

2015-01-01

communities can help document causal relationships between metal contamination and biological effects. Total or total-recoverable metal concentrations in sediments are the most common measure of metal contamination in sediments, but metal concentrations in labile sediment fractions (e.g., determined as part of selective sediment extraction protocols) may better represent metal bioavailability. Metals released by the weak-acid extraction of acid-volatile sulfide (AVS), termed simultaneously-extracted metals (SEM), are widely used to estimate the ‘potentially-bioavailable’ fraction of metals that is not bound to sulfides (i.e., SEM-AVS). Metal concentrations in pore water are widely considered to be direct measures of metal bioavailability, and predictions of toxicity based on pore-water metal concentrations may be further improved by modeling interactions of metals with other pore-water constituents using Biotic Ligand Models. Data from sediment toxicity tests and metal analyses has provided the basis for development of sediment quality guidelines, which estimate thresholds for toxicity of metals in sediments. Empirical guidelines such as Probable Effects Concentrations or (PECs) are based on associations between sediment metal concentrations and occurrence of toxic effects in large datasets. PECs do not model bioavailable metals, but they can be used to estimate the toxicity of metal mixtures using by calculation of probable effect quotients (PEQ = sediment metal concentration/PEC). In contrast, mechanistic guidelines, such as Equilibrium Partitioning Sediment Benchmarks (ESBs) attempt to predict both bioavailability and mixture toxicity. Application of these simple bioavailability models requires more extensive chemical characterization of sediments or pore water, compared to empirical guidelines, but may provide more reliable estimates of metal toxicity across a wide range of sediment types

Nitrite addition to acidified sludge significantly improves digestibility, toxic metal removal, dewaterability and pathogen reduction

Science.gov (United States)

Du, Fangzhou; Keller, Jürg; Yuan, Zhiguo; Batstone, Damien J.; Freguia, Stefano; Pikaar, Ilje

2016-12-01

Sludge management is a major issue for water utilities globally. Poor digestibility and dewaterability are the main factors determining the cost for sludge management, whereas pathogen and toxic metal concentrations limit beneficial reuse. In this study, the effects of low level nitrite addition to acidified sludge to simultaneously enhance digestibility, toxic metal removal, dewaterability and pathogen reduction were investigated. Waste activated sludge (WAS) from a full-scale waste water treatment plant was treated at pH 2 with 10 mg NO2--N/L for 5 h. Biochemical methane potential tests showed an increase in the methane production of 28%, corresponding to an improvement from 247 ± 8 L CH4/kg VS to 317 ± 1 L CH4/kg VS. The enhanced removal of toxic metals further increased the methane production by another 18% to 360 ± 6 L CH4/kg VS (a total increase of 46%). The solids content of dewatered sludge increased from 14.6 ± 1.4% in the control to 18.2 ± 0.8%. A 4-log reduction for both total coliforms and E. coli was achieved. Overall, this study highlights the potential of acidification with low level nitrite addition as an effective and simple method achieving multiple improvements in terms of sludge management.

Improved synthesis of N-benzylaminoferrocene-based prodrugs and evaluation of their toxicity and antileukemic activity.

Science.gov (United States)

Daum, Steffen; Chekhun, Vasiliy F; Todor, Igor N; Lukianova, Natalia Yu; Shvets, Yulia V; Sellner, Leopold; Putzker, Kerstin; Lewis, Joe; Zenz, Thorsten; de Graaf, Inge A M; Groothuis, Geny M M; Casini, Angela; Zozulia, Oleksii; Hampel, Frank; Mokhir, Andriy

2015-02-26

We report on an improved method of synthesis of N-benzylaminoferrocene-based prodrugs and demonstrate its applicability by preparing nine new aminoferrocenes. Their effect on the viability of selected cancer cells having different p53 status was studied. The obtained data are in agreement with the hypothesis that the toxicity of aminoferrocenes is not dependent upon p53 status. Subsequently the toxicity of a selected prodrug (4) was investigated ex vivo using rat precision cut liver slices and in vivo on hybrid male mice BDF1. In both experiments no toxicity was observed: ex vivo, up to 10 μM; in vivo, up to 6 mg/kg. Finally, prodrug 4 was shown to extend the survival of BDF1 mice carrying L1210 leukemia from 13.7 ± 0.6 days to 17.5 ± 0.7 days when injected daily 6 times at a dose of 26 μg/kg starting from the second day after injection of L1210 cells.

Improving orbit prediction accuracy through supervised machine learning

Science.gov (United States)

Peng, Hao; Bai, Xiaoli

2018-05-01

Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs' trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO's orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.

Combining gene prediction methods to improve metagenomic gene annotation

Directory of Open Access Journals (Sweden)

Rosen Gail L

2011-01-01

Full Text Available Abstract Background Traditional gene annotation methods rely on characteristics that may not be available in short reads generated from next generation technology, resulting in suboptimal performance for metagenomic (environmental samples. Therefore, in recent years, new programs have been developed that optimize performance on short reads. In this work, we benchmark three metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation. Results We not only analyze the programs' performance at different read-lengths like similar studies, but also separate different types of reads, including intra- and intergenic regions, for analysis. The main deficiencies are in the algorithms' ability to predict non-coding regions and gene edges, resulting in more false-positives and false-negatives than desired. In fact, the specificities of the algorithms are notably worse than the sensitivities. By combining the programs' predictions, we show significant improvement in specificity at minimal cost to sensitivity, resulting in 4% improvement in accuracy for 100 bp reads with ~1% improvement in accuracy for 200 bp reads and above. To correctly annotate the start and stop of the genes, we find that a consensus of all the predictors performs best for shorter read lengths while a unanimous agreement is better for longer read lengths, boosting annotation accuracy by 1-8%. We also demonstrate use of the classifier combinations on a real dataset. Conclusions To optimize the performance for both prediction and annotation accuracies, we conclude that the consensus of all methods (or a majority vote is the best for reads 400 bp and shorter, while using the intersection of GeneMark and Orphelia predictions is the best for reads 500 bp and longer. We demonstrate that most methods predict over 80% coding (including partially coding reads on a real human gut sample sequenced by Illumina technology.

In silico prediction of microRNAs on fluoride induced sperm toxicity in mice.

Science.gov (United States)

Raghunath, Azhwar; Jeyabaskar, Dhivyalakshmi; Sundarraj, Kiruthika; Panneerselvam, Lakshmikanthan; Perumal, Ekambaram

2016-12-01

Fluorosis is an endemic global problem causing male reproductive impairment. F mediates male reproductive toxicity in mice down-regulating 63 genes involved in diverse biological processes - apoptosis, cell cycle, cell signaling, chemotaxis, electron transport, glycolysis, oxidative stress, sperm capacitation and spermatogenesis. We predicted the miRNAs down-regulating these 63 genes using TargetScan, DIANA and MicroCosm. The prediction tools identified 3059 miRNAs targeting 63 genes. Of the predicted interactions, 11 miRNAs (mmu-miR-103, -107, -122, -188a, -199a-5p, -205, -340-5p, -345-3p, -452-5p, -499, -878-3p) were commonly found in the three tools utilized and seven miRNAs (miR-9-5p, miR-511-3p, miR-7b-5p, miR-30e-5p, miR-17-5p, miR-122-5p and miR-541-5p) targeting six genes (Traf3, Rock2, Rgs8, Atp1b2, Cacna2d1 and Aldoa) were already validated experimentally in mice. The miRNA-mRNA network of the predicted miRNAs with its respective targets revealed the complex interaction within a biological process leading to sperm dysfunction on exposure to F. Our findings not only suggest that the predicted miRs furnish evidence, but also have the potential to serve as non-invasive biomarkers on F-induced sperm dysfunction. Our data contribute towards elucidating the function of miRNAs in the fluoride induced infertility. miRNA molecular pathways in F intoxication will open new avenues on the use of antagomirs in recovering fertility. Copyright © 2016 Elsevier Ltd. All rights reserved.

Improved Wind Speed Prediction Using Empirical Mode Decomposition

Directory of Open Access Journals (Sweden)

ZHANG, Y.

2018-05-01

Full Text Available Wind power industry plays an important role in promoting the development of low-carbon economic and energy transformation in the world. However, the randomness and volatility of wind speed series restrict the healthy development of the wind power industry. Accurate wind speed prediction is the key to realize the stability of wind power integration and to guarantee the safe operation of the power system. In this paper, combined with the Empirical Mode Decomposition (EMD, the Radial Basis Function Neural Network (RBF and the Least Square Support Vector Machine (SVM, an improved wind speed prediction model based on Empirical Mode Decomposition (EMD-RBF-LS-SVM is proposed. The prediction result indicates that compared with the traditional prediction model (RBF, LS-SVM, the EMD-RBF-LS-SVM model can weaken the random fluctuation to a certain extent and improve the short-term accuracy of wind speed prediction significantly. In a word, this research will significantly reduce the impact of wind power instability on the power grid, ensure the power grid supply and demand balance, reduce the operating costs in the grid-connected systems, and enhance the market competitiveness of the wind power.

A New Model for Predicting Acute Mucosal Toxicity in Head-and-Neck Cancer Patients Undergoing Radiotherapy With Altered Schedules

International Nuclear Information System (INIS)

Strigari, Lidia; Pedicini, Piernicola; D’Andrea, Marco; Pinnarò, Paola; Marucci, Laura; Giordano, Carolina; Benassi, Marcello

2012-01-01

Purpose: One of the worst radiation-induced acute effects in treating head-and-neck (HN) cancer is grade 3 or higher acute (oral and pharyngeal) mucosal toxicity (AMT), caused by the killing/depletion of mucosa cells. Here we aim to testing a predictive model of the AMT in HN cancer patients receiving different radiotherapy schedules. Methods and Materials: Various radiotherapeutic schedules have been reviewed and classified as tolerable or intolerable based on AMT severity. A modified normal tissue complication probability (NTCP) model has been investigated to describe AMT data in radiotherapy regimens, both conventional and altered in dose and overall treatment time (OTT). We tested the hypothesis that such a model could also be applied to identify intolerable treatment and to predict AMT. This AMT NTCP model has been compared with other published predictive models to identify schedules that are either tolerable or intolerable. The area under the curve (AUC) was calculated for all models, assuming treatment tolerance as the gold standard. The correlation between AMT and the predicted toxicity rate was assessed by a Pearson correlation test. Results: The AMT NTCP model was able to distinguish between acceptable and intolerable schedules among the data available for the study (AUC = 0.84, 95% confidence interval = 0.75-0.92). In the equivalent dose at 2 Gy/fraction (EQD2) vs OTT space, the proposed model shows a trend similar to that of models proposed by other authors, but was superior in detecting some intolerable schedules. Moreover, it was able to predict the incidence of ≥G3 AMT. Conclusion: The proposed model is able to predict ≥G3 AMT after HN cancer radiotherapy, and could be useful for designing altered/hypofractionated schedules to reduce the incidence of AMT.

Improvement of gas entrainment prediction method. Introduction of surface tension effect

International Nuclear Information System (INIS)

Ito, Kei; Sakai, Takaaki; Ohshima, Hiroyuki; Uchibori, Akihiro; Eguchi, Yuzuru; Monji, Hideaki; Xu, Yongze

2010-01-01

A gas entrainment (GE) prediction method has been developed to establish design criteria for the large-scale sodium-cooled fast reactor (JSFR) systems. The prototype of the GE prediction method was already confirmed to give reasonable gas core lengths by simple calculation procedures. However, for simplification, the surface tension effects were neglected. In this paper, the evaluation accuracy of gas core lengths is improved by introducing the surface tension effects into the prototype GE prediction method. First, the mechanical balance between gravitational, centrifugal, and surface tension forces is considered. Then, the shape of a gas core tip is approximated by a quadratic function. Finally, using the approximated gas core shape, the authors determine the gas core length satisfying the mechanical balance. This improved GE prediction method is validated by analyzing the gas core lengths observed in simple experiments. Results show that the analytical gas core lengths calculated by the improved GE prediction method become shorter in comparison to the prototype GE prediction method, and are in good agreement with the experimental data. In addition, the experimental data under different temperature and surfactant concentration conditions are reproduced by the improved GE prediction method. (author)

Antimony Toxicity

OpenAIRE

Sundar, Shyam; Chakravarty, Jaya

2010-01-01

Antimony toxicity occurs either due to occupational exposure or during therapy. Occupational exposure may cause respiratory irritation, pneumoconiosis, antimony spots on the skin and gastrointestinal symptoms. In addition antimony trioxide is possibly carcinogenic to humans. Improvements in working conditions have remarkably decreased the incidence of antimony toxicity in the workplace. As a therapeutic, antimony has been mostly used for the treatment of leishmaniasis and schistosomiasis. The...

The influence of toxicity constraints in models of chemotherapeutic protocol escalation

KAUST Repository

Boston, E. A. J.

2011-04-06

The prospect of exploiting mathematical and computational models to gain insight into the influence of scheduling on cancer chemotherapeutic effectiveness is increasingly being considered. However, the question of whether such models are robust to the inclusion of additional tumour biology is relatively unexplored. In this paper, we consider a common strategy for improving protocol scheduling that has foundations in mathematical modelling, namely the concept of dose densification, whereby rest phases between drug administrations are reduced. To maintain a manageable scope in our studies, we focus on a single cell cycle phase-specific agent with uncomplicated pharmacokinetics, as motivated by 5-Fluorouracil-based adjuvant treatments of liver micrometastases. In particular, we explore predictions of the effectiveness of dose densification and other escalations of the protocol scheduling when the influence of toxicity constraints, cell cycle phase specificity and the evolution of drug resistance are all represented within the modelling. For our specific focus, we observe that the cell cycle and toxicity should not simply be neglected in modelling studies. Our explorations also reveal the prediction that dose densification is often, but not universally, effective. Furthermore, adjustments in the duration of drug administrations are predicted to be important, especially when dose densification in isolation does not yield improvements in protocol outcomes. © The author 2011. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

The use of adverse outcome pathway-based toxicity predictions: A case study evaluating the effects of imazalil on fathead minnow reproduction

Science.gov (United States)

Product Description: As a means to increase the efficiency of chemical safety assessment, there is an interest in using data from molecular and cellular bioassays, conducted in a highly automated fashion using modern robotics, to predict toxicity in humans and wildlife. The prese...

Plant water potential improves prediction of empirical stomatal models.

Directory of Open Access Journals (Sweden)

William R L Anderegg

Full Text Available Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.

Recent advances in prediction of emission of hazardous air pollutants from coal-fired power plants

International Nuclear Information System (INIS)

Senior, C.L.; Helble, J.J.; Sarofim, A.F.

2000-01-01

Coal-fired power plants are a primary source of mercury discharge into the atmosphere along with fine particulates containing arsenic, selenium, cadmium, and other hazardous air pollutants. Information regarding the speciation of these toxic metals is necessary to accurately predict their atmospheric transport and fate in the environment. New predictive tools have been developed to allow utilities to better estimate the emissions of toxic metals from coal-fired power plants. These prediction equations are based on fundamental physics and chemistry and can be applied to a wide variety of fuel types and combustion conditions. The models have significantly improved the ability to predict the emissions of air toxic metals in fine particulate and gas-phase mercury. In this study, the models were successfully tested using measured mercury speciation and mass balance information collected from coal-fired power plants

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

Science.gov (United States)

Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

2017-01-01

Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others

An Improved Optimal Slip Ratio Prediction considering Tyre Inflation Pressure Changes

Directory of Open Access Journals (Sweden)

Guoxing Li

2015-01-01

Full Text Available The prediction of optimal slip ratio is crucial to vehicle control systems. Many studies have verified there is a definitive impact of tyre pressure change on the optimal slip ratio. However, the existing method of optimal slip ratio prediction has not taken into account the influence of tyre pressure changes. By introducing a second-order factor, an improved optimal slip ratio prediction considering tyre inflation pressure is proposed in this paper. In order to verify and evaluate the performance of the improved prediction, a cosimulation platform is developed by using MATLAB/Simulink and CarSim software packages, achieving a comprehensive simulation study of vehicle braking performance cooperated with an ABS controller. The simulation results show that the braking distances and braking time under different tyre pressures and initial braking speeds are effectively shortened with the improved prediction of optimal slip ratio. When the tyre pressure is slightly lower than the nominal pressure, the difference of braking performances between original optimal slip ratio and improved optimal slip ratio is the most obvious.

Metabolic enzyme microarray coupled with miniaturized cell-culture array technology for high-throughput toxicity screening.

Science.gov (United States)

Lee, Moo-Yeal; Dordick, Jonathan S; Clark, Douglas S

2010-01-01

Due to poor drug candidate safety profiles that are often identified late in the drug development process, the clinical progression of new chemical entities to pharmaceuticals remains hindered, thus resulting in the high cost of drug discovery. To accelerate the identification of safer drug candidates and improve the clinical progression of drug candidates to pharmaceuticals, it is important to develop high-throughput tools that can provide early-stage predictive toxicology data. In particular, in vitro cell-based systems that can accurately mimic the human in vivo response and predict the impact of drug candidates on human toxicology are needed to accelerate the assessment of drug candidate toxicity and human metabolism earlier in the drug development process. The in vitro techniques that provide a high degree of human toxicity prediction will be perhaps more important in cosmetic and chemical industries in Europe, as animal toxicity testing is being phased out entirely in the immediate future.We have developed a metabolic enzyme microarray (the Metabolizing Enzyme Toxicology Assay Chip, or MetaChip) and a miniaturized three-dimensional (3D) cell-culture array (the Data Analysis Toxicology Assay Chip, or DataChip) for high-throughput toxicity screening of target compounds and their metabolic enzyme-generated products. The human or rat MetaChip contains an array of encapsulated metabolic enzymes that is designed to emulate the metabolic reactions in the human or rat liver. The human or rat DataChip contains an array of 3D human or rat cells encapsulated in alginate gels for cell-based toxicity screening. By combining the DataChip with the complementary MetaChip, in vitro toxicity results are obtained that correlate well with in vivo rat data.

Recent Improvements in IERS Rapid Service/Prediction Center Products

National Research Council Canada - National Science Library

Stamatakos, N; Luzum, B; Wooden, W

2007-01-01

...) at USNO has made several improvements to its combination and pre- diction products. These improvements are due to the inclusion of new input data sources as well as modifications to the combination and prediction algorithms...

Prediction of clinical toxicity in localized cervical carcinoma by radio-induced apoptosis study in peripheral blood lymphocytes (PBLs)

International Nuclear Information System (INIS)

Bordón, Elisa; Henríquez Hernández, Luis Alberto; Lara, Pedro C; Pinar, Beatriz; Fontes, Fausto; Rodríguez Gallego, Carlos; Lloret, Marta

2009-01-01

Cervical cancer is treated mainly by surgery and radiotherapy. Toxicity due to radiation is a limiting factor for treatment success. Determination of lymphocyte radiosensitivity by radio-induced apoptosis arises as a possible method for predictive test development. The aim of this study was to analyze radio-induced apoptosis of peripheral blood lymphocytes. Ninety four consecutive patients suffering from cervical carcinoma, diagnosed and treated in our institution, and four healthy controls were included in the study. Toxicity was evaluated using the Lent-Soma scale. Peripheral blood lymphocytes were isolated and irradiated at 0, 1, 2 and 8 Gy during 24, 48 and 72 hours. Apoptosis was measured by flow cytometry using annexin V/propidium iodide to determine early and late apoptosis. Lymphocytes were marked with CD45 APC-conjugated monoclonal antibody. Radiation-induced apoptosis (RIA) increased with radiation dose and time of incubation. Data strongly fitted to a semi logarithmic model as follows: RIA = βln(Gy) + α. This mathematical model was defined by two constants: α, is the origin of the curve in the Y axis and determines the percentage of spontaneous cell death and β, is the slope of the curve and determines the percentage of cell death induced at a determined radiation dose (β = ΔRIA/Δln(Gy)). Higher β values (increased rate of RIA at given radiation doses) were observed in patients with low sexual toxicity (Exp(B) = 0.83, C.I. 95% (0.73-0.95), p = 0.007; Exp(B) = 0.88, C.I. 95% (0.82-0.94), p = 0.001; Exp(B) = 0.93, C.I. 95% (0.88-0.99), p = 0.026 for 24, 48 and 72 hours respectively). This relation was also found with rectal (Exp(B) = 0.89, C.I. 95% (0.81-0.98), p = 0.026; Exp(B) = 0.95, C.I. 95% (0.91-0.98), p = 0.013 for 48 and 72 hours respectively) and urinary (Exp(B) = 0.83, C.I. 95% (0.71-0.97), p = 0.021 for 24 hours) toxicity. Radiation induced apoptosis at different time points and radiation doses fitted to a semi logarithmic model defined

Is It Time to Tailor the Prediction of Radio-Induced Toxicity in Prostate Cancer Patients? Building the First Set of Nomograms for Late Rectal Syndrome

International Nuclear Information System (INIS)

Valdagni, Riccardo; Kattan, Michael W.; Rancati, Tiziana; Yu Changhong; Vavassori, Vittorio; Fellin, Giovanni; Cagna, Elena; Gabriele, Pietro; Mauro, Flora Anna; Baccolini, Micaela; Bianchi, Carla; Menegotti, Loris; Monti, Angelo F.; Stasi, Michele; Giganti, Maria Olga

2012-01-01

Purpose: Development of user-friendly tools for the prediction of single-patient probability of late rectal toxicity after conformal radiotherapy for prostate cancer. Methods and Materials: This multicenter protocol was characterized by the prospective evaluation of rectal toxicity through self-assessed questionnaires (minimum follow-up, 36 months) by 718 adult men in the AIROPROS 0102 trial. Doses were between 70 and 80 Gy. Nomograms were created based on multivariable logistic regression analysis. Three endpoints were considered: G2 to G3 late rectal bleeding (52/718 events), G3 late rectal bleeding (24/718 events), and G2 to G3 late fecal incontinence (LINC, 19/718 events). Results: Inputs for the nomogram for G2 to G3 late rectal bleeding estimation were as follows: presence of abdominal surgery before RT, percentage volume of rectum receiving >75 Gy (V75Gy), and nomogram-based estimation of the probability of G2 to G3 acute gastrointestinal toxicity (continuous variable, which was estimated using a previously published nomogram). G3 late rectal bleeding estimation was based on abdominal surgery before RT, V75Gy, and NOMACU. Prediction of G2 to G3 late fecal incontinence was based on abdominal surgery before RT, presence of hemorrhoids, use of antihypertensive medications (protective factor), and percentage volume of rectum receiving >40 Gy. Conclusions: We developed and internally validated the first set of nomograms available in the literature for the prediction of radio-induced toxicity in prostate cancer patients. Calculations included dosimetric as well as clinical variables to help radiation oncologists predict late rectal morbidity, thus introducing the possibility of RT plan corrections to better tailor treatment to the patient’s characteristics, to avoid unnecessary worsening of quality of life, and to provide support to the patient in selecting the best therapeutic approach.

Is It Time to Tailor the Prediction of Radio-Induced Toxicity in Prostate Cancer Patients? Building the First Set of Nomograms for Late Rectal Syndrome

Energy Technology Data Exchange (ETDEWEB)

Valdagni, Riccardo [Prostate Program, Scientific Directorate, Fondazione IRCCS-Istituto Nazionale Tumori, Milan (Italy); Radiotherapy, Fondazione IRCCS - Istituto Nazionale dei Tumori, Milan (Italy); Kattan, Michael W. [Department of Quantitative Health Sciences, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH (United States); Rancati, Tiziana, E-mail: tiziana.rancati@istitutotumori.mi.it [Prostate Program, Scientific Directorate, Fondazione IRCCS-Istituto Nazionale Tumori, Milan (Italy); Yu Changhong [Department of Quantitative Health Sciences, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH (United States); Vavassori, Vittorio [Radiotherapy and Medical Physics, Ospedale di Circolo, Varese (Italy); Department of Radiotherapy, Humanitas - Gavazzeni, Bergamo (Italy); Fellin, Giovanni [Radiotherapy and Medical Physics, Ospedale Santa Chiara, Trento (Italy); Cagna, Elena [Department of Radiotherapy and Medical Physics, Ospedale Sant' Anna, Como (Italy); Gabriele, Pietro [Department of Radiotherapy and Medical Physics, Institute for Cancer Research and Treatment, Candiolo (Italy); Mauro, Flora Anna; Baccolini, Micaela [Department of Radiotherapy and Medical Physics, Ospedale Villa Maria Cecilia, Lugo (Italy); Bianchi, Carla [Radiotherapy and Medical Physics, Ospedale di Circolo, Varese (Italy); Menegotti, Loris [Radiotherapy and Medical Physics, Ospedale Santa Chiara, Trento (Italy); Monti, Angelo F. [Department of Radiotherapy and Medical Physics, Ospedale Sant' Anna, Como (Italy); Stasi, Michele [Department of Radiotherapy and Medical Physics, Institute for Cancer Research and Treatment, Candiolo (Italy); Giganti, Maria Olga [Prostate Program, Scientific Directorate, Fondazione IRCCS-Istituto Nazionale Tumori, Milan (Italy); Dept. of Oncology, Ospedale Niguarda, Milan (Italy); and others

2012-04-01

Purpose: Development of user-friendly tools for the prediction of single-patient probability of late rectal toxicity after conformal radiotherapy for prostate cancer. Methods and Materials: This multicenter protocol was characterized by the prospective evaluation of rectal toxicity through self-assessed questionnaires (minimum follow-up, 36 months) by 718 adult men in the AIROPROS 0102 trial. Doses were between 70 and 80 Gy. Nomograms were created based on multivariable logistic regression analysis. Three endpoints were considered: G2 to G3 late rectal bleeding (52/718 events), G3 late rectal bleeding (24/718 events), and G2 to G3 late fecal incontinence (LINC, 19/718 events). Results: Inputs for the nomogram for G2 to G3 late rectal bleeding estimation were as follows: presence of abdominal surgery before RT, percentage volume of rectum receiving >75 Gy (V75Gy), and nomogram-based estimation of the probability of G2 to G3 acute gastrointestinal toxicity (continuous variable, which was estimated using a previously published nomogram). G3 late rectal bleeding estimation was based on abdominal surgery before RT, V75Gy, and NOMACU. Prediction of G2 to G3 late fecal incontinence was based on abdominal surgery before RT, presence of hemorrhoids, use of antihypertensive medications (protective factor), and percentage volume of rectum receiving >40 Gy. Conclusions: We developed and internally validated the first set of nomograms available in the literature for the prediction of radio-induced toxicity in prostate cancer patients. Calculations included dosimetric as well as clinical variables to help radiation oncologists predict late rectal morbidity, thus introducing the possibility of RT plan corrections to better tailor treatment to the patient's characteristics, to avoid unnecessary worsening of quality of life, and to provide support to the patient in selecting the best therapeutic approach.

Acute toxicity of anionic and non-ionic surfactants to aquatic organisms.

Science.gov (United States)

Lechuga, M; Fernández-Serrano, M; Jurado, E; Núñez-Olea, J; Ríos, F

2016-03-01

The environmental risk of surfactants requires toxicity measurements. As different test organisms have different sensitivity to the toxics, it is necessary to establish the most appropriate organism to classify the surfactant as very toxic, toxic, harmful or safe, in order to establish the maximum permissible concentrations in aquatic ecosystems. We have determined the toxicity values of various anionic surfactants ether carboxylic derivatives using four test organisms: the freshwater crustacean Daphnia magna, the luminescent bacterium Vibrio fischeri, the microalgae Selenastrum capricornutum (freshwater algae) and Phaeodactylum tricornutum (seawater algae). In addition, in order to compare and classify the different families of surfactants, we have included a compilation of toxicity data of surfactants collected from literature. The results indicated that V. fischeri was more sensitive to the toxic effects of the surfactants than was D. magna or the microalgae, which was the least sensitive. This result shows that the most suitable toxicity assay for surfactants may be the one using V. fischeri. The toxicity data revealed considerable variation in toxicity responses with the structure of the surfactants regardless of the species tested. The toxicity data have been related to the structure of the surfactants, giving a mathematical relationship that helps to predict the toxic potential of a surfactant from its structure. Model-predicted toxicity agreed well with toxicity values reported in the literature for several surfactants previously studied. Predictive models of toxicity is a handy tool for providing a risk assessment that can be useful to establish the toxicity range for each surfactant and the different test organisms in order to select efficient surfactants with a lower impact on the aquatic environment. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting molybdenum toxicity to higher plants: Influence of soil properties

International Nuclear Information System (INIS)

McGrath, S.P.; Mico, C.; Curdy, R.; Zhao, F.J.

2010-01-01

The effect of soil properties on the toxicity of molybdenum (Mo) to four plant species was investigated. Soil organic carbon or ammonium-oxalate extractable Fe oxides were found to be the best predictors of the 50% effective dose (ED 50 ) of Mo in different soils, explaining > 65% of the variance in ED 50 for four species except for ryegrass (26-38%). Molybdenum concentrations in soil solution and consequently plant uptake were increased when soil pH was artificially raised because sorption of Mo to amorphous oxides is greatly reduced at high pH. The addition of sulphate significantly decreased Mo uptake by oilseed rape. For risk assessment, we suggest that Mo toxicity values for plants should be normalised using soil amorphous iron oxide concentrations. - Amorphous iron oxides or organic carbon were found to be the best predictors of the toxicity threshold values of Mo to higher plants on different soils.

Improving anticancer activity and reducing systemic toxicity of doxorubicin by self-assembled polymeric micelles

International Nuclear Information System (INIS)

Gou Maling; Shi Huashan; Guo Gang; Men Ke; Zhang Juan; Li Zhiyong; Luo Feng; Qian Zhiyong; Wei Yuquan; Zheng Lan; Zhao Xia

2011-01-01

In an attempt to improve anticancer activity and reduce systemic toxicity of doxorubicin (Dox), we encapsulated Dox in monomethoxy poly(ethylene glycol)-poly(ε-caprolactone) (MPEG-PCL) micelles by a novel self-assembly procedure without using surfactants, organic solvents or vigorous stirring. These Dox encapsulated MPEG-PCL (Dox/MPEG-PCL) micelles with drug loading of 4.2% were monodisperse and ∼ 20 nm in diameter. The Dox can be released from the Dox/MPEG-PCL micelles; the Dox-release at pH 5.5 was faster than that at pH 7.0. Encapsulation of Dox in MPEG-PCL micelles enhanced the cellular uptake and cytotoxicity of Dox on the C-26 colon carcinoma cell in vitro, and slowed the extravasation of Dox in the transgenic zebrafish model. Compared to free Dox, Dox/MPEG-PCL micelles were more effective in inhibiting tumor growth in the subcutaneous C-26 colon carcinoma and Lewis lung carcinoma models, and prolonging survival of mice bearing these tumors. Dox/MPEG-PCL micelles also induced lower systemic toxicity than free Dox. In conclusion, incorporation of Dox in MPEG-PCL micelles enhanced the anticancer activity and decreased the systemic toxicity of Dox; these Dox/MPEG-PCL micelles are an interesting formulation of Dox and may have potential clinical applications in cancer therapy.

Improving anticancer activity and reducing systemic toxicity of doxorubicin by self-assembled polymeric micelles

Energy Technology Data Exchange (ETDEWEB)

Gou Maling; Shi Huashan; Guo Gang; Men Ke; Zhang Juan; Li Zhiyong; Luo Feng; Qian Zhiyong; Wei Yuquan [State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu 610041 (China); Zheng Lan; Zhao Xia, E-mail: anderson-qian@163.com [West China Second University Hospital, West China Women' s and Children' s Hospital, Sichuan University, Chengdu 610041 (China)

2011-03-04

In an attempt to improve anticancer activity and reduce systemic toxicity of doxorubicin (Dox), we encapsulated Dox in monomethoxy poly(ethylene glycol)-poly({epsilon}-caprolactone) (MPEG-PCL) micelles by a novel self-assembly procedure without using surfactants, organic solvents or vigorous stirring. These Dox encapsulated MPEG-PCL (Dox/MPEG-PCL) micelles with drug loading of 4.2% were monodisperse and {approx} 20 nm in diameter. The Dox can be released from the Dox/MPEG-PCL micelles; the Dox-release at pH 5.5 was faster than that at pH 7.0. Encapsulation of Dox in MPEG-PCL micelles enhanced the cellular uptake and cytotoxicity of Dox on the C-26 colon carcinoma cell in vitro, and slowed the extravasation of Dox in the transgenic zebrafish model. Compared to free Dox, Dox/MPEG-PCL micelles were more effective in inhibiting tumor growth in the subcutaneous C-26 colon carcinoma and Lewis lung carcinoma models, and prolonging survival of mice bearing these tumors. Dox/MPEG-PCL micelles also induced lower systemic toxicity than free Dox. In conclusion, incorporation of Dox in MPEG-PCL micelles enhanced the anticancer activity and decreased the systemic toxicity of Dox; these Dox/MPEG-PCL micelles are an interesting formulation of Dox and may have potential clinical applications in cancer therapy.

Zebrafish embryotoxicity test for developmental (neuro)toxicity : Demo case of an integrated screening approach system using anti-epileptic drugs

NARCIS (Netherlands)

Beker van Woudenberg, Anna; Snel, Cor; Rijkmans, Eke; De Groot, Didima; Bouma, Marga; Hermsen, Sanne; Piersma, Aldert; Menke, Aswin; Wolterbeek, André

2014-01-01

To improve the predictability of the zebrafish embryotoxicity test (ZET) for developmental (neuro)toxicity screening, we used a multiple-endpoints strategy, including morphology, motor activity (MA), histopathology and kinetics. The model compounds used were antiepileptic drugs (AEDs): valproic acid

Zebrafish embryotoxicity test for developmental (neuro)toxicity: Demo case of an integrated screening approach system using anti-epileptic drugs

NARCIS (Netherlands)

Beker van Woudenberg, A.; Snel, C.; Rijkmans, E.; Groot, D. de; Bouma, M.; Hermsen, S.; Piersma, A.; Menke, A.; Wolterbeek, A.

2014-01-01

To improve the predictability of the zebrafish embryotoxicity test (ZET) for developmental (neuro)toxicity screening, we used a multiple-endpoints strategy, including morphology, motor activity (MA), histopathology and kinetics. The model compounds used were antiepileptic drugs (AEDs): valproic acid

Improving toxicity assessment of pesticide mixtures: the use of polar passive sampling devices extracts in microalgae toxicity tests

Directory of Open Access Journals (Sweden)

Sandra KIM TIAM

2016-09-01

Full Text Available Complexity of contaminants exposure needs to be taking in account for an appropriate evaluation of risks related to mixtures of pesticides released in the ecosystems. Toxicity assessment of such mixtures can be made through a variety of toxicity tests reflecting different level of biological complexity. This paper reviews the recent developments of passive sampling techniques for polar compounds, especially Polar Organic Chemical Integrative Samplers (POCIS and Chemcatcher® and the principal assessment techniques using microalgae in laboratory experiments. The progresses permitted by the coupled use of such passive samplers and ecotoxicology testing as well as their limitations are presented. Case studies combining passive sampling devices (PSD extracts and toxicity assessment toward microorganisms at different biological scales from single organisms to communities level are presented. These case studies, respectively aimed i at characterizing the toxic potential of waters using dose-response curves, and ii at performing microcosm experiments with increased environmental realism in the toxicant exposure in term of cocktail composition and concentration. Finally perspectives and limitations of such approaches for future applications in the area of environmental risk assessment are discussed.

Neurophysiology in preschool improves behavioral prediction of reading ability throughout primary school.

Science.gov (United States)

Maurer, Urs; Bucher, Kerstin; Brem, Silvia; Benz, Rosmarie; Kranz, Felicitas; Schulz, Enrico; van der Mark, Sanne; Steinhausen, Hans-Christoph; Brandeis, Daniel

2009-08-15

More struggling readers could profit from additional help at the beginning of reading acquisition if dyslexia prediction were more successful. Currently, prediction is based only on behavioral assessment of early phonological processing deficits associated with dyslexia, but it might be improved by adding brain-based measures. In a 5-year longitudinal study of children with (n = 21) and without (n = 23) familial risk for dyslexia, we tested whether neurophysiological measures of automatic phoneme and tone deviance processing obtained in kindergarten would improve prediction of reading over behavioral measures alone. Together, neurophysiological and behavioral measures obtained in kindergarten significantly predicted reading in school. Particularly the late mismatch negativity measure that indicated hemispheric lateralization of automatic phoneme processing improved prediction of reading ability over behavioral measures. It was also the only significant predictor for long-term reading success in fifth grade. Importantly, this result also held for the subgroup of children at familial risk. The results demonstrate that brain-based measures of processing deficits associated with dyslexia improve prediction of reading and thus may be further evaluated to complement clinical practice of dyslexia prediction, especially in targeted populations, such as children with a familial risk.

