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Sample records for automated biomarker discovery

  1. Novel automated biomarker discovery work flow for urinary peptidomics

    DEFF Research Database (Denmark)

    Balog, Crina I.; Hensbergen, Paul J.; Derks, Rico

    2009-01-01

    eluted peptides using MALDI-TOF, Fourier transform ion cyclotron resonance, and liquid chromatography-iontrap mass spectrometry. We determined qualitative and quantitative reproducibility of the system and robustness of the method using BSA digests and urine samples, and we used a selected set of urine......Urine is potentially a rich source of peptide biomarkers, but reproducible, high-throughput peptidomic analysis is often hampered by the inherent variability in factors such as pH and salt concentration. Our goal was to develop a generally applicable, rapid, and robust method for screening large...... samples from Schistosoma haematobium-infected individuals to evaluate clinical applicability. RESULTS: The automated RP-SCX sample cleanup and fractionation system exhibits a high qualitative and quantitative reproducibility, with both BSA standards and urine samples. Because of the relatively high...

  2. Automated Sample Preparation Platform for Mass Spectrometry-Based Plasma Proteomics and Biomarker Discovery

    Directory of Open Access Journals (Sweden)

    Vilém Guryča

    2014-03-01

    Full Text Available The identification of novel biomarkers from human plasma remains a critical need in order to develop and monitor drug therapies for nearly all disease areas. The discovery of novel plasma biomarkers is, however, significantly hampered by the complexity and dynamic range of proteins within plasma, as well as the inherent variability in composition from patient to patient. In addition, it is widely accepted that most soluble plasma biomarkers for diseases such as cancer will be represented by tissue leakage products, circulating in plasma at low levels. It is therefore necessary to find approaches with the prerequisite level of sensitivity in such a complex biological matrix. Strategies for fractionating the plasma proteome have been suggested, but improvements in sensitivity are often negated by the resultant process variability. Here we describe an approach using multidimensional chromatography and on-line protein derivatization, which allows for higher sensitivity, whilst minimizing the process variability. In order to evaluate this automated process fully, we demonstrate three levels of processing and compare sensitivity, throughput and reproducibility. We demonstrate that high sensitivity analysis of the human plasma proteome is possible down to the low ng/mL or even high pg/mL level with a high degree of technical reproducibility.

  3. Automated Morphological and Morphometric Analysis of Mass Spectrometry Imaging Data: Application to Biomarker Discovery

    Science.gov (United States)

    Picard de Muller, Gaël; Ait-Belkacem, Rima; Bonnel, David; Longuespée, Rémi; Stauber, Jonathan

    2017-12-01

    Mass spectrometry imaging datasets are mostly analyzed in terms of average intensity in regions of interest. However, biological tissues have different morphologies with several sizes, shapes, and structures. The important biological information, contained in this highly heterogeneous cellular organization, could be hidden by analyzing the average intensities. Finding an analytical process of morphology would help to find such information, describe tissue model, and support identification of biomarkers. This study describes an informatics approach for the extraction and identification of mass spectrometry image features and its application to sample analysis and modeling. For the proof of concept, two different tissue types (healthy kidney and CT-26 xenograft tumor tissues) were imaged and analyzed. A mouse kidney model and tumor model were generated using morphometric - number of objects and total surface - information. The morphometric information was used to identify m/z that have a heterogeneous distribution. It seems to be a worthwhile pursuit as clonal heterogeneity in a tumor is of clinical relevance. This study provides a new approach to find biomarker or support tissue classification with more information. [Figure not available: see fulltext.

  4. Exosomes in urine biomarker discovery.

    Science.gov (United States)

    Huebner, Alyssa R; Somparn, Poorichaya; Benjachat, Thitima; Leelahavanichkul, Asada; Avihingsanon, Yingyos; Fenton, Robert A; Pisitkun, Trairak

    2015-01-01

    Nanovesicles present in urine the so-called urinary exosomes have been found to be secreted by every epithelial cell type lining the urinary tract system in human. Urinary exosomes are an appealing source for biomarker discovery as they contain molecular constituents of their cell of origin, including proteins and genetic materials, and they can be isolated in a non-invasive manner. Following the discovery of urinary exosomes in 2004, many studies have been performed using urinary exosomes as a starting material to identify biomarkers in various renal, urogenital, and systemic diseases. Here, we describe the discovery of urinary exosomes and address the issues on the collection, isolation, and normalization of urinary exosomes as well as delineate the systems biology approach to biomarker discovery using urinary exosomes.