Small Bowel Dose Parameters Predicting Grade ≥3 Acute Toxicity in Rectal Cancer Patients Treated With Neoadjuvant Chemoradiation: An Independent Validation Study Comparing Peritoneal Space Versus Small Bowel Loop Contouring Techniques

International Nuclear Information System (INIS)

Banerjee, Robyn; Chakraborty, Santam; Nygren, Ian; Sinha, Richie

2013-01-01

Purpose: To determine whether volumes based on contours of the peritoneal space can be used instead of individual small bowel loops to predict for grade ≥3 acute small bowel toxicity in patients with rectal cancer treated with neoadjuvant chemoradiation therapy. Methods and Materials: A standardized contouring method was developed for the peritoneal space and retrospectively applied to the radiation treatment plans of 67 patients treated with neoadjuvant chemoradiation therapy for rectal cancer. Dose-volume histogram (DVH) data were extracted and analyzed against patient toxicity. Receiver operating characteristic analysis and logistic regression were carried out for both contouring methods. Results: Grade ≥3 small bowel toxicity occurred in 16% (11/67) of patients in the study. A highly significant dose-volume relationship between small bowel irradiation and acute small bowel toxicity was supported by the use of both small bowel loop and peritoneal space contouring techniques. Receiver operating characteristic analysis demonstrated that, for both contouring methods, the greatest sensitivity for predicting toxicity was associated with the volume receiving between 15 and 25 Gy. Conclusion: DVH analysis of peritoneal space volumes accurately predicts grade ≥3 small bowel toxicity in patients with rectal cancer receiving neoadjuvant chemoradiation therapy, suggesting that the contours of the peritoneal space provide a reasonable surrogate for the contours of individual small bowel loops. The study finds that a small bowel V15 less than 275 cc and a peritoneal space V15 less than 830 cc are associated with a less than 10% risk of grade ≥3 acute toxicity

Predicting the formation and the dispersion of toxic combustion products from the fires of dangerous substances

Science.gov (United States)

Nevrlý, V.; Bitala, P.; Danihelka, P.; Dobeš, P.; Dlabka, J.; Hejzlar, T.; Baudišová, B.; Míček, D.; Zelinger, Z.

2012-04-01

Natural events, such as wildfires, lightning or earthquakes represent a frequent trigger of industrial fires involving dangerous substances. Dispersion of smoke plume from such fires and the effects of toxic combustion products are one of the reference scenarios expected in the framework of major accident prevention. Nowadays, tools for impact assessment of these events are rather missing. Detailed knowledge of burning material composition, atmospheric conditions, and other factors are required in order to describe quantitatively the source term of toxic fire products and to evaluate the parameters of smoke plume. Nevertheless, an assessment of toxic emissions from large scale fires involves a high degree of uncertainty, because of the complex character of physical and chemical processes in the harsh environment of uncontrolled flame. Among the others, soot particle formation can be mentioned as still being one of the unresolved problems in combustion chemistry, as well as decomposition pathways of chemical substances. Therefore, simplified approach for estimating the emission factors from outdoor fires of dangerous chemicals, utilizable for major accident prevention and preparedness, was developed and the case study illustrating the application of the proposed method was performed. ALOFT-FT software tool based on large eddy simulation of buoyant fire plumes was employed for predicting the local toxic contamination in the down-wind vicinity of the fire. The database of model input parameters can be effectively modified enabling the simulation of the smoke plume from pool fires or jet fires of arbitrary flammable (or combustible) gas, liquid or solid. This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic via the project LD11012 (in the frame of the COST CM0901 Action) and the Ministry of Environment of the Czech Republic (project no. SPII 1a10 45/70).

Predicting molybdenum toxicity to higher plants: Influence of soil properties

Energy Technology Data Exchange (ETDEWEB)

McGrath, S.P., E-mail: steve.mcgrath@bbsrc.ac.u [Soil Science Department, Centre for Soils and Ecosystems Functions, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ (United Kingdom); Mico, C. [Soil Science Department, Centre for Soils and Ecosystems Functions, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ (United Kingdom); Curdy, R. [Laboratory for Environmental Biotechnology (LBE), Swiss Federal Institute of Technology Lausanne (EPFL) Station 6 CH, 1015 Lausanne (Switzerland); Zhao, F.J. [Soil Science Department, Centre for Soils and Ecosystems Functions, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ (United Kingdom)

2010-10-15

The effect of soil properties on the toxicity of molybdenum (Mo) to four plant species was investigated. Soil organic carbon or ammonium-oxalate extractable Fe oxides were found to be the best predictors of the 50% effective dose (ED{sub 50}) of Mo in different soils, explaining > 65% of the variance in ED{sub 50} for four species except for ryegrass (26-38%). Molybdenum concentrations in soil solution and consequently plant uptake were increased when soil pH was artificially raised because sorption of Mo to amorphous oxides is greatly reduced at high pH. The addition of sulphate significantly decreased Mo uptake by oilseed rape. For risk assessment, we suggest that Mo toxicity values for plants should be normalised using soil amorphous iron oxide concentrations. - Amorphous iron oxides or organic carbon were found to be the best predictors of the toxicity threshold values of Mo to higher plants on different soils.

Species differences in drug glucuronidation: Humanized UDP-glucuronosyltransferase 1 mice and their application for predicting drug glucuronidation and drug-induced toxicity in humans.

Science.gov (United States)

Fujiwara, Ryoichi; Yoda, Emiko; Tukey, Robert H

2018-02-01

More than 20% of clinically used drugs are glucuronidated by a microsomal enzyme UDP-glucuronosyltransferase (UGT). Inhibition or induction of UGT can result in an increase or decrease in blood drug concentration. To avoid drug-drug interactions and adverse drug reactions in individuals, therefore, it is important to understand whether UGTs are involved in metabolism of drugs and drug candidates. While most of glucuronides are inactive metabolites, acyl-glucuronides that are formed from compounds with a carboxylic acid group can be highly toxic. Animals such as mice and rats are widely used to predict drug metabolism and drug-induced toxicity in humans. However, there are marked species differences in the expression and function of drug-metabolizing enzymes including UGTs. To overcome the species differences, mice in which certain drug-metabolizing enzymes are humanized have been recently developed. Humanized UGT1 (hUGT1) mice were created in 2010 by crossing Ugt1-null mice with human UGT1 transgenic mice in a C57BL/6 background. hUGT1 mice can be promising tools to predict human drug glucuronidation and acyl-glucuronide-associated toxicity. In this review article, studies of drug metabolism and toxicity in the hUGT1 mice are summarized. We further discuss research and strategic directions to advance the understanding of drug glucuronidation in humans. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

In vitro assessments of nanomaterial toxicity.

Science.gov (United States)

Jones, Clinton F; Grainger, David W

2009-06-21

Nanotechnology has grown from a scientific interest to a major industry with both commodity and specialty nanomaterial exposure to global populations and ecosystems. Sub-micron materials are currently used in a wide variety of consumer products and in clinical trials as drug delivery carriers and imaging agents. Due to the expected growth in this field and the increasing public exposure to nanomaterials, both from intentional administration and inadvertent contact, improved characterization and reliable toxicity screening tools are required for new and existing nanomaterials. This review discusses current methodologies used to assess nanomaterial physicochemical properties and their in vitro effects. Current methods lack the desired sensitivity, reliability, correlation and sophistication to provide more than limited, often equivocal, pieces of the overall nanomaterial performance parameter space, particularly in realistic physiological or environmental models containing cells, proteins and solutes. Therefore, improved physicochemical nanomaterial assays are needed to provide accurate exposure risk assessments and genuine predictions of in vivo behavior and therapeutic value. Simpler model nanomaterial systems in buffer do not accurately duplicate this complexity or predict in vivo behavior. A diverse portfolio of complementary material characterization tools and bioassays are required to validate nanomaterial properties in physiology.

Machine Learning Principles Can Improve Hip Fracture Prediction

DEFF Research Database (Denmark)

Kruse, Christian; Eiken, Pia; Vestergaard, Peter

2017-01-01

Apply machine learning principles to predict hip fractures and estimate predictor importance in Dual-energy X-ray absorptiometry (DXA)-scanned men and women. Dual-energy X-ray absorptiometry data from two Danish regions between 1996 and 2006 were combined with national Danish patient data.......89 [0.82; 0.95], but with poor calibration in higher probabilities. A ten predictor subset (BMD, biochemical cholesterol and liver function tests, penicillin use and osteoarthritis diagnoses) achieved a test AUC of 0.86 [0.78; 0.94] using an “xgbTree” model. Machine learning can improve hip fracture...... prediction beyond logistic regression using ensemble models. Compiling data from international cohorts of longer follow-up and performing similar machine learning procedures has the potential to further improve discrimination and calibration....

Extending the Derek-Meteor Workflow to Predict Chemical-Toxicity Space Impacted by Metabolism: Application to ToxCast and Tox21 Chemical Inventories

Science.gov (United States)

A central aim of EPA’s ToxCast project is to use in vitro high-throughput screening (HTS) profiles to build predictive models of in vivo toxicity. Where assays lack metabolic capability, such efforts may need to anticipate the role of metabolic activation (or deactivation). A wo...

Improving urban wind flow predictions through data assimilation

Science.gov (United States)

Sousa, Jorge; Gorle, Catherine

2017-11-01

Computational fluid dynamic is fundamentally important to several aspects in the design of sustainable and resilient urban environments. The prediction of the flow pattern for example can help to determine pedestrian wind comfort, air quality, optimal building ventilation strategies, and wind loading on buildings. However, the significant variability and uncertainty in the boundary conditions poses a challenge when interpreting results as a basis for design decisions. To improve our understanding of the uncertainties in the models and develop better predictive tools, we started a pilot field measurement campaign on Stanford University's campus combined with a detailed numerical prediction of the wind flow. The experimental data is being used to investigate the potential use of data assimilation and inverse techniques to better characterize the uncertainty in the results and improve the confidence in current wind flow predictions. We consider the incoming wind direction and magnitude as unknown parameters and perform a set of Reynolds-averaged Navier-Stokes simulations to build a polynomial chaos expansion response surface at each sensor location. We subsequently use an inverse ensemble Kalman filter to retrieve an estimate for the probabilistic density function of the inflow parameters. Once these distributions are obtained, the forward analysis is repeated to obtain predictions for the flow field in the entire urban canopy and the results are compared with the experimental data. We would like to acknowledge high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR.

Predicting competitive adsorption behavior of major toxic anionic elements onto activated alumina: A speciation-based approach

International Nuclear Information System (INIS)

Su Tingzhi; Guan Xiaohong; Tang Yulin; Gu Guowei; Wang Jianmin

2010-01-01

Toxic anionic elements such as arsenic, selenium, and vanadium often co-exist in groundwater. These elements may impact each other when adsorption methods are used to remove them. In this study, we investigated the competitive adsorption behavior of As(V), Se(IV), and V(V) onto activated alumina under different pH and surface loading conditions. Results indicated that these anionic elements interfered with each other during adsorption. A speciation-based model was developed to quantify the competitive adsorption behavior of these elements. This model could predict the adsorption data well over the pH range of 1.5-12 for various surface loading conditions, using the same set of adsorption constants obtained from single-sorbate systems. This model has great implications in accurately predicting the field capacity of activated alumina under various local water quality conditions when multiple competitive anionic elements are present.

Characterizing toxicity of metal-contaminated sediments from mining areas

Science.gov (United States)

Besser, John M.; Brumbaugh, William G.; Ingersoll, Christopher G.

2015-01-01

of acid-volatile sulfide (AVS), termed simultaneously-extracted metals (SEM), are widely used to estimate the ‘potentially-bioavailable’ fraction of metals that is not bound to sulfides (i.e., SEM-AVS). Metal concentrations in pore water are widely considered to be direct measures of metal bioavailability, and predictions of toxicity based on pore-water metal concentrations may be further improved by modeling interactions of metals with other pore-water constituents using Biotic Ligand Models. Data from sediment toxicity tests and metal analyses has provided the basis for development of sediment quality guidelines, which estimate thresholds for toxicity of metals in sediments. Empirical guidelines such as Probable Effects Concentrations or (PECs) are based on associations between sediment metal concentrations and occurrence of toxic effects in large datasets. PECs do not model bioavailable metals, but they can be used to estimate the toxicity of metal mixtures using by calculation of probable effect quotients (PEQ = sediment metal concentration/PEC). In contrast, mechanistic guidelines, such as Equilibrium Partitioning Sediment Benchmarks (ESBs) attempt to predict both bioavailability and mixture toxicity. Application of these simple bioavailability models requires more extensive chemical characterization of sediments or pore water, compared to empirical guidelines, but may provide more reliable estimates of metal toxicity across a wide range of sediment types.

Genetic tests for predicting the toxicity and efficacy of anticancer chemotherapy.

Science.gov (United States)

Mladosievicova, B; Carter, A; Kristova, V

2007-01-01

The standard anticancer therapy based "on one size fits all" modality has been determined to be ineffective or to be the cause of adverse drug reactions in many oncologic patients. Most pharmacogenetic and pharmacogenomic studies so far have been focused on toxicity of anticancer drugs such as 6-mercaptopurine, thioguanine, irinotecan, methotrexate, 5-fluorouracil (5-FU). Variation in genes are known to influence not only toxicity, but also efficacy of chemotherapeutics such as platinum analogues, 5-FU and irinotecan. The majority of current pharmacogenetic studies focus on single enzyme deficiencies as predictors of drug effects; however effects of most anticancer drugs are determined by the interplay of several gene products. These effects are polygenic in nature. This review briefly describes genetic variations that may impact efficacy and toxicity of drugs used in cancer chemotherapy.

Building a developmental toxicity ontology.

Science.gov (United States)

Baker, Nancy; Boobis, Alan; Burgoon, Lyle; Carney, Edward; Currie, Richard; Fritsche, Ellen; Knudsen, Thomas; Laffont, Madeleine; Piersma, Aldert H; Poole, Alan; Schneider, Steffen; Daston, George

2018-04-03

As more information is generated about modes of action for developmental toxicity and more data are generated using high-throughput and high-content technologies, it is becoming necessary to organize that information. This report discussed the need for a systematic representation of knowledge about developmental toxicity (i.e., an ontology) and proposes a method to build one based on knowledge of developmental biology and mode of action/ adverse outcome pathways in developmental toxicity. This report is the result of a consensus working group developing a plan to create an ontology for developmental toxicity that spans multiple levels of biological organization. This report provide a description of some of the challenges in building a developmental toxicity ontology and outlines a proposed methodology to meet those challenges. As the ontology is built on currently available web-based resources, a review of these resources is provided. Case studies on one of the most well-understood morphogens and developmental toxicants, retinoic acid, are presented as examples of how such an ontology might be developed. This report outlines an approach to construct a developmental toxicity ontology. Such an ontology will facilitate computer-based prediction of substances likely to induce human developmental toxicity. © 2018 Wiley Periodicals, Inc.

Photodegradation kinetics, transformation, and toxicity prediction of ketoprofen, carprofen, and diclofenac acid in aqueous solutions.

Science.gov (United States)

Li, Jian; Ma, Li-Yun; Li, Lu-Shuang; Xu, Li

2017-12-01

Photodegradation of 3 commonly used nonsteroidal anti-inflammatory drugs, ketoprofen, carprofen, and diclofenac acid, was conducted under ultraviolet (UV) irradiation. The kinetic results showed that the 3 pharmaceuticals obeyed the first-order reaction with decreasing rate constants of 1.54 × 10 -4 , 5.91 × 10 -5 , and 7.78 × 10 -6  s -1 for carprofen, ketoprofen, and diclofenac acid, respectively. Moreover, the main transformation products were identified by ion-pair liquid-liquid extraction combined with injection port derivatization-gas chromatography-mass spectrometry and high-performance liquid chromatography-quadrupole-time of flight mass spectrometric analysis. There were 8, 3, and 6 transformation products identified for ketoprofen, carprofen, and diclofenac acid, respectively. Decarboxylation, dechlorination, oxidation, demethylation, esterification, and cyclization were proposed to be associated with the transformation of the 3 pharmaceuticals. Toxicity prediction of the transformation products was conducted on the EPI Suite software based on ECOSAR model, and the results indicate that some of the transformation products were more toxic than the parent compounds. The present study provides the foundation to understand the transformation behavior of the studied pharmaceuticals under UV irradiation. Environ Toxicol Chem 2017;36:3232-3239. © 2017 SETAC. © 2017 SETAC.

Comparative analysis of pharmaceuticals versus industrial chemicals acute aquatic toxicity classification according to the United Nations classification system for chemicals. Assessment of the (Q)SAR predictability of pharmaceuticals acute aquatic toxicity and their predominant acute toxic mode-of-action

DEFF Research Database (Denmark)

Sanderson, Hans; Thomsen, Marianne

2009-01-01

data. Pharmaceuticals were found to be more frequent than industrial chemicals in GHS category III. Acute toxicity was predictable (>92%) using a generic (Q)SAR ((Quantitative) Structure Activity Relationship) suggesting a narcotic MOA. Analysis of model prediction error suggests that 68...... a comprehensive database based on OECD's standardized measured ecotoxicological data and to evaluate if there is generally cause of greater concern with regards to pharmaceutical aquatic toxicological profiles relative to industrial chemicals. Comparisons were based upon aquatic ecotoxicity classification under...... the United Nations Global Harmonized System for classification and labeling of chemicals (GHS). Moreover, we statistically explored whether the predominant mode-of-action (MOA) for pharmaceuticals is narcosis. We found 275 pharmaceuticals with 569 acute aquatic effect data; 23 pharmaceuticals had chronic...

Nanoparticle delivery of chemosensitizers improve chemotherapy efficacy without incurring additional toxicity

Science.gov (United States)

Caster, Joseph M.; Sethi, Manish; Kowalczyk, Sonya; Wang, Edina; Tian, Xi; Nabeel Hyder, Sayed; Wagner, Kyle T.; Zhang, Ying-Ao; Kapadia, Chintan; Man Au, Kin; Wang, Andrew Z.

2015-01-01

Chemosensitizers can improve the therapeutic index of chemotherapy and overcome treatment resistance. Successful translation of chemosensitizers depends on the development of strategies that can preferentially deliver chemosensitizers to tumors while avoiding normal tissue. We hypothesized that nanoparticle (NP) formulation of chemosensitizers can improve their delivery to tumors which can in turn improve their therapeutic index. To demonstrate the proof of principle of this approach, we engineered NP formulations of two chemosensitizers, the PI3-kindase inhibitor wortmanin (Wtmn) and the PARP inhibitor olaparib. NP Wtmn and NP olaparib were evaluated as chemosensitizers using lung cancer cells and breast cancer cells respectively. We found Wtmn to be an efficient chemosensitizer in all tested lung-cancer cell lines reducing tumor cell growth between 20 and 60% compared to drug alone. NP formulation did not decrease its efficacy in vitro. Olaparib showed less consistent chemosensitization as a free drug or in NP formulation. NP Wtmn was further evaluated as a chemosensitizer using mouse models of lung cancer. We found that NP Wtmn is an effective chemosensitizer and more effective than free Wtmn showing a 32% reduction in tumor growth compared to free Wtmn when given with etoposide. Importantly, NP Wtmn was able to sensitize the multi-drug resistant H69AR cells to etoposide. Additionally, the combination of NP Wtmn and etoposide chemotherapy did not significantly increase toxicity. The present study demonstrates the proof of principle of using NP formulation of chemosensitizing drugs to improve the therapeutic index of chemotherapy.

An overview of data mining algorithms in drug induced toxicity prediction.

Science.gov (United States)

Omer, Ankur; Singh, Poonam; Yadav, N K; Singh, R K

2014-04-01

The growth in chemical diversity has increased the need to adjudicate the toxicity of different chemical compounds raising the burden on the demand of animal testing. The toxicity evaluation requires time consuming and expensive undertaking, leading to the deprivation of the methods employed for screening chemicals pointing towards the need to develop more efficient toxicity assessment systems. Computational approaches have reduced the time as well as the cost for evaluating the toxicity and kinetic behavior of any chemical. The accessibility of a large amount of data and the intense need of turning this data into useful information have attracted the attention towards data mining. Machine Learning, one of the powerful data mining techniques has evolved as the most effective and potent tool for exploring new insights on combinatorial relationships among various experimental data generated. The article accounts on some sophisticated machine learning algorithms like Artificial Neural Networks (ANN), Support Vector Machine (SVM), k-mean clustering and Self Organizing Maps (SOM) with some of the available tools used for classification, sorting and toxicological evaluation of data, clarifying, how data mining and machine learning interact cooperatively to facilitate knowledge discovery. Addressing the association of some commonly used expert systems, we briefly outline some real world applications to consider the crucial role of data set partitioning.

Coupled Geochemical and Hydrological Processes Governing the Fate and Transport of Radionuclides and Toxic Metals Beneath the Hanford Tank Farms

International Nuclear Information System (INIS)

Scott Fendorf; Phil Jardine

2006-01-01

The goal of this research was to provide an improved understanding and predictive capability of coupled hydrological and geochemical mechanisms that are responsible for the accelerated migration and immobilization of radionuclides and toxic metals in the vadose zone beneath the Hanford Tank Farms

Improved insecticidal toxicity by fusing Cry1Ac of Bacillus thuringiensis with Av3 of Anemonia viridis.

Science.gov (United States)

Yan, Fu; Cheng, Xing; Ding, Xuezhi; Yao, Ting; Chen, Hanna; Li, Wenping; Hu, Shengbiao; Yu, Ziquan; Sun, Yunjun; Zhang, Youming; Xia, Liqiu

2014-05-01

Av3, a neurotoxin of Anemonia viridis, is toxic to crustaceans and cockroaches but inactive in mammals. In the present study, Av3 was expressed in Escherichia coli Origami B (DE3) and purified by reversed-phase liquid chromatography. The purified Av3 was injected into the hemocoel of Helicoverpa armigera, rendering the worm paralyzed. Then, Av3 was expressed alone or fusion expressed with the Cry1Ac in acrystalliferous strain Cry(-)B of Bacillus thuringiensis. The shape of Cry1Ac was changed by fusion with Av3. The expressed fusion protein, Cry1AcAv3, formed irregular rhombus- or crescent-shaped crystalline inclusions, which is quite different from the shape of original Cry1Ac crystals. The toxicity of Cry1Ac was improved by fused expression. Compared with original Cry1Ac expressed in Cry(-)B, the oral toxicity of Cry1AcAv3 to H. armigera was elevated about 2.6-fold. No toxicity was detected when Av3 was expressed in Cry(-)B alone. The present study confirmed that marine toxins could be used in bio-control and implied that fused expression with other insecticidal proteins could be an efficient way for their application.

Assessment of the Developmental Toxicity of Epidermal Growth ...

African Journals Online (AJOL)

Purpose: To determine whether epidermal growth factor (EGF) is involved in reproductive developmental toxicity, using the embryonic stem cell test (EST), as well as ascertain how EGF influences embryonic development. Methods: To predict developmental toxicity on the basis of reducing cell viability and inhibition of ...

Prediction of earth rotation parameters based on improved weighted least squares and autoregressive model

Directory of Open Access Journals (Sweden)

Sun Zhangzhen

2012-08-01

Full Text Available In this paper, an improved weighted least squares (WLS, together with autoregressive (AR model, is proposed to improve prediction accuracy of earth rotation parameters(ERP. Four weighting schemes are developed and the optimal power e for determination of the weight elements is studied. The results show that the improved WLS-AR model can improve the ERP prediction accuracy effectively, and for different prediction intervals of ERP, different weight scheme should be chosen.

Toxicity and cosmetic outcome of hypofractionated whole-breast radiotherapy: predictive clinical and dosimetric factors

International Nuclear Information System (INIS)

Ciammella, Patrizia; Podgornii, Ala; Galeandro, Maria; Micera, Renato; Ramundo, Dafne; Palmieri, Tamara; Cagni, Elisabetta; Iotti, Cinzia

2014-01-01

The objective of this study is to evaluate toxicity and cosmetic outcome in breast cancer patients treated with adjuvant hypo fractionated radiotherapy to the whole breast, and to identify the risk factors for toxicity. Two hundred twelve women with early breast cancer underwent conserving surgery were enrolled in the study. The patients received 40.05 Gy in 15 daily fractions, 2.67 Gy per fraction. The boost to the tumor bed was administered with a total dose of 9 Gy in 3 consecutive fractions in 55 women. Physician-rated acute and late toxicity and cosmetic outcome (both subjective and objective) were prospectively assessed during and after radiotherapy. In our population study the mean age was 63 with the 17% (36 pts) of the women younger than 50 years. The median follow-up was 34 months. By the end of RT, 35 patients out of 212 (16%) no acute toxicity, according to the RTOG criteria, while 145 (68%) and 31 patients (15%) developed grade 1 and grade 2 acute skin toxicity, respectively. Late skin toxicity evaluation was available for all 212 patients with a minimum follow up of 8 months. The distribution of toxicity was: 39 pts (18%) with grade 1 and 2 pts (1%) with grade 2. No worse late skin toxicity was observed. Late subcutaneous grade 0-1 toxicity was recorded in 208 patients (98%) and grade 2 toxicity in 3 patients (2%), while grade 3 was observed in 1 patient only. At last follow up, a subjective and objective good or excellent cosmetic outcome was reported in 93% and 92% of the women, respectively. At univariate and multivariate analysis, the late skin toxicity was correlated with the additional boost delivery (p=0.007 and p=0.023). Regarding the late subcutaneous tissue, a correlation with diabetes was found (p=0.0283). These results confirm the feasibility and safety of the hypofractionated radiotherapy in patients with early breast cancer. In our population the boost administration was resulted to be a significant adverse prognostic factor for acute

Modeling Aquatic Toxicity through Chromatographic Systems.

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Fernández-Pumarega, Alejandro; Amézqueta, Susana; Farré, Sandra; Muñoz-Pascual, Laura; Abraham, Michael H; Fuguet, Elisabet; Rosés, Martí

2017-08-01

Environmental risk assessment requires information about the toxicity of the growing number of chemical products coming from different origins that can contaminate water and become toxicants to aquatic species or other living beings via the trophic chain. Direct toxicity measurements using sensitive aquatic species can be carried out but they may become expensive and ethically questionable. Literature refers to the use of chromatographic measurements that correlate to the toxic effect of a compound over a specific aquatic species as an alternative to get toxicity information. In this work, we have studied the similarity in the response of the toxicity to different species and we have selected eight representative aquatic species (including tadpoles, fish, water fleas, protozoan, and bacteria) with known nonspecific toxicity to chemical substances. Next, we have selected four chromatographic systems offering good perspectives for surrogation of the eight selected aquatic systems, and thus prediction of toxicity from the chromatographic measurement. Then toxicity has been correlated to the chromatographic retention factor. Satisfactory correlation results have been obtained to emulate toxicity in five of the selected aquatic species through some of the chromatographic systems. Other aquatic species with similar characteristics to these five representative ones could also be emulated by using the same chromatographic systems. The final aim of this study is to model chemical products toxicity to aquatic species by means of chromatographic systems to reduce in vivo testing.

Plasma exchange combining with plasma bilirubin adsorption effectively removes toxic substances and improves liver functions of hepatic failure patients.

Science.gov (United States)

Che, X-Q; Li, Z-Q; Chen, Z; Guo, D; Jia, Q-Y; Jiang, S-C; Cai, J

2018-02-01

Hepatic failure (HF) is a kind of complex disease characterizing with liver dysfunction and a few clinical complications. Artificial liver support system (ALSS) has been applied to HF patients to improve dysfunctional liver in recent years. This study aims to evaluate therapeutic effects of ALSS approaches, including plasma exchange (PE), plasma diafiltration (PDF) and plasma bilirubin adsorption (PBA), on liver function of HF patients. This study is a retrospective analysis involving 516 patients diagnosed as HF between February 2014 and February 2015. Patients were randomly divided into PE, PDF, PE plus PBA, and PDF plus PBA group. Meanwhile, single-drug group and combined-drug group were also divided. The liver functions, capability of removing toxic substances and coagulation functions were evaluated both pre-treatment and post-treatment. The side effects and hospital improvement rate were also observed post-treatment. Hospital improvement rate achieves to 69.6%. TBIL levels and MELD scores were significantly decreased post-treatment compared to pre-treatment (phigher compared to PE and PDF group (p=0.002, 0.002, respectively). MELD scores were significantly decreased post-treatment compared to pre-treatment in each group (pbetter role in removing toxic substances, improving liver functions of HF patients.

Periodical assessment of genitourinary and gastrointestinal toxicity in patients who underwent prostate low-dose-rate brachytherapy

International Nuclear Information System (INIS)

Tanaka, Nobumichi; Asakawa, Isao; Anai, Satoshi; Hirayama, Akihide; Hasegawa, Masatoshi; Konishi, Noboru; Fujimoto, Kiyohide

2013-01-01

To compare the periodical incidence rates of genitourinary (GU) and gastrointestinal (GI) toxicity in patients who underwent prostate low-dose-rate brachytherapy between the monotherapy group (seed implantation alone) and the boost group (in combination with external beam radiation therapy (EBRT)). A total of 218 patients with a median follow-up of 42.5 months were enrolled. The patients were divided into 2 groups by treatment modality, namely, the monotherapy group (155 patients) and the boost group (63 patients). The periodical incidence rates of GU and GI toxicity were separately evaluated and compared between the monotherapy group and the boost group using the National Cancer Institute - Common Terminology Criteria for Adverse Events, version 3.0. To elucidate an independent factor among clinical and postdosimetric parameters to predict grade 2 or higher GU and GI toxicity in the acute and late phases, univariate and multivariate logistic regression analyses were carried out. Of all patients, 78.0% showed acute GU toxicity, and 7.8% showed acute GI toxicity, while 63.8% showed late GU toxicity, and 21.1% showed late GI toxicity. The incidence rates of late GU and GI toxicity were significantly higher in the boost group. Multivariate analysis showed that the International Prostate Symptom Score (IPSS) before seed implantation was a significant parameter to predict acute GU toxicity, while there were no significant predictive parameters for acute GI toxicity. On the other hand, combination with EBRT was a significant predictive parameter for late GU toxicity, and rectal volume (mL) receiving 100% of the prescribed dose (R100) was a significant predictive parameter for late GI toxicity. The boost group showed higher incidence rates of both GU and GI toxicity. Higher IPSS before seed implantation, combination with EBRT and a higher R100 were significant predictors for acute GU, late GU and late GI toxicity

Synthesis of β-cyclodextrin hydrogel nanoparticles for improving the solubility of dexibuprofen: characterization and toxicity evaluation.

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Khalid, Qandeel; Ahmad, Mahmood; Minhas, Muhammad Usman

2017-11-01

This study was aimed to enhance aqueous solubility of dexibuprofen through designing β-cyclodextrin (βCD) hydrogel nanoparticles and to evaluate toxicological potential through acute toxicity studies in rats. Dexibuprofen is a non-steroidal analgesic and anti-inflammatory drug that is one of safest over the counter medications. However, its clinical effectiveness is hampered due to poor aqueous solubility. βCD hydrogel nanoparticles were prepared and characterized by percent yield, drug loading, solubilization efficiency, FTIR, XRD, DSC, FESEM and in-vitro dissolution studies. Acute oral toxicity study was conducted to assess safety of oral administration of prepared βCD hydrogel nanoparticles. βCD hydrogel nanoparticles dramatically enhanced the drug loading and solubilization efficiency of dexibuprofen in aqueous media. FTIR, TGA and DSC studies confirmed the formation of new and a stable nano-polymeric network and interactions of dexibuprofen with these nanoparticles. Resulting nanoparticles were highly porous with 287 nm in size. XRD analysis revealed pronounced reduction in crystalline nature of dexibuprofen within nanoparticles. Release of dexibuprofen in βCD hydrogel nanoparticles was significantly higher compared with dexibuprofen tablet at pH 1.2 and 6.8. In acute toxicity studies, no significant changes in behavioral, physiological, biochemical or histopathologic parameters of animals were observed. The efficient preparation, high solubility, excellent physicochemical characteristics, improved dissolution and non-toxic βCD hydrogel nanoparticles may be a promising approach for oral delivery of lipophilic drugs.

Pesticide Toxicity Index: a tool for assessing potential toxicity of pesticide mixtures to freshwater aquatic organisms

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Nowell, Lisa H.; Norman, Julia E.; Moran, Patrick W.; Martin, Jeffrey D.; Stone, Wesley W.

2014-01-01

Pesticide mixtures are common in streams with agricultural or urban influence in the watershed. The Pesticide Toxicity Index (PTI) is a screening tool to assess potential aquatic toxicity of complex pesticide mixtures by combining measures of pesticide exposure and acute toxicity in an additive toxic-unit model. The PTI is determined separately for fish, cladocerans, and benthic invertebrates. This study expands the number of pesticides and degradates included in previous editions of the PTI from 124 to 492 pesticides and degradates, and includes two types of PTI for use in different applications, depending on study objectives. The Median-PTI was calculated from median toxicity values for individual pesticides, so is robust to outliers and is appropriate for comparing relative potential toxicity among samples, sites, or pesticides. The Sensitive-PTI uses the 5th percentile of available toxicity values, so is a more sensitive screening-level indicator of potential toxicity. PTI predictions of toxicity in environmental samples were tested using data aggregated from published field studies that measured pesticide concentrations and toxicity to Ceriodaphnia dubia in ambient stream water. C. dubia survival was reduced to ≤ 50% of controls in 44% of samples with Median-PTI values of 0.1–1, and to 0% in 96% of samples with Median-PTI values > 1. The PTI is a relative, but quantitative, indicator of potential toxicity that can be used to evaluate relationships between pesticide exposure and biological condition.

The Effects of Temperature and Hydrostatic Pressure on Metal Toxicity: Insights into Toxicity in the Deep Sea.

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Brown, Alastair; Thatje, Sven; Hauton, Chris

2017-09-05

Mineral prospecting in the deep sea is increasing, promoting concern regarding potential ecotoxicological impacts on deep-sea fauna. Technological difficulties in assessing toxicity in deep-sea species has promoted interest in developing shallow-water ecotoxicological proxy species. However, it is unclear how the low temperature and high hydrostatic pressure prevalent in the deep sea affect toxicity, and whether adaptation to deep-sea environmental conditions moderates any effects of these factors. To address these uncertainties we assessed the effects of temperature and hydrostatic pressure on lethal and sublethal (respiration rate, antioxidant enzyme activity) toxicity in acute (96 h) copper and cadmium exposures, using the shallow-water ecophysiological model organism Palaemon varians. Low temperature reduced toxicity in both metals, but reduced cadmium toxicity significantly more. In contrast, elevated hydrostatic pressure increased copper toxicity, but did not affect cadmium toxicity. The synergistic interaction between copper and cadmium was not affected by low temperature, but high hydrostatic pressure significantly enhanced the synergism. Differential environmental effects on toxicity suggest different mechanisms of action for copper and cadmium, and highlight that mechanistic understanding of toxicity is fundamental to predicting environmental effects on toxicity. Although results infer that sensitivity to toxicants differs across biogeographic ranges, shallow-water species may be suitable ecotoxicological proxies for deep-sea species, dependent on adaptation to habitats with similar environmental variability.

Integrating geophysics and hydrology for reducing the uncertainty of groundwater model predictions and improved prediction performance

DEFF Research Database (Denmark)

Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty

the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... and the resulting predictions can be compared with predictions from the ‘true’ model. By performing this analysis we expect to give the modeler insight into how the uncertainty of model-based prediction can be reduced.......A major purpose of groundwater modeling is to help decision-makers in efforts to manage the natural environment. Increasingly, it is recognized that both the predictions of interest and their associated uncertainties should be quantified to support robust decision making. In particular, decision...

Assessment of Pharmacogenomic Panel Assay for Prediction of Taxane Toxicities: Preliminary Results

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Raffaele Di Francia

2017-11-01

Full Text Available Backbone: Paclitaxel and docetaxel are the primary taxane anticancer drugs regularly used to treat, breast, gastric, ovarian, head/neck, lung, and genitourinary neoplasm. Suspension of taxane treatments compromising patient benefits is more frequently caused by peripheral neuropathy and allergy, than to tumor progression. Several strategies for preventing toxicity have been investigated so far. Recently, findings on the genetic variants associated with toxicity and resistance to taxane-based chemotherapy have been reported.Methods: An extensive panel of five polymorphisms on four candidate genes (ABCB1, CYP2C8*3, CYP3A4*1B, XRCC3, previously validated as significant markers related to paclitaxel and Docetaxel toxicity, are analyzed and discussed. We genotyped 76 cancer patients, and 35 of them received paclitaxel or docetaxel-based therapy. What is more, an early outline evaluation of the genotyping costs and benefit was assessed.Results: Out of 35 patients treated with a taxane, six (17.1% had adverse neuropathy events. Pharmacogenomics analysis showed no correlation between candidate gene polymorphisms and toxicity, except for the XRCC3 AG+GG allele [OR 2.61 (95% CI: 0.91–7.61] which showed a weak significant trend of risk of neurotoxicities vs. the AG allele [OR 1.52 (95% CI: 0.51–4.91] P = 0.03.Summary: Based on our experimental results and data from the literature, we propose a useful and low-cost genotyping panel assay for the prevention of toxicity in patients undergoing taxane-based therapy. With the individual pharmacogenomics profile, clinicians will have additional information to plan the better treatment for their patients to minimize toxicity and maximize benefits, including determining cost-effectiveness for national healthcare sustainability.

Predicting the risk of developmental toxicity from in vitro assays

International Nuclear Information System (INIS)

Spielmann, Horst

2005-01-01

Reproductive toxicity refers to the adverse effects of a substance on any aspect of the reproductive cycle, including the impairment of reproductive function, the induction of adverse effects in the embryo, such as growth retardation, malformations, and death. Due to the complexity of the mammalian reproductive cycle, it is impossible to model the whole cycle in a single in vitro system in order to detect chemical effects on mammalian reproduction. However, the cycle can be broken down in its biological components which may be studied individually or in combination. This approach has the advantage that the target tissue/organ of a developmental toxicant can be identified. In specific areas of developmental toxicity, a number of useful and promising in vitro models are already available. The individual tests may be used as building blocks of a tiered testing strategy. So far, research has focused on developing and validating tests covering only a few components of the reproductive cycle, in particular organogenesis of the embryo, reflecting important concerns for teratogenic chemicals. During the last three decades, a number of established models and promising new developments have emerged that will be discussed, e.g. culture of mammalian embryos and embryonic cells and tissues and the use of embryonic stem cells

NOAA's Strategy to Improve Operational Weather Prediction Outlooks at Subseasonal Time Range

Science.gov (United States)

Schneider, T.; Toepfer, F.; Stajner, I.; DeWitt, D.

2017-12-01

NOAA is planning to extend operational global numerical weather prediction to sub-seasonal time range under the auspices of its Next Generation Global Prediction System (NGGPS) and Extended Range Outlook Programs. A unification of numerical prediction capabilities for weather and subseasonal to seasonal (S2S) timescales is underway at NOAA using the Finite Volume Cubed Sphere (FV3) dynamical core as the basis for the emerging unified system. This presentation will overview NOAA's strategic planning and current activities to improve prediction at S2S time-scales that are ongoing in response to the Weather Research and Forecasting Innovation Act of 2017, Section 201. Over the short-term, NOAA seeks to improve the operational capability through improvements to its ensemble forecast system to extend its range to 30 days using the new FV3 Global Forecast System model, and by using this system to provide reforecast and re-analyses. In parallel, work is ongoing to improve NOAA's operational product suite for 30 day outlooks for temperature, precipitation and extreme weather phenomena.

Explicit Modeling of Ancestry Improves Polygenic Risk Scores and BLUP Prediction.

Science.gov (United States)

Chen, Chia-Yen; Han, Jiali; Hunter, David J; Kraft, Peter; Price, Alkes L

2015-09-01

Polygenic prediction using genome-wide SNPs can provide high prediction accuracy for complex traits. Here, we investigate the question of how to account for genetic ancestry when conducting polygenic prediction. We show that the accuracy of polygenic prediction in structured populations may be partly due to genetic ancestry. However, we hypothesized that explicitly modeling ancestry could improve polygenic prediction accuracy. We analyzed three GWAS of hair color (HC), tanning ability (TA), and basal cell carcinoma (BCC) in European Americans (sample size from 7,440 to 9,822) and considered two widely used polygenic prediction approaches: polygenic risk scores (PRSs) and best linear unbiased prediction (BLUP). We compared polygenic prediction without correction for ancestry to polygenic prediction with ancestry as a separate component in the model. In 10-fold cross-validation using the PRS approach, the R(2) for HC increased by 66% (0.0456-0.0755; P ancestry, which prevents ancestry effects from entering into each SNP effect and being overweighted. Surprisingly, explicitly modeling ancestry produces a similar improvement when using the BLUP approach, which fits all SNPs simultaneously in a single variance component and causes ancestry to be underweighted. We validate our findings via simulations, which show that the differences in prediction accuracy will increase in magnitude as sample sizes increase. In summary, our results show that explicitly modeling ancestry can be important in both PRS and BLUP prediction. © 2015 WILEY PERIODICALS, INC.

WE-F-BRB-02: Setting the Stage for Incorporation of Toxicity Measures in Treatment Plan Assessments

International Nuclear Information System (INIS)

Mayo, C.

2015-01-01

Advancements in informatics in radiotherapy are opening up opportunities to improve our ability to assess treatment plans. Models on individualizing patient dose constraints from prior patient data and shape relationships have been extensively researched and are now making their way into commercial products. New developments in knowledge based treatment planning involve understanding the impact of the radiation dosimetry on the patient. Akin to radiobiology models that have driven intensity modulated radiotherapy optimization, toxicity and outcome predictions based on treatment plans and prior patient experiences may be the next step in knowledge based planning. In order to realize these predictions, it is necessary to understand how the clinical information can be captured, structured and organized with ontologies and databases designed for recall. Large databases containing radiation dosimetry and outcomes present the opportunity to evaluate treatment plans against predictions of toxicity and disease response. Such evaluations can be based on dose volume histogram or even the full 3-dimensional dose distribution and its relation to the critical anatomy. This session will provide an understanding of ontologies and standard terminologies used to capture clinical knowledge into structured databases; How data can be organized and accessed to utilize the knowledge in planning; and examples of research and clinical efforts to incorporate that clinical knowledge into planning for improved care for our patients. Learning Objectives: Understand the role of standard terminologies, ontologies and data organization in oncology Understand methods to capture clinical toxicity and outcomes in a clinical setting Understand opportunities to learn from clinical data and its application to treatment planning Todd McNutt receives funding from Philips, Elekta and Toshiba for some of the work presented

CNNcon: improved protein contact maps prediction using cascaded neural networks.

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Wang Ding

Full Text Available BACKGROUNDS: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly. METHODS: CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction. RESULTS: The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective

QSTR with extended topochemical atom (ETA) indices. 16. Development of predictive classification and regression models for toxicity of ionic liquids towards Daphnia magna

International Nuclear Information System (INIS)

Roy, Kunal; Das, Rudra Narayan

2013-01-01

Highlights: • Ionic liquids are not intrinsically ‘green chemicals’ and require toxicological assessment. • Predictive QSTR models have been developed for toxicity of ILs to Daphnia magna. • Simple two dimensional descriptors were used to reduce the computational burden. • Discriminant and regression based models showed appreciable predictivity and reproducibility. • The extracted features can be explored in designing novel environmentally-friendly agents. -- Abstract: Ionic liquids have been judged much with respect to their wide applicability than their considerable harmful effects towards the living ecosystem which has been observed in many instances. Hence, toxicological introspection of these chemicals by the development of predictive mathematical models can be of good help. This study presents an attempt to develop predictive classification and regression models correlating the structurally derived chemical information of a group of 62 diverse ionic liquids with their toxicity towards Daphnia magna and their interpretation. We have principally used the extended topochemical atom (ETA) indices along with various topological non-ETA and thermodynamic parameters as independent variables. The developed quantitative models have been subjected to extensive statistical tests employing multiple validation strategies from which acceptable results have been reported. The best models obtained from classification and regression studies captured necessary structural information on lipophilicity, branching pattern, electronegativity and chain length of the cationic substituents for explaining ecotoxicity of ionic liquids towards D. magna. The derived information can be successfully used to design better ionic liquid analogues acquiring the qualities of a true eco-friendly green chemical

The discovery and development of proteomic safety biomarkers for the detection of drug-induced liver toxicity

International Nuclear Information System (INIS)

Amacher, David E.

2010-01-01

biological fluids with varying immunoreactivity which can present bioanalytical challenges when first discovered. The potential success of these efforts is greatly enhanced by recent advances in two closely linked technologies, toxicoproteomics and targeted, quantitative mass spectrometry. This review focuses on the examination of the current status of these technologies as they relate to the discovery and development of novel preclinical biomarkers of hepatotoxicity. A critical assessment of the current literature reveals two distinct lines of safety biomarker investigation, (1) peripheral fluid biomarkers of organ toxicity and (2) tissue or cell-based toxicity signatures. Improved peripheral fluid biomarkers should allow the sensitive detection of potential organ toxicity prior to the onset of overt organ pathology. Advancements in tissue or cell-based toxicity biomarkers will provide sensitive in vitro or ex vivo screening systems based on toxicity pathway markers. An examination of the current practices in clinical pathology and the critical evaluation of some recently proposed biomarker candidates in comparison to the desired characteristics of an ideal toxicity biomarker lead this author to conclude that a combination of selected biomarkers will be more informative if not predictive of potential animal organ toxicity than any single biomarker, new or old. For the practical assessment of combinations of conventional and/or novel toxicity biomarkers in rodent and large animal preclinical species, mass spectrometry has emerged as the premier analytical tool compared to specific immunoassays or functional assays. Selected and multiple reaction monitoring mass spectrometry applications make it possible for this same basic technology to be used in the progressive stages of biomarker discovery, development, and more importantly, routine study applications without the use of specific antibody reagents. This technology combined with other 'omics' technologies can provide added

Prediction of Chemical Carcinogenicity in Rodents from in vitro Genetic Toxicity Assays

Science.gov (United States)

Tennant, Raymond W.; Margolin, Barry H.; Shelby, Michael D.; Zeiger, Errol; Haseman, Joseph K.; Spalding, Judson; Caspary, William; Resnick, Michael; Stasiewicz, Stanley; Anderson, Beth; Minor, Robert

1987-05-01

Four widely used in vitro assays for genetic toxicity were evaluated for their ability to predict the carcinogenicity of selected chemicals in rodents. These assays were mutagenesis in Salmonella and mouse lymphoma cells and chromosome aberrations and sister chromatid exchanges in Chinese hamster ovary cells. Seventy-three chemicals recently tested in 2-year carcinogenicity studies conducted by the National Cancer Institute and the National Toxicology Program were used in this evaluation. Test results from the four in vitro assays did not show significant differences in individual concordance with the rodent carcinogenicity results; the concordance of each assay was approximately 60 percent. Within the limits of this study there was no evidence of complementarity among the four assays, and no battery of tests constructed from these assays improved substantially on the overall performance of the Salmonella assay. The in vitro assays which represented a range of three cell types and four end points did show substantial agreement among themselves, indicating that chemicals positive in one in vitro assay tended to be positive in the other in vitro assays. To help put this project into its proper context, we emphasize certain features of the study: 1) Standard protocols were used to mimic the major use of STTs worldwide--screening for mutagens and carcinogens; no attempt was made to optimize protocols for specific chemicals. 2) The 73 NTP chemicals and their 60% incidence of carcinogenicity are probably not representative of the universe of chemicals but rather reflect the recent chemical selection process for the NTP carcinogenicity assay. 3) The small, diverse group of chemicals precludes a meaningful evaluation of the predictive utility of chemical structure information. 4) The NTP is currently testing these same 73 chemicals in two in vivo STTs for chromosomal effects. 5) Complete data for an additional group of 30 to 40 NTP chemicals will be gathered on

Combining Physical and Biologic Parameters to Predict Radiation-Induced Lung Toxicity in Patients With Non-Small-Cell Lung Cancer Treated With Definitive Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Stenmark, Matthew H. [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Cai Xuwei [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Radiation Oncology, Shanghai Cancer Hospital, Fudan University, Shanghai (China); Shedden, Kerby [Department of Biostatistics, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Hayman, James A. [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Yuan Shuanghu [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Radiation Oncology, Shangdong Cancer Hospital, Jinan (China); Ritter, Timothy [Veterans Affairs Medical Center, Ann Arbor, Michigan (United States); Ten Haken, Randall K.; Lawrence, Theodore S. [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Kong Fengming, E-mail: fengkong@med.umich.edu [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Veterans Affairs Medical Center, Ann Arbor, Michigan (United States)

2012-10-01

Purpose: To investigate the plasma dynamics of 5 proinflammatory/fibrogenic cytokines, including interleukin-1beta (IL-1{beta}), IL-6, IL-8, tumor necrosis factor alpha (TNF-{alpha}), and transforming growth factor beta1 (TGF-{beta}1) to ascertain their value in predicting radiation-induced lung toxicity (RILT), both individually and in combination with physical dosimetric parameters. Methods and Materials: Treatments of patients receiving definitive conventionally fractionated radiation therapy (RT) on clinical trial for inoperable stages I-III lung cancer were prospectively evaluated. Circulating cytokine levels were measured prior to and at weeks 2 and 4 during RT. The primary endpoint was symptomatic RILT, defined as grade 2 and higher radiation pneumonitis or symptomatic pulmonary fibrosis. Minimum follow-up was 18 months. Results: Of 58 eligible patients, 10 (17.2%) patients developed RILT. Lower pretreatment IL-8 levels were significantly correlated with development of RILT, while radiation-induced elevations of TGF-ss1 were weakly correlated with RILT. Significant correlations were not found for any of the remaining 3 cytokines or for any clinical or dosimetric parameters. Using receiver operator characteristic curves for predictive risk assessment modeling, we found both individual cytokines and dosimetric parameters were poor independent predictors of RILT. However, combining IL-8, TGF-ss1, and mean lung dose into a single model yielded an improved predictive ability (P<.001) compared to either variable alone. Conclusions: Combining inflammatory cytokines with physical dosimetric factors may provide a more accurate model for RILT prediction. Future study with a larger number of cases and events is needed to validate such findings.

Aquatic Toxic Analysis by Monitoring Fish Behavior Using Computer Vision: A Recent Progress

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Chunlei Xia

2018-01-01

Full Text Available Video tracking based biological early warning system achieved a great progress with advanced computer vision and machine learning methods. Ability of video tracking of multiple biological organisms has been largely improved in recent years. Video based behavioral monitoring has become a common tool for acquiring quantified behavioral data for aquatic risk assessment. Investigation of behavioral responses under chemical and environmental stress has been boosted by rapidly developed machine learning and artificial intelligence. In this paper, we introduce the fundamental of video tracking and present the pioneer works in precise tracking of a group of individuals in 2D and 3D space. Technical and practical issues suffered in video tracking are explained. Subsequently, the toxic analysis based on fish behavioral data is summarized. Frequently used computational methods and machine learning are explained with their applications in aquatic toxicity detection and abnormal pattern analysis. Finally, advantages of recent developed deep learning approach in toxic prediction are presented.

A Conceptual Framework for Predicting the Toxicity of Reactive Chemicals: Modeling Soft Electrophilicity

Science.gov (United States)

Although the literature is replete with QSAR models developed for many toxic effects caused by reversible chemical interactions, the development of QSARs for the toxic effects of reactive chemicals lacks a consistent approach. While limitations exit, an appropriate starting-point...

Designing quantitative structure activity relationships to predict specific toxic endpoints for polybrominated diphenyl ethers in mammalian cells.

Science.gov (United States)

Rawat, S; Bruce, E D

2014-01-01

Polybrominated diphenyl ethers (PBDEs) are known as effective flame retardants and have vast industrial application in products like plastics, building materials and textiles. They are found to be structurally similar to thyroid hormones that are responsible for regulating metabolism in the body. Structural similarity with the hormones poses a threat to human health because, once in the system, PBDEs have the potential to affect thyroid hormone transport and metabolism. This study was aimed at designing quantitative structure-activity relationship (QSAR) models for predicting toxic endpoints, namely cell viability and apoptosis, elicited by PBDEs in mammalian cells. Cell viability was evaluated quantitatively using a general cytotoxicity bioassay using Janus Green dye and apoptosis was evaluated using a caspase assay. This study has thus modelled the overall cytotoxic influence of PBDEs at an early and a late endpoint by the Genetic Function Approximation method. This research was a twofold process including running in vitro bioassays to collect data on the toxic endpoints and modeling the evaluated endpoints using QSARs. Cell viability and apoptosis responses for Hep G2 cells exposed to PBDEs were successfully modelled with an r(2) of 0.97 and 0.94, respectively.

Improving Flash Flood Prediction in Multiple Environments

Science.gov (United States)

Broxton, P. D.; Troch, P. A.; Schaffner, M.; Unkrich, C.; Goodrich, D.; Wagener, T.; Yatheendradas, S.

2009-12-01

Flash flooding is a major concern in many fast responding headwater catchments . There are many efforts to model and to predict these flood events, though it is not currently possible to adequately predict the nature of flash flood events with a single model, and furthermore, many of these efforts do not even consider snow, which can, by itself, or in combination with rainfall events, cause destructive floods. The current research is aimed at broadening the applicability of flash flood modeling. Specifically, we will take a state of the art flash flood model that is designed to work with warm season precipitation in arid environments, the KINematic runoff and EROSion model (KINEROS2), and combine it with a continuous subsurface flow model and an energy balance snow model. This should improve its predictive capacity in humid environments where lateral subsurface flow significantly contributes to streamflow, and it will make possible the prediction of flooding events that involve rain-on-snow or rapid snowmelt. By modeling changes in the hydrologic state of a catchment before a flood begins, we can also better understand the factors or combination of factors that are necessary to produce large floods. Broadening the applicability of an already state of the art flash flood model, such as KINEROS2, is logical because flash floods can occur in all types of environments, and it may lead to better predictions, which are necessary to preserve life and property.

Predictive Maintenance: One key to improved power plant availability

International Nuclear Information System (INIS)

Mobley; Allen, J.W.

1986-01-01

Recent developments in microprocessor technology has provided the ability to routinely monitor the actual mechanical condition of all rotating and reciprocating machinery and process variables (i.e. pressure, temperature, flow, etc.) of other process equipment within an operating electric power generating plant. This direct correlation between frequency domain vibration and actual mechanical condition of machinery and trending process variables of non-rotating equipment can provide the ''key'' to improving the availability and reliability, thermal efficiency and provide the baseline information necessary for developing a realistic plan for extending the useful life of power plants. The premise of utilizing microprocessor-based Predictive Maintenance to improve power plant operation has been proven by a number of utilities. This paper provides a comprehensive discussion of the TEC approach to Predictive Maintenance and examples of successful programs

Suppression of a NAC-like transcription factor gene improves boron-toxicity tolerance in rice.

Science.gov (United States)

Ochiai, Kumiko; Shimizu, Akifumi; Okumoto, Yutaka; Fujiwara, Toru; Matoh, Toru

2011-07-01

We identified a gene responsible for tolerance to boron (B) toxicity in rice (Oryza sativa), named BORON EXCESS TOLERANT1. Using recombinant inbred lines derived from the B-toxicity-sensitive indica-ecotype cultivar IR36 and the tolerant japonica-ecotype cultivar Nekken 1, the region responsible for tolerance to B toxicity was narrowed to 49 kb on chromosome 4. Eight genes are annotated in this region. The DNA sequence in this region was compared between the B-toxicity-sensitive japonica cultivar Wataribune and the B-toxicity-tolerant japonica cultivar Nipponbare by eco-TILLING analysis and revealed a one-base insertion mutation in the open reading frame sequence of the gene Os04g0477300. The gene encodes a NAC (NAM, ATAF, and CUC)-like transcription factor and the function of the transcript is abolished in B-toxicity-tolerant cultivars. Transgenic plants in which the expression of Os04g0477300 is abolished by RNA interference gain tolerance to B toxicity.

Analyzing the capacity of the Daphnia magna and Pseudokirchneriella subcapitata bioavailability models to predict chronic zinc toxicity at high pH and low calcium concentrations and formulation of a generalized bioavailability model for D. magna.

Science.gov (United States)

Van Regenmortel, Tina; Berteloot, Olivier; Janssen, Colin R; De Schamphelaere, Karel A C

2017-10-01

Risk assessment in the European Union implements Zn bioavailability models to derive predicted-no-effect concentrations for Zn. These models are validated within certain boundaries (i.e., pH ≤ 8 and Ca concentrations ≥ 5mg/L), but a substantial fraction of the European surface waters falls outside these boundaries. Therefore, we evaluated whether the chronic Zn biotic ligand model (BLM) for Daphnia magna and the chronic bioavailability model for Pseudokirchneriella subcapitata could be extrapolated to pH > 8 and Ca concentrations model can accurately predict Zn toxicity for Ca concentrations down to 0.8 mg/L and pH values up to 8.5. Because the chronic Zn BLM for D. magna could not be extrapolated beyond its validity boundaries for pH, a generalized bioavailability model (gBAM) was developed. Of 4 gBAMs developed, we recommend the use of gBAM-D, which combines a log-linear relation between the 21-d median effective concentrations (expressed as free Zn 2+ ion activity) and pH, with more conventional BLM-type competition constants for Na, Ca, and Mg. This model is a first step in further improving the accuracy of chronic toxicity predictions of Zn as a function of water chemistry, which can decrease the uncertainty in implementing the bioavailability-based predicted-no-effect concentration in the risk assessment of high-pH and low-Ca concentration regions in Europe. Environ Toxicol Chem 2017;36:2781-2798. © 2017 SETAC. © 2017 SETAC.

Patients Undergoing Radiation Therapy Are at Risk of Financial Toxicity: A Patient-based Prospective Survey Study.

Science.gov (United States)

Palmer, Joshua D; Patel, Tejash T; Eldredge-Hindy, Harriet; Keith, Scott W; Patel, Tapas; Malatesta, Theresa; DiNome, Jessie; Lowther, Anne; Ferguson, Linda; Wagenborg, Sally; Smyles, John; Babaria, Usha; Stabile, Richard; Gressen, Eric; Rudoler, Shari; Fisher, Scot A

2018-06-01

Little is known about the financial burden experienced by patients receiving radiation therapy. Furthermore, currently, no financial toxicity screening tools have been validated for use in radiation oncology. Physician surveys were used to gauge provider understanding of treatment costs and their willingness to adopt the use of financial toxicity screening tools. Post-treatment patient surveys were used to investigate the covariates of treatment-induced financial risk. Of the 210 radiation oncologists who completed our survey, 53% reported being "very concerned" with treatment-related costs negatively affecting their patients, and 80% believed that a financial toxicity screening tool would be useful in practice. An analysis of patient surveys using logistic regression found age and cancer site to be the most important variables associated with financial toxicity. Thirty-four patients (22%) experienced financial toxicity related to treatment. The financial toxicities experienced were loss of job (28%), loss of income (24%), difficulty paying their rent or mortgage (20%), difficulty paying for transportation (15%), and difficulty paying for meals (13%). Financial toxicity is an important measure for patients and providers and is experienced by approximately one quarter of patients. Further studies to improve models to predict financial toxicity and how financial toxicity is related to patient outcomes and quality of life are warranted. Copyright © 2018 Elsevier Inc. All rights reserved.

Ionizing radiation-induced DNA damage and repair as a potential biomarker in biodosimetry, cancer risk analysis and for prediction of radiotherapy induced toxicity

International Nuclear Information System (INIS)

Satish Rao, B.S.

2017-01-01

Lymphocytes isolated from peripheral blood from 100 healthy individuals, 232 cancer patients (cervical, breast cancer and head and neck cancer) irradiated in vitro or in vivo were used for measuring DNA damage and repair. The microscopic method of the γ-H2AX assay was adopted to elucidate the significance of DSB in biodosimetry, cancer risk susceptibility, and normal tissue toxicity prediction. We validated the use of H2AX assay in early triage biodosimetry by using lymphocytes from cervical cancer patients exposed to radiotherapy. Further, the basal and residual damage was significantly higher in cancer individuals compared to the healthy individuals. In cancer patients undergoing radiotherapy, we could able to show the increase in normal tissue toxicity with decreased DSB repair capacity. In conclusion this study indicates the DSB estimation by γ-H2AX foci analysis can serve as a tool to understand the triage of radiation exposed individuals, identifying individuals at cancer risk and normal tissue toxicity

Identification and comparison of predictive models of rectal and bladder toxicity in the case of prostatic irradiation; Identification et comparaison de modeles predictifs de toxicite rectale et vesicale en cas d'irradiation prostatique

Energy Technology Data Exchange (ETDEWEB)

Gnep, K.; Chira, C.; Le Prise, E.; Crevoisier, R. de [Centre Eugene-Marquis, Rennes (France); Zhu, J.; Simon, A.; Ospina Arango, J.D. [Inserm U642, Rennes (France); Messai, T.; Bossi, A. [Institut Gustave-Roussy, Villejuif (France); Beckendorf, V. [Centre Alexis-Vautrin, Nancy (France)

2011-10-15

More than 400 patients have been treated by conformational radiation therapy for a localized prostate adenocarcinoma and some have been selected according to the availability of dose-volume histograms. Predictive models of rectal and bladder toxicity have been compared: LKB, Logit EUD and Poisson EUD for rectal toxicity, LKB, Logit EUD, Poisson EUD and Schultheiss for bladder toxicity. Results suggest that these models could be used during the inverse planning of intensity-modulated radiation therapy in order to decrease toxicity. Short communication

Improved survival for elderly married glioblastoma patients. Better treatment delivery, less toxicity, and fewer disease complications

International Nuclear Information System (INIS)

Putz, Florian; Goerig, Nicole; Knippen, Stefan; Gryc, Thomas; Semrau, Sabine; Lettmaier, Sebastian; Fietkau, Rainer; Putz, Tobias; Eyuepoglu, Ilker; Roessler, Karl

2016-01-01

Marital status is a well-described prognostic factor in patients with gliomas but the observed survival difference is unexplained in the available population-based studies. A series of 57 elderly glioblastoma patients (≥70 years) were analyzed retrospectively. Patients received radiotherapy or chemoradiation with temozolomide. The prognostic significance of marital status was assessed. Disease complications, toxicity, and treatment delivery were evaluated in detail. Overall survival was significantly higher in married than in unmarried patients (median, 7.9 vs. 4.0 months; p = 0.006). The prognostic significance of marital status was preserved in the multivariate analysis (HR, 0.41; p = 0.011). Married patients could receive significantly higher daily temozolomide doses (mean, 53.7 mg/m"2 vs. 33.1 mg/m"2; p = 0.020), were more likely to receive maintenance temozolomide (45.7 % vs. 11.8 %; p = 0.016), and had to be hospitalized less frequently during radiotherapy (55.0 % vs. 88.2 %; p = 0.016). Of the patients receiving temozolomide, married patients showed significantly lower rates of hematologic and liver toxicity. Most complications were infectious or neurologic in nature. Complications of any grade were more frequent in unmarried patients (58.8 % vs. 30.0 %; p = 0.041) with the incidence of grade 3-5 complications being particularly elevated (47.1 % vs. 15.0 %; p = 0.004). We found poorer treatment delivery as well as an unexpected severe increase in toxicity and disease complications in elderly unmarried glioblastoma patients. Marital status may be an important predictive factor for clinical decision-making and should be addressed in further studies. (orig.) [de

Benthic Light Availability Improves Predictions of Riverine Primary Production

Science.gov (United States)

Kirk, L.; Cohen, M. J.

2017-12-01

Light is a fundamental control on photosynthesis, and often the only control strongly correlated with gross primary production (GPP) in streams and rivers; yet it has received far less attention than nutrients. Because benthic light is difficult to measure in situ, surrogates such as open sky irradiance are often used. Several studies have now refined methods to quantify canopy and water column attenuation of open sky light in order to estimate the amount of light that actually reaches the benthos. Given the additional effort that measuring benthic light requires, we should ask if benthic light always improves our predictions of GPP compared to just open sky irradiance. We use long-term, high-resolution dissolved oxygen, turbidity, dissolved organic matter (fDOM), and irradiance data from streams and rivers in north-central Florida, US across gradients of size and color to build statistical models of benthic light that predict GPP. Preliminary results on a large, clear river show only modest model improvements over open sky irradiance, even in heavily canopied reaches with pulses of tannic water. However, in another spring-fed river with greater connectivity to adjacent wetlands - and hence larger, more frequent pulses of tannic water - the model improved dramatically with the inclusion of fDOM (model R2 improved from 0.28 to 0.68). River shade modeling efforts also suggest that knowing benthic light will greatly enhance our ability to predict GPP in narrower, forested streams flowing in particular directions. Our objective is to outline conditions where an assessment of benthic light conditions would be necessary for riverine metabolism studies or management strategies.

Pitfalls in Prediction Modeling for Normal Tissue Toxicity in Radiation Therapy: An Illustration With the Individual Radiation Sensitivity and Mammary Carcinoma Risk Factor Investigation Cohorts

Energy Technology Data Exchange (ETDEWEB)

Mbah, Chamberlain, E-mail: chamberlain.mbah@ugent.be [Department of Basic Medical Sciences, Faculty of Health Sciences, Ghent University, Ghent (Belgium); Department of Mathematical Modeling, Statistics, and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent (Belgium); Thierens, Hubert [Department of Basic Medical Sciences, Faculty of Health Sciences, Ghent University, Ghent (Belgium); Thas, Olivier [Department of Mathematical Modeling, Statistics, and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent (Belgium); National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, New South Wales (Australia); De Neve, Jan [Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent (Belgium); Chang-Claude, Jenny; Seibold, Petra; Botma, Akke [Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg (Germany); West, Catharine [Translational Radiobiology Group, Institute of Cancer Sciences, Radiotherapy Related Research, Christie Hospital NHS Trust, University of Manchester, Manchester (United Kingdom); De Ruyck, Kim [Department of Basic Medical Sciences, Faculty of Health Sciences, Ghent University, Ghent (Belgium)

2016-08-01

Purpose: To identify the main causes underlying the failure of prediction models for radiation therapy toxicity to replicate. Methods and Materials: Data were used from two German cohorts, Individual Radiation Sensitivity (ISE) (n=418) and Mammary Carcinoma Risk Factor Investigation (MARIE) (n=409), of breast cancer patients with similar characteristics and radiation therapy treatments. The toxicity endpoint chosen was telangiectasia. The LASSO (least absolute shrinkage and selection operator) logistic regression method was used to build a predictive model for a dichotomized endpoint (Radiation Therapy Oncology Group/European Organization for the Research and Treatment of Cancer score 0, 1, or ≥2). Internal areas under the receiver operating characteristic curve (inAUCs) were calculated by a naïve approach whereby the training data (ISE) were also used for calculating the AUC. Cross-validation was also applied to calculate the AUC within the same cohort, a second type of inAUC. Internal AUCs from cross-validation were calculated within ISE and MARIE separately. Models trained on one dataset (ISE) were applied to a test dataset (MARIE) and AUCs calculated (exAUCs). Results: Internal AUCs from the naïve approach were generally larger than inAUCs from cross-validation owing to overfitting the training data. Internal AUCs from cross-validation were also generally larger than the exAUCs, reflecting heterogeneity in the predictors between cohorts. The best models with largest inAUCs from cross-validation within both cohorts had a number of common predictors: hypertension, normalized total boost, and presence of estrogen receptors. Surprisingly, the effect (coefficient in the prediction model) of hypertension on telangiectasia incidence was positive in ISE and negative in MARIE. Other predictors were also not common between the 2 cohorts, illustrating that overcoming overfitting does not solve the problem of replication failure of prediction models completely

Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm.

Science.gov (United States)

Bai, Li-Yue; Dai, Hao; Xu, Qin; Junaid, Muhammad; Peng, Shao-Liang; Zhu, Xiaolei; Xiong, Yi; Wei, Dong-Qing

2018-02-05

Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters) were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.

Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm

Directory of Open Access Journals (Sweden)

Li-Yue Bai

2018-02-01

Full Text Available Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.

PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes.

Science.gov (United States)

Yu, Nancy Y; Wagner, James R; Laird, Matthew R; Melli, Gabor; Rey, Sébastien; Lo, Raymond; Dao, Phuong; Sahinalp, S Cenk; Ester, Martin; Foster, Leonard J; Brinkman, Fiona S L

2010-07-01

PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program. We developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. It features an improved standalone program, with a new batch results delivery system complementing its web interface. We evaluated the most accurate SCL predictors using 5-fold cross validation plus we performed an independent proteomics analysis, showing that PSORTb 3.0 is the most accurate but can benefit from being complemented by Proteome Analyst predictions. http://www.psort.org/psortb (download open source software or use the web interface). psort-mail@sfu.ca Supplementary data are available at Bioinformatics online.

Immunotoxicity, genotoxicity and epigenetic toxicity of nanomaterials: New strategies for toxicity testing?

Science.gov (United States)

Dusinska, Maria; Tulinska, Jana; El Yamani, Naouale; Kuricova, Miroslava; Liskova, Aurelia; Rollerova, Eva; Rundén-Pran, Elise; Smolkova, Bozena

2017-11-01

The unique properties of nanomaterials (NMs) are beneficial in numerous industrial and medical applications. However, they could also induce unintended effects. Thus, a proper strategy for toxicity testing is essential in human hazard and risk assessment. Toxicity can be tested in vivo and in vitro; in compliance with the 3Rs, alternative strategies for in vitro testing should be further developed for NMs. Robust, standardized methods are of great importance in nanotoxicology, with comprehensive material characterization and uptake as an integral part of the testing strategy. Oxidative stress has been shown to be an underlying mechanism of possible toxicity of NMs, causing both immunotoxicity and genotoxicity. For testing NMs in vitro, a battery of tests should be performed on cells of human origin, either cell lines or primary cells, in conditions as close as possible to an in vivo situation. Novel toxicity pathways, particularly epigenetic modification, should be assessed along with conventional toxicity testing methods. However, to initiate epigenetic toxicity screens for NM exposure, there is a need to better understand their adverse effects on the epigenome, to identify robust and reproducible causal links between exposure, epigenetic changes and adverse phenotypic endpoints, and to develop improved assays to monitor epigenetic toxicity. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

The eTOX Data-Sharing Project to Advance in Silico Drug-Induced Toxicity Prediction

Directory of Open Access Journals (Sweden)

Montserrat Cases

2014-11-01

Full Text Available The high-quality in vivo preclinical safety data produced by the pharmaceutical industry during drug development, which follows numerous strict guidelines, are mostly not available in the public domain. These safety data are sometimes published as a condensed summary for the few compounds that reach the market, but the majority of studies are never made public and are often difficult to access in an automated way, even sometimes within the owning company itself. It is evident from many academic and industrial examples, that useful data mining and model development requires large and representative data sets and careful curation of the collected data. In 2010, under the auspices of the Innovative Medicines Initiative, the eTOX project started with the objective of extracting and sharing preclinical study data from paper or pdf archives of toxicology departments of the 13 participating pharmaceutical companies and using such data for establishing a detailed, well-curated database, which could then serve as source for read-across approaches (early assessment of the potential toxicity of a drug candidate by comparison of similar structure and/or effects and training of predictive models. The paper describes the efforts undertaken to allow effective data sharing intellectual property (IP protection and set up of adequate controlled vocabularies and to establish the database (currently with over 4000 studies contributed by the pharma companies corresponding to more than 1400 compounds. In addition, the status of predictive models building and some specific features of the eTOX predictive system (eTOXsys are presented as decision support knowledge-based tools for drug development process at an early stage.

Improving Permafrost Hydrology Prediction Through Data-Model Integration

Science.gov (United States)

Wilson, C. J.; Andresen, C. G.; Atchley, A. L.; Bolton, W. R.; Busey, R.; Coon, E.; Charsley-Groffman, L.

2017-12-01

The CMIP5 Earth System Models were unable to adequately predict the fate of the 16GT of permafrost carbon in a warming climate due to poor representation of Arctic ecosystem processes. The DOE Office of Science Next Generation Ecosystem Experiment, NGEE-Arctic project aims to reduce uncertainty in the Arctic carbon cycle and its impact on the Earth's climate system by improved representation of the coupled physical, chemical and biological processes that drive how much buried carbon will be converted to CO2 and CH4, how fast this will happen, which form will dominate, and the degree to which increased plant productivity will offset increased soil carbon emissions. These processes fundamentally depend on permafrost thaw rate and its influence on surface and subsurface hydrology through thermal erosion, land subsidence and changes to groundwater flow pathways as soil, bedrock and alluvial pore ice and massive ground ice melts. LANL and its NGEE colleagues are co-developing data and models to better understand controls on permafrost degradation and improve prediction of the evolution of permafrost and its impact on Arctic hydrology. The LANL Advanced Terrestrial Simulator was built using a state of the art HPC software framework to enable the first fully coupled 3-dimensional surface-subsurface thermal-hydrology and land surface deformation simulations to simulate the evolution of the physical Arctic environment. Here we show how field data including hydrology, snow, vegetation, geochemistry and soil properties, are informing the development and application of the ATS to improve understanding of controls on permafrost stability and permafrost hydrology. The ATS is being used to inform parameterizations of complex coupled physical, ecological and biogeochemical processes for implementation in the DOE ACME land model, to better predict the role of changing Arctic hydrology on the global climate system. LA-UR-17-26566.

Combining Gene Signatures Improves Prediction of Breast Cancer Survival

Science.gov (United States)

Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

2011-01-01

Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast

Combining gene signatures improves prediction of breast cancer survival.

Directory of Open Access Journals (Sweden)

Xi Zhao

Full Text Available BACKGROUND: Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123 and test set (n = 81, respectively. Gene sets from eleven previously published gene signatures are included in the study. PRINCIPAL FINDINGS: To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014. Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001. The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. CONCLUSION: Combining the predictive strength of multiple gene signatures improves

Can biomechanical variables predict improvement in crouch gait?

Science.gov (United States)

Hicks, Jennifer L.; Delp, Scott L.; Schwartz, Michael H.