  5. Automated Supernova Discovery (Abstract)

    Science.gov (United States)

    Post, R. S.

    2015-12-01

    (Abstract only) We are developing a system of robotic telescopes for automatic recognition of Supernovas as well as other transient events in collaboration with the Puckett Supernova Search Team. At the SAS2014 meeting, the discovery program, SNARE, was first described. Since then, it has been continuously improved to handle searches under a wide variety of atmospheric conditions. Currently, two telescopes are used to build a reference library while searching for PSN with a partial library. Since data is taken every night without clouds, we must deal with varying atmospheric and high background illumination from the moon. Software is configured to identify a PSN, reshoot for verification with options to change the run plan to acquire photometric or spectrographic data. The telescopes are 24-inch CDK24, with Alta U230 cameras, one in CA and one in NM. Images and run plans are sent between sites so the CA telescope can search while photometry is done in NM. Our goal is to find bright PSNs with magnitude 17.5 or less which is the limit of our planned spectroscopy. We present results from our first automated PSN discoveries and plans for PSN data acquisition.

  6. Shotgun Proteomics and Biomarker Discovery

    Directory of Open Access Journals (Sweden)

    W. Hayes McDonald

    2002-01-01

    Full Text Available Coupling large-scale sequencing projects with the amino acid sequence information that can be gleaned from tandem mass spectrometry (MS/MS has made it much easier to analyze complex mixtures of proteins. The limits of this “shotgun” approach, in which the protein mixture is proteolytically digested before separation, can be further expanded by separating the resulting mixture of peptides prior to MS/MS analysis. Both single dimensional high pressure liquid chromatography (LC and multidimensional LC (LC/LC can be directly interfaced with the mass spectrometer to allow for automated collection of tremendous quantities of data. While there is no single technique that addresses all proteomic challenges, the shotgun approaches, especially LC/LC-MS/MS-based techniques such as MudPIT (multidimensional protein identification technology, show advantages over gel-based techniques in speed, sensitivity, scope of analysis, and dynamic range. Advances in the ability to quantitate differences between samples and to detect for an array of post-translational modifications allow for the discovery of classes of protein biomarkers that were previously unassailable.

  7. Glycoscience aids in biomarker discovery

    Directory of Open Access Journals (Sweden)

    Serenus Hua1,2 & Hyun Joo An1,2,*

    2012-06-01

    Full Text Available The glycome consists of all glycans (or carbohydrates within abiological system, and modulates a wide range of important biologicalactivities, from protein folding to cellular communications.The mining of the glycome for disease markers representsa new paradigm for biomarker discovery; however, this effortis severely complicated by the vast complexity and structuraldiversity of glycans. This review summarizes recent developmentsin analytical technology and methodology as applied tothe fields of glycomics and glycoproteomics. Mass spectrometricstrategies for glycan compositional profiling are described, as arepotential refinements which allow structure-specific profiling.Analytical methods that can discern protein glycosylation at aspecific site of modification are also discussed in detail.Biomarker discovery applications are shown at each level ofanalysis, highlighting the key role that glycoscience can play inhelping scientists understand disease biology.

  8. Systems biology and biomarker discovery

    Energy Technology Data Exchange (ETDEWEB)

    Rodland, Karin D.

    2010-12-01

    Medical practitioners have always relied on surrogate markers of inaccessible biological processes to make their diagnosis, whether it was the pallor of shock, the flush of inflammation, or the jaundice of liver failure. Obviously, the current implementation of biomarkers for disease is far more sophisticated, relying on highly reproducible, quantitative measurements of molecules that are often mechanistically associated with the disease in question, as in glycated hemoglobin for the diagnosis of diabetes [1] or the presence of cardiac troponins in the blood for confirmation of myocardial infarcts [2]. In cancer, where the initial symptoms are often subtle and the consequences of delayed diagnosis often drastic for disease management, the impetus to discover readily accessible, reliable, and accurate biomarkers for early detection is compelling. Yet despite years of intense activity, the stable of clinically validated, cost-effective biomarkers for early detection of cancer is pathetically small and still dominated by a handful of markers (CA-125, CEA, PSA) first discovered decades ago. It is time, one could argue, for a fresh approach to the discovery and validation of disease biomarkers, one that takes full advantage of the revolution in genomic technologies and in the development of computational tools for the analysis of large complex datasets. This issue of Disease Markers is dedicated to one such new approach, loosely termed the 'Systems Biology of Biomarkers'. What sets the Systems Biology approach apart from other, more traditional approaches, is both the types of data used, and the tools used for data analysis - and both reflect the revolution in high throughput analytical methods and high throughput computing that has characterized the start of the twenty first century.