2011-01-01

Many patients respond positively to treatments for crouch gait, yet surgical outcomes are inconsistent and unpredictable. In this study, we developed a multivariable regression model to determine if biomechanical variables and other subject characteristics measured during a physical exam and gait analysis can predict which subjects with crouch gait will demonstrate improved knee kinematics on a follow-up gait analysis. We formulated the model and tested its performance by retrospectively analyzing 353 limbs of subjects who walked with crouch gait. The regression model was able to predict which subjects would demonstrate ‘improved’ and ‘unimproved’ knee kinematics with over 70% accuracy, and was able to explain approximately 49% of the variance in subjects’ change in knee flexion between gait analyses. We found that improvement in stance phase knee flexion was positively associated with three variables that were drawn from knowledge about the biomechanical contributors to crouch gait: i) adequate hamstrings lengths and velocities, possibly achieved via hamstrings lengthening surgery, ii) normal tibial torsion, possibly achieved via tibial derotation osteotomy, and iii) sufficient muscle strength. PMID:21616666

Improving Saliency Models by Predicting Human Fixation Patches

KAUST Repository

Dubey, Rachit

2015-04-16

There is growing interest in studying the Human Visual System (HVS) to supplement and improve the performance of computer vision tasks. A major challenge for current visual saliency models is predicting saliency in cluttered scenes (i.e. high false positive rate). In this paper, we propose a fixation patch detector that predicts image patches that contain human fixations with high probability. Our proposed model detects sparse fixation patches with an accuracy of 84 % and eliminates non-fixation patches with an accuracy of 84 % demonstrating that low-level image features can indeed be used to short-list and identify human fixation patches. We then show how these detected fixation patches can be used as saliency priors for popular saliency models, thus, reducing false positives while maintaining true positives. Extensive experimental results show that our proposed approach allows state-of-the-art saliency methods to achieve better prediction performance on benchmark datasets.

Improving Saliency Models by Predicting Human Fixation Patches

KAUST Repository

Dubey, Rachit; Dave, Akshat; Ghanem, Bernard

2015-01-01

There is growing interest in studying the Human Visual System (HVS) to supplement and improve the performance of computer vision tasks. A major challenge for current visual saliency models is predicting saliency in cluttered scenes (i.e. high false positive rate). In this paper, we propose a fixation patch detector that predicts image patches that contain human fixations with high probability. Our proposed model detects sparse fixation patches with an accuracy of 84 % and eliminates non-fixation patches with an accuracy of 84 % demonstrating that low-level image features can indeed be used to short-list and identify human fixation patches. We then show how these detected fixation patches can be used as saliency priors for popular saliency models, thus, reducing false positives while maintaining true positives. Extensive experimental results show that our proposed approach allows state-of-the-art saliency methods to achieve better prediction performance on benchmark datasets.

Ringer's lactate improves liver recovery in a murine model of acetaminophen toxicity

Directory of Open Access Journals (Sweden)

Yang Runkuan

2011-11-01

Full Text Available Abstract Background Acetaminophen (APAP overdose induces massive hepatocyte necrosis. Liver regeneration is a vital process for survival after a toxic insult. Since hepatocytes are mostly in a quiescent state (G0, the regeneration process requires the priming of hepatocytes by cytokines such as TNF-α and IL-6. Ringer's lactate solution (RLS has been shown to increase serum TNF-α and IL-6 in patients and experimental animals; in addition, RLS also provides lactate, which can be used as an alternative metabolic fuel to meet the higher energy demand by liver regeneration. Therefore, we tested whether RLS therapy improves liver recovery after APAP overdose. Methods C57BL/6 male mice were intraperitoneally injected with a single dose of APAP (300 mg/kg dissolved in 1 mL sterile saline. Following 2 hrs of APAP challenge, the mice were given 1 mL RLS or Saline treatment every 12 hours for a total of 72 hours. Results 72 hrs after APAP challenge, compared to saline-treated group, RLS treatment significantly lowered serum transaminases (ALT/AST and improved liver recovery seen in histopathology. This beneficial effect was associated with increased hepatic tissue TNF-α concentration, enhanced hepatic NF-κB DNA binding and increased expression of cell cycle protein cyclin D1, three important factors in liver regeneration. Conclusion RLS improves liver recovery from APAP hepatotoxicity.

Improved hybrid optimization algorithm for 3D protein structure prediction.

Science.gov (United States)

Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

2014-07-01

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

Controlling air toxics through advanced coal preparation

Energy Technology Data Exchange (ETDEWEB)

Straszheim, W.E.; Buttermore, W.H.; Pollard, J.L. [Iowa State Univ., Ames, IA (United States)

1995-11-01

This project involves the assessment of advanced coal preparation methods for removing trace elements from coal to reduce the potential for air toxic emissions upon combustion. Scanning electron microscopy-based automated image analysis (SEM-AIA) and advanced washability analyses are being applied with state-of-the-art analytical procedures to predict the removal of elements of concern by advanced column flotation and to confirm the effectiveness of preparation on the quality of quantity of clean coal produced. Specific objectives are to maintain an acceptable recovery of combustible product, while improving the rejection of mineral-associated trace elements. Current work has focused on determining conditions for controlling column flotation system across its operating range and on selection and analysis of samples for determining trace element cleanability.

Innovative predictive maintenance concepts to improve life cycle management

NARCIS (Netherlands)

Tinga, Tiedo

2014-01-01

For naval systems with typically long service lives, high sustainment costs and strict availability requirements, an effective and efficient life cycle management process is very important. In this paper four approaches are discussed to improve that process: physics of failure based predictive

Prediction of paraquat exposure and toxicity in clinically ill poisoned patients: a model based approach.

Science.gov (United States)

Wunnapuk, Klintean; Mohammed, Fahim; Gawarammana, Indika; Liu, Xin; Verbeeck, Roger K; Buckley, Nicholas A; Roberts, Michael S; Musuamba, Flora T

2014-10-01

Paraquat poisoning is a medical problem in many parts of Asia and the Pacific. The mortality rate is extremely high as there is no effective treatment. We analyzed data collected during an ongoing cohort study on self-poisoning and from a randomized controlled trial assessing the efficacy of immunosuppressive therapy in hospitalized paraquat-intoxicated patients. The aim of this analysis was to characterize the toxicokinetics and toxicodynamics of paraquat in this population. A non-linear mixed effects approach was used to perform a toxicokinetic/toxicodynamic population analysis in a cohort of 78 patients. The paraquat plasma concentrations were best fitted by a two compartment toxicokinetic structural model with first order absorption and first order elimination. Changes in renal function were used for the assessment of paraquat toxicodynamics. The estimates of toxicokinetic parameters for the apparent clearance, the apparent volume of distribution and elimination half-life were 1.17 l h(-1) , 2.4 l kg(-1) and 87 h, respectively. Renal function, namely creatinine clearance, was the most significant covariate to explain between patient variability in paraquat clearance.This model suggested that a reduction in paraquat clearance occurred within 24 to 48 h after poison ingestion, and afterwards the clearance was constant over time. The model estimated that a paraquat concentration of 429 μg l(-1) caused 50% of maximum renal toxicity. The immunosuppressive therapy tested during this study was associated with only 8% improvement of renal function. The developed models may be useful as prognostic tools to predict patient outcome based on patient characteristics on admission and to assess drug effectiveness during antidote drug development. © 2014 The British Pharmacological Society.

Prediction of the relative toxicity of environmental toxins as a function of behavioral and non-behavioral endpoints

International Nuclear Information System (INIS)

Young, R.W.

1979-01-01

This study was conducted in order to examine the differential effects of behavioral and non-behavioral endpoints on the prediction of the relative toxicity of an environmental toxin. The effects of ionizing radiation were taken as the model for this evaluation. Forty rhesus monkeys were irradiated in groups of four at five different dose levels of high energy neuton and Bremsstrahlung radiations. Measures of behavioral performance, emesis and mortality were taken for each subject in order to test the hypotheses that behavioral indices would be more sensitive to gamma radiation than would physiological indices and that the physiological indices would be more sensitive to neutron radiations than would behavioral indices. The results supported these hypotheses

Solar radio proxies for improved satellite orbit prediction

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Yaya, Philippe; Hecker, Louis; Dudok de Wit, Thierry; Fèvre, Clémence Le; Bruinsma, Sean

2017-12-01

Specification and forecasting of solar drivers to thermosphere density models is critical for satellite orbit prediction and debris avoidance. Satellite operators routinely forecast orbits up to 30 days into the future. This requires forecasts of the drivers to these orbit prediction models such as the solar Extreme-UV (EUV) flux and geomagnetic activity. Most density models use the 10.7 cm radio flux (F10.7 index) as a proxy for solar EUV. However, daily measurements at other centimetric wavelengths have also been performed by the Nobeyama Radio Observatory (Japan) since the 1950's, thereby offering prospects for improving orbit modeling. Here we present a pre-operational service at the Collecte Localisation Satellites company that collects these different observations in one single homogeneous dataset and provides a 30 days forecast on a daily basis. Interpolation and preprocessing algorithms were developed to fill in missing data and remove anomalous values. We compared various empirical time series prediction techniques and selected a multi-wavelength non-recursive analogue neural network. The prediction of the 30 cm flux, and to a lesser extent that of the 10.7 cm flux, performs better than NOAA's present prediction of the 10.7 cm flux, especially during periods of high solar activity. In addition, we find that the DTM-2013 density model (Drag Temperature Model) performs better with (past and predicted) values of the 30 cm radio flux than with the 10.7 flux.

Solar radio proxies for improved satellite orbit prediction

Directory of Open Access Journals (Sweden)

Yaya Philippe

2017-01-01

Full Text Available Specification and forecasting of solar drivers to thermosphere density models is critical for satellite orbit prediction and debris avoidance. Satellite operators routinely forecast orbits up to 30 days into the future. This requires forecasts of the drivers to these orbit prediction models such as the solar Extreme-UV (EUV flux and geomagnetic activity. Most density models use the 10.7 cm radio flux (F10.7 index as a proxy for solar EUV. However, daily measurements at other centimetric wavelengths have also been performed by the Nobeyama Radio Observatory (Japan since the 1950's, thereby offering prospects for improving orbit modeling. Here we present a pre-operational service at the Collecte Localisation Satellites company that collects these different observations in one single homogeneous dataset and provides a 30 days forecast on a daily basis. Interpolation and preprocessing algorithms were developed to fill in missing data and remove anomalous values. We compared various empirical time series prediction techniques and selected a multi-wavelength non-recursive analogue neural network. The prediction of the 30 cm flux, and to a lesser extent that of the 10.7 cm flux, performs better than NOAA's present prediction of the 10.7 cm flux, especially during periods of high solar activity. In addition, we find that the DTM-2013 density model (Drag Temperature Model performs better with (past and predicted values of the 30 cm radio flux than with the 10.7 flux.

A two-stage approach for improved prediction of residue contact maps

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Pollastri Gianluca

2006-03-01

Full Text Available Abstract Background Protein topology representations such as residue contact maps are an important intermediate step towards ab initio prediction of protein structure. Although improvements have occurred over the last years, the problem of accurately predicting residue contact maps from primary sequences is still largely unsolved. Among the reasons for this are the unbalanced nature of the problem (with far fewer examples of contacts than non-contacts, the formidable challenge of capturing long-range interactions in the maps, the intrinsic difficulty of mapping one-dimensional input sequences into two-dimensional output maps. In order to alleviate these problems and achieve improved contact map predictions, in this paper we split the task into two stages: the prediction of a map's principal eigenvector (PE from the primary sequence; the reconstruction of the contact map from the PE and primary sequence. Predicting the PE from the primary sequence consists in mapping a vector into a vector. This task is less complex than mapping vectors directly into two-dimensional matrices since the size of the problem is drastically reduced and so is the scale length of interactions that need to be learned. Results We develop architectures composed of ensembles of two-layered bidirectional recurrent neural networks to classify the components of the PE in 2, 3 and 4 classes from protein primary sequence, predicted secondary structure, and hydrophobicity interaction scales. Our predictor, tested on a non redundant set of 2171 proteins, achieves classification performances of up to 72.6%, 16% above a base-line statistical predictor. We design a system for the prediction of contact maps from the predicted PE. Our results show that predicting maps through the PE yields sizeable gains especially for long-range contacts which are particularly critical for accurate protein 3D reconstruction. The final predictor's accuracy on a non-redundant set of 327 targets is 35

Research on Improved Depth Belief Network-Based Prediction of Cardiovascular Diseases

Directory of Open Access Journals (Sweden)

Peng Lu

2018-01-01

Full Text Available Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper, cardiovascular disease prediction model based on improved deep belief network (DBN is proposed. Using the reconstruction error, the network depth is determined independently, and unsupervised training and supervised optimization are combined. It ensures the accuracy of model prediction while guaranteeing stability. Thirty experiments were performed independently on the Statlog (Heart and Heart Disease Database data sets in the UCI database. Experimental results showed that the mean of prediction accuracy was 91.26% and 89.78%, respectively. The variance of prediction accuracy was 5.78 and 4.46, respectively.

Genetic markers for prediction of normal tissue toxicity after radiotherapy

DEFF Research Database (Denmark)

Alsner, Jan; Andreassen, Christian Nicolaj; Overgaard, Jens

2008-01-01

During the last decade, a number of studies have supported the hypothesis that there is an important genetic component to the observed interpatient variability in normal tissue toxicity after radiotherapy. This review summarizes the candidate gene association studies published so far on the risk...

Healthy, wealthy, and wise: retirement planning predicts employee health improvements.

Science.gov (United States)

Gubler, Timothy; Pierce, Lamar

2014-09-01

Are poor physical and financial health driven by the same underlying psychological factors? We found that the decision to contribute to a 401(k) retirement plan predicted whether an individual acted to correct poor physical-health indicators revealed during an employer-sponsored health examination. Using this examination as a quasi-exogenous shock to employees' personal-health knowledge, we examined which employees were more likely to improve their health, controlling for differences in initial health, demographics, job type, and income. We found that existing retirement-contribution patterns and future health improvements were highly correlated. Employees who saved for the future by contributing to a 401(k) showed improvements in their abnormal blood-test results and health behaviors approximately 27% more often than noncontributors did. These findings are consistent with an underlying individual time-discounting trait that is both difficult to change and domain interdependent, and that predicts long-term individual behaviors in multiple dimensions. © The Author(s) 2014.

Prediction of pesticide acute toxicity using two-dimensional chemical descriptors and target species classification

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Previous modelling of the median lethal dose (oral rat LD50) has indicated that local class-based models yield better correlations than global models. We evaluated the hypothesis that dividing the dataset by pesticidal mechanisms would improve prediction accuracy. A linear discri...

DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.

Science.gov (United States)

Adhikari, Badri; Hou, Jie; Cheng, Jianlin

2018-05-01

Significant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the recent progress in ab initio protein structure prediction, as demonstrated in the recent CASP experiments. Continuing the development of new methods to reliably predict contact maps is essential to further improve ab initio structure prediction. In this paper we discuss DNCON2, an improved protein contact map predictor based on two-level deep convolutional neural networks. It consists of six convolutional neural networks-the first five predict contacts at 6, 7.5, 8, 8.5 and 10 Å distance thresholds, and the last one uses these five predictions as additional features to predict final contact maps. On the free-modeling datasets in CASP10, 11 and 12 experiments, DNCON2 achieves mean precisions of 35, 50 and 53.4%, respectively, higher than 30.6% by MetaPSICOV on CASP10 dataset, 34% by MetaPSICOV on CASP11 dataset and 46.3% by Raptor-X on CASP12 dataset, when top L/5 long-range contacts are evaluated. We attribute the improved performance of DNCON2 to the inclusion of short- and medium-range contacts into training, two-level approach to prediction, use of the state-of-the-art optimization and activation functions, and a novel deep learning architecture that allows each filter in a convolutional layer to access all the input features of a protein of arbitrary length. The web server of DNCON2 is at http://sysbio.rnet.missouri.edu/dncon2/ where training and testing datasets as well as the predictions for CASP10, 11 and 12 free-modeling datasets can also be downloaded. Its source code is available at https://github.com/multicom-toolbox/DNCON2/. chengji@missouri.edu. Supplementary data are available at Bioinformatics online.

Improved prediction of signal peptides: SignalP 3.0

DEFF Research Database (Denmark)

Bendtsen, Jannick Dyrløv; Nielsen, Henrik; von Heijne, G.

2004-01-01

We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the ...

Compound toxicity screening and structure-activity relationship modeling in Escherichia coli.

Science.gov (United States)

Planson, Anne-Gaëlle; Carbonell, Pablo; Paillard, Elodie; Pollet, Nicolas; Faulon, Jean-Loup

2012-03-01

Synthetic biology and metabolic engineering are used to develop new strategies for producing valuable compounds ranging from therapeutics to biofuels in engineered microorganisms. When developing methods for high-titer production cells, toxicity is an important element to consider. Indeed the production rate can be limited due to toxic intermediates or accumulation of byproducts of the heterologous biosynthetic pathway of interest. Conversely, highly toxic molecules are desired when designing antimicrobials. Compound toxicity in bacteria plays a major role in metabolic engineering as well as in the development of new antibacterial agents. Here, we screened a diversified chemical library of 166 compounds for toxicity in Escherichia coli. The dataset was built using a clustering algorithm maximizing the chemical diversity in the library. The resulting assay data was used to develop a toxicity predictor that we used to assess the toxicity of metabolites throughout the metabolome. This new tool for predicting toxicity can thus be used for fine-tuning heterologous expression and can be integrated in a computational-framework for metabolic pathway design. Many structure-activity relationship tools have been developed for toxicology studies in eukaryotes [Valerio (2009), Toxicol Appl Pharmacol, 241(3): 356-370], however, to the best of our knowledge we present here the first E. coli toxicity prediction web server based on QSAR models (EcoliTox server: http://www.issb.genopole.fr/∼faulon/EcoliTox.php). Copyright © 2011 Wiley Periodicals, Inc.

Suppression of a NAC-Like Transcription Factor Gene Improves Boron-Toxicity Tolerance in Rice1

Science.gov (United States)

Ochiai, Kumiko; Shimizu, Akifumi; Okumoto, Yutaka; Fujiwara, Toru; Matoh, Toru

2011-01-01

We identified a gene responsible for tolerance to boron (B) toxicity in rice (Oryza sativa), named BORON EXCESS TOLERANT1. Using recombinant inbred lines derived from the B-toxicity-sensitive indica-ecotype cultivar IR36 and the tolerant japonica-ecotype cultivar Nekken 1, the region responsible for tolerance to B toxicity was narrowed to 49 kb on chromosome 4. Eight genes are annotated in this region. The DNA sequence in this region was compared between the B-toxicity-sensitive japonica cultivar Wataribune and the B-toxicity-tolerant japonica cultivar Nipponbare by eco-TILLING analysis and revealed a one-base insertion mutation in the open reading frame sequence of the gene Os04g0477300. The gene encodes a NAC (NAM, ATAF, and CUC)-like transcription factor and the function of the transcript is abolished in B-toxicity-tolerant cultivars. Transgenic plants in which the expression of Os04g0477300 is abolished by RNA interference gain tolerance to B toxicity. PMID:21543724

Lead toxicity to Lemna minor predicted using a metal speciation chemistry approach.

Science.gov (United States)

Antunes, Paula M C; Kreager, Nancy J

2014-10-01

In the present study, predictive measures for Pb toxicity and Lemna minor were developed from bioassays with 7 surface waters having varied chemistries (0.5-12.5 mg/L dissolved organic carbon, pH of 5.4-8.3, and water hardness of 8-266 mg/L CaCO3 ). As expected based on water quality, 10%, 20%, and 50% inhibitory concentration (IC10, IC20, and IC50, respectively) values expressed as percent net root elongation (%NRE) varied widely (e.g., IC20s ranging from 306 nM to >6920 nM total dissolved Pb), with unbounded values limited by Pb solubility. In considering chemical speciation, %NRE variability was better explained when both Pb hydroxides and the free lead ion were defined as bioavailable (i.e., f{OH} ) and colloidal Fe(III)(OH)3 precipitates were permitted to form and sorb metals (using FeOx as the binding phase). Although cause and effect could not be established because of covariance with alkalinity (p = 0.08), water hardness correlated strongly (r(2)  = 0.998, p minor and highlight the importance of chemical speciation in Pb-based risk assessments for aquatic macrophytes. © 2014 SETAC.

Can decadal climate predictions be improved by ocean ensemble dispersion filtering?

Science.gov (United States)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-12-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years inadvance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-termweather forecasts represent an initial value problem and long-term climate projections represent a boundarycondition problem, the decadal climate prediction falls in-between these two time scales. The ocean memorydue to its heat capacity holds big potential skill on the decadal scale. In recent years, more precise initializationtechniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions.Ensembles are another important aspect. Applying slightly perturbed predictions results in an ensemble. Insteadof using and evaluating one prediction, but the whole ensemble or its ensemble average, improves a predictionsystem. However, climate models in general start losing the initialized signal and its predictive skill from oneforecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improvedby a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. Wefound that this procedure, called ensemble dispersion filter, results in more accurate results than the standarddecadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions showan increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with largerensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from oceanensemble dispersion filtering toward the ensemble mean. This study is part of MiKlip (fona-miklip.de) - a major project on decadal climate prediction in Germany.We focus on the Max-Planck-Institute Earth System Model using the low-resolution version (MPI-ESM-LR) andMiKlip's basic initialization strategy as in 2017 published decadal climate forecast: http

TMDIM: an improved algorithm for the structure prediction of transmembrane domains of bitopic dimers

Science.gov (United States)

Cao, Han; Ng, Marcus C. K.; Jusoh, Siti Azma; Tai, Hio Kuan; Siu, Shirley W. I.

2017-09-01

α-Helical transmembrane proteins are the most important drug targets in rational drug development. However, solving the experimental structures of these proteins remains difficult, therefore computational methods to accurately and efficiently predict the structures are in great demand. We present an improved structure prediction method TMDIM based on Park et al. (Proteins 57:577-585, 2004) for predicting bitopic transmembrane protein dimers. Three major algorithmic improvements are introduction of the packing type classification, the multiple-condition decoy filtering, and the cluster-based candidate selection. In a test of predicting nine known bitopic dimers, approximately 78% of our predictions achieved a successful fit (RMSD PHP, MySQL and Apache, with all major browsers supported.

Systematic Review of Radiation Therapy Toxicity Reporting in Randomized Controlled Trials of Rectal Cancer: A Comparison of Patient-Reported Outcomes and Clinician Toxicity Reporting

Energy Technology Data Exchange (ETDEWEB)

Gilbert, Alexandra, E-mail: a.gilbert@leeds.ac.uk [Leeds Institute of Cancer & Pathology, University of Leeds, Leeds (United Kingdom); Ziegler, Lucy; Martland, Maisie [Leeds Institute of Cancer & Pathology, University of Leeds, Leeds (United Kingdom); Davidson, Susan [The Christie Hospital, Manchester (United Kingdom); Efficace, Fabio [Italian Group for Adult Hematologic Diseases, Rome (Italy); Sebag-Montefiore, David; Velikova, Galina [Leeds Institute of Cancer & Pathology, University of Leeds, Leeds (United Kingdom)

2015-07-01

The use of multimodal treatments for rectal cancer has improved cancer-related outcomes but makes monitoring toxicity challenging. Optimizing future radiation therapy regimens requires collection and publication of detailed toxicity data. This review evaluated the quality of toxicity information provided in randomized controlled trials (RCTs) of radiation therapy in rectal cancer and focused on the difference between clinician-reported and patient-reported toxicity. Medline, EMBASE, and the Cochrane Library were searched (January 1995-July 2013) for RCTs reporting late toxicity in patients treated with regimens including preoperative (chemo)radiation therapy. Data on toxicity measures and information on toxicity reported were extracted using Quantitative Analyses of Normal Tissue Effects in the Clinic recommendations. International Society for Quality of Life Research standards on patient-reported outcomes (PROs) were used to evaluate the quality of patient-reported toxicity. Twenty-one RCT publications met inclusion criteria out of 4144 articles screened. All PRO studies reported higher rates of toxicity symptoms than clinician-reported studies and reported on a wider range and milder symptoms. No clinician-reported study published data on sexual dysfunction. Of the clinician-reported studies, 55% grouped toxicity data related to an organ system together (eg “Bowel”), and 45% presented data only on more-severe (grade ≥3) toxicity. In comparison, all toxicity grades were reported in 79% of PRO publications, and all studies (100%) presented individual symptom toxicity data (eg bowel urgency). However, PRO reporting quality was variable. Only 43% of PRO studies presented baseline data, 28% did not use any psychometrically validated instruments, and only 29% of studies described statistical methods for managing missing data. Analysis of these trials highlights the lack of reporting standards for adverse events and reveals the differences between clinician and

Regional Models for Sediment Toxicity Assessment

Science.gov (United States)

This paper investigates the use of empirical models to predict the toxicity of sediment samples within a region to laboratory test organisms based on sediment chemistry. In earlier work, we used a large nationwide database of matching sediment chemistry and marine amphipod sedim...

Improved Helicopter Rotor Performance Prediction through Loose and Tight CFD/CSD Coupling

Science.gov (United States)

Ickes, Jacob C.

Helicopters and other Vertical Take-Off or Landing (VTOL) vehicles exhibit an interesting combination of structural dynamic and aerodynamic phenomena which together drive the rotor performance. The combination of factors involved make simulating the rotor a challenging and multidisciplinary effort, and one which is still an active area of interest in the industry because of the money and time it could save during design. Modern tools allow the prediction of rotorcraft physics from first principles. Analysis of the rotor system with this level of accuracy provides the understanding necessary to improve its performance. There has historically been a divide between the comprehensive codes which perform aeroelastic rotor simulations using simplified aerodynamic models, and the very computationally intensive Navier-Stokes Computational Fluid Dynamics (CFD) solvers. As computer resources become more available, efforts have been made to replace the simplified aerodynamics of the comprehensive codes with the more accurate results from a CFD code. The objective of this work is to perform aeroelastic rotorcraft analysis using first-principles simulations for both fluids and structural predictions using tools available at the University of Toledo. Two separate codes are coupled together in both loose coupling (data exchange on a periodic interval) and tight coupling (data exchange each time step) schemes. To allow the coupling to be carried out in a reliable and efficient way, a Fluid-Structure Interaction code was developed which automatically performs primary functions of loose and tight coupling procedures. Flow phenomena such as transonics, dynamic stall, locally reversed flow on a blade, and Blade-Vortex Interaction (BVI) were simulated in this work. Results of the analysis show aerodynamic load improvement due to the inclusion of the CFD-based airloads in the structural dynamics analysis of the Computational Structural Dynamics (CSD) code. Improvements came in the form

Enhancing the applicability and predictability of the embryonic stem cell test for developmental toxicity

NARCIS (Netherlands)

de Jong, E.

2012-01-01

Within the full risk assessment of a chemical, developmental toxicity testing is one of the endpoints that require the highest percentage of experimental animals. With the high number of experimental animals, cost and time involved in in vivo developmental toxicity testing there is an urgent need

Improved survival for elderly married glioblastoma patients : Better treatment delivery, less toxicity, and fewer disease complications.

Science.gov (United States)

Putz, Florian; Putz, Tobias; Goerig, Nicole; Knippen, Stefan; Gryc, Thomas; Eyüpoglu, Ilker; Rössler, Karl; Semrau, Sabine; Lettmaier, Sebastian; Fietkau, Rainer

2016-11-01

Marital status is a well-described prognostic factor in patients with gliomas but the observed survival difference is unexplained in the available population-based studies. A series of 57 elderly glioblastoma patients (≥70 years) were analyzed retrospectively. Patients received radiotherapy or chemoradiation with temozolomide. The prognostic significance of marital status was assessed. Disease complications, toxicity, and treatment delivery were evaluated in detail. Overall survival was significantly higher in married than in unmarried patients (median, 7.9 vs. 4.0 months; p = 0.006). The prognostic significance of marital status was preserved in the multivariate analysis (HR, 0.41; p = 0.011). Married patients could receive significantly higher daily temozolomide doses (mean, 53.7 mg/m² vs. 33.1 mg/m²; p = 0.020), were more likely to receive maintenance temozolomide (45.7 % vs. 11.8 %; p = 0.016), and had to be hospitalized less frequently during radiotherapy (55.0 % vs. 88.2 %; p = 0.016). Of the patients receiving temozolomide, married patients showed significantly lower rates of hematologic and liver toxicity. Most complications were infectious or neurologic in nature. Complications of any grade were more frequent in unmarried patients (58.8 % vs. 30.0 %; p = 0.041) with the incidence of grade 3-5 complications being particularly elevated (47.1 % vs. 15.0 %; p = 0.004). We found poorer treatment delivery as well as an unexpected severe increase in toxicity and disease complications in elderly unmarried glioblastoma patients. Marital status may be an important predictive factor for clinical decision-making and should be addressed in further studies.

Improved prediction of aerodynamic noise from wind turbines

Energy Technology Data Exchange (ETDEWEB)

Guidati, G.; Bareiss, R.; Wagner, S. [Univ. of Stuttgart, Inst. of Aerodynamics and Gasdynamics, Stuttgart (Germany)

1997-12-31

This paper focuses on an improved prediction model for inflow-turbulence noise which takes the true airfoil shape into account. Predictions are compared to the results of acoustic measurements on three 2D-models of 0.25 m chord. Two of the models have NACA-636xx airfoils of 12% and 18% relative thickness. The third airfoil was acoustically optimized by using the new prediction model. In the experiments the turbulence intensity of the flow was strongly increased by mounting a grid with 60 mm wide meshes and 12 mm thick rods onto the tunnel exhaust nozzle. The sound radiated from the airfoil was distinguished by the tunnel background noise by using an acoustic antenna consisting of a cross array of 36 microphones in total. An application of a standard beam-forming algorithm allows to determine how much noise is radiated from different parts of the models. This procedure normally results in a peak at the leading and trailing edge of the airfoil. The strength of the leading-edge peak is taken as the source strength for inflow-turbulence noise. (LN) 14 refs.

BAYESIAN FORECASTS COMBINATION TO IMPROVE THE ROMANIAN INFLATION PREDICTIONS BASED ON ECONOMETRIC MODELS

Directory of Open Access Journals (Sweden)

Mihaela Simionescu

2014-12-01

Full Text Available There are many types of econometric models used in predicting the inflation rate, but in this study we used a Bayesian shrinkage combination approach. This methodology is used in order to improve the predictions accuracy by including information that is not captured by the econometric models. Therefore, experts’ forecasts are utilized as prior information, for Romania these predictions being provided by Institute for Economic Forecasting (Dobrescu macromodel, National Commission for Prognosis and European Commission. The empirical results for Romanian inflation show the superiority of a fixed effects model compared to other types of econometric models like VAR, Bayesian VAR, simultaneous equations model, dynamic model, log-linear model. The Bayesian combinations that used experts’ predictions as priors, when the shrinkage parameter tends to infinite, improved the accuracy of all forecasts based on individual models, outperforming also zero and equal weights predictions and naïve forecasts.

Understanding mechanisms of toxicity: Insights from drug discovery research

International Nuclear Information System (INIS)

Houck, Keith A.; Kavlock, Robert J.

2008-01-01

Toxicology continues to rely heavily on use of animal testing for prediction of potential for toxicity in humans. Where mechanisms of toxicity have been elucidated, for example endocrine disruption by xenoestrogens binding to the estrogen receptor, in vitro assays have been developed as surrogate assays for toxicity prediction. This mechanistic information can be combined with other data such as exposure levels to inform a risk assessment for the chemical. However, there remains a paucity of such mechanistic assays due at least in part to lack of methods to determine specific mechanisms of toxicity for many toxicants. A means to address this deficiency lies in utilization of a vast repertoire of tools developed by the drug discovery industry for interrogating the bioactivity of chemicals. This review describes the application of high-throughput screening assays as experimental tools for profiling chemicals for potential for toxicity and understanding underlying mechanisms. The accessibility of broad panels of assays covering an array of protein families permits evaluation of chemicals for their ability to directly modulate many potential targets of toxicity. In addition, advances in cell-based screening have yielded tools capable of reporting the effects of chemicals on numerous critical cell signaling pathways and cell health parameters. Novel, more complex cellular systems are being used to model mammalian tissues and the consequences of compound treatment. Finally, high-throughput technology is being applied to model organism screens to understand mechanisms of toxicity. However, a number of formidable challenges to these methods remain to be overcome before they are widely applicable. Integration of successful approaches will contribute towards building a systems approach to toxicology that will provide mechanistic understanding of the effects of chemicals on biological systems and aid in rationale risk assessments

Molecular characterization and identification of markers for toxic and non-toxic varieties of Jatropha curcas L. using RAPD, AFLP and SSR markers.

Science.gov (United States)

Sudheer Pamidimarri, D V N; Singh, Sweta; Mastan, Shaik G; Patel, Jalpa; Reddy, Muppala P

2009-07-01

Jatropha curcas L., a multipurpose shrub has acquired significant economic importance for its seed oil which can be converted to biodiesel, is emerging as an alternative to petro-diesel. The deoiled seed cake remains after oil extraction is toxic and cannot be used as a feed despite having best nutritional contents. No quantitative and qualitative differences were observed between toxic and non-toxic varieties of J. curcas except for phorbol esters content. Development of molecular marker will enable to differentiate non-toxic from toxic variety in a mixed population and also help in improvement of the species through marker assisted breeding programs. The present investigation was undertaken to characterize the toxic and non-toxic varieties at molecular level and to develop PCR based molecular markers for distinguishing non-toxic from toxic or vice versa. The polymorphic markers were successfully identified specific to non-toxic and toxic variety using RAPD and AFLP techniques. Totally 371 RAPD, 1,442 AFLP markers were analyzed and 56 (15.09%) RAPD, 238 (16.49%) AFLP markers were found specific to either of the varieties. Genetic similarity between non-toxic and toxic verity was found to be 0.92 by RAPD and 0.90 by AFLP fingerprinting. In the present study out of 12 microsatellite markers analyzed, seven markers were found polymorphic. Among these seven, jcms21 showed homozygous allele in the toxic variety. The study demonstrated that both RAPD and AFLP techniques were equally competitive in identifying polymorphic markers and differentiating both the varieties of J. curcas. Polymorphism of SSR markers prevailed between the varieties of J. curcas. These RAPD and AFLP identified markers will help in selective cultivation of specific variety and along with SSRs these markers can be exploited for further improvement of the species through breeding and Marker Assisted Selection (MAS).

Emerging approaches in predictive toxicology.

Science.gov (United States)

Zhang, Luoping; McHale, Cliona M; Greene, Nigel; Snyder, Ronald D; Rich, Ivan N; Aardema, Marilyn J; Roy, Shambhu; Pfuhler, Stefan; Venkatactahalam, Sundaresan

2014-12-01

Predictive toxicology plays an important role in the assessment of toxicity of chemicals and the drug development process. While there are several well-established in vitro and in vivo assays that are suitable for predictive toxicology, recent advances in high-throughput analytical technologies and model systems are expected to have a major impact on the field of predictive toxicology. This commentary provides an overview of the state of the current science and a brief discussion on future perspectives for the field of predictive toxicology for human toxicity. Computational models for predictive toxicology, needs for further refinement and obstacles to expand computational models to include additional classes of chemical compounds are highlighted. Functional and comparative genomics approaches in predictive toxicology are discussed with an emphasis on successful utilization of recently developed model systems for high-throughput analysis. The advantages of three-dimensional model systems and stem cells and their use in predictive toxicology testing are also described. © 2014 Wiley Periodicals, Inc.

A novel method for improved accuracy of transcription factor binding site prediction

KAUST Repository

Khamis, Abdullah M.; Motwalli, Olaa Amin; Oliva, Romina; Jankovic, Boris R.; Medvedeva, Yulia; Ashoor, Haitham; Essack, Magbubah; Gao, Xin; Bajic, Vladimir B.

2018-01-01

Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.

A novel method for improved accuracy of transcription factor binding site prediction

KAUST Repository

Khamis, Abdullah M.

2018-03-20

Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.

HLA-G 3′UTR Polymorphisms Predict Drug-Induced G3-4 Toxicity Related to Folinic Acid/5-Fluorouracil/Oxaliplatin (FOLFOX4) Chemotherapy in Non-Metastatic Colorectal Cancer

Science.gov (United States)

Garziera, Marica; Virdone, Saverio; De Mattia, Elena; Scarabel, Lucia; Cecchin, Erika; Polesel, Jerry; D’Andrea, Mario; Pella, Nicoletta; Buonadonna, Angela; Favaretto, Adolfo; Toffoli, Giuseppe

2017-01-01

Polymorphisms in drug-metabolizing enzymes might not completely explain inter-individual differences in toxicity profiles of patients with colorectal cancer (CRC) that receive folinic acid/5-fluorouracil/oxaliplatin (FOLFOX4). Recent data indicate that the immune system could contribute to FOLFOX4 outcomes. In light of the immune inhibitory nature of human leukocyte antigen-G (HLA-G), a non-classical major histocompatibility complex (MHC) class I molecule, we aimed to identify novel genomic markers of grades 3 and 4 (G3-4) toxicity related to FOLFOX4 therapy in patients with CRC. We retrospectively analyzed data for 144 patients with stages II-III CRC to identify HLA-G 3′ untranslated region (3′UTR) polymorphisms and related haplotypes and evaluate their impact on the risk of developing G3-4 toxicities (i.e., neutropenia, hematological/non-hematological toxicity, neurotoxicity) with logistic regression. The rs1610696-G/G polymorphism was associated with increased risk of G3-4 neutropenia (OR = 3.76, p = 0.015) and neurotoxicity (OR = 8.78, p = 0.016); rs371194629-Ins/Ins was associated with increased risk of neurotoxicity (OR = 5.49, p = 0.027). HLA-G 3′UTR-2, which contains rs1610696-G/G and rs371194629-Ins/Ins polymorphisms, was associated with increased risk of G3-4 neutropenia (OR = 3.92, p = 0.017) and neurotoxicity (OR = 11.29, p = 0.009). A bootstrap analysis confirmed the predictive value of rs1610696 and rs371194629, but the UTR-2 haplotype was validated only for neurotoxicity. This exploratory study identified new HLA-G 3′UTR polymorphisms/haplotypes as potential predictive markers of G3-4 toxicities in CRC. PMID:28653974

HLA-G 3′UTR Polymorphisms Predict Drug-Induced G3-4 Toxicity Related to Folinic Acid/5-Fluorouracil/Oxaliplatin (FOLFOX4 Chemotherapy in Non-Metastatic Colorectal Cancer

Directory of Open Access Journals (Sweden)

Marica Garziera

2017-06-01

Full Text Available Polymorphisms in drug-metabolizing enzymes might not completely explain inter-individual differences in toxicity profiles of patients with colorectal cancer (CRC that receive folinic acid/5-fluorouracil/oxaliplatin (FOLFOX4. Recent data indicate that the immune system could contribute to FOLFOX4 outcomes. In light of the immune inhibitory nature of human leukocyte antigen-G (HLA-G, a non-classical major histocompatibility complex (MHC class I molecule, we aimed to identify novel genomic markers of grades 3 and 4 (G3-4 toxicity related to FOLFOX4 therapy in patients with CRC. We retrospectively analyzed data for 144 patients with stages II-III CRC to identify HLA-G 3′ untranslated region (3′UTR polymorphisms and related haplotypes and evaluate their impact on the risk of developing G3-4 toxicities (i.e., neutropenia, hematological/non-hematological toxicity, neurotoxicity with logistic regression. The rs1610696-G/G polymorphism was associated with increased risk of G3-4 neutropenia (OR = 3.76, p = 0.015 and neurotoxicity (OR = 8.78, p = 0.016; rs371194629-Ins/Ins was associated with increased risk of neurotoxicity (OR = 5.49, p = 0.027. HLA-G 3′UTR-2, which contains rs1610696-G/G and rs371194629-Ins/Ins polymorphisms, was associated with increased risk of G3-4 neutropenia (OR = 3.92, p = 0.017 and neurotoxicity (OR = 11.29, p = 0.009. A bootstrap analysis confirmed the predictive value of rs1610696 and rs371194629, but the UTR-2 haplotype was validated only for neurotoxicity. This exploratory study identified new HLA-G 3′UTR polymorphisms/haplotypes as potential predictive markers of G3-4 toxicities in CRC.

Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredients.

Science.gov (United States)

Hisaki, Tomoka; Aiba Née Kaneko, Maki; Yamaguchi, Masahiko; Sasa, Hitoshi; Kouzuki, Hirokazu

2015-04-01

Use of laboratory animals for systemic toxicity testing is subject to strong ethical and regulatory constraints, but few alternatives are yet available. One possible approach to predict systemic toxicity of chemicals in the absence of experimental data is quantitative structure-activity relationship (QSAR) analysis. Here, we present QSAR models for prediction of maximum "no observed effect level" (NOEL) for repeated-dose, developmental and reproductive toxicities. NOEL values of 421 chemicals for repeated-dose toxicity, 315 for reproductive toxicity, and 156 for developmental toxicity were collected from Japan Existing Chemical Data Base (JECDB). Descriptors to predict toxicity were selected based on molecular orbital (MO) calculations, and QSAR models employing multiple independent descriptors as the input layer of an artificial neural network (ANN) were constructed to predict NOEL values. Robustness of the models was indicated by the root-mean-square (RMS) errors after 10-fold cross-validation (0.529 for repeated-dose, 0.508 for reproductive, and 0.558 for developmental toxicity). Evaluation of the models in terms of the percentages of predicted NOELs falling within factors of 2, 5 and 10 of the in-vivo-determined NOELs suggested that the model is applicable to both general chemicals and the subset of chemicals listed in International Nomenclature of Cosmetic Ingredients (INCI). Our results indicate that ANN models using in silico parameters have useful predictive performance, and should contribute to integrated risk assessment of systemic toxicity using a weight-of-evidence approach. Availability of predicted NOELs will allow calculation of the margin of safety, as recommended by the Scientific Committee on Consumer Safety (SCCS).

Validation of a biotic ligand model on site-specific copper toxicity to Daphnia magna in the Yeongsan River, Korea.

Science.gov (United States)

Park, Jinhee; Ra, Jin-Sung; Rho, Hojung; Cho, Jaeweon; Kim, Sang Don

2018-03-01

The objective of this study was to determine whether the water effect ratio (WER) or biotic ligand model (BLM) could be applied to efficiently develop water quality criteria (WQC) in Korea. Samples were collected from 12 specific sites along the Yeongsan River (YSR), Korea, including two sewage treatment plants and one estuary lake. A copper toxicity test using Daphnia magna was performed to determine the WER and to compare to the BLM prediction. The results of the WER from YSR samples also indicated significantly different copper toxicities in all sites. The model-based predictions showed that effluent and estuary waters had significantly different properties in regard to their ability to be used to investigate water characteristics and copper toxicity. It was supposed that the slight water characteristics changes, such as pH, DOC, hardness, conductivity, among others, influence copper toxicity, and these variable effects on copper toxicity interacted with the water composition. The 38% prediction was outside of the validation range by a factor of two in all sites, showing a poor predictive ability, especially in STPs and streams adjacent to the estuary, while the measured toxicity was more stable. The samples that ranged from pH 7.3-7.7 generated stable predictions, while other samples, including those with lower and the higher pH values, led to more unstable predictions. The results also showed that the toxicity of Cu in sample waters to D. magna was closely proportional to the amounts of acidity, including the carboxylic and phenolic groups, as well as the DOC concentrations. Consequently, the acceptable prediction of metal toxicity in various water samples needs the site-specific results considering the water characteristics such as pH and DOC properties particularly in STPs and estuary regions. Copyright © 2017 Elsevier Inc. All rights reserved.

IMPROVING STRUCTURE-LINKED ACCESS TO PUBLICLY AVAILABLE CHEMICAL TOXICITY INFORMATION

Science.gov (United States)

Hepatotoxicity of the Herbicide Alachlor Associated with Glutathione Depletion, Oxidative Damage and Protein S-Cysteinyl Adduction.Toxicity of the herbicide alachlor (2-chloro-2',6'-diethtl-N-[methoxtmethtl]-acetanilide) has been attributed to cytochrome P450-dependent me...

Ethambutol/Linezolid Toxic Optic Neuropathy.

Science.gov (United States)

Libershteyn, Yevgeniya

2016-02-01

To report a rare toxic optic neuropathy after long-term use of two medications: ethambutol and linezolid. A 65-year-old man presented to the Miami Veterans Affairs Medical Center in December 2014 for evaluation of progressive vision decrease in both eyes. The patient presented with best-corrected visual acuities of 20/400 in the right eye and counting fingers at 5 feet in the left eye. Color vision was significantly reduced in both eyes. Visual fields revealed a cecocentral defect in both eyes. His fundus and optic nerve examination was unremarkable. Because vision continued to decline after discontinuation of ethambutol, linezolid was also discontinued, after which vision, color vision, and visual fields improved. Because of these findings, the final diagnosis was toxic optic neuropathy. Final visual outcome was 20/30 in the right eye and 20/40 in the left eye. Drug-associated toxic optic neuropathy is a rare but vision-threatening condition. Diagnosis is made based on an extensive case history and careful clinical examination. The examination findings include varying decrease in vision, normal pupils and extraocular muscles, and unremarkable fundoscopy, with the possibility of swollen optic discs in the acute stage of the optic neuropathy. Other important findings descriptive of toxic optic neuropathy include decreased color vision and cecocentral visual field defects. This case illustrates the importance of knowledge of all medications and/or substances a patient consumes that may cause a toxic reaction and discontinuing them immediately if the visual functions are worsening or not improving.

Developing Predictive Maintenance Expertise to Improve Plant Equipment Reliability

International Nuclear Information System (INIS)

Wurzbach, Richard N.

2002-01-01

On-line equipment condition monitoring is a critical component of the world-class production and safety histories of many successful nuclear plant operators. From addressing availability and operability concerns of nuclear safety-related equipment to increasing profitability through support system reliability and reduced maintenance costs, Predictive Maintenance programs have increasingly become a vital contribution to the maintenance and operation decisions of nuclear facilities. In recent years, significant advancements have been made in the quality and portability of many of the instruments being used, and software improvements have been made as well. However, the single most influential component of the success of these programs is the impact of a trained and experienced team of personnel putting this technology to work. Changes in the nature of the power generation industry brought on by competition, mergers, and acquisitions, has taken the historically stable personnel environment of power generation and created a very dynamic situation. As a result, many facilities have seen a significant turnover in personnel in key positions, including predictive maintenance personnel. It has become the challenge for many nuclear operators to maintain the consistent contribution of quality data and information from predictive maintenance that has become important in the overall equipment decision process. These challenges can be met through the implementation of quality training to predictive maintenance personnel and regular updating and re-certification of key technology holders. The use of data management tools and services aid in the sharing of information across sites within an operating company, and with experts who can contribute value-added data management and analysis. The overall effectiveness of predictive maintenance programs can be improved through the incorporation of newly developed comprehensive technology training courses. These courses address the use of

Common toxicity criteria: version 2.0. an improved reference for grading the acute effects of cancer treatment: impact on radiotherapy

International Nuclear Information System (INIS)

Trotti, Andy; Byhardt, Roger; Stetz, Joanne; Gwede, Clement; Corn, Benjamin; Fu, Karen; Gunderson, Leonard; McCormick, Beryl; Morris, Mitchell; Rich, Tyvin; Shipley, William; Curran, Walter

2000-01-01

In 1997, the National Cancer Institute (NCI) led an effort to revise and expand the Common Toxicity Criteria (CTC) with the goal of integrating systemic agent, radiation, and surgical criteria into a comprehensive and standardized system. Representatives from the Radiation Therapy Oncology Group (RTOG) participated in this process in an effort to improve acute radiation related criteria and to achieve better clarity and consistency among modalities. CTC v. 2.0 replaces the previous NCI CTC and the RTOG Acute Radiation Morbidity Scoring Criteria and includes more than 260 individual adverse events with more than 100 of these applicable to acute radiation effects. One of the advantages of the revised criteria for radiation oncology is the opportunity to grade acute radiation effects not adequately captured under the previous RTOG system. A pilot study conducted by the RTOG indicated the new criteria are indeed more comprehensive and were preferred by research associates. CTC v. 2.0 represents an improvement in the evaluation and grading of acute toxicity for all modalities

Improved prediction of breast cancer outcome by identifying heterogeneous biomarkers.

Science.gov (United States)

Choi, Jonghwan; Park, Sanghyun; Yoon, Youngmi; Ahn, Jaegyoon

2017-11-15

Identification of genes that can be used to predict prognosis in patients with cancer is important in that it can lead to improved therapy, and can also promote our understanding of tumor progression on the molecular level. One of the common but fundamental problems that render identification of prognostic genes and prediction of cancer outcomes difficult is the heterogeneity of patient samples. To reduce the effect of sample heterogeneity, we clustered data samples using K-means algorithm and applied modified PageRank to functional interaction (FI) networks weighted using gene expression values of samples in each cluster. Hub genes among resulting prioritized genes were selected as biomarkers to predict the prognosis of samples. This process outperformed traditional feature selection methods as well as several network-based prognostic gene selection methods when applied to Random Forest. We were able to find many cluster-specific prognostic genes for each dataset. Functional study showed that distinct biological processes were enriched in each cluster, which seems to reflect different aspect of tumor progression or oncogenesis among distinct patient groups. Taken together, these results provide support for the hypothesis that our approach can effectively identify heterogeneous prognostic genes, and these are complementary to each other, improving prediction accuracy. https://github.com/mathcom/CPR. jgahn@inu.ac.kr. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Can dosimetric parameters predict acute hematologic toxicity in rectal cancer patients treated with intensity-modulated pelvic radiotherapy?

International Nuclear Information System (INIS)

Wan, Juefeng; Liu, Kaitai; Li, Kaixuan; Li, Guichao; Zhang, Zhen

2015-01-01

To identify dosimetric parameters associated with acute hematologic toxicity (HT) in rectal cancer patients undergoing concurrent chemotherapy and intensity-modulated pelvic radiotherapy. Ninety-three rectal cancer patients receiving concurrent capecitabine and pelvic intensity-modulated radiation therapy (IMRT) were analyzed. Pelvic bone marrow (PBM) was contoured for each patient and divided into three subsites: lumbosacral spine (LSS), ilium, and lower pelvis (LP). The volume of each site receiving 5–40 Gy (V 5, V10, V15, V20, V30, and V40, respectively) as well as patient baseline clinical characteristics was calculated. The endpoint for hematologic toxicity was grade ≥ 2 (HT2+) leukopenia, neutropenia, anemia or thrombocytopenia. Logistic regression was used to analyze correlation between dosimetric parameters and grade ≥ 2 hematologic toxicity. Twenty-four in ninety-three patients experienced grade ≥ 2 hematologic toxicity. Only the dosimetric parameter V40 of lumbosacral spine was correlated with grade ≥ 2 hematologic toxicity. Increased pelvic lumbosacral spine V40 (LSS-V40) was associated with an increased grade ≥ 2 hematologic toxicity (p = 0.041). Patients with LSS-V40 ≥ 60 % had higher rates of grade ≥ 2 hematologic toxicity than did patients with lumbosacral spine V40 < 60 % (38.3 %, 18/47 vs.13 %, 6/46, p =0.005). On univariate and multivariate logistic regression analysis, lumbosacral spine V40 and gender was also the variable associated with grade ≥ 2 hematologic toxicity. Female patients were observed more likely to have grade ≥ 2 hematologic toxicity than male ones (46.9 %, 15/32 vs 14.8 %, 9/61, p =0.001). Lumbosacral spine -V40 was associated with clinically significant grade ≥ 2 hematologic toxicity. Keeping the lumbosacral spine -V40 < 60 % was associated with a 13 % risk of grade ≥ 2 hematologic toxicity in rectal cancer patients undergoing concurrent chemoradiotherapy

Development of thresholds of excess toxicity for environmental species and their application to identification of modes of acute toxic action.

Science.gov (United States)

Li, Jin J; Zhang, Xu J; Yang, Yi; Huang, Tao; Li, Chao; Su, Limin; Zhao, Yuan H; Cronin, Mark T D

2018-03-01

The acute toxicity of organic pollutants to fish, Daphnia magna, Tetrahymena pyriformis, and Vibrio fischeri was investigated. The results indicated that the Toxicity Ratio (TR) threshold of log TR =1, which has been based on the distribution of toxicity data to fish, can also be used to discriminate reactive or specifically acting compounds from baseline narcotics for Daphnia magna and Vibrio fischeri. A log TR=0.84 is proposed for Tetrahymena pyriformis following investigation of the relationships between the species sensitivity and the absolute averaged residuals (AAR) between the predicted baseline toxicity and the experimental toxicity. Less inert compounds exhibit relatively higher toxicity to the lower species (Tetrahymena pyriformis and Vibrio fischeri) than the higher species (fish and Daphnia magna). A greater number of less inert compounds with log TR greater than the thresholds was observed for Tetrahymena pyriformis and Vibrio fischeri. This may be attributed to the hydrophilic compounds which may pass more easily through cell membranes than the skin or exoskeleton of organisms and have higher bioconcentration factors in the lower species, leading to higher toxicity. Most of classes of chemical associated with excess toxicity to one species also exhibited excess toxicity to other species, however, a few classes with excess toxicity to one species exhibiting narcotic toxicity to other species and thus may have different MOAs between species. Some ionizable compounds have log TR much lower than one because of the over-estimated log K OW . The factors that influence the toxicity ratio calculated from baseline level are discussed in this paper. Copyright © 2017 Elsevier B.V. All rights reserved.

Testing strategies for embryo-fetal toxicity of human pharmaceuticals. Animal models vs. in vitro approaches: a workshop report.

Science.gov (United States)

van der Laan, Jan Willem; Chapin, Robert E; Haenen, Bert; Jacobs, Abigail C; Piersma, Aldert

2012-06-01

Reproductive toxicity testing is characterized by high animal use. For registration of pharmaceutical compounds, developmental toxicity studies are usually conducted in both rat and rabbits. Efforts have been underway for a long time to design alternatives to animal use. Implementation has lagged, partly because of uncertainties about the applicability domain of the alternatives. The reproductive cycle is complex and not all mechanisms of development can be mimicked in vitro. Therefore, efforts are underway to characterize the available alternative tests with regard to the mechanism of action they include. One alternative test is the mouse embryonic stem cell test (EST), which has been studied since the late 1990s. It is a genuine 3R "alternative" assay as it is essentially animal-free. A meeting was held to review the state-of-the-art of various in vitro models for prediction of developmental toxicity. Although the predictivity of individual assays is improving, a battery of several assays is likely to have even higher predictivity, which is necessary for regulatory acceptance. The workshop concluded that an important first step is a thorough survey of the existing rat and rabbit studies, to fully characterize the frequency of responses and the types of effects seen. At the same time, it is important to continue the optimization of in vitro assays. As more experience accumulates, the optimal conditions, assay structure, and applicability of the alternative assays are expected to emerge. Copyright © 2012 Elsevier Inc. All rights reserved.

Precision-cut intestinal slices as an in vitro model to predict NSAID induced intestinal toxicity

NARCIS (Netherlands)

Niu, Xiaoyu; van der Bijl, Henk; Groothuis, Geny; de Graaf, Inge

2013-01-01

Non-steroidal anti-inflammatory drugs (NSAIDs) are associated with high prevalence of gastro-intestinal side-effects. In vivo studies suggest that uncoupling of oxidative phosphorylation is an important cause of the toxicity and that the toxicity is aggravated by enterohepatic circulation.

Improved prediction of genetic predisposition to psychiatric disorders using genomic feature best linear unbiased prediction models

DEFF Research Database (Denmark)

Rohde, Palle Duun; Demontis, Ditte; Børglum, Anders

is enriched for causal variants. Here we apply the GFBLUP model to a small schizophrenia case-control study to test the promise of this model on psychiatric disorders, and hypothesize that the performance will be increased when applying the model to a larger ADHD case-control study if the genomic feature...... contains the causal variants. Materials and Methods: The schizophrenia study consisted of 882 controls and 888 schizophrenia cases genotyped for 520,000 SNPs. The ADHD study contained 25,954 controls and 16,663 ADHD cases with 8,4 million imputed genotypes. Results: The predictive ability for schizophrenia.......6% for the null model). Conclusion: The improvement in predictive ability for schizophrenia was marginal, however, greater improvement is expected for the larger ADHD data....

Incorporating Scale-Dependent Fracture Stiffness for Improved Reservoir Performance Prediction

Science.gov (United States)

Crawford, B. R.; Tsenn, M. C.; Homburg, J. M.; Stehle, R. C.; Freysteinson, J. A.; Reese, W. C.

2017-12-01

We present a novel technique for predicting dynamic fracture network response to production-driven changes in effective stress, with the potential for optimizing depletion planning and improving recovery prediction in stress-sensitive naturally fractured reservoirs. A key component of the method involves laboratory geomechanics testing of single fractures in order to develop a unique scaling relationship between fracture normal stiffness and initial mechanical aperture. Details of the workflow are as follows: tensile, opening mode fractures are created in a variety of low matrix permeability rocks with initial, unstressed apertures in the micrometer to millimeter range, as determined from image analyses of X-ray CT scans; subsequent hydrostatic compression of these fractured samples with synchronous radial strain and flow measurement indicates that both mechanical and hydraulic aperture reduction varies linearly with the natural logarithm of effective normal stress; these stress-sensitive single-fracture laboratory observations are then upscaled to networks with fracture populations displaying frequency-length and length-aperture scaling laws commonly exhibited by natural fracture arrays; functional relationships between reservoir pressure reduction and fracture network porosity, compressibility and directional permeabilities as generated by such discrete fracture network modeling are then exported to the reservoir simulator for improved naturally fractured reservoir performance prediction.

Thermal Stress and Toxicity | Science Inventory | US EPA

Science.gov (United States)

Elevating ambient temperature above thermoneutrality exacerbates toxicity of most air pollutants, insecticides, and other toxic chemicals. On the other hand, safety and toxicity testing of toxicants and drugs is usually performed in mice and rats maintained at subthermoneutral temperatures of —22 °C. When exposed to chemical toxicants under these relatively cool conditions, rodents typically undergo a regulated hypothermic response, characterized by preference for cooler ambient temperatures and controlled reduction in core temperature. Reducing core temperature delays the clearance of most toxicants from the body; however, a mild hypothermia also improves recovery and survival from the toxicant. Raising ambient temperature to thermoneutrality and above increases the rate of clearance of the toxicant but also exacerbates toxicity. Furthermore, heat stress combined with work or exercise is likely to worsen toxicity. Body temperature of large mammals, including humans, does not decrease as much in response to exposure to a toxicant. However, heat stress tan nonetheless worsen toxic outcome in humans through a variety of mechanisms. For example, heat-induced sweating and elevation in skin blood flow accelerates uptake of some insecticides. Epidemiological studies suggest that thermal stress may exacerbate the toxicity of airborne pollutants such as ozone and particulate matter. Overall, translating results of studies in rodents to that of humans is a formidable

Mediating toxic emotions in the workplace--the impact of abusive supervision.

Science.gov (United States)

Chu, Li-Chuan

2014-11-01

This study explores whether abusive supervision can effectively predict employees' counterproductive work behaviour (CWB) and organisational citizenship behaviour (OCB) and the role of toxic emotions at work as a potential mediator of these relationships in nursing settings. Workplace bullying is widespread in nursing. Despite the growing literature on abusive supervision and employees' counterproductive work behaviour and organisational citizenship behaviour, few studies have examined the relationships between abusive supervision and these work behaviours from the viewpoint of the victimed employee's emotion process. This study adopted a two-stage survey of 212 nurses, all of whom were employed by hospitals in Taiwan. Hypotheses were tested through the use of hierarchical multiple regression. The results showed that abusive supervision was positively associated with toxic emotions. Moreover, toxic emotions could effectively predict nurses' counterproductive work behaviour and organisational citizenship behaviour. Finally, it was found that toxic emotions partially mediated the negative effects of abusive supervision on both work behaviours. Toxic emotions at work are a critical mediating variable between abusive supervision and both counterproductive work behaviour and organisational citizenship behaviour. Hospital administrators can implement policies designed to manage events effectively that can spark toxic emotions in their employees. Work empowerment may be an effective way to reduce counterproductive work behaviour and to enhance organisational citizenship behaviour among nurses when supervisors do not promote a healthy work environment for them. © 2013 John Wiley & Sons Ltd.

Improving the accuracy of protein secondary structure prediction using structural alignment

Directory of Open Access Journals (Sweden)

Gallin Warren J

2006-06-01

Full Text Available Abstract Background The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3 of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences, the probability of a newly identified sequence having a structural homologue is actually quite high. Results We have developed a method that performs structure-based sequence alignments as part of the secondary structure prediction process. By mapping the structure of a known homologue (sequence ID >25% onto the query protein's sequence, it is possible to predict at least a portion of that query protein's secondary structure. By integrating this structural alignment approach with conventional (sequence-based secondary structure methods and then combining it with a "jury-of-experts" system to generate a consensus result, it is possible to attain very high prediction accuracy. Using a sequence-unique test set of 1644 proteins from EVA, this new method achieves an average Q3 score of 81.3%. Extensive testing indicates this is approximately 4–5% better than any other method currently available. Assessments using non sequence-unique test sets (typical of those used in proteome annotation or structural genomics indicate that this new method can achieve a Q3 score approaching 88%. Conclusion By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called PROTEUS, that performs these secondary structure predictions is accessible at http://wishart.biology.ualberta.ca/proteus. For high throughput or batch sequence analyses, the PROTEUS programs

Clonal Evaluation of Prostate Cancer by ERG/SPINK1 Status to Improve Prognosis Prediction

Science.gov (United States)

2017-12-01

19 NIH Exploiting drivers of androgen receptor signaling negative prostate cancer for precision medicine Goal(s): Identify novel potential drivers...AWARD NUMBER: W81XWH-14-1-0466 TITLE: Clonal evaluation of prostate cancer by ERG/SPINK1 status to improve prognosis prediction PRINCIPAL...Sept 2017 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Clonal Evaluation of Prostate Cancer by ERG/SPINK1 Status to Improve Prognosis Prediction 5b

Evaluating the Zebrafish Embryo Toxicity Test for Pesticide ...

Science.gov (United States)

Given the numerous chemicals used in society, it is critical to develop tools for accurate and efficient evaluation of potential risks to human and ecological receptors. Fish embryo acute toxicity tests are 1 tool that has been shown to be highly predictive of standard, more resource-intensive, juvenile fish acute toxicity tests. However, there is also evidence that fish embryos are less sensitive than juvenile fish for certain types of chemicals, including neurotoxicants. The utility of fish embryos for pesticide hazard assessment was investigated by comparing published zebrafish embryo toxicity data from pesticides with median lethal concentration 50% (LC50) data for juveniles of 3 commonly tested fish species: rainbow trout, bluegill sunfish, and sheepshead minnow. A poor, albeit significant, relationship (r2 = 0.28; p embryo and juvenile fish toxicity when pesticides were considered as a single group, but a much better relationship (r2 = 0.64; p embryo toxicity test endpoints are particularly insensitive to neurotoxicants. These results indicate that it is still premature to replace juvenile fish toxicity tests with embryo-based tests such as the Organisation for Economic Co-op

Comparison of Toxicities to Vibrio fischeri and Fish Based on Discrimination of Excess Toxicity from Baseline Level

Science.gov (United States)

Wang, Xiao H.; Yu, Yang; Huang, Tao; Qin, Wei C.; Su, Li M.; Zhao, Yuan H.

2016-01-01

Investigations on the relationship of toxicities between species play an important role in the understanding of toxic mechanisms to environmental organisms. In this paper, the toxicity data of 949 chemicals to fish and 1470 chemicals to V. fischeri were used to investigate the modes of action (MOAs) between species. The results show that although there is a positive interspecies correlation, the relationship is poor. Analysis on the excess toxicity calculated from toxic ratios (TR) shows that many chemicals have close toxicities and share the same MOAs between the two species. Linear relationships between the toxicities and octanol/water partition coefficient (log KOW) for baseline and less inert compounds indicate that the internal critical concentrations (CBRs) approach a constant both to fish and V. fischeri for neutral hydrophobic compounds. These compounds share the same toxic mechanisms and bio-uptake processes between species. On the other hand, some hydrophilic compounds exhibit different toxic effects with greatly different log TR values between V. fischeri and fish species. These hydrophilic compounds were identified as reactive MOAs to V. fischeri, but not to fish. The interspecies correlation is improved by adding a hydrophobic descriptor into the correlation equation. This indicates that the differences in the toxic ratios between fish and V. fischeri for these hydrophilic compounds can be partly attributed to the differences of bioconcentration between the two species, rather than the differences of reactivity with the target macromolecules. These hydrophilic compounds may more easily pass through the cell membrane of V. fischeri than the gill and skin of fish, react with the target macromolecules and exhibit excess toxicity. The compounds with log KOW > 7 exhibiting very low toxicity (log TR toxicity and MOAs. PMID:26901437

Temperature prediction model of asphalt pavement in cold regions based on an improved BP neural network

International Nuclear Information System (INIS)

Xu, Bo; Dan, Han-Cheng; Li, Liang

2017-01-01

Highlights: • Pavement temperature prediction model is presented with improved BP neural network. • Dynamic and static methods are presented to predict pavement temperature. • Pavement temperature can be excellently predicted in next 3 h. - Abstract: Ice cover on pavement threatens traffic safety, and pavement temperature is the main factor used to determine whether the wet pavement is icy or not. In this paper, a temperature prediction model of the pavement in winter is established by introducing an improved Back Propagation (BP) neural network model. Before the application of the BP neural network model, many efforts were made to eliminate chaos and determine the regularity of temperature on the pavement surface (e.g., analyze the regularity of diurnal and monthly variations of pavement temperature). New dynamic and static prediction methods are presented by improving the algorithms to intelligently overcome the prediction inaccuracy at the change point of daily temperature. Furthermore, some scenarios have been compared for different dates and road sections to verify the reliability of the prediction model. According to the analysis results, the daily pavement temperatures can be accurately predicted for the next 3 h from the time of prediction by combining the dynamic and static prediction methods. The presented method in this paper can provide technical references for temperature prediction of the pavement and the development of an early-warning system for icy pavements in cold regions.

Identification of Chemical Toxicity Using Ontology Information of Chemicals

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Zhanpeng Jiang

2015-01-01

Full Text Available With the advance of the combinatorial chemistry, a large number of synthetic compounds have surged. However, we have limited knowledge about them. On the other hand, the speed of designing new drugs is very slow. One of the key causes is the unacceptable toxicities of chemicals. If one can correctly identify the toxicity of chemicals, the unsuitable chemicals can be discarded in early stage, thereby accelerating the study of new drugs and reducing the R&D costs. In this study, a new prediction method was built for identification of chemical toxicities, which was based on ontology information of chemicals. By comparing to a previous method, our method is quite effective. We hope that the proposed method may give new insights to study chemical toxicity and other attributes of chemicals.

Characterisation of a radiation-resistant strain of bacillus thuringiensis subsp. Aizawai with improved toxicity to larval plutella xylostella

International Nuclear Information System (INIS)

Mahadi, N.M.; Boo, J.M.L.; Jangi, M.S.

1989-01-01

A radiation-resistant strain of Bacillus thuringiensis subsp. Aizawai which was previously shown to be more toxic against larval Plutell xylostella was further characterized. Some of the growth characteristics of the mutant strain were quite different from those of the parent strain. In shake flask culture, its lag period was shorter and its cell yield was lower. The growth rate, however, was the same as that of the parent. Electron microscope studies show that the insecticidal parasporal crystals from the mutant strain are significantly bigger than those produced by the parent strain. The average length and width of the crystals were 1.25 and 0.53 um respectively whereas those of the parent were 0.87 and 0.35 um, respectively. The crystals from the mutant strain were also more toxic. The LC 50 was 0.30 ug crystal protein per ml as against 0.66 ug crystal protein per ml for those from the parent strain. Protein profile of the crystals obtained with SDS-PA gel electrophoresis showed that the mutant strain produced an additional polypeptide of 143 KDa polypeptide. The mutant strain also has an additional high molecular weight plasmid. The improved toxicity may have been brought about by a number of factors including an alteration in the regulatory mechanism that control the synthesis of the polypeptides that make up the crystals. (Auth.). 5 figs.; 21 refs.; 2 tabs

Improved Trust Prediction in Business Environments by Adaptive Neuro Fuzzy Inference Systems

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Ali Azadeh

2015-06-01

Full Text Available Trust prediction turns out to be an important challenge when cooperation among intelligent agents with an impression of trust in their mind, is investigated. In other words, predicting trust values for future time slots help partners to identify the probability of continuing a relationship. Another important case to be considered is the context of trust, i.e. the services and business commitments for which a relationship is defined. Hence, intelligent agents should focus on improving trust to provide a stable and confident context. Modelling of trust between collaborating parties seems to be an important component of the business intelligence strategy. In this regard, a set of metrics have been considered by which the value of confidence level for predicted trust values has been estimated. These metrics are maturity, distance and density (MD2. Prediction of trust for future mutual relationships among agents is a problem that is addressed in this study. We introduce a simulation-based model which utilizes linguistic variables to create various scenarios. Then, future trust values among agents are predicted by the concept of adaptive neuro-fuzzy inference system (ANFIS. Mean absolute percentage errors (MAPEs resulted from ANFIS are compared with confidence levels which are determined by applying MD2. Results determine the efficiency of MD2 for forecasting trust values. This is the first study that utilizes the concept of MD2 for improvement of business trust prediction.

Non-toxic brominated perfluorocarbons radiopaque agents

International Nuclear Information System (INIS)

Long, D.M. Jr.

1976-01-01

Non-toxic bromofluorocarbon radiopaque agents are disclosed. Certain monobrominated acyclic fluorocarbons, e.g., CF 3 (CF 2 ) 6 CF 2 Br, are improved non-toxic radiopaque agents useful in diagnostic roentgenology, for example in visualizing the gastrointestinal tract, the tracheobronchial tree, the alveolar spaces or parenchyma of the lung, the spleen, the urinary bladder and ureters, the common bile duct and its radicals, the pancreatic ducts, the blood vessels, etc. 13 claims, no drawings

Aluminum toxicity in dialysis patients: Radiographic findings and establishment of biopsy-sparing criteria

International Nuclear Information System (INIS)

Kriegshauser, J.S.; Swee, R.G.; McCarthy, J.T.; Hauser, M.F.

1986-01-01

Aluminum toxicity in dialysis patients currently requires bone biopsy for diagnosis. The authors retrospectively reviewed the findings in 63 dialysis patients who had undergone bone biopsies. In 30 patients biopsies were negative for aluminum toxicity and in 33 patients biopsies were positive. In 17 of the 30 biopsy-negative patients, absence of aluminum toxicity could be predicted by a high parathyroid hormone (iPTH) level (>200 μEq/ml) and fewer than three fractures, or by the presence of osteosclerosis on radiographs. No biopsy-positive patients met these criteria (P < .001). In 16 of 33 biopsy-positive patients aluminum toxicity could be predicted by a low iPTH level (<500 μEq/ml) and more than three fractures. No biopsy-negative patient met these criteria (P < .001). Thus, based on the criteria we have identified, 52.4% of the patients could have been spared biopsy

Toxicity of common ions to marine organisms

International Nuclear Information System (INIS)

Pillard, D.A.; DuFresne, D.L.; Evans, J.

1995-01-01

Produced waters from oil and gas drilling operations are typically very saline, and these may cause acute toxicity to marine organisms due to osmotic imbalances as well as to an excess or deficiency of specific common ions. In order to better understand the relationship between toxicity and ion concentration, laboratory toxicity tests were conducted using mysid shrimp (Mysidopsis bahia), sheepshead minnow (Cyprinodon variegatus), and inland silverside (Menidia beryllina). For each species the ionic concentration of standard laboratory water was proportionally increased or decreased to produce test solutions with a range of salinities. Organisms were exposed for 48 hours. Individual ions (sodium, potassium, calcium, magnetsium, strontium, chloride, bromide, sulfate, bicarbonate, and borate) were also manipulated to examine individual ion toxicity. The three test species differ in their tolerance of salinity. Mysid shrimp show a marked decrease in survival at salinities less than approximately 5 ppt. Both fish species tolerated low salinity water, however, silversides were less tolerant of saline waters (salinity greater than 40 ppt). There were also significant differences in the responses of the organisms to different ions. The results show that the salinity of the test solution may play an important role in the responses of the organisms to the produced water effluent. Predictable toxicity/ion relationships developed in this study can be used to estimate whether toxicity in a produced water is a result of common ions, salinity, or some other unknown toxicant

Tomotherapy for prostate adenocarcinoma: A report on acute toxicity

International Nuclear Information System (INIS)

Keiler, Louis; Dobbins, Donald; Kulasekere, Ravi; Einstein, Douglas

2007-01-01

Background and purpose: To analyze the impact of Tomotherapy (TOMO) intensity modulated radiotherapy (IMRT) on acute gastrointestinal (GI) and genitourinary (GU) toxicity in prostate cancer. Materials and methods: The records of 55 consecutively treated TOMO patients were reviewed. Additionally a well-matched group of 43 patients treated with LINAC-based step and shoot IMRT (LINAC) was identified. Acute toxicity was scored according to Radiation Therapy Oncology Group acute toxicity criterion. Results: The grade 2-3 acute GU toxicity rates for the TOMO vs. LINAC groups were 51% vs. 28% (p = 0.001). Acute grade 2 GI toxicity was 25% vs. 40% (p = 0.024), with no grade 3 GI toxicity in either group. In univariate analysis, androgen deprivation, prostate volume, pre-treatment urinary toxicity, and prostate dose homogeneity correlated with acute GI and GU toxicity. With multivariate analysis use of Tomotherapy, median bladder dose and bladder dose homogeneity remained significantly correlated with GU toxicity. Conclusions: Acute GI toxicity for prostate cancer is improved with Tomotherapy at a cost of increased acute GU toxicity possibly due to differences in bladder and prostate dose distribution

Development of a Combined In Vitro Physiologically Based Kinetic (PBK) and Monte Carlo Modelling Approach to Predict Interindividual Human Variation in Phenol-Induced Developmental Toxicity.