  9. Mass spectrometry for protein quantification in biomarker discovery.

    Science.gov (United States)

    Wang, Mu; You, Jinsam

    2012-01-01

    Major technological advances have made proteomics an extremely active field for biomarker discovery in recent years due primarily to the development of newer mass spectrometric technologies and the explosion in genomic and protein bioinformatics. This leads to an increased emphasis on larger scale, faster, and more efficient methods for detecting protein biomarkers in human tissues, cells, and biofluids. Most current proteomic methodologies for biomarker discovery, however, are not highly automated and are generally labor-intensive and expensive. More automation and improved software programs capable of handling a large amount of data are essential to reduce the cost of discovery and to increase throughput. In this chapter, we discuss and describe mass spectrometry-based proteomic methods for quantitative protein analysis.

  10. Biomarkers: in medicine, drug discovery, and environmental health

    National Research Council Canada - National Science Library

    Vaidya, Vishal S; Bonventre, Joseph V

    2010-01-01

    ... Identification Using Mass Spectrometry Sample Preparation Protein Quantitation Examples of Biomarker Discovery and Evaluation Challenges in Proteomic Biomarker Discovery The Road Forward: Targeted ...

  11. Sparse Mbplsr for Metabolomics Data and Biomarker Discovery

    DEFF Research Database (Denmark)

    Karaman, İbrahim

    2014-01-01

    Metabolomics is part of systems biology and a rapidly evolving field. It is a tool to analyze multiple metabolic changes in biofluids and tissues and aims at determining biomarkers in the metabolism. LC-MS (liquid chromatography – mass spectrometry), GC-MS (gas chromatography – mass spectrometry...... the link between high throughput metabolomics data generated on different analytical platforms, discover important metabolites deriving from the digestion processes in the gut, and automate metabolic pathway discovery from mass spectrometry. PLS (partial least squares) based chemometric methods were......, potential biomarkers from LC-MS and NMR data could be detected and the relationships among the measurement variables of both analytical methods could be studied. Detection of potential biomarkers is followed up by an identification process through online metabolite and pathway databases. This process...

  12. Mass Spectrometry-Based Biomarker Discovery.

    Science.gov (United States)

    Zhou, Weidong; Petricoin, Emanuel F; Longo, Caterina

    2017-01-01

    The discovery of candidate biomarkers within the entire proteome is one of the most important and challenging goals in proteomic research. Mass spectrometry-based proteomics is a modern and promising technology for semiquantitative and qualitative assessment of proteins, enabling protein sequencing and identification with exquisite accuracy and sensitivity. For mass spectrometry analysis, protein extractions from tissues or body fluids and subsequent protein fractionation represent an important and unavoidable step in the workflow for biomarker discovery. Following extraction of proteins, the protein mixture must be digested, reduced, alkylated, and cleaned up prior to mass spectrometry. The aim of our chapter is to provide comprehensible and practical lab procedures for sample digestion, protein fractionation, and subsequent mass spectrometry analysis.

  13. Using Aptamers for Cancer Biomarker Discovery

    Directory of Open Access Journals (Sweden)

    Yun Min Chang

    2013-01-01

    Full Text Available Aptamers are single-stranded synthetic DNA- or RNA-based oligonucleotides that fold into various shapes to bind to a specific target, which includes proteins, metals, and molecules. Aptamers have high affinity and high specificity that are comparable to that of antibodies. They are obtained using iterative method, called (Systematic Evolution of Ligands by Exponential Enrichment SELEX and cell-based SELEX (cell-SELEX. Aptamers can be paired with recent advances in nanotechnology, microarray, microfluidics, and other technologies for applications in clinical medicine. One particular area that aptamers can shed a light on is biomarker discovery. Biomarkers are important in diagnosis and treatment of cancer. In this paper, we will describe ways in which aptamers can be used to discover biomarkers for cancer diagnosis and therapeutics.

  14. Computational Analyses for Transplant Biomarker Discovery

    Directory of Open Access Journals (Sweden)

    Anyou eWang

    2015-09-01

    Full Text Available Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called omics provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key computational approaches for selecting efficiently the best subset of biomarkers from high dimensional omics data are highlighted. Prediction models are also introduced and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems.