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Strikwold, Marije; Spenkelink, Bert; Woutersen, Ruud A; Rietjens, Ivonne M C M; Punt, Ans

2017-06-01

With our recently developed in vitro physiologically based kinetic (PBK) modelling approach, we could extrapolate in vitro toxicity data to human toxicity values applying PBK-based reverse dosimetry. Ideally information on kinetic differences among human individuals within a population should be considered. In the present study, we demonstrated a modelling approach that integrated in vitro toxicity data, PBK modelling and Monte Carlo simulations to obtain insight in interindividual human kinetic variation and derive chemical specific adjustment factors (CSAFs) for phenol-induced developmental toxicity. The present study revealed that UGT1A6 is the primary enzyme responsible for the glucuronidation of phenol in humans followed by UGT1A9. Monte Carlo simulations were performed taking into account interindividual variation in glucuronidation by these specific UGTs and in the oral absorption coefficient. Linking Monte Carlo simulations with PBK modelling, population variability in the maximum plasma concentration of phenol for the human population could be predicted. This approach provided a CSAF for interindividual variation of 2.0 which covers the 99th percentile of the population, which is lower than the default safety factor of 3.16 for interindividual human kinetic differences. Dividing the dose-response curve data obtained with in vitro PBK-based reverse dosimetry, with the CSAF provided a dose-response curve that reflects the consequences of the interindividual variability in phenol kinetics for the developmental toxicity of phenol. The strength of the presented approach is that it provides insight in the effect of interindividual variation in kinetics for phenol-induced developmental toxicity, based on only in vitro and in silico testing. © The Author 2017. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score.

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Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G

2014-09-01

The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

Biomarkers improve mortality prediction by prognostic scales in community-acquired pneumonia.

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Menéndez, R; Martínez, R; Reyes, S; Mensa, J; Filella, X; Marcos, M A; Martínez, A; Esquinas, C; Ramirez, P; Torres, A

2009-07-01

Prognostic scales provide a useful tool to predict mortality in community-acquired pneumonia (CAP). However, the inflammatory response of the host, crucial in resolution and outcome, is not included in the prognostic scales. The aim of this study was to investigate whether information about the initial inflammatory cytokine profile and markers increases the accuracy of prognostic scales to predict 30-day mortality. To this aim, a prospective cohort study in two tertiary care hospitals was designed. Procalcitonin (PCT), C-reactive protein (CRP) and the systemic cytokines tumour necrosis factor alpha (TNFalpha) and interleukins IL6, IL8 and IL10 were measured at admission. Initial severity was assessed by PSI (Pneumonia Severity Index), CURB65 (Confusion, Urea nitrogen, Respiratory rate, Blood pressure, > or = 65 years of age) and CRB65 (Confusion, Respiratory rate, Blood pressure, > or = 65 years of age) scales. A total of 453 hospitalised CAP patients were included. The 36 patients who died (7.8%) had significantly increased levels of IL6, IL8, PCT and CRP. In regression logistic analyses, high levels of CRP and IL6 showed an independent predictive value for predicting 30-day mortality, after adjustment for prognostic scales. Adding CRP to PSI significantly increased the area under the receiver operating characteristic curve (AUC) from 0.80 to 0.85, that of CURB65 from 0.82 to 0.85 and that of CRB65 from 0.79 to 0.85. Adding IL6 or PCT values to CRP did not significantly increase the AUC of any scale. When using two scales (PSI and CURB65/CRB65) and CRP simultaneously the AUC was 0.88. Adding CRP levels to PSI, CURB65 and CRB65 scales improves the 30-day mortality prediction. The highest predictive value is reached with a combination of two scales and CRP. Further validation of that improvement is needed.

Exploring BSEP Inhibition-Mediated Toxicity with a Mechanistic Model of Drug-Induced Liver Injury

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Jeffrey L Woodhead

2014-11-01

Full Text Available Inhibition of the bile salt export pump (BSEP has been linked to incidence of drug-induced liver injury (DILI, presumably by the accumulation of toxic bile acids in the liver. We have previously constructed and validated a model of bile acid disposition within DILIsym®, a mechanistic model of DILI. In this paper, we use DILIsym® to simulate the DILI response of the hepatotoxic BSEP inhibitors bosentan and CP-724,714 and the non-hepatotoxic BSEP inhibitor telmisartan in humans in order to explore whether we can predict that hepatotoxic BSEP inhibitors can cause bile acid accumulation to reach toxic levels. We also simulate bosentan in rats in order to illuminate potential reasons behind the lack of toxicity in rats compared to the toxicity observed in humans. DILIsym® predicts that bosentan, but not telmisartan, will cause mild hepatocellular ATP decline and serum ALT elevation in a simulated population of humans. The difference in hepatotoxic potential between bosentan and telmisartan is consistent with clinical observations. However, DILIsym® underpredicts the incidence of bosentan toxicity. DILIsym® also predicts that bosentan will not cause toxicity in a simulated population of rats, and that the difference between the response to bosentan in rats and in humans is primarily due to the less toxic bile acid pool in rats. Our simulations also suggest a potential synergistic role for bile acid accumulation and mitochondrial electron transport chain inhibition in producing the observed toxicity in CP-724,714, and suggest that CP-724,714 metabolites may also play a role in the observed toxicity. Our work also compares the impact of competitive and noncompetitive BSEP inhibition for CP-724,714 and demonstrates that noncompetitive inhibition leads to much greater bile acid accumulation and potential toxicity. Our research demonstrates the potential for mechanistic modeling to contribute to the understanding of how bile acid transport inhibitors

Improving consensus contact prediction via server correlation reduction.

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Gao, Xin; Bu, Dongbo; Xu, Jinbo; Li, Ming

2009-05-06

Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them. In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top L/5 predicted contacts are evaluated where L is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively. Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.

Improving consensus contact prediction via server correlation reduction

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Xu Jinbo

2009-05-01

Full Text Available Abstract Background Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them. Results In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top L/5 predicted contacts are evaluated where L is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively. Conclusion Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.

Improving Multi-Sensor Drought Monitoring, Prediction and Recovery Assessment Using Gravimetry Information

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Aghakouchak, Amir; Tourian, Mohammad J.

2015-04-01

Development of reliable drought monitoring, prediction and recovery assessment tools are fundamental to water resources management. This presentation focuses on how gravimetry information can improve drought assessment. First, we provide an overview of the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) which offers near real-time drought information using remote sensing observations and model simulations. Then, we present a framework for integration of satellite gravimetry information for improving drought prediction and recovery assessment. The input data include satellite-based and model-based precipitation, soil moisture estimates and equivalent water height. Previous studies show that drought assessment based on one single indicator may not be sufficient. For this reason, GIDMaPS provides drought information based on multiple drought indicators including Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI) and the Multivariate Standardized Drought Index (MSDI) which combines SPI and SSI probabilistically. MSDI incorporates the meteorological and agricultural drought conditions and provides composite multi-index drought information for overall characterization of droughts. GIDMaPS includes a seasonal prediction component based on a statistical persistence-based approach. The prediction component of GIDMaPS provides the empirical probability of drought for different severity levels. In this presentation we present a new component in which the drought prediction information based on SPI, SSI and MSDI are conditioned on equivalent water height obtained from the Gravity Recovery and Climate Experiment (GRACE). Using a Bayesian approach, GRACE information is used to evaluate persistence of drought. Finally, the deficit equivalent water height based on GRACE is used for assessing drought recovery. In this presentation, both monitoring and prediction components of GIDMaPS will be discussed, and the results from 2014

Chemical toxicity approach for emergency response

International Nuclear Information System (INIS)

Bauer, T.

2009-01-01

In the event of an airborne release of chemical agent or toxic industrial chemical by accidental or intentional means, emergency responders must have a reasonable estimate of the location and size of the resulting hazard area. Emergency responders are responsible for warning persons downwind of the hazard to evacuate or shelter-in-place and must know where to look for casualties after the hazard has passed or dissipated. Given the same source characterization, modern hazard assessment models provide comparable concentration versus location and time estimates. Even urban hazard assessment models often provide similar predictions. There is a major shortcoming, though, in applying model output to estimating human toxicity effects. There exist a variety of toxicity values for non-lethal effects ranging from short-term to occupational to lifetime exposures. For health and safety purposes, these estimates are all safe-sided in converting animal data to human effects and in addressing the most sensitive subset of the population. In addition, these values are usually based on an assumed 1 hour exposure duration at constant concentration and do not reflect either a passing clouds concentration profile or duration. Emergency responders need expected value toxicity parameters rather than the existing safe-sided ones. This presentation will specify the types of toxicity values needed to provide appropriate chemical hazard estimates to emergency responders and will demonstrate how dramatically their use changes the hazard area.(author)

Semi-quantitative assessments of dextran toxicity on corneal endothelium: conceptual design of a predictive algorithm.

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Filev, Filip; Oezcan, Ceprail; Feuerstacke, Jana; Linke, Stephan J; Wulff, Birgit; Hellwinkel, Olaf J C

2017-03-01

Dextran is added to corneal culture medium for at least 8 h prior to transplantation to ensure that the cornea is osmotically dehydrated. It is presumed that dextran has a certain toxic effect on corneal endothelium but the degree and the kinetics of this effect have not been quantified so far. We consider that such data regarding the toxicity of dextran on the corneal endothelium could have an impact on scheduling and logistics of corneal preparation in eye banking. In retrospective statistic analyses, we compared the progress of corneal endothelium (endothelium cell loss per day) of 1334 organ-cultured corneal explants in media with and without dextran. Also, the influence of donor-age, sex and cause of death on the observed dextran-mediated effect on endothelial cell counts was studied. Corneas cultured in dextran-free medium showed a mean endothelium cell count decrease of 0.7% per day. Dextran supplementation led to a mean endothelium cell loss of 2.01% per day; this reflects an increase by the factor of 2.9. The toxic impact of dextran was found to be time dependent; while the prevailing part of the effect was observed within the first 24 h after dextran-addition. Donor age, sex and cause of death did not seem to have an influence on the dextran-mediated toxicity. Based on these findings, we could design an algorithm which approximately describes the kinetics of dextran-toxicity. We reproduced the previously reported toxic effect of dextran on the corneal endothelium in vitro. Additionally, this is the first work that provides an algorithmic instrument for the semi-quantitative calculation of the putative endothelium cell count decrease in dextran containing medium for a given incubation time and could thus influence the time management and planning of corneal transplantations.

Cumulative toxicity of neonicotinoid insecticide mixtures to Chironomus dilutus under acute exposure scenarios.

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Maloney, Erin M; Morrissey, Christy A; Headley, John V; Peru, Kerry M; Liber, Karsten

2017-11-01

Extensive agricultural use of neonicotinoid insecticide products has resulted in the presence of neonicotinoid mixtures in surface waters worldwide. Although many aquatic insect species are known to be sensitive to neonicotinoids, the impact of neonicotinoid mixtures is poorly understood. In the present study, the cumulative toxicities of binary and ternary mixtures of select neonicotinoids (imidacloprid, clothianidin, and thiamethoxam) were characterized under acute (96-h) exposure scenarios using the larval midge Chironomus dilutus as a representative aquatic insect species. Using the MIXTOX approach, predictive parametric models were fitted and statistically compared with observed toxicity in subsequent mixture tests. Single-compound toxicity tests yielded median lethal concentration (LC50) values of 4.63, 5.93, and 55.34 μg/L for imidacloprid, clothianidin, and thiamethoxam, respectively. Because of the similar modes of action of neonicotinoids, concentration-additive cumulative mixture toxicity was the predicted model. However, we found that imidacloprid-clothianidin mixtures demonstrated response-additive dose-level-dependent synergism, clothianidin-thiamethoxam mixtures demonstrated concentration-additive synergism, and imidacloprid-thiamethoxam mixtures demonstrated response-additive dose-ratio-dependent synergism, with toxicity shifting from antagonism to synergism as the relative concentration of thiamethoxam increased. Imidacloprid-clothianidin-thiamethoxam ternary mixtures demonstrated response-additive synergism. These results indicate that, under acute exposure scenarios, the toxicity of neonicotinoid mixtures to C. dilutus cannot be predicted using the common assumption of additive joint activity. Indeed, the overarching trend of synergistic deviation emphasizes the need for further research into the ecotoxicological effects of neonicotinoid insecticide mixtures in field settings, the development of better toxicity models for neonicotinoid mixture

Regenerative toxicology: the role of stem cells in the development of chronic toxicities.

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Canovas-Jorda, David; Louisse, Jochem; Pistollato, Francesca; Zagoura, Dimitra; Bremer, Susanne

2014-01-01

Human stem cell lines and their derivatives, as alternatives to the use of animal cells or cancer cell lines, have been widely discussed as cellular models in predictive toxicology. However, the role of stem cells in the development of long-term toxicities and carcinogenesis has not received great attention so far, despite growing evidence indicating the relationship of stem cell damage to adverse effects later in life. However, testing this in vitro is a scientific/technical challenge in particular due to the complex interplay of factors existing under physiological conditions. Current major research programs in stem cell toxicity are not aiming to demonstrate that stem cells can be targeted by toxicants. Therefore, this knowledge gap needs to be addressed in additional research activities developing technical solutions and defining appropriate experimental designs. The current review describes selected examples of the role of stem cells in the development of long-term toxicities in the brain, heart or liver and in the development of cancer. The presented examples illustrate the need to analyze the contribution of stem cells to chronic toxicity in order to make a final conclusion whether stem cell toxicities are an underestimated risk in mechanism-based safety assessments. This requires the development of predictive in vitro models allowing the assessment of adverse effects to stem cells on chronic toxicity and carcinogenicity.

Consensus report on the future of animal-free systemic toxicity testing.

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Leist, Marcel; Hasiwa, Nina; Rovida, Costanza; Daneshian, Mardas; Basketter, David; Kimber, Ian; Clewell, Harvey; Gocht, Tilman; Goldberg, Alan; Busquet, Francois; Rossi, Anna-Maria; Schwarz, Michael; Stephens, Martin; Taalman, Rob; Knudsen, Thomas B; McKim, James; Harris, Georgina; Pamies, David; Hartung, Thomas

2014-01-01

Since March 2013, animal use for cosmetics testing for the European market has been banned. This requires a renewed view on risk assessment in this field. However, in other fields as well, traditional animal experimentation does not always satisfy requirements in safety testing, as the need for human-relevant information is ever increasing. A general strategy for animal-free test approaches was outlined by the US National Research Council`s vision document for Toxicity Testing in the 21st Century in 2007. It is now possible to provide a more defined roadmap on how to implement this vision for the four principal areas of systemic toxicity evaluation: repeat dose organ toxicity, carcinogenicity, reproductive toxicity and allergy induction (skin sensitization), as well as for the evaluation of toxicant metabolism (toxicokinetics) (Fig. 1). CAAT-Europe assembled experts from Europe, America and Asia to design a scientific roadmap for future risk assessment approaches and the outcome was then further discussed and refined in two consensus meetings with over 200 stakeholders. The key recommendations include: focusing on improving existing methods rather than favoring de novo design; combining hazard testing with toxicokinetics predictions; developing integrated test strategies; incorporating new high content endpoints to classical assays; evolving test validation procedures; promoting collaboration and data-sharing of different industrial sectors; integrating new disciplines, such as systems biology and high throughput screening; and involving regulators early on in the test development process. A focus on data quality, combined with increased attention to the scientific background of a test method, will be important drivers. Information from each test system should be mapped along adverse outcome pathways. Finally, quantitative information on all factors and key events will be fed into systems biology models that allow a probabilistic risk assessment with flexible

Toxicity identification evaluation of cosmetics industry wastewater.

Science.gov (United States)

de Melo, Elisa Dias; Mounteer, Ann H; Leão, Lucas Henrique de Souza; Bahia, Renata Cibele Barros; Campos, Izabella Maria Ferreira

2013-01-15

The cosmetics industry has shown steady growth in many developing countries over the past several years, yet little research exists on toxicity of wastewaters it generates. This study describes a toxicity identification evaluation conducted on wastewater from a small Brazilian hair care products manufacturing plant. Physicochemical and ecotoxicological analyses of three wastewater treatment plant inlet and outlet samples collected over a six month period revealed inefficient operation of the treatment system and thus treated wastewater organic matter, suspended solids and surfactants contents consistently exceeded discharge limits. Treated wastewater also presented high acute toxicity to Daphnia similis and chronic toxicity to Ceriodaphnia dubia and Pseudokirchneriella subcapitata. This toxicity was associated with suspended solids, volatile or sublatable and non-polar to moderately polar organic compounds that could be recovered in filtration and aeration residues. Seven surfactants used in the largest quantities in the production process were highly toxic to P. subcapitata and D. similis. These results indicated that surfactants, important production raw materials, are a probable source of toxicity, although other possible sources, such as fragrances, should not be discarded. Improved treatment plant operational control may reduce toxicity and lower impact of wastewater discharge to receiving waters. Copyright © 2012 Elsevier B.V. All rights reserved.

Relationships between soil properties and toxicity of copper and nickel to bok choy and tomato in Chinese soils.

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Li, Bo; Zhang, Hongtao; Ma, Yibing; McLaughlin, Mike J

2013-10-01

The toxicity of copper (Cu) and nickel (Ni) to bok choy and tomato shoot growth was investigated in a wide range of Chinese soils with and without leaching with artificial rainwater. The results showed that the variations of Ni toxicity induced by soil properties were wider than those of Cu toxicity to both tomato and bok choy plant growth. Leaching generally decreased the toxicity of Cu and Ni added to soils, which also depended on soils, metals, and test plant species. Soil factors controlling metal phytotoxicity were found to be soil pH and soil organic carbon content for Cu, and soil pH for Ni. It was also found that soil pH had stronger effects on Ni toxicity than on Cu toxicity. Predictive toxicity models based on these soil factors were developed. These toxicity models for Cu and Ni toxicity to tomato plant growth were validated using an independent data set for European soils. These models could be applied to predict the Cu and Ni phytotoxicity in not only Chinese soils but also European soils. © 2013 SETAC.

High-throughput respirometric assay identifies predictive toxicophore of mitochondrial injury

Energy Technology Data Exchange (ETDEWEB)

Wills, Lauren P. [MitoHealth Inc., Charleston, SC 29403 (United States); Beeson, Gyda C.; Trager, Richard E.; Lindsey, Christopher C. [Department of Drug Discovery and Biomedical Sciences, Medical University of South Carolina, Charleston, SC 29425 (United States); Beeson, Craig C. [MitoHealth Inc., Charleston, SC 29403 (United States); Peterson, Yuri K. [Department of Drug Discovery and Biomedical Sciences, Medical University of South Carolina, Charleston, SC 29425 (United States); Schnellmann, Rick G., E-mail: schnell@musc.edu [Department of Drug Discovery and Biomedical Sciences, Medical University of South Carolina, Charleston, SC 29425 (United States); Ralph H. Johnson VA Medical Center, Charleston, SC 29401 (United States)

2013-10-15

Many environmental chemicals and drugs negatively affect human health through deleterious effects on mitochondrial function. Currently there is no chemical library of mitochondrial toxicants, and no reliable methods for predicting mitochondrial toxicity. We hypothesized that discrete toxicophores defined by distinct chemical entities can identify previously unidentified mitochondrial toxicants. We used a respirometric assay to screen 1760 compounds (5 μM) from the LOPAC and ChemBridge DIVERSet libraries. Thirty-one of the assayed compounds decreased uncoupled respiration, a stress test for mitochondrial dysfunction, prior to a decrease in cell viability and reduced the oxygen consumption rate in isolated mitochondria. The mitochondrial toxicants were grouped by chemical similarity and two clusters containing four compounds each were identified. Cheminformatic analysis of one of the clusters identified previously uncharacterized mitochondrial toxicants from the ChemBridge DIVERSet. This approach will enable the identification of mitochondrial toxicants and advance the prediction of mitochondrial toxicity for both drug discovery and risk assessment. - Highlights: • Respirometric assay conducted in RPTC to create mitochondrial toxicant database. • Chemically similar mitochondrial toxicants aligned as mitochondrial toxicophores • Mitochondrial toxicophore identifies five novel mitochondrial toxicants.

High-throughput respirometric assay identifies predictive toxicophore of mitochondrial injury

International Nuclear Information System (INIS)

Wills, Lauren P.; Beeson, Gyda C.; Trager, Richard E.; Lindsey, Christopher C.; Beeson, Craig C.; Peterson, Yuri K.; Schnellmann, Rick G.

2013-01-01

Many environmental chemicals and drugs negatively affect human health through deleterious effects on mitochondrial function. Currently there is no chemical library of mitochondrial toxicants, and no reliable methods for predicting mitochondrial toxicity. We hypothesized that discrete toxicophores defined by distinct chemical entities can identify previously unidentified mitochondrial toxicants. We used a respirometric assay to screen 1760 compounds (5 μM) from the LOPAC and ChemBridge DIVERSet libraries. Thirty-one of the assayed compounds decreased uncoupled respiration, a stress test for mitochondrial dysfunction, prior to a decrease in cell viability and reduced the oxygen consumption rate in isolated mitochondria. The mitochondrial toxicants were grouped by chemical similarity and two clusters containing four compounds each were identified. Cheminformatic analysis of one of the clusters identified previously uncharacterized mitochondrial toxicants from the ChemBridge DIVERSet. This approach will enable the identification of mitochondrial toxicants and advance the prediction of mitochondrial toxicity for both drug discovery and risk assessment. - Highlights: • Respirometric assay conducted in RPTC to create mitochondrial toxicant database. • Chemically similar mitochondrial toxicants aligned as mitochondrial toxicophores • Mitochondrial toxicophore identifies five novel mitochondrial toxicants

An evaluation of the whole effluent toxicity test method

International Nuclear Information System (INIS)

Osteen, D.V.

1999-01-01

Whole effluent toxicity (WET) testing has become increasingly more important to the Environmental Protection Agency (EPA) and the States in the permitting of wastewater discharges from industry and municipalities. The primary purpose of the WET test is to protect aquatic life by predicting the effect of an effluent on the receiving stream. However, there are both scientific and regulatory concerns that using WET tests to regulate industrial effluents may result in either false positives and/or false negatives. In order to realistically predict the effect of an effluent on the receiving stream, the test should be as representative as possible of the conditions in the receiving stream. Studies (Rand and Petrocelli 1985) suggested several criteria for an ideal aquatic toxicity test organism, one of which is that the organism be indigenous to, or representative of, the ecosystem receiving the effluent. The other component needed in the development of a predictive test is the use of the receiving stream water or similar synthetic water as the control and dilution water in the test method. Use of an indigenous species and receiving water in the test should help reduce the variability in the method and allow the test to predict the effect of the effluent on the receiving stream. The experience with toxicity testing at the Savannah River Site (SRS) has yielded inconclusive data because of the inconsistency and unreliability of the results. The SRS contention is that the WET method in its present form does not adequately mimic actual biological/chemical conditions of the receiving streams and is neither reasonable nor accurate. This paper discusses the rationale for such a position by SRS on toxicity testing in terms of historical permitting requirements, outfall effluent test results, standard test method evaluation, scientific review of alternate test species, and concerns over the test method expressed by other organizations. This paper presents the Savannah River Site

Constitutive gene expression profile segregates toxicity in locally advanced breast cancer patients treated with high-dose hyperfractionated radical radiotherapy

International Nuclear Information System (INIS)

Henríquez Hernández, Luis Alberto; Lara, Pedro Carlos; Pinar, Beatriz; Bordón, Elisa; Gallego, Carlos Rodríguez; Bilbao, Cristina; Pérez, Leandro Fernández; Morales, Amílcar Flores

2009-01-01

Breast cancer patients show a wide variation in normal tissue reactions after radiotherapy. The individual sensitivity to x-rays limits the efficiency of the therapy. Prediction of individual sensitivity to radiotherapy could help to select the radiation protocol and to improve treatment results. The aim of this study was to assess the relationship between gene expression profiles of ex vivo un-irradiated and irradiated lymphocytes and the development of toxicity due to high-dose hyperfractionated radiotherapy in patients with locally advanced breast cancer. Raw data from microarray experiments were uploaded to the Gene Expression Omnibus Database http://www.ncbi.nlm.nih.gov/geo/ (GEO accession GSE15341). We obtained a small group of 81 genes significantly regulated by radiotherapy, lumped in 50 relevant pathways. Using ANOVA and t-test statistical tools we found 20 and 26 constitutive genes (0 Gy) that segregate patients with and without acute and late toxicity, respectively. Non-supervised hierarchical clustering was used for the visualization of results. Six and 9 pathways were significantly regulated respectively. Concerning to irradiated lymphocytes (2 Gy), we founded 29 genes that separate patients with acute toxicity and without it. Those genes were gathered in 4 significant pathways. We could not identify a set of genes that segregates patients with and without late toxicity. In conclusion, we have found an association between the constitutive gene expression profile of peripheral blood lymphocytes and the development of acute and late toxicity in consecutive, unselected patients. These observations suggest the possibility of predicting normal tissue response to irradiation in high-dose non-conventional radiation therapy regimens. Prospective studies with higher number of patients are needed to validate these preliminary results

Metal and pharmaceutical mixtures: Is ion loss the mechanism underlying acute toxicity and widespread additive toxicity in zebrafish?

Energy Technology Data Exchange (ETDEWEB)

Alsop, Derek, E-mail: alsopde@mcmaster.ca; Wood, Chris M.

2013-09-15

Highlights: •Zebrafish larvae were used to test the acute toxicity of contaminant mixtures. •Interactions were observed between metals, ammonia and pharmaceuticals. •Larval Na{sup +} loss was observed with exposure to all acutely toxic contaminants tested. •Water quality criteria should recognize the toxic interactions between contaminants. -- Abstract: The acute toxicities and mechanisms of action of a variety of environmental contaminants were examined using zebrafish larvae (Danio rerio; 4–8 days post fertilization). Toxic interactions were observed between metals. For example, the addition of a sublethal level of nickel (15% of the LC{sub 50}, one third of the LC{sub 01}) to all copper treatments decreased the copper 96 h LC{sub 50} by 58%, while sublethal copper exposure (6% of the copper LC{sub 50}, 13% of the LC{sub 01}) decreased the cadmium 96 h LC{sub 50} by 47%. Two predictive models were assessed, the concentration addition (CA) model, which assumes similar mechanisms of action, and the independent action (IA) model, which assumes different mechanisms of action. Quantitative comparisons indicated the CA model performed better than the IA model; the latter tended to underestimate combined toxicity to a greater extent. The effects of mixtures with nickel or ammonia were typically additive, while mixtures with copper or cadmium were typically greater than additive. Larvae exposed to cadmium, copper or nickel experienced whole body ion loss. Decreases were greatest for Na{sup +} followed by K{sup +} (as high as 19% and 9%, respectively, in 24 h). Additive toxicity between copper and other pharmaceutical compounds such as fluoxetine (Prozac™), β-naphthoflavone, estrogen and 17α-ethinylestradiol were also observed. Similar to metals, acutely toxic concentrations of fluoxetine, β-naphthoflavone and ammonia all decreased whole body Na{sup +} and K{sup +}. Overall, whole body Na{sup +} loss showed the greatest correlation with mortality across a

Metal and pharmaceutical mixtures: Is ion loss the mechanism underlying acute toxicity and widespread additive toxicity in zebrafish?

International Nuclear Information System (INIS)

Alsop, Derek; Wood, Chris M.

2013-01-01

Highlights: •Zebrafish larvae were used to test the acute toxicity of contaminant mixtures. •Interactions were observed between metals, ammonia and pharmaceuticals. •Larval Na + loss was observed with exposure to all acutely toxic contaminants tested. •Water quality criteria should recognize the toxic interactions between contaminants. -- Abstract: The acute toxicities and mechanisms of action of a variety of environmental contaminants were examined using zebrafish larvae (Danio rerio; 4–8 days post fertilization). Toxic interactions were observed between metals. For example, the addition of a sublethal level of nickel (15% of the LC 50 , one third of the LC 01 ) to all copper treatments decreased the copper 96 h LC 50 by 58%, while sublethal copper exposure (6% of the copper LC 50 , 13% of the LC 01 ) decreased the cadmium 96 h LC 50 by 47%. Two predictive models were assessed, the concentration addition (CA) model, which assumes similar mechanisms of action, and the independent action (IA) model, which assumes different mechanisms of action. Quantitative comparisons indicated the CA model performed better than the IA model; the latter tended to underestimate combined toxicity to a greater extent. The effects of mixtures with nickel or ammonia were typically additive, while mixtures with copper or cadmium were typically greater than additive. Larvae exposed to cadmium, copper or nickel experienced whole body ion loss. Decreases were greatest for Na + followed by K + (as high as 19% and 9%, respectively, in 24 h). Additive toxicity between copper and other pharmaceutical compounds such as fluoxetine (Prozac™), β-naphthoflavone, estrogen and 17α-ethinylestradiol were also observed. Similar to metals, acutely toxic concentrations of fluoxetine, β-naphthoflavone and ammonia all decreased whole body Na + and K + . Overall, whole body Na + loss showed the greatest correlation with mortality across a variety of toxicants. We theorize that a disruption of

Towards Global QSAR Model Building for Acute Toxicity: Munro Database Case Study

Directory of Open Access Journals (Sweden)

Swapnil Chavan

2014-10-01

Full Text Available A series of 436 Munro database chemicals were studied with respect to their corresponding experimental LD50 values to investigate the possibility of establishing a global QSAR model for acute toxicity. Dragon molecular descriptors were used for the QSAR model development and genetic algorithms were used to select descriptors better correlated with toxicity data. Toxic values were discretized in a qualitative class on the basis of the Globally Harmonized Scheme: the 436 chemicals were divided into 3 classes based on their experimental LD50 values: highly toxic, intermediate toxic and low to non-toxic. The k-nearest neighbor (k-NN classification method was calibrated on 25 molecular descriptors and gave a non-error rate (NER equal to 0.66 and 0.57 for internal and external prediction sets, respectively. Even if the classification performances are not optimal, the subsequent analysis of the selected descriptors and their relationship with toxicity levels constitute a step towards the development of a global QSAR model for acute toxicity.

An analysis of decrements in vital capacity as an index of pulmonary oxygen toxicity.

Science.gov (United States)

Harabin, A L; Homer, L D; Weathersby, P K; Flynn, E T

1987-09-01

Decrements in vital capacity (% delta VC) were proposed by the Pennsylvania group in the early 1970s as an index of O2-induced lung damage. These workers used the combined effects of PO2 and time of exposure to develop recommendations to limit expected % delta VC. Adopting this general approach, we fitted human pulmonary O2 toxicity data to the hyperbolic equation % delta VC = Bs.(PO2 - B1).(time)B3 using a nonlinear least squares analysis. In addition to the data considered in 1970, our analysis included new data available from the literature. The best fit was obtained when 1) an individual slope parameter, Bs, was estimated for each subject instead of an average slope; 2) PO2 asymptote B1 = 0.38 ATA; and 3) exponent B3 = 1.0. Wide individual variation imposed large uncertainty on any % delta VC prediction. A 12-h exposure to a PO2 of 1 ATA would be expected to yield a median VC decrement of 4%. The 80% confidence limits, however, included changes from +1.0 and -12% delta VC. Until an improved index of pulmonary O2 toxicity is developed, a simplified expression % delta VC = -0.011.(PO2 - 0.5).time (PO2 in ATA and time in min) can be used to predict a median response with little loss in predictability. The limitations of changes in VC as an index are discussed.

Advanced Materials Test Methods for Improved Life Prediction of Turbine Engine Components

National Research Council Canada - National Science Library

Stubbs, Jack

2000-01-01

Phase I final report developed under SBIR contract for Topic # AF00-149, "Durability of Turbine Engine Materials/Advanced Material Test Methods for Improved Use Prediction of Turbine Engine Components...

Predictive Models of target organ and Systemic toxicities (BOSC)

Science.gov (United States)

The objective of this work is to predict the hazard classification and point of departure (PoD) of untested chemicals in repeat-dose animal testing studies. We used supervised machine learning to objectively evaluate the predictive accuracy of different classification and regress...

Guidance on health effects of toxic chemicals. Safety Analysis Report Update Program

Energy Technology Data Exchange (ETDEWEB)

Foust, C.B.; Griffin, G.D.; Munro, N.B.; Socolof, M.L.

1994-02-01

Martin Marietta Energy Systems, Inc. (MMES), and Martin Marietta Utility Services, Inc. (MMUS), are engaged in phased programs to update the safety documentation for the existing US Department of Energy (DOE)-owned facilities. The safety analysis of potential toxic hazards requires a methodology for evaluating human health effects of predicted toxic exposures. This report provides a consistent set of health effects and documents toxicity estimates corresponding to these health effects for some of the more important chemicals found within MMES and MMUS. The estimates are based on published toxicity information and apply to acute exposures for an ``average`` individual. The health effects (toxicological endpoints) used in this report are (1) the detection threshold; (2) the no-observed adverse effect level; (3) the onset of irritation/reversible effects; (4) the onset of irreversible effects; and (5) a lethal exposure, defined to be the 50% lethal level. An irreversible effect is defined as a significant effect on a person`s quality of life, e.g., serious injury. Predicted consequences are evaluated on the basis of concentration and exposure time.

Improved model predictive control for high voltage quality in microgrid applications

DEFF Research Database (Denmark)

Dragicevic, T.; Al hasheem, Mohamed; Lu, M.

2017-01-01

This paper proposes an improvement of the finite control set model predictive control (FCS-MPC) strategy for enhancing the voltage regulation performance of a voltage source converter (VSC) used for standalone microgrid and uninterrupted power supply (UPS) applications. The modification is based...

Proteome Profiling Reveals Potential Toxicity and Detoxification Pathways Following Exposure of BEAS-2B Cells to Engineered Nanoparticle Titanium Dioxide

Science.gov (United States)

Identification of toxicity pathways linked to chemical -exposure is critical for a better understanding of biological effects of the exposure, toxic mechanisms, and for enhancement of the prediction of chemical toxicity and adverse health outcomes. To identify toxicity pathways a...

Quantitative Prediction of Systemic Toxicity Points of Departure (OpenTox USA 2017)

Science.gov (United States)

Human health risk assessment associated with environmental chemical exposure is limited by the tens of thousands of chemicals little or no experimental in vivo toxicity data. Data gap filling techniques, such as quantitative models based on chemical structure information, are c...

Data Prediction for Public Events in Professional Domains Based on Improved RNN- LSTM

Science.gov (United States)

Song, Bonan; Fan, Chunxiao; Wu, Yuexin; Sun, Juanjuan

2018-02-01

The traditional data services of prediction for emergency or non-periodic events usually cannot generate satisfying result or fulfill the correct prediction purpose. However, these events are influenced by external causes, which mean certain a priori information of these events generally can be collected through the Internet. This paper studied the above problems and proposed an improved model—LSTM (Long Short-term Memory) dynamic prediction and a priori information sequence generation model by combining RNN-LSTM and public events a priori information. In prediction tasks, the model is qualified for determining trends, and its accuracy also is validated. This model generates a better performance and prediction results than the previous one. Using a priori information can increase the accuracy of prediction; LSTM can better adapt to the changes of time sequence; LSTM can be widely applied to the same type of prediction tasks, and other prediction tasks related to time sequence.

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

Science.gov (United States)

Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

2018-02-01

Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

Methods to improve genomic prediction and GWAS using combined Holstein populations

DEFF Research Database (Denmark)

Li, Xiujin

The thesis focuses on methods to improve GWAS and genomic prediction using combined Holstein populations and investigations G by E interaction. The conclusions are: 1) Prediction reliabilities for Brazilian Holsteins can be increased by adding Nordic and Frensh genotyped bulls and a large G by E...... interaction exists between populations. 2) Combining data from Chinese and Danish Holstein populations increases the power of GWAS and detects new QTL regions for milk fatty acid traits. 3) The novel multi-trait Bayesian model efficiently estimates region-specific genomic variances, covariances...

Improved prediction of residue flexibility by embedding optimized amino acid grouping into RSA-based linear models.

Science.gov (United States)

Zhang, Hua; Kurgan, Lukasz

2014-12-01

Knowledge of protein flexibility is vital for deciphering the corresponding functional mechanisms. This knowledge would help, for instance, in improving computational drug design and refinement in homology-based modeling. We propose a new predictor of the residue flexibility, which is expressed by B-factors, from protein chains that use local (in the chain) predicted (or native) relative solvent accessibility (RSA) and custom-derived amino acid (AA) alphabets. Our predictor is implemented as a two-stage linear regression model that uses RSA-based space in a local sequence window in the first stage and a reduced AA pair-based space in the second stage as the inputs. This method is easy to comprehend explicit linear form in both stages. Particle swarm optimization was used to find an optimal reduced AA alphabet to simplify the input space and improve the prediction performance. The average correlation coefficients between the native and predicted B-factors measured on a large benchmark dataset are improved from 0.65 to 0.67 when using the native RSA values and from 0.55 to 0.57 when using the predicted RSA values. Blind tests that were performed on two independent datasets show consistent improvements in the average correlation coefficients by a modest value of 0.02 for both native and predicted RSA-based predictions.

Scale invariance properties of intracerebral EEG improve seizure prediction in mesial temporal lobe epilepsy.

Directory of Open Access Journals (Sweden)

Kais Gadhoumi

Full Text Available Although treatment for epilepsy is available and effective for nearly 70 percent of patients, many remain in need of new therapeutic approaches. Predicting the impending seizures in these patients could significantly enhance their quality of life if the prediction performance is clinically practical. In this study, we investigate the improvement of the performance of a seizure prediction algorithm in 17 patients with mesial temporal lobe epilepsy by means of a novel measure. Scale-free dynamics of the intracerebral EEG are quantified through robust estimates of the scaling exponents--the first cumulants--derived from a wavelet leader and bootstrap based multifractal analysis. The cumulants are investigated for the discriminability between preictal and interictal epochs. The performance of our recently published patient-specific seizure prediction algorithm is then out-of-sample tested on long-lasting data using combinations of cumulants and state similarity measures previously introduced. By using the first cumulant in combination with state similarity measures, up to 13 of 17 patients had seizures predicted above chance with clinically practical levels of sensitivity (80.5% and specificity (25.1% of total time under warning for prediction horizons above 25 min. These results indicate that the scale-free dynamics of the preictal state are different from those of the interictal state. Quantifiers of these dynamics may carry a predictive power that can be used to improve seizure prediction performance.

OECD validation study to assess intra- and inter-laboratory reproducibility of the zebrafish embryo toxicity test for acute aquatic toxicity testing.

Science.gov (United States)

Busquet, François; Strecker, Ruben; Rawlings, Jane M; Belanger, Scott E; Braunbeck, Thomas; Carr, Gregory J; Cenijn, Peter; Fochtman, Przemyslaw; Gourmelon, Anne; Hübler, Nicole; Kleensang, André; Knöbel, Melanie; Kussatz, Carola; Legler, Juliette; Lillicrap, Adam; Martínez-Jerónimo, Fernando; Polleichtner, Christian; Rzodeczko, Helena; Salinas, Edward; Schneider, Katharina E; Scholz, Stefan; van den Brandhof, Evert-Jan; van der Ven, Leo T M; Walter-Rohde, Susanne; Weigt, Stefan; Witters, Hilda; Halder, Marlies

2014-08-01

The OECD validation study of the zebrafish embryo acute toxicity test (ZFET) for acute aquatic toxicity testing evaluated the ZFET reproducibility by testing 20 chemicals at 5 different concentrations in 3 independent runs in at least 3 laboratories. Stock solutions and test concentrations were analytically confirmed for 11 chemicals. Newly fertilised zebrafish eggs (20/concentration and control) were exposed for 96h to chemicals. Four apical endpoints were recorded daily as indicators of acute lethality: coagulation of the embryo, lack of somite formation, non-detachment of the tail bud from the yolk sac and lack of heartbeat. Results (LC50 values for 48/96h exposure) show that the ZFET is a robust method with a good intra- and inter-laboratory reproducibility (CV30%) for some very toxic or volatile chemicals, and chemicals tested close to their limit of solubility. The ZFET is now available as OECD Test Guideline 236. Considering the high predictive capacity of the ZFET demonstrated by Belanger et al. (2013) in their retrospective analysis of acute fish toxicity and fish embryo acute toxicity data, the ZFET is ready to be considered for acute fish toxicity for regulatory purposes. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Prostate Health Index improves multivariable risk prediction of aggressive prostate cancer.

Science.gov (United States)

Loeb, Stacy; Shin, Sanghyuk S; Broyles, Dennis L; Wei, John T; Sanda, Martin; Klee, George; Partin, Alan W; Sokoll, Lori; Chan, Daniel W; Bangma, Chris H; van Schaik, Ron H N; Slawin, Kevin M; Marks, Leonard S; Catalona, William J

2017-07-01

To examine the use of the Prostate Health Index (PHI) as a continuous variable in multivariable risk assessment for aggressive prostate cancer in a large multicentre US study. The study population included 728 men, with prostate-specific antigen (PSA) levels of 2-10 ng/mL and a negative digital rectal examination, enrolled in a prospective, multi-site early detection trial. The primary endpoint was aggressive prostate cancer, defined as biopsy Gleason score ≥7. First, we evaluated whether the addition of PHI improves the performance of currently available risk calculators (the Prostate Cancer Prevention Trial [PCPT] and European Randomised Study of Screening for Prostate Cancer [ERSPC] risk calculators). We also designed and internally validated a new PHI-based multivariable predictive model, and created a nomogram. Of 728 men undergoing biopsy, 118 (16.2%) had aggressive prostate cancer. The PHI predicted the risk of aggressive prostate cancer across the spectrum of values. Adding PHI significantly improved the predictive accuracy of the PCPT and ERSPC risk calculators for aggressive disease. A new model was created using age, previous biopsy, prostate volume, PSA and PHI, with an area under the curve of 0.746. The bootstrap-corrected model showed good calibration with observed risk for aggressive prostate cancer and had net benefit on decision-curve analysis. Using PHI as part of multivariable risk assessment leads to a significant improvement in the detection of aggressive prostate cancer, potentially reducing harms from unnecessary prostate biopsy and overdiagnosis. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

RELATIONSHIP BETWEEN COMPOSITION AND TOXICITY OF ENGINE EMISSION SAMPLES

Energy Technology Data Exchange (ETDEWEB)

(1)Mauderly, J; Seagrave, J; McDonald; J (2)Eide,I (3)Zielinska, B (4)Lawson, D

2003-08-24

Differences in the lung toxicity and bacterial mutagenicity of seven samples from gasoline and diesel vehicle emissions were reported previously [1]. Filter and vapor-phase semivolatile organic samples were collected from normal and high-emitter gasoline and diesel vehicles operated on chassis dynamometers on the Unified Driving Cycle, and the compositions of the samples were measured in detail. The two fractions of each sample were combined in their original mass collection ratios, and the toxicity of the seven samples was compared by measuring inflammation and tissue damage in rat lungs and mutagenicity in bacteria. There was good agreement among the toxicity response variables in ranking the samples and demonstrating a five-fold range of toxicity. The relationship between chemical composition and toxicity was analyzed by a combination of principal component analysis (PCA) and partial least squares regression (PLS, also known as projection to latent surfaces). The PCA /PLS analysis revealed the chemical constituents co-varying most strongly with toxicity and produced models predicting the relative toxicity of the samples with good accuracy. The results demonstrated the utility of the PCA/PLS approach, which is now being applied to additional samples, and it also provided a starting point for confirming the compounds that actually cause the effects.

Development and application of freshwater sediment-toxicity benchmarks for currently used pesticides

Energy Technology Data Exchange (ETDEWEB)

Nowell, Lisa H., E-mail: lhnowell@usgs.gov [U.S. Geological Survey, California Water Science Center, Placer Hall, 6000 J Street, Sacramento, CA 95819 (United States); Norman, Julia E., E-mail: jnorman@usgs.gov [U.S. Geological Survey, Oregon Water Science Center, 2130 SW 5" t" h Avenue, Portland, OR 97201 (United States); Ingersoll, Christopher G., E-mail: cingersoll@usgs.gov [U.S. Geological Survey, Columbia Environmental Research Center, 4200 New Haven Road, Columbia, MO 65021 (United States); Moran, Patrick W., E-mail: pwmoran@usgs.gov [U.S. Geological Survey, Washington Water Science Center, 934 Broadway, Suite 300, Tacoma, WA 98402 (United States)

2016-04-15

of sediment, and uncertainty in TEB values. Additional evaluations of benchmarks in relation to sediment chemistry and toxicity are ongoing. - Highlights: • Sediment-toxicity benchmarks are developed for 129 pesticides in whole sediment. • Benchmarks can be used to predict or interpret pesticide toxicity in whole sediment. • Benchmarks are based on spiked-sediment bioassays or equilibrium partitioning. • Benchmarks correctly predicted amphipod toxicity in 74% of samples in a case study. • Whole-sediment benchmarks may not always represent bioavailable concentrations.

Development and application of freshwater sediment-toxicity benchmarks for currently used pesticides

International Nuclear Information System (INIS)

Nowell, Lisa H.; Norman, Julia E.; Ingersoll, Christopher G.; Moran, Patrick W.

2016-01-01

of sediment, and uncertainty in TEB values. Additional evaluations of benchmarks in relation to sediment chemistry and toxicity are ongoing. - Highlights: • Sediment-toxicity benchmarks are developed for 129 pesticides in whole sediment. • Benchmarks can be used to predict or interpret pesticide toxicity in whole sediment. • Benchmarks are based on spiked-sediment bioassays or equilibrium partitioning. • Benchmarks correctly predicted amphipod toxicity in 74% of samples in a case study. • Whole-sediment benchmarks may not always represent bioavailable concentrations.

Metabolic Toxicity Screening Using Electrochemiluminescence Arrays Coupled with Enzyme-DNA Biocolloid Reactors and Liquid Chromatography–Mass Spectrometry

Science.gov (United States)

Hvastkovs, Eli G.; Schenkman, John B.; Rusling, James F.

2012-01-01

New chemicals or drugs must be guaranteed safe before they can be marketed. Despite widespread use of bioassay panels for toxicity prediction, products that are toxic to a subset of the population often are not identified until clinical trials. This article reviews new array methodologies based on enzyme/DNA films that form and identify DNA-reactive metabolites that are indicators of potentially genotoxic species. This molecularly based methodology is designed in a rapid screening array that utilizes electrochemiluminescence (ECL) to detect metabolite-DNA reactions, as well as biocolloid reactors that provide the DNA adducts and metabolites for liquid chromatography–mass spectrometry (LC-MS) analysis. ECL arrays provide rapid toxicity screening, and the biocolloid reactor LC-MS approach provides a valuable follow-up on structure, identification, and formation rates of DNA adducts for toxicity hits from the ECL array screening. Specific examples using this strategy are discussed. Integration of high-throughput versions of these toxicity-screening methods with existing drug toxicity bioassays should allow for better human toxicity prediction as well as more informed decision making regarding new chemical and drug candidates. PMID:22482786

Complex mixtures of dissolved pesticides show potential aquatic toxicity in a synoptic study of Midwestern U.S. streams

Science.gov (United States)

Nowell, Lisa H.; Moran, Patrick W.; Schmidt, Travis S.; Norman, Julia E.; Nakagaki, Naomi; Shoda, Megan E.; Mahler, Barbara J.; Van Metre, Peter C.; Stone, Wesley W.; Sandstrom, Mark W.; Hladik, Michelle L.

2018-01-01

Aquatic organisms in streams are exposed to pesticide mixtures that vary in composition over time in response to changes in flow conditions, pesticide inputs to the stream, and pesticide fate and degradation within the stream. To characterize mixtures of dissolved-phase pesticides and degradates in Midwestern streams, a synoptic study was conducted at 100 streams during May–August 2013. In weekly water samples, 94 pesticides and 89 degradates were detected, with a median of 25 compounds detected per sample and 54 detected per site. In a screening-level assessment using aquatic-life benchmarks and the Pesticide Toxicity Index (PTI), potential effects on fish were unlikely in most streams. For invertebrates, potential chronic toxicity was predicted in 53% of streams, punctuated in 12% of streams by acutely toxic exposures. For aquatic plants, acute but likely reversible effects on biomass were predicted in 75% of streams, with potential longer-term effects on plant communities in 9% of streams. Relatively few pesticides in water—atrazine, acetochlor, metolachlor, imidacloprid, fipronil, organophosphate insecticides, and carbendazim—were predicted to be major contributors to potential toxicity. Agricultural streams had the highest potential for effects on plants, especially in May–June, corresponding to high spring-flush herbicide concentrations. Urban streams had higher detection frequencies and concentrations of insecticides and most fungicides than in agricultural streams, and higher potential for invertebrate toxicity, which peaked during July–August. Toxicity-screening predictions for invertebrates were supported by quantile regressions showing significant associations for the Benthic Invertebrate-PTI and imidacloprid concentrations with invertebrate community metrics for MSQA streams, and by mesocosm toxicity testing with imidacloprid showing effects on invertebrate communities at environmentally relevant concentrations. This study documents the most

Respiratory sinus arrhythmia reactivity to a sad film predicts depression symptom improvement and symptomatic trajectory.

Science.gov (United States)

Panaite, Vanessa; Hindash, Alexandra Cowden; Bylsma, Lauren M; Small, Brent J; Salomon, Kristen; Rottenberg, Jonathan

2016-01-01

Respiratory sinus arrhythmia (RSA) reactivity, an index of cardiac vagal tone, has been linked to self-regulation and the severity and course of depression (Rottenberg, 2007). Although initial data supports the proposition that RSA withdrawal during a sad film is a specific predictor of depression course (Fraguas, 2007; Rottenberg, 2005), the robustness and specificity of this finding are unclear. To provide a stronger test, RSA reactivity to three emotion films (happy, sad, fear) and to a more robust stressor, a speech task, were examined in currently depressed individuals (n=37), who were assessed for their degree of symptomatic improvement over 30weeks. Robust RSA reactivity to the sad film uniquely predicted overall symptom improvement over 30weeks. RSA reactivity to both sad and stressful stimuli predicted the speed and maintenance of symptomatic improvement. The current analyses provide the most robust support to date that RSA withdrawal to sad stimuli (but not stressful) has specificity in predicting the overall symptomatic improvement. In contrast, RSA reactivity to negative stimuli (both sad and stressful) predicted the trajectory of depression course. Patients' engagement with sad stimuli may be an important sign to attend to in therapeutic settings. Copyright © 2015 Elsevier B.V. All rights reserved.

Catchment coevolution: A useful framework for improving predictions of hydrological change?

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Troch, Peter A.

2017-04-01

The notion that landscape features have co-evolved over time is well known in the Earth sciences. Hydrologists have recently called for a more rigorous connection between emerging spatial patterns of landscape features and the hydrological response of catchments, and have termed this concept catchment coevolution. In this presentation we present a general framework of catchment coevolution that could improve predictions of hydrologic change. We first present empirical evidence of the interaction and feedback of landscape evolution and changes in hydrological response. From this review it is clear that the independent drivers of catchment coevolution are climate, geology, and tectonics. We identify common currency that allows comparing the levels of activity of these independent drivers, such that, at least conceptually, we can quantify the rate of evolution or aging. Knowing the hydrologic age of a catchment by itself is not very meaningful without linking age to hydrologic response. Two avenues of investigation have been used to understand the relationship between (differences in) age and hydrological response: (i) one that is based on relating present landscape features to runoff processes that are hypothesized to be responsible for the current fingerprints in the landscape; and (ii) one that takes advantage of an experimental design known as space-for-time substitution. Both methods have yielded significant insights in the hydrologic response of landscapes with different histories. If we want to make accurate predictions of hydrologic change, we will also need to be able to predict how the catchment will further coevolve in association with changes in the activity levels of the drivers (e.g., climate). There is ample evidence in the literature that suggests that whole-system prediction of catchment coevolution is, at least in principle, plausible. With this imperative we outline a research agenda that implements the concepts of catchment coevolution for building

The use of patient factors to improve the prediction of operative duration using laparoscopic cholecystectomy.

Science.gov (United States)

Thiels, Cornelius A; Yu, Denny; Abdelrahman, Amro M; Habermann, Elizabeth B; Hallbeck, Susan; Pasupathy, Kalyan S; Bingener, Juliane

2017-01-01

Reliable prediction of operative duration is essential for improving patient and care team satisfaction, optimizing resource utilization and reducing cost. Current operative scheduling systems are unreliable and contribute to costly over- and underestimation of operative time. We hypothesized that the inclusion of patient-specific factors would improve the accuracy in predicting operative duration. We reviewed all elective laparoscopic cholecystectomies performed at a single institution between 01/2007 and 06/2013. Concurrent procedures were excluded. Univariate analysis evaluated the effect of age, gender, BMI, ASA, laboratory values, smoking, and comorbidities on operative duration. Multivariable linear regression models were constructed using the significant factors (p historical surgeon-specific and procedure-specific operative duration. External validation was done using the ACS-NSQIP database (n = 11,842). A total of 1801 laparoscopic cholecystectomy patients met inclusion criteria. Female sex was associated with reduced operative duration (-7.5 min, p < 0.001 vs. male sex) while increasing BMI (+5.1 min BMI 25-29.9, +6.9 min BMI 30-34.9, +10.4 min BMI 35-39.9, +17.0 min BMI 40 + , all p < 0.05 vs. normal BMI), increasing ASA (+7.4 min ASA III, +38.3 min ASA IV, all p < 0.01 vs. ASA I), and elevated liver function tests (+7.9 min, p < 0.01 vs. normal) were predictive of increased operative duration on univariate analysis. A model was then constructed using these predictive factors. The traditional surgical scheduling system was poorly predictive of actual operative duration (R 2  = 0.001) compared to the patient factors model (R 2  = 0.08). The model remained predictive on external validation (R 2  = 0.14).The addition of surgeon as a variable in the institutional model further improved predictive ability of the model (R 2  = 0.18). The use of routinely available pre-operative patient factors improves the prediction of operative

Human contamination by persistent toxic substances: the rationale to improve exposure assessment.

Science.gov (United States)

Porta, Miquel

2015-10-01

We know quite a lot about the generalized human contamination by environmental chemical agents; this statement is fully compatible with the view that most countries lack the necessary monitoring systems. We also know quite a lot about the toxic effects of environmental pollutants; this statement is fully compatible with the proposal that we need both more research and more energetic policies to decrease human contamination by such pollutants. Unsurprisingly, we know too little about the (environmental and social) causes and the etiopathogenesis (mechanisms) of the most prevalent diseases, and we will continue to miss relevant causes and mechanisms if we neglect the toxic chemicals that commonly contaminate humans, worldwide. Basic, clinical end environmental-epidemiological research on human health should more often consider integrating biomarkers of internal dose of environmental chemical pollutants. When we act in more responsible, rational, and scientific ways; when we become less dismissive towards environmental hazards; and when we thus neglect less the generalized human contamination by environmental chemical agents and their toxic effects, we will expand mechanistic biologic knowledge, and we shall as well increase the effectiveness of interventions and policies that enable the primary prevention of human diseases which cause huge amounts of economic burden and human suffering.

Metal and proton toxicity to lake zooplankton: A chemical speciation based modelling approach

International Nuclear Information System (INIS)

Stockdale, Anthony; Tipping, Edward; Lofts, Stephen; Fott, Jan; Garmo, Øyvind A.; Hruska, Jakub; Keller, Bill; Löfgren, Stefan; Maberly, Stephen C.; Majer, Vladimir; Nierzwicki-Bauer, Sandra A.; Persson, Gunnar; Schartau, Ann-Kristin; Thackeray, Stephen J.

2014-01-01

The WHAM-F TOX model quantifies the combined toxic effects of protons and metal cations towards aquatic organisms through the toxicity function (F TOX ), a linear combination of the products of organism-bound cation and a toxic potency coefficient for each cation. We describe the application of the model to predict an observable ecological field variable, species richness of pelagic lake crustacean zooplankton, studied with respect to either acidification or the impacts of metals from smelters. The fitted results give toxic potencies increasing in the order H + TOX to relate combined toxic effects of protons and metal cations towards lake crustacean zooplankton. • The fitted results give toxic potencies increasing in the order H + TOX model has been applied to field data for pelagic lake crustacean zooplankton. The fitted results give metal toxic potencies increasing in the order H + < Al < Cu < Zn < Ni

Adjusting the Stems Regional Forest Growth Model to Improve Local Predictions

Science.gov (United States)

W. Brad Smith

1983-01-01

A simple procedure using double sampling is described for adjusting growth in the STEMS regional forest growth model to compensate for subregional variations. Predictive accuracy of the STEMS model (a distance-independent, individual tree growth model for Lake States forests) was improved by using this procedure

Hematological toxicity in radioimmunotherapy is predicted both by the computed absorbed whole body dose (cGy) and by the administered dose (mCi)

International Nuclear Information System (INIS)

Marquez, Sheri D.; Knox, Susan J.; Trisler, Kirk D.; Goris, Michael L.

1997-01-01

Purpose/Objective: Radioimmunotherapy (RIT) has yielded encouraging response rates in patients with recurrent non-Hodgkin's lymphoma, but myelotoxicity remains the dose limiting factor. Dose optimization is theoretically possible, since a pretreatment biodistribution study with tracer doses allows for a fairly accurate estimate of the whole body (and by implication the bone marrow) dose in patients. It has been shown that the radiation dose as a function of the administered dose varies widely from patient to patient. The pretreatment study could therefore be used to determine the maximum tolerable dose for each individual patient. The purpose of this study was to examine whether the administered dose or the estimated whole body absorbed radiation dose were indeed predictors of bone marrow toxicity. Materials and Methods: We studied two cohorts of patients to determine if the computed integral whole body or marrow dose is predictive of myelotoxicity. The first cohort consisted of 13 patients treated with Yttrium-90 labeled anti-CD20 (2B8) monoclonal antibody. Those patients were treated in a dose escalation protocol, based on the administered dose, without correction for weight or body surface. The computed whole body dose varied from 41 to 129 cGy. The second cohort (6 patients) were treated with Iodine-131 labeled anti-CD20 (B1) antibody. In this group the administered dose was tailored to deliver an estimated 75 cGy whole body dose. The administered dose varied from 54 to 84 mCi of Iodine-131. For each patient, white blood cell count with differential, hemoglobin, hematocrit, and platelet levels were measured before and at regular intervals after RIT was administered. Using linear regression analysis, a relationship between administered dose, absorbed dose and myelotoxicity was determined for each patient cohort. Results: Marrow toxicity was measured by the absolute decrease in white blood cell (DWBC), platelet (DPLAT), and neutrophil (DN) values. In the Yttrium

Oxygen Toxicity and Special Operations Forces Diving: Hidden and Dangerous

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Thijs T. Wingelaar

2017-07-01

Full Text Available In Special Operations Forces (SOF closed-circuit rebreathers with 100% oxygen are commonly utilized for covert diving operations. Exposure to high partial pressures of oxygen (PO2 could cause damage to the central nervous system (CNS and pulmonary system. Longer exposure time and higher PO2 leads to faster development of more serious pathology. Exposure to a PO2 above 1.4 ATA can cause CNS toxicity, leading to a wide range of neurologic complaints including convulsions. Pulmonary oxygen toxicity develops over time when exposed to a PO2 above 0.5 ATA and can lead to inflammation and fibrosis of lung tissue. Oxygen can also be toxic for the ocular system and may have systemic effects on the inflammatory system. Moreover, some of the effects of oxygen toxicity are irreversible. This paper describes the pathophysiology, epidemiology, signs and symptoms, risk factors and prediction models of oxygen toxicity, and their limitations on SOF diving.

LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs

DEFF Research Database (Denmark)

Will, Sebastian; Joshi, Tejal; Hofacker, Ivo L.

2012-01-01

Current genomic screens for noncoding RNAs (ncRNAs) predict a large number of genomic regions containing potential structural ncRNAs. The analysis of these data requires highly accurate prediction of ncRNA boundaries and discrimination of promising candidate ncRNAs from weak predictions. Existing...... methods struggle with these goals because they rely on sequence-based multiple sequence alignments, which regularly misalign RNA structure and therefore do not support identification of structural similarities. To overcome this limitation, we compute columnwise and global reliabilities of alignments based...... on sequence and structure similarity; we refer to these structure-based alignment reliabilities as STARs. The columnwise STARs of alignments, or STAR profiles, provide a versatile tool for the manual and automatic analysis of ncRNAs. In particular, we improve the boundary prediction of the widely used nc...

Modelling interactions of toxicants and density dependence in wildlife populations

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Schipper, Aafke M.; Hendriks, Harrie W.M.; Kauffman, Matthew J.; Hendriks, A. Jan; Huijbregts, Mark A.J.

2013-01-01

1. A major challenge in the conservation of threatened and endangered species is to predict population decline and design appropriate recovery measures. However, anthropogenic impacts on wildlife populations are notoriously difficult to predict due to potentially nonlinear responses and interactions with natural ecological processes like density dependence. 2. Here, we incorporated both density dependence and anthropogenic stressors in a stage-based matrix population model and parameterized it for a density-dependent population of peregrine falcons Falco peregrinus exposed to two anthropogenic toxicants [dichlorodiphenyldichloroethylene (DDE) and polybrominated diphenyl ethers (PBDEs)]. Log-logistic exposure–response relationships were used to translate toxicant concentrations in peregrine falcon eggs to effects on fecundity. Density dependence was modelled as the probability of a nonbreeding bird acquiring a breeding territory as a function of the current number of breeders. 3. The equilibrium size of the population, as represented by the number of breeders, responded nonlinearly to increasing toxicant concentrations, showing a gradual decrease followed by a relatively steep decline. Initially, toxicant-induced reductions in population size were mitigated by an alleviation of the density limitation, that is, an increasing probability of territory acquisition. Once population density was no longer limiting, the toxicant impacts were no longer buffered by an increasing proportion of nonbreeders shifting to the breeding stage, resulting in a strong decrease in the equilibrium number of breeders. 4. Median critical exposure concentrations, that is, median toxicant concentrations in eggs corresponding with an equilibrium population size of zero, were 33 and 46 μg g−1 fresh weight for DDE and PBDEs, respectively. 5. Synthesis and applications. Our modelling results showed that particular life stages of a density-limited population may be relatively insensitive to

Free and Open Source Chemistry Software in Research of Quantitative Structure-Toxicity Relationship of Pesticides

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Rastija Vesna

2017-01-01

Full Text Available Pesticides are toxic chemicals aimed for the destroying pest on crops. Numerous data evidence about toxicity of pesticides on aquatic organisms. Since pesticides with similar properties tend to have similar biological activities, toxicity may be predicted from structure. Their structure feature and properties are encoded my means of molecular descriptors. Molecular descriptors can capture quite simple two-dimensional (2D chemical structures to highly complex three-dimensional (3D chemical structures. Quantitative structure-toxicity relationship (QSTR method uses linear regression analyses for correlation toxicity of chemical with their structural feature using molecular descriptors. Molecular descriptors were calculated using open source software PaDEL and in-house built PyMOL plugin (PyDescriptor. PyDescriptor is a new script implemented with the commonly used visualization software PyMOL for calculation of a large and diverse set of easily interpretable molecular descriptors encoding pharmacophoric patterns and atomic fragments. PyDescriptor has several advantages like free and open source, can work on all major platforms (Windows, Linux, MacOS. QSTR method allows prediction of toxicity of pesticides without experimental assay. In the present work, QSTR analysis for toxicity of a dataset of mixtures of 5 classes of pesticides comprising has been performed.

Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.

Science.gov (United States)

Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne; Bourgeois, Stephane; Svensson, Peter J; Wadelius, Mia; Deloukas, Panos; Montgomery, Stephen B; Altman, Russ B

2017-11-24

Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects. Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

Anticoagulant rodenticide toxicity to non-target wildlife under controlled exposure conditions

Science.gov (United States)

Rattner, Barnett A.; Mastrota, F. Nicholas; van den Brink, Nico; Elliott, J.; Shore, R.; Rattner, B.

2018-01-01

-target wildlife. Ecological risk assessments of anticoagulant rodenticides would be improved with additional data on (i) interspecific differences in sensitivity, particularly for understudied taxa, (ii) sublethal effects unrelated to coagulopathy, (iii) responses to mixtures and sequential exposures, and (iv) the role of vitamin K status on toxicity, and significance of inclusion of supplemental vitamin K or menadione (provitamin) in the diet of test organisms. A more complete understanding of the toxicity of anticoagulant rodenticides in non-target wildlife would enable regulators and natural resource managers to better predict and even mitigate risk.

Predicting dietborne metal toxicity from metal influxes

Science.gov (United States)

Croteau, M.-N.; Luoma, S.N.

2009-01-01

Dietborne metal uptake prevails for many species in nature. However, the links between dietary metal exposure and toxicity are not well understood. Sources of uncertainty include the lack of suitable tracers to quantify exposure for metals such as copper, the difficulty to assess dietary processes such as food ingestion rate, and the complexity to link metal bioaccumulation and effects. We characterized dietborne copper, nickel, and cadmium influxes in a freshwater gastropod exposed to diatoms labeled with enriched stable metal isotopes. Metal influxes in Lymnaea stagnalis correlated linearly with dietborne metal concentrations over a range encompassing most environmental exposures. Dietary Cd and Ni uptake rate constants (kuf) were, respectively, 3.3 and 2.3 times higher than that for Cu. Detoxification rate constants (k detox) were similar among metals and appeared 100 times higher than efflux rate constants (ke). Extremely high Cu concentrations reduced feeding rates, causing the relationship between exposure and influx to deviate from linearity; i.e., Cu uptake rates leveled off between 1500 and 1800 nmol g-1 day-1. L. stagnalis rapidly takes up Cu, Cd, and Ni from food but detoxifies the accumulated metals, instead of reducing uptake or intensifying excretion. Above a threshold uptake rate, however, the detoxification capabilities of L. stagnalis are overwhelmed.

Predictive modelling of interventions to improve iodine intake in New Zealand.

Science.gov (United States)

Schiess, Sonja; Cressey, Peter J; Thomson, Barbara M

2012-10-01

The potential effects of four interventions to improve iodine intakes of six New Zealand population groups are assessed. A model was developed to estimate iodine intake when (i) bread is manufactured with or without iodized salt, (ii) recommended foods are consumed to augment iodine intake, (iii) iodine supplementation as recommended for pregnant women is taken and (iv) the level of iodization for use in bread manufacture is doubled from 25-65 mg to 100 mg iodine/kg salt. New Zealanders have low and decreasing iodine intakes and low iodine status. Predictive modelling is a useful tool to assess the likely impact, and potential risk, of nutrition interventions. Food consumption information was sourced from 24 h diet recall records for 4576 New Zealanders aged over 5 years. Most consumers (73-100 %) are predicted to achieve an adequate iodine intake when salt iodized at 25-65 mg iodine/kg salt is used in bread manufacture, except in pregnant females of whom 37 % are likely to meet the estimated average requirement. Current dietary advice to achieve estimated average requirements is challenging for some consumers. Pregnant women are predicted to achieve adequate but not excessive iodine intakes when 150 μg of supplemental iodine is taken daily, assuming iodized salt in bread. The manufacture of bread with iodized salt and supplemental iodine for pregnant women are predicted to be effective interventions to lift iodine intakes in New Zealand. Current estimations of iodine intake will be improved with information on discretionary salt and supplemental iodine usage.

Skill of Predicting Heavy Rainfall Over India: Improvement in Recent Years Using UKMO Global Model

Science.gov (United States)

Sharma, Kuldeep; Ashrit, Raghavendra; Bhatla, R.; Mitra, A. K.; Iyengar, G. R.; Rajagopal, E. N.

2017-11-01

The quantitative precipitation forecast (QPF) performance for heavy rains is still a challenge, even for the most advanced state-of-art high-resolution Numerical Weather Prediction (NWP) modeling systems. This study aims to evaluate the performance of UK Met Office Unified Model (UKMO) over India for prediction of high rainfall amounts (>2 and >5 cm/day) during the monsoon period (JJAS) from 2007 to 2015 in short range forecast up to Day 3. Among the various modeling upgrades and improvements in the parameterizations during this period, the model horizontal resolution has seen an improvement from 40 km in 2007 to 17 km in 2015. Skill of short range rainfall forecast has improved in UKMO model in recent years mainly due to increased horizontal and vertical resolution along with improved physics schemes. Categorical verification carried out using the four verification metrics, namely, probability of detection (POD), false alarm ratio (FAR), frequency bias (Bias) and Critical Success Index, indicates that QPF has improved by >29 and >24% in case of POD and FAR. Additionally, verification scores like EDS (Extreme Dependency Score), EDI (Extremal Dependence Index) and SEDI (Symmetric EDI) are used with special emphasis on verification of extreme and rare rainfall events. These scores also show an improvement by 60% (EDS) and >34% (EDI and SEDI) during the period of study, suggesting an improved skill of predicting heavy rains.

Tracking pyrethroid toxicity in surface water samples: Exposure dynamics and toxicity identification tools for laboratory tests with Hyalella azteca (Amphipoda).

Science.gov (United States)

Deanovic, Linda A; Stillway, Marie; Hammock, Bruce G; Fong, Stephanie; Werner, Inge

2018-02-01

Pyrethroid insecticides are commonly used in pest control and are present at toxic concentrations in surface waters of agricultural and urban areas worldwide. Monitoring is challenging as a result of their high hydrophobicity and low toxicity thresholds, which often fall below the analytical methods detection limits (MDLs). Standard daphnid bioassays used in surface water monitoring are not sensitive enough to protect more susceptible invertebrate species such as the amphipod Hyalella azteca and chemical loss during toxicity testing is of concern. In the present study, we quantified toxicity loss during storage and testing, using both natural and synthetic water, and presented a tool to enhance toxic signal strength for improved sensitivity of H. azteca toxicity tests. The average half-life during storage in low-density polyethylene (LDPE) cubitainers (Fisher Scientific) at 4 °C of 5 pyrethroids (permethrin, bifenthrin, lambda-cyhalothrin, cyfluthrin, and esfenvalerate) and one organophosphate (chlorpyrifos; used as reference) was 1.4 d, and piperonyl butoxide (PBO) proved an effective tool to potentiate toxicity. We conclude that toxicity tests on ambient water samples containing these hydrophobic insecticides are likely to underestimate toxicity present in the field, and mimic short pulse rather than continuous exposures. Where these chemicals are of concern, the addition of PBO during testing can yield valuable information on their presence or absence. Environ Toxicol Chem 2018;37:462-472. © 2017 SETAC. © 2017 SETAC.

Multilevel eEmpirical Bayes modeling for improved estimation of toxicant formulations tosuppress parasitic sea lamprey in the Upper Great Lakes

Science.gov (United States)

Hatfield, Laura A.; Gutreuter, Steve; Boogaard, Michael A.; Carlin, Bradley P.

2011-01-01

Estimation of extreme quantal-response statistics, such as the concentration required to kill 99.9% of test subjects (LC99.9), remains a challenge in the presence of multiple covariates and complex study designs. Accurate and precise estimates of the LC99.9 for mixtures of toxicants are critical to ongoing control of a parasitic invasive species, the sea lamprey, in the Laurentian Great Lakes of North America. The toxicity of those chemicals is affected by local and temporal variations in water chemistry, which must be incorporated into the modeling. We develop multilevel empirical Bayes models for data from multiple laboratory studies. Our approach yields more accurate and precise estimation of the LC99.9 compared to alternative models considered. This study demonstrates that properly incorporating hierarchical structure in laboratory data yields better estimates of LC99.9 stream treatment values that are critical to larvae control in the field. In addition, out-of-sample prediction of the results of in situ tests reveals the presence of a latent seasonal effect not manifest in the laboratory studies, suggesting avenues for future study and illustrating the importance of dual consideration of both experimental and observational data.

Predictability of extreme weather events for NE U.S.: improvement of the numerical prediction using a Bayesian regression approach

Science.gov (United States)

Yang, J.; Astitha, M.; Anagnostou, E. N.; Hartman, B.; Kallos, G. B.

2015-12-01

Weather prediction accuracy has become very important for the Northeast U.S. given the devastating effects of extreme weather events in the recent years. Weather forecasting systems are used towards building strategies to prevent catastrophic losses for human lives and the environment. Concurrently, weather forecast tools and techniques have evolved with improved forecast skill as numerical prediction techniques are strengthened by increased super-computing resources. In this study, we examine the combination of two state-of-the-science atmospheric models (WRF and RAMS/ICLAMS) by utilizing a Bayesian regression approach to improve the prediction of extreme weather events for NE U.S. The basic concept behind the Bayesian regression approach is to take advantage of the strengths of two atmospheric modeling systems and, similar to the multi-model ensemble approach, limit their weaknesses which are related to systematic and random errors in the numerical prediction of physical processes. The first part of this study is focused on retrospective simulations of seventeen storms that affected the region in the period 2004-2013. Optimal variances are estimated by minimizing the root mean square error and are applied to out-of-sample weather events. The applicability and usefulness of this approach are demonstrated by conducting an error analysis based on in-situ observations from meteorological stations of the National Weather Service (NWS) for wind speed and wind direction, and NCEP Stage IV radar data, mosaicked from the regional multi-sensor for precipitation. The preliminary results indicate a significant improvement in the statistical metrics of the modeled-observed pairs for meteorological variables using various combinations of the sixteen events as predictors of the seventeenth. This presentation will illustrate the implemented methodology and the obtained results for wind speed, wind direction and precipitation, as well as set the research steps that will be

Prospective evaluation of radiation-induced skin toxicity in a race/ethnically diverse breast cancer population

International Nuclear Information System (INIS)

Wright, Jean L.; Takita, Cristiane; Reis, Isildinha M.; Zhao, Wei; Lee, Eunkyung; Nelson, Omar L.; Hu, Jennifer J.