  15. The Process Chain for Peptidomic Biomarker Discovery

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    Michael Schrader

    2006-01-01

    Full Text Available Over the last few years the interest in diagnostic markers for specific diseases has increased continuously. It is expected that they not only improve a patient's medical treatment but also contribute to accelerating the process of drug development. This demand for new biomarkers is caused by a lack of specific and sensitive diagnosis in many diseases. Moreover, diseases usually occur in different types or stages which may need different diagnostic and therapeutic measures. Their differentiation has to be considered in clinical studies as well. Therefore, it is important to translate a macroscopic pathological or physiological finding into a microscopic view of molecular processes and vice versa, though it is a difficult and tedious task. Peptides play a central role in many physiological processes and are of importance in several areas of drug research. Exploration of endogenous peptides in biologically relevant sources may directly lead to new drug substances, serve as key information on a new target and can as well result in relevant biomarker candidates. A comprehensive analysis of peptides and small proteins of a biological system corresponding to the respective genomic information (peptidomics®methods was a missing link in proteomics. A new peptidomic technology platform addressing peptides was recently presented, developed by adaptation of the striving proteomic technologies. Here, concepts of using peptidomics technologies for biomarker discovery are presented and illustrated with examples. It is discussed how the biological hypothesis and sample quality determine the result of the study. A detailed study design, appropriate choice and application of technology as well as thorough data interpretation can lead to significant results which have to be interpreted in the context of the underlying disease. The identified biomarker candidates will be characterised in validation studies before use. This approach for discovery of peptide

  16. Cancer Biomarker Discovery: Lectin-Based Strategies Targeting Glycoproteins

    Directory of Open Access Journals (Sweden)

    David Clark

    2012-01-01

    Full Text Available Biomarker discovery can identify molecular markers in various cancers that can be used for detection, screening, diagnosis, and monitoring of disease progression. Lectin-affinity is a technique that can be used for the enrichment of glycoproteins from a complex sample, facilitating the discovery of novel cancer biomarkers associated with a disease state.

  17. Biomarker Gene Signature Discovery Integrating Network Knowledge

    Directory of Open Access Journals (Sweden)

    Holger Fröhlich

    2012-02-01

    Full Text Available Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step towards a better personalized medicine. During the last decade various methods, mainly coming from the machine learning or statistical domain, have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinical diagnosis is the typical low reproducibility of these signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. Here we review the current state of research in this field by giving an overview about so-far proposed approaches.

  18. Emerging Concepts and Methodologies in Cancer Biomarker Discovery.

    Science.gov (United States)

    Lu, Meixia; Zhang, Jinxiang; Zhang, Lanjing

    2017-01-01

    Cancer biomarker discovery is a critical part of cancer prevention and treatment. Despite the decades of effort, only a small number of cancer biomarkers have been identified for and validated in clinical settings. Conceptual and methodological breakthroughs may help accelerate the discovery of additional cancer biomarkers, particularly their use for diagnostics. In this review, we have attempted to review the emerging concepts in cancer biomarker discovery, including real-world evidence, open access data, and data paucity in rare or uncommon cancers. We have also summarized the recent methodological progress in cancer biomarker discovery, such as high-throughput sequencing, liquid biopsy, big data, artificial intelligence (AI), and deep learning and neural networks. Much attention has been given to the methodological details and comparison of the methodologies. Notably, these concepts and methodologies interact with each other and will likely lead to synergistic effects when carefully combined. Newer, more innovative concepts and methodologies are emerging as the current emerging ones became mainstream and widely applied to the field. Some future challenges are also discussed. This review contributes to the development of future theoretical frameworks and technologies in cancer biomarker discovery and will contribute to the discovery of more useful cancer biomarkers.

  19. Biomarker discovery in high grade sarcomas by mass spectrometry imaging

    OpenAIRE

    Lou, S.

    2017-01-01

    This thesis demonstrates a detailed biomarker discovery Mass Spectrometry Imaging workflow for histologically heterogeneous high grade sarcomas. Panels of protein and metabolite signatures were discovered either distinguishing different histological subtypes or stratifying high risk patients with poor survival.