2016-01-01

We evaluated predictors of radiation-induced skin toxicity in a prospective study of a tri-racial/ethnic breast cancer population. We evaluated patient demographics, tumor characteristics, and treatment variables in the first 392 patients in a prospective study assessing radiation-induced skin toxicity. Logistic regression analyses were conducted to evaluate potential predictors of skin toxicity. The study consists of 59 non-Hispanic whites (NHW; 15%), 241 Hispanic Whites (HW; 62%), 79 black or African Americans (AA; 20%), and 13 others (3%). Overall, 48% developed grade 0–1 skin toxicity, 49.8% grade 2, and 2.2% grade 3 by the National Cancer Institute's Common Toxicity Criteria for Adverse Events (CTCAE) scale. Twenty-one percent developed moist desquamation. In multivariate analysis, higher body mass index (BMI; OR = 2.09; 95%CI = 1.15, 3.82), higher disease stage (OR = 1.82; 95%CI = 1.06, 3.11), ER-positive/PR-negative status (OR = 2.74; 95%CI = 1.26, 5.98), and conventionally fractionated regimens (OR = 3.25; 95%CI = 1.76, 6.01) were significantly associated with higher skin toxicity grade after adjustment for age, race, ethnicity, ER status, and breast volume. BMI specifically predicted for moist desquamation, but not degree of erythema. In this racially and ethnically diverse cohort of breast cancer patients receiving radiation to the intact breast, risk factors including BMI, disease stage, and conventionally fractionated radiation predicted for higher skin toxicity grade, whereas age, race, ethnicity, and breast volume did not. BMI specifically predicted for moist desquamation, suggesting that preventive measures to address this particular outcome should be investigated

Thyroid cancer in toxic and non-toxic multinodular goiter

Directory of Open Access Journals (Sweden)

Cerci C

2007-01-01

Full Text Available Background : Many authors have claimed that hyperthyroidism protects against thyroid cancer and believed that the incidence of malignancy is lower in patients with toxic multinodular goiter (TMG than in those with non-toxic multinodular goiter. But in recent studies, it was reported that the incidence of malignancy with TMG is not as low as previously thought. Aim : To compare the thyroid cancer incidence in patients with toxic and non-toxic multinodular goiter. Settings and Design : Histology reports of patients treated surgically with a preoperative diagnosis of toxic and non-toxic multinodular goiter were reviewed to identify the thyroid cancer incidence. Patients having a history of neck irradiation or radioactive iodine therapy were excluded from the study. Materials and Methods : We reviewed 294 patients operated between 2001-2005 from toxic and non-toxic multinodular goiter. One hundred and twenty-four of them were toxic and 170 were non-toxic. Hyperthyroidism was diagnosed by elevated tri-iodothyroinine / thyroxine ratios and low thyroid-stimulating hormone with clinical signs and symptoms. All patients were evaluated with ultrasonography and scintigraphy and fine needle aspiration biopsy. Statistical Analysis Used : Significance of the various parameters was calculated by using ANOVA test. Results : The incidence of malignancy was 9% in the toxic and 10.58% in the non-toxic multinodular goiter group. Any significant difference in the incidence of cancer and tumor size between the two groups could not be detected. Conclusions : The incidence of malignancy in toxic multinodular goiter is not very low as thought earlier and is nearly the same in non-toxic multinodular goiter.

QSAR Models for Reproductive Toxicity and Endocrine Disruption Activity

Directory of Open Access Journals (Sweden)

Marjan Vračko

2010-03-01

Full Text Available Reproductive toxicity is an important regulatory endpoint, which is required in registration procedures of chemicals used for different purposes (for example pesticides. The in vivo tests are expensive, time consuming and require large numbers of animals, which must be sacrificed. Therefore an effort is ongoing to develop alternative In vitro and in silico methods to evaluate reproductive toxicity. In this review we describe some modeling approaches. In the first example we describe the CAESAR model for prediction of reproductive toxicity; the second example shows a classification model for endocrine disruption potential based on counter propagation artificial neural networks; the third example shows a modeling of relative binding affinity to rat estrogen receptor, and the fourth one shows a receptor dependent modeling experiment.

Factors predicting visual improvement post pars plana vitrectomy for proliferative diabetic retinopathy

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Evelyn Tai Li Min

2017-08-01

Full Text Available AIM: To identify factors predicting visual improvement post vitrectomy for sequelae of proliferative diabetic retinopathy(PDR.METHODS: This was a retrospective analysis of pars plana vitrectomy indicated for sequelae of PDR from Jan. to Dec. 2014 in Hospital Sultanah Bahiyah, Alor Star, Kedah, Malaysia. Data collected included patient demographics, baseline visual acuity(VAand post-operative logMAR best corrected VA at 1y. Data analysis was performed with IBM SPSS Statistics Version 22.0. RESULTS: A total of 103 patients were included. The mean age was 51.2y. On multivariable analysis, each pre-operative positive deviation of 1 logMAR from a baseline VA of 0 logMAR was associated with a post-operative improvement of 0.859 logMAR(P0.001. Likewise, an attached macula pre-operatively was associated with a 0.374(P=0.003logMAR improvement post vitrectomy. Absence of iris neovascularisation and absence of post-operative complications were associated with a post vitrectomy improvement in logMAR by 1.126(P=0.001and 0.377(P=0.005respectively. Absence of long-acting intraocular tamponade was associated with a 0.302(P=0.010improvement of logMAR post vitrectomy.CONCLUSION: Factors associated with visual improvement after vitrectomy are poor pre-operative VA, an attached macula, absence of iris neovascularisation, absence of post-operative complications and abstaining from use of long-acting intraocular tamponade. A thorough understanding of the factors predicting visual improvement will facilitate decision-making in vitreoretinal surgery.

Non-animal Replacements for Acute Toxicity Testing.

Science.gov (United States)

Barker-Treasure, Carol; Coll, Kevin; Belot, Nathalie; Longmore, Chris; Bygrave, Karl; Avey, Suzanne; Clothier, Richard

2015-07-01

Current approaches to predicting adverse effects in humans from acute toxic exposure to cosmetic ingredients still heavily necessitate the use of animals under EU legislation, particularly in the context of the REACH system, when cosmetic ingredients are also destined for use in other industries. These include the LD50 test, the Up-and-Down Procedure and the Fixed Dose Procedure, which are regarded as having notable scientific deficiencies and low transferability to humans. By expanding on previous in vitro tests, such as the animal cell-based 3T3 Neutral Red Uptake (NRU) assay, this project aims to develop a truly animal-free predictive test for the acute toxicity of cosmetic ingredients in humans, by using human-derived cells and a prediction model that does not rely on animal data. The project, funded by Innovate UK, will incorporate the NRU assay with human dermal fibroblasts in animal product-free culture, to generate an in vitro protocol that can be validated as an accepted replacement for the currently available in vivo tests. To date, the project has successfully completed an assessment of the robustness and reproducibility of the method, by using sodium lauryl sulphate (SLS) as a positive control, and displaying analogous results to those of the original studies with mouse 3T3 cells. Currently, the testing of five known ingredients from key groups (a surfactant, a preservative, a fragrance, a colour and an emulsifier) is under way. The testing consists of initial range-finding runs followed by three valid runs of a main experiment with the appropriate concentration ranges, to generate IC50 values. Expanded blind trials of 20 ingredients will follow. Early results indicate that this human cell-based test holds the potential to replace aspects of in vivo animal acute toxicity testing, particularly with reference to cosmetic ingredients. 2015 FRAME.

Improvement of Risk Prediction After Transcatheter Aortic Valve Replacement by Combining Frailty With Conventional Risk Scores.

Science.gov (United States)

Schoenenberger, Andreas W; Moser, André; Bertschi, Dominic; Wenaweser, Peter; Windecker, Stephan; Carrel, Thierry; Stuck, Andreas E; Stortecky, Stefan

2018-02-26

This study sought to evaluate whether frailty improves mortality prediction in combination with the conventional scores. European System for Cardiac Operative Risk Evaluation (EuroSCORE) or Society of Thoracic Surgeons (STS) score have not been evaluated in combined models with frailty for mortality prediction after transcatheter aortic valve replacement (TAVR). This prospective cohort comprised 330 consecutive TAVR patients ≥70 years of age. Conventional scores and a frailty index (based on assessment of cognition, mobility, nutrition, and activities of daily living) were evaluated to predict 1-year all-cause mortality using Cox proportional hazards regression (providing hazard ratios [HRs] with confidence intervals [CIs]) and measures of test performance (providing likelihood ratio [LR] chi-square test statistic and C-statistic [CS]). All risk scores were predictive of the outcome (EuroSCORE, HR: 1.90 [95% CI: 1.45 to 2.48], LR chi-square test statistic 19.29, C-statistic 0.67; STS score, HR: 1.51 [95% CI: 1.21 to 1.88], LR chi-square test statistic 11.05, C-statistic 0.64; frailty index, HR: 3.29 [95% CI: 1.98 to 5.47], LR chi-square test statistic 22.28, C-statistic 0.66). A combination of the frailty index with either EuroSCORE (LR chi-square test statistic 38.27, C-statistic 0.72) or STS score (LR chi-square test statistic 28.71, C-statistic 0.68) improved mortality prediction. The frailty index accounted for 58.2% and 77.6% of the predictive information in the combined model with EuroSCORE and STS score, respectively. Net reclassification improvement and integrated discrimination improvement confirmed that the added frailty index improved risk prediction. This is the first study showing that the assessment of frailty significantly enhances prediction of 1-year mortality after TAVR in combined risk models with conventional risk scores and relevantly contributes to this improvement. Copyright © 2018 American College of Cardiology Foundation

Simple test guidelines for screening oilspill sorbents for toxicity

International Nuclear Information System (INIS)

Blenkinsopp, S.A.; Sergy, G.; Doe, K.; Jackman, P.; Huybers, A.

1998-01-01

Environment Canada's Emergencies Science Division has established a program to develop a standard test method suitable for evaluating the toxicity of common sorbent materials. Sorbents are used to absorb or adsorb spilled oil and other hazardous materials. They vary widely in composition and packaging. They are often treated with oleophilic and hydrophobic compounds to improve performance and have been used in large quantities during oil spills. Until now, their potential toxicity has never been considered. Three tests have been evaluated to determine how appropriate they are in screening the toxicity of sorbents. Seven toxicity test recommendations for sorbents were presented. 7 refs., 3 tabs., 2 figs

Toxicity after post-prostatectomy image-guided intensity-modulated radiotherapy using Australian guidelines.

Science.gov (United States)

Chin, Stephen; Aherne, Noel J; Last, Andrew; Assareh, Hassan; Shakespeare, Thomas P

2017-12-01

We evaluated single institution toxicity outcomes after post-prostatectomy radiotherapy (PPRT) via image-guided intensity-modulated radiation therapy (IG-IMRT) with implanted fiducial markers following national eviQ guidelines, for which late toxicity outcomes have not been published. Prospectively collected toxicity data were retrospectively reviewed for 293 men who underwent 64-66 Gy IG-IMRT to the prostate bed between 2007 and 2015. Median follow-up after PPRT was 39 months. Baseline grade ≥2 genitourinary (GU), gastrointestinal (GI) and sexual toxicities were 20.5%, 2.7% and 43.7%, respectively, reflecting ongoing toxicity after radical prostatectomy. Incidence of new (compared to baseline) acute grade ≥2 GU and GI toxicity was 5.8% and 10.6%, respectively. New late grade ≥2 GU, GI and sexual toxicity occurred in 19.1%, 4.7% and 20.2%, respectively. However, many patients also experienced improvements in toxicities. For this reason, prevalence of grade ≥2 GU, GI and sexual toxicities 4 years after PPRT was similar to or lower than baseline (21.7%, 2.6% and 17.4%, respectively). There were no grade ≥4 toxicities. Post-prostatectomy IG-IMRT using Australian contouring guidelines appears to have tolerable acute and late toxicity. The 4-year prevalence of grade ≥2 GU and GI toxicity was virtually unchanged compared to baseline, and sexual toxicity improved over baseline. This should reassure radiation oncologists following these guidelines. Late toxicity rates of surgery and PPRT are higher than following definitive IG-IMRT, and this should be taken into account if patients are considering surgery and likely to require PPRT. © 2017 The Royal Australian and New Zealand College of Radiologists.

Generation of human pluripotent stem cell-derived hepatocyte-like cells for drug toxicity screening.

Science.gov (United States)

Takayama, Kazuo; Mizuguchi, Hiroyuki

2017-02-01

Because drug-induced liver injury is one of the main reasons for drug development failures, it is important to perform drug toxicity screening in the early phase of pharmaceutical development. Currently, primary human hepatocytes are most widely used for the prediction of drug-induced liver injury. However, the sources of primary human hepatocytes are limited, making it difficult to supply the abundant quantities required for large-scale drug toxicity screening. Therefore, there is an urgent need for a novel unlimited, efficient, inexpensive, and predictive model which can be applied for large-scale drug toxicity screening. Human embryonic stem (ES) cells and induced pluripotent stem (iPS) cells are able to replicate indefinitely and differentiate into most of the body's cell types, including hepatocytes. It is expected that hepatocyte-like cells generated from human ES/iPS cells (human ES/iPS-HLCs) will be a useful tool for drug toxicity screening. To apply human ES/iPS-HLCs to various applications including drug toxicity screening, homogenous and functional HLCs must be differentiated from human ES/iPS cells. In this review, we will introduce the current status of hepatocyte differentiation technology from human ES/iPS cells and a novel method to predict drug-induced liver injury using human ES/iPS-HLCs. Copyright © 2016 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

Measured Copper Toxicity to Cnesterodon decemmaculatus (Pisces: Poeciliidae and Predicted by Biotic Ligand Model in Pilcomayo River Water: A Step for a Cross-Fish-Species Extrapolation

Directory of Open Access Journals (Sweden)

María Victoria Casares

2012-01-01

Full Text Available In order to determine copper toxicity (LC50 to a local species (Cnesterodon decemmaculatus in the South American Pilcomayo River water and evaluate a cross-fish-species extrapolation of Biotic Ligand Model, a 96 h acute copper toxicity test was performed. The dissolved copper concentrations tested were 0.05, 0.19, 0.39, 0.61, 0.73, 1.01, and 1.42 mg Cu L-1. The 96 h Cu LC50 calculated was 0.655 mg L-1 (0.823-0.488. 96-h Cu LC50 predicted by BLM for Pimephales promelas was 0.722 mg L-1. Analysis of the inter-seasonal variation of the main water quality parameters indicates that a higher protective effect of calcium, magnesium, sodium, sulphate, and chloride is expected during the dry season. The very high load of total suspended solids in this river might be a key factor in determining copper distribution between solid and solution phases. A cross-fish-species extrapolation of copper BLM is valid within the water quality parameters and experimental conditions of this toxicity test.

An Improved Generalized Predictive Control in a Robust Dynamic Partial Least Square Framework

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Jin Xin

2015-01-01

Full Text Available To tackle the sensitivity to outliers in system identification, a new robust dynamic partial least squares (PLS model based on an outliers detection method is proposed in this paper. An improved radial basis function network (RBFN is adopted to construct the predictive model from inputs and outputs dataset, and a hidden Markov model (HMM is applied to detect the outliers. After outliers are removed away, a more robust dynamic PLS model is obtained. In addition, an improved generalized predictive control (GPC with the tuning weights under dynamic PLS framework is proposed to deal with the interaction which is caused by the model mismatch. The results of two simulations demonstrate the effectiveness of proposed method.

Comparative analysis of pharmaceuticals versus industrial chemicals acute aquatic toxicity classification according to the United Nations classification system for chemicals. Assessment of the (Q)SAR predictability of pharmaceuticals acute aquatic toxicity and their predominant acute toxic mode-of-action.

Science.gov (United States)

Sanderson, Hans; Thomsen, Marianne

2009-06-01

Pharmaceuticals have been reported to be ubiquitously present in surface waters prompting concerns of effects of these bioactive substances. Meanwhile, there is a general scarcity of publicly available ecotoxicological data concerning pharmaceuticals. The aim of this paper was to compile a comprehensive database based on OECD's standardized measured ecotoxicological data and to evaluate if there is generally cause of greater concern with regards to pharmaceutical aquatic toxicological profiles relative to industrial chemicals. Comparisons were based upon aquatic ecotoxicity classification under the United Nations Global Harmonized System for classification and labeling of chemicals (GHS). Moreover, we statistically explored whether the predominant mode-of-action (MOA) for pharmaceuticals is narcosis. We found 275 pharmaceuticals with 569 acute aquatic effect data; 23 pharmaceuticals had chronic data. Pharmaceuticals were found to be more frequent than industrial chemicals in GHS category III. Acute toxicity was predictable (>92%) using a generic (Q)SAR ((Quantitative) Structure Activity Relationship) suggesting a narcotic MOA. Analysis of model prediction error suggests that 68% of the pharmaceuticals have a non-specific MOA. Additionally, the acute-to-chronic ratio (ACR) for 70% of the analyzed pharmaceuticals was below 25 further suggesting a non-specific MOA. Sub-lethal receptor-mediated effects may however have a more specific MOA.

A REVIEW OF SINGLE SPECIES TOXICITY TESTS: ARE THE TESTS RELIABLE PREDICTORS OF AQUATIC ECOSYSTEM COMMUNITY RESPONSES?

Science.gov (United States)

This document provides a comprehensive review to evaluate the reliability of indicator species toxicity test results in predicting aquatic ecosystem impacts, also called the ecological relevance of laboratory single species toxicity tests.

Copper bioavailability and toxicity to Mytilus galloprovincialis in Shelter Island Yacht Basin, San Diego, CA.

Science.gov (United States)

Bosse, Casey; Rosen, Gunther; Colvin, Marienne; Earley, Patrick; Santore, Robert; Rivera-Duarte, Ignacio

2014-08-15

The bioavailability and toxicity of copper (Cu) in Shelter Island Yacht Basin (SIYB), San Diego, CA, USA, was assessed with simultaneous toxicological, chemical, and modeling approaches. Toxicological measurements included laboratory toxicity testing with Mytilus galloprovincialis (Mediterranean mussel) embryos added to both site water (ambient) and site water spiked with multiple Cu concentrations. Chemical assessment of ambient samples included total and dissolved Cu concentrations, and Cu complexation capacity measurements. Modeling was based on chemical speciation and predictions of bioavailability and toxicity using a marine Biotic Ligand Model (BLM). Cumulatively, these methods assessed the natural buffering capacity of Cu in SIYB during singular wet and dry season sampling events. Overall, the three approaches suggested negligible bioavailability, and isolated observed or predicted toxicity, despite an observed gradient of increasing Cu concentration, both horizontally and vertically within the water body, exceeding current water quality criteria for saltwater. Published by Elsevier Ltd.

A study on improvement of analytical prediction model for spacer grid pressure loss coefficients

International Nuclear Information System (INIS)

Lim, Jonh Seon

2002-02-01

Nuclear fuel assemblies used in the nuclear power plants consist of the nuclear fuel rods, the control rod guide tubes, an instrument guide tube, spacer grids,a bottom nozzle, a top nozzle. The spacer grid is the most important component of the fuel assembly components for thermal hydraulic and mechanical design and analyses. The spacer grids fixed with the guide tubes support the fuel rods and have the very important role to activate thermal energy transfer by the coolant mixing caused to the turbulent flow and crossflow in the subchannels. In this paper, the analytical spacer grid pressure loss prediction model has been studied and improved by considering the test section wall to spacer grid gap pressure loss independently and applying the appropriate friction drag coefficient to predict pressure loss more accurately at the low Reynolds number region. The improved analytical model has been verified based on the hydraulic pressure drop test results for the spacer grids of three types with 5x5, 16x16, 17x17 arrays, respectively. The pressure loss coefficients predicted by the improved analytical model are coincident with those test results within ±12%. This result shows that the improved analytical model can be used for research and design change of the nuclear fuel assembly

ExpoCast: Exposure Science for Prioritization and Toxicity Testing

Science.gov (United States)

The US EPA is completing the Phase I pilot for a chemical prioritization research program, called ToxCastTM. Here EPA is developing methods for using computational chemistry, high-throughput screening, and toxicogenomic technologies to predict potential toxicity and prioritize l...

Dose-volume effects for pelvic bone marrow in predicting hematological toxicity in prostate cancer radiotherapy with pelvic node irradiation.

Science.gov (United States)

Sini, Carla; Fiorino, Claudio; Perna, Lucia; Noris Chiorda, Barbara; Deantoni, Chiara Lucrezia; Bianchi, Marco; Sacco, Vincenzo; Briganti, Alberto; Montorsi, Francesco; Calandrino, Riccardo; Di Muzio, Nadia; Cozzarini, Cesare

2016-01-01

To prospectively identify clinical/dosimetric predictors of acute/late hematologic toxicity (HT) in chemo-naÏve patients treated with whole-pelvis radiotherapy (WPRT) for prostate cancer. Data of 121 patients treated with adjuvant/salvage WPRT were analyzed (static-field IMRT n=19; VMAT/Rapidarc n=57; Tomotherapy n=45). Pelvic bone marrow (BM) was delineated as ilium (IL), lumbosacral, lower and whole pelvis (WP), and the relative DVHs were calculated. HT was graded both according to CTCAE v4.03 and as variation in percentage relative to baseline. Logistic regression was used to analyze association between HT and clinical/DVHs factors. Significant differences (p<0.005) in the DVH of BM volumes between different techniques were found: Tomotherapy was associated with larger volumes receiving low doses (3-20 Gy) and smaller receiving 40-50 Gy. Lower baseline absolute values of WBC, neutrophils and lymphocytes (ALC) predicted acute/late HT (p ⩽ 0.001). Higher BM V40 was associated with higher risk of acute Grade3 (OR=1.018) or late Grade2 lymphopenia (OR=1.005). Two models predicting lymphopenia were developed, both including baseline ALC, and BM WP-V40 (AUC=0.73) and IL-V40+smoking (AUC=0.904) for acute/late respectively. Specific regions of pelvic BM predicting acute/late lymphopenia, a risk factor for viral infections, were identified. The 2-variable models including specific constraints to BM may help reduce HT. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Predictive power of theoretical modelling of the nuclear mean field: examples of improving predictive capacities

Science.gov (United States)

Dedes, I.; Dudek, J.

2018-03-01

We examine the effects of the parametric correlations on the predictive capacities of the theoretical modelling keeping in mind the nuclear structure applications. The main purpose of this work is to illustrate the method of establishing the presence and determining the form of parametric correlations within a model as well as an algorithm of elimination by substitution (see text) of parametric correlations. We examine the effects of the elimination of the parametric correlations on the stabilisation of the model predictions further and further away from the fitting zone. It follows that the choice of the physics case and the selection of the associated model are of secondary importance in this case. Under these circumstances we give priority to the relative simplicity of the underlying mathematical algorithm, provided the model is realistic. Following such criteria, we focus specifically on an important but relatively simple case of doubly magic spherical nuclei. To profit from the algorithmic simplicity we chose working with the phenomenological spherically symmetric Woods–Saxon mean-field. We employ two variants of the underlying Hamiltonian, the traditional one involving both the central and the spin orbit potential in the Woods–Saxon form and the more advanced version with the self-consistent density-dependent spin–orbit interaction. We compare the effects of eliminating of various types of correlations and discuss the improvement of the quality of predictions (‘predictive power’) under realistic parameter adjustment conditions.

Toxicity of the fluoroquinolone antibiotics enrofloxacin and ciprofloxacin to photoautotrophic aquatic organisms.

Science.gov (United States)

Ebert, Ina; Bachmann, Jean; Kühnen, Ute; Küster, Anette; Kussatz, Carola; Maletzki, Dirk; Schlüter, Christoph

2011-12-01

The present study investigated the growth inhibition effect of the fluoroquinolone antibiotics enrofloxacin and ciprofloxacin on four photoautotrophic aquatic species: the freshwater microalga Desmodesmus subspicatus, the cyanobacterium Anabaena flos-aquae, the monocotyledonous macrophyte Lemna minor, and the dicotyledonous macrophyte Myriophyllum spicatum. Both antibiotics, which act by inhibiting the bacterial DNA gyrase, demonstrated high toxicity to A. flos-aquae and L. minor and moderate to slight toxicity to D. subspicatus and M. spicatum. The cyanobacterium was the most sensitive species with median effective concentration (EC50) values of 173 and 10.2 µg/L for enrofloxacin and ciprofloxacin, respectively. Lemna minor proved to be similarly sensitive, with EC50 values of 107 and 62.5 µg/L for enrofloxacin and ciprofloxacin, respectively. While enrofloxacin was more toxic to green algae, ciprofloxacin was more toxic to cyanobacteria. Calculated EC50s for D. subspicatus were 5,568 µg/L and >8,042 µg/L for enrofloxacin and ciprofloxacin, respectively. These data, as well as effect data from the literature, were compared with predicted and reported environmental concentrations. For two of the four species, a risk was identified at ciprofloxacin concentrations found in surface waters, sewage treatment plant influents and effluents, as well as in hospital effluents. For ciprofloxacin the results of the present study indicate a risk even at the predicted environmental concentration. In contrast, for enrofloxacin no risk was identified at predicted and measured concentrations. Copyright © 2011 SETAC.

Improving Wind Farm Dispatchability Using Model Predictive Control for Optimal Operation of Grid-Scale Energy Storage

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Douglas Halamay

2014-09-01

Full Text Available This paper demonstrates the use of model-based predictive control for energy storage systems to improve the dispatchability of wind power plants. Large-scale wind penetration increases the variability of power flow on the grid, thus increasing reserve requirements. Large energy storage systems collocated with wind farms can improve dispatchability of the wind plant by storing energy during generation over-the-schedule and sourcing energy during generation under-the-schedule, essentially providing on-site reserves. Model predictive control (MPC provides a natural framework for this application. By utilizing an accurate energy storage system model, control actions can be planned in the context of system power and state-of-charge limitations. MPC also enables the inclusion of predicted wind farm performance over a near-term horizon that allows control actions to be planned in anticipation of fast changes, such as wind ramps. This paper demonstrates that model-based predictive control can improve system performance compared with a standard non-predictive, non-model-based control approach. It is also demonstrated that secondary objectives, such as reducing the rate of change of the wind plant output (i.e., ramps, can be considered and successfully implemented within the MPC framework. Specifically, it is shown that scheduling error can be reduced by 81%, reserve requirements can be improved by up to 37%, and the number of ramp events can be reduced by 74%.

Improving Predictive Modeling in Pediatric Drug Development: Pharmacokinetics, Pharmacodynamics, and Mechanistic Modeling

Energy Technology Data Exchange (ETDEWEB)

Slikker, William; Young, John F.; Corley, Rick A.; Dorman, David C.; Conolly, Rory B.; Knudsen, Thomas; Erstad, Brian L.; Luecke, Richard H.; Faustman, Elaine M.; Timchalk, Chuck; Mattison, Donald R.

2005-07-26

A workshop was conducted on November 18?19, 2004, to address the issue of improving predictive models for drug delivery to developing humans. Although considerable progress has been made for adult humans, large gaps remain for predicting pharmacokinetic/pharmacodynamic (PK/PD) outcome in children because most adult models have not been tested during development. The goals of the meeting included a description of when, during development, infants/children become adultlike in handling drugs. The issue of incorporating the most recent advances into the predictive models was also addressed: both the use of imaging approaches and genomic information were considered. Disease state, as exemplified by obesity, was addressed as a modifier of drug pharmacokinetics and pharmacodynamics during development. Issues addressed in this workshop should be considered in the development of new predictive and mechanistic models of drug kinetics and dynamics in the developing human.

Toxic stress and child refugees.

Science.gov (United States)

Murray, John S

2018-01-01

The purpose of this article was to describe the phenomenon of toxic stress and its impact on the physical and mental health of child refugees. Almost two decades ago, researchers found that recurring adverse childhood events (ACEs; e.g., physical, psychological, and sexual abuse, neglect, and household dysfunction such as substance abuse, mental illness, and criminal behavior) were associated with a significant increase in serious illnesses during adulthood. Illnesses include heart, lung, and liver disease, cancer, and bone fractures. The scientists reported that experiencing four or more ACEs during childhood significantly increases the risk for toxic stress. Toxic stress is defined as the exposure to extreme, frequent, and persistent adverse events without the presence of a supportive caretaker. There is a paucity of literature related to toxic stress and child refugees. However, it has been clearly established that the prolonged brutal and traumatizing war in Syria is having a profound impact on the physical and mental health of child refugees at a distressing rate. Prevention of toxic stress should be a primary goal of all pediatric healthcare professionals working with child refugees. While this seems daunting given the population, and the seemingly insurmountable stressors they experience, some basic interventions should be considered. Providing basic anticipatory guidance to parents and caregivers of child refugees, to encourage positive parenting and strengthening support networks, will be highly effective in developing the requisite buffers that mitigate the effects of stress and avoid toxic stress. Efforts should also be focused on addressing caregiver stress and improving their ability to provide safe, reliable, and nurturing care that will help to mitigate any stress response experienced by a child. It is critical that greater awareness be placed on the effects of toxic stress on child refugees who are exposed to significant adverse events early in life

Parametric Bayesian priors and better choice of negative examples improve protein function prediction.

Science.gov (United States)

Youngs, Noah; Penfold-Brown, Duncan; Drew, Kevin; Shasha, Dennis; Bonneau, Richard

2013-05-01

Computational biologists have demonstrated the utility of using machine learning methods to predict protein function from an integration of multiple genome-wide data types. Yet, even the best performing function prediction algorithms rely on heuristics for important components of the algorithm, such as choosing negative examples (proteins without a given function) or determining key parameters. The improper choice of negative examples, in particular, can hamper the accuracy of protein function prediction. We present a novel approach for choosing negative examples, using a parameterizable Bayesian prior computed from all observed annotation data, which also generates priors used during function prediction. We incorporate this new method into the GeneMANIA function prediction algorithm and demonstrate improved accuracy of our algorithm over current top-performing function prediction methods on the yeast and mouse proteomes across all metrics tested. Code and Data are available at: http://bonneaulab.bio.nyu.edu/funcprop.html

Clinical use of the hyperthermia treatment planning system HyperPlan to predict effectiveness and toxicity

International Nuclear Information System (INIS)

Sreenivasa, Geetha; Gellermann, Johanna; Rau, Beate; Nadobny, Jacek; Schlag, Peter; Deuflhard, Peter; Felix, Roland; Wust, Peter

2003-01-01

Purpose: The main aim is to prove the clinical practicability of the hyperthermia treatment planning system HyperPlan on a β-test level. Data and observations obtained from clinical hyperthermia are compared with the numeric methods FE (finite element) and FDTD (finite difference time domain), respectively. Methods and Materials: The planning system HyperPlan is built on top of the modular, object-oriented platform for visualization and model generation AMIRA. This system already contains powerful algorithms for image processing, geometric modeling, and three-dimensional graphics display. A number of hyperthermia-specific modules are provided, enabling the creation of three-dimensional tetrahedral patient models suitable for treatment planning. Two numeric methods, FE and FDTD, are implemented in HyperPlan for solving Maxwell's equations. Both methods base their calculations on segmented (contour based) CT or MR image data. A tetrahedral grid is generated from the segmented tissue boundaries, consisting of approximately 80,000 tetrahedrons per patient. The FE method necessitates, primarily, this tetrahedral grid for the calculation of the E-field. The FDTD method, on the other hand, calculates the E-field on a cubical grid, but also requires a tetrahedral grid for correction at electrical interfaces. In both methods, temperature distributions are calculated on the tetrahedral grid by solving the bioheat transfer equation with the FE method. Segmentation, grid generation, E-field, and temperature calculation can be carried out in clinical practice at an acceptable time expenditure of about 1-2 days. Results: All 30 patients we analyzed with cervical, rectal, and prostate carcinoma exhibit a good correlation between the model calculations and the attained clinical data regarding acute toxicity (hot spots), prediction of easy-to-heat or difficult-to-heat patients, and the dependency on various other individual parameters. We could show sufficient agreement between

Predicting_Systemic_Toxicity_Effects_ArchTox_2017_Data

Data.gov (United States)

U.S. Environmental Protection Agency — In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was...

Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening.

Science.gov (United States)

Ain, Qurrat Ul; Aleksandrova, Antoniya; Roessler, Florian D; Ballester, Pedro J

2015-01-01

Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure-based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine-learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine-learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert-selected structural features can be strongly improved by a machine-learning approach based on nonlinear regression allied with comprehensive data-driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405-424. doi: 10.1002/wcms.1225 For further resources related to this article, please visit the WIREs website.

Metal mixture toxicity to aquatic biota in laboratory experiments: Application of the WHAM-F{sub TOX} model

Energy Technology Data Exchange (ETDEWEB)

Tipping, E., E-mail: et@ceh.ac.uk; Lofts, S.

2013-10-15

Highlights: •Metal accumulation by living organisms is successfully simulated with WHAM. •Modelled organism-bound metal provides a measure of toxic exposure. •The toxic potency of individual bound metals is quantified by fitting toxicity data. •Eleven laboratory mixture toxicity data sets were parameterised. •Relatively little variability amongst individual test organisms is indicated. -- Abstract: The WHAM-F{sub TOX} model describes the combined toxic effects of protons and metal cations towards aquatic organisms through the toxicity function (F{sub TOX}), a linear combination of the products of organism-bound cation and a toxic potency coefficient (α{sub i}) for each cation. Organism-bound, metabolically-active, cation is quantified by the proxy variable, amount bound by humic acid (HA), as predicted by the WHAM chemical speciation model. We compared published measured accumulations of metals by living organisms (bacteria, algae, invertebrates) in different solutions, with WHAM predictions of metal binding to humic acid in the same solutions. After adjustment for differences in binding site density, the predictions were in reasonable line with observations (for logarithmic variables, r{sup 2} = 0.89, root mean squared deviation = 0.44), supporting the use of HA binding as a proxy. Calculated loadings of H{sup +}, Al, Cu, Zn, Cd, Pb and UO{sub 2} were used to fit observed toxic effects in 11 published mixture toxicity experiments involving bacteria, macrophytes, invertebrates and fish. Overall, WHAM-F{sub TOX} gave slightly better fits than a conventional additive model based on solution concentrations. From the derived values of α{sub i}, the toxicity of bound cations can tentatively be ranked in the order: H < Al < (Zn–Cu–Pb–UO{sub 2}) < Cd. The WHAM-F{sub TOX} analysis indicates much narrower ranges of differences amongst individual organisms in metal toxicity tests than was previously thought. The model potentially provides a means to

Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans

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Assaf Gottlieb

2017-11-01

Full Text Available Abstract Background Genome-wide association studies are useful for discovering genotype–phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into “gene level” effects. Methods Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression—on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. Results We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Conclusions Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort

Reliable B cell epitope predictions: impacts of method development and improved benchmarking

DEFF Research Database (Denmark)

Kringelum, Jens Vindahl; Lundegaard, Claus; Lund, Ole

2012-01-01

biomedical applications such as; rational vaccine design, development of disease diagnostics and immunotherapeutics. However, experimental mapping of epitopes is resource intensive making in silico methods an appealing complementary approach. To date, the reported performance of methods for in silico mapping...... evaluation data set improved from 0.712 to 0.727. Our results thus demonstrate that given proper benchmark definitions, B-cell epitope prediction methods achieve highly significant predictive performances suggesting these tools to be a powerful asset in rational epitope discovery. The updated version...

Comparison and avoidance of toxicity of penetrating cryoprotectants.

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Edyta A Szurek

Full Text Available The objective of this study was to elucidate the toxicity of widely used penetrating cryoprotective agents (CPAs to mammalian oocytes. To this end, mouse metaphase II (M II oocytes were exposed to 1.5 M solutions of dimethylsulfoxide (DMSO, ethylene glycol (EG, or propanediol (PROH prepared in phosphate buffered saline (PBS containing 10% fetal bovine serum. To address the time- and temperature-dependence of the CPA toxicity, M II oocytes were exposed to the aforementioned CPAs at room temperature (RT, ∼23°C and 37°C for 15 or 30 minutes. Subsequently, the toxicity of each CPA was evaluated by examining post-exposure survival, fertilization, embryonic development, chromosomal abnormalities, and parthenogenetic activation of treated oocytes. Untreated oocytes served as controls. Exposure of MII oocytes to 1.5 M DMSO or 1.5 M EG at RT for 15 min did not adversely affect any of the evaluated criteria. In contrast, 1.5 M PROH induced a significant increase in oocyte degeneration (54.2% and parthenogenetic activation (16% under same conditions. When the CPA exposure was performed at 37°C, the toxic effect of PROH further increased, resulting in lower survival (15% and no fertilization while the toxicity of DMSO and EG was still insignificant. Nevertheless, it was possible to completely avoid the toxicity of PROH by decreasing its concentration to 0.75 M and combining it with 0.75 M DMSO to bring the total CPA concentration to a cryoprotective level. Moreover, combining lower concentrations (i.e., 0.75 M of PROH and DMSO significantly improved the cryosurvival of MII oocytes compared to the equivalent concentration of DMSO alone. Taken together, our results suggest that from the perspective of CPA toxicity, DMSO and EG are safer to use in slow cooling protocols while a lower concentration of PROH can be combined with another CPA to avoid its toxicity and to improve the cryosurvival as well.

Nanosecond pulsed electric field incorporation technique to predict molecular mechanisms of teratogenicity and developmental toxicity of estradiol-17β on medaka embryos.