  20. Mass spectrometry imaging enriches biomarker discovery approaches with candidate mapping.

    Science.gov (United States)

    Scott, Alison J; Jones, Jace W; Orschell, Christie M; MacVittie, Thomas J; Kane, Maureen A; Ernst, Robert K

    2014-01-01

    Integral to the characterization of radiation-induced tissue damage is the identification of unique biomarkers. Biomarker discovery is a challenging and complex endeavor requiring both sophisticated experimental design and accessible technology. The resources within the National Institute of Allergy and Infectious Diseases (NIAID)-sponsored Consortium, Medical Countermeasures Against Radiological Threats (MCART), allow for leveraging robust animal models with novel molecular imaging techniques. One such imaging technique, MALDI (matrix-assisted laser desorption ionization) mass spectrometry imaging (MSI), allows for the direct spatial visualization of lipids, proteins, small molecules, and drugs/drug metabolites-or biomarkers-in an unbiased manner. MALDI-MSI acquires mass spectra directly from an intact tissue slice in discrete locations across an x, y grid that are then rendered into a spatial distribution map composed of ion mass and intensity. The unique mass signals can be plotted to generate a spatial map of biomarkers that reflects pathology and molecular events. The crucial unanswered questions that can be addressed with MALDI-MSI include identification of biomarkers for radiation damage that reflect the response to radiation dose over time and the efficacy of therapeutic interventions. Techniques in MALDI-MSI also enable integration of biomarker identification among diverse animal models. Analysis of early, sublethally irradiated tissue injury samples from diverse mouse tissues (lung and ileum) shows membrane phospholipid signatures correlated with histological features of these unique tissues. This paper will discuss the application of MALDI-MSI for use in a larger biomarker discovery pipeline.

  1. Secreted proteins as a fundamental source for biomarker discovery

    Czech Academy of Sciences Publication Activity Database

    Šťastná, Miroslava; Van Eyk, J.E.

    2012-01-01

    Roč. 12, 4-5 (2012), s. 722-735 ISSN 1615-9853 Institutional research plan: CEZ:AV0Z40310501 Keywords : conditioned media * secreted proteins * proteomics * biomarker discovery Subject RIV: CB - Analytical Chemistry, Separation Impact factor: 4.132, year: 2012

  2. Exhaled Breath Condensate for Proteomic Biomarker Discovery

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    Sean W. Harshman

    2014-07-01

    Full Text Available Exhaled breath condensate (EBC has been established as a potential source of respiratory biomarkers. Compared to the numerous small molecules identified, the protein content of EBC has remained relatively unstudied due to the methodological and technical difficulties surrounding EBC analysis. In this review, we discuss the proteins identified in EBC, by mass spectrometry, focusing on the significance of those proteins identified. We will also review the limitations surrounding mass spectral EBC protein analysis emphasizing recommendations to enhance EBC protein identifications by mass spectrometry. Finally, we will provide insight into the future directions of the EBC proteomics field.

  3. Biomarker discovery in mass spectrometry-based urinary proteomics.

    Science.gov (United States)

    Thomas, Samuel; Hao, Ling; Ricke, William A; Li, Lingjun

    2016-04-01

    Urinary proteomics has become one of the most attractive topics in disease biomarker discovery. MS-based proteomic analysis has advanced continuously and emerged as a prominent tool in the field of clinical bioanalysis. However, only few protein biomarkers have made their way to validation and clinical practice. Biomarker discovery is challenged by many clinical and analytical factors including, but not limited to, the complexity of urine and the wide dynamic range of endogenous proteins in the sample. This article highlights promising technologies and strategies in the MS-based biomarker discovery process, including study design, sample preparation, protein quantification, instrumental platforms, and bioinformatics. Different proteomics approaches are discussed, and progresses in maximizing urinary proteome coverage and standardization are emphasized in this review. MS-based urinary proteomics has great potential in the development of noninvasive diagnostic assays in the future, which will require collaborative efforts between analytical scientists, systems biologists, and clinicians. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  4. Application of “omics” to Prion Biomarker Discovery

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    Rhiannon L. C. H. Huzarewich

    2010-01-01

    Full Text Available The advent of genomics and proteomics has been a catalyst for the discovery of biomarkers able to discriminate biological processes such as the pathogenesis of complex diseases. Prompt detection of prion diseases is particularly desirable given their transmissibility, which is responsible for a number of human health risks stemming from exogenous sources of prion protein. Diagnosis relies on the ability to detect the biomarker PrPSc, a pathological isoform of the host protein PrPC, which is an essential component of the infectious prion. Immunochemical detection of PrPSc is specific and sensitive enough for antemortem testing of brain tissue, however, this is not the case in accessible biological fluids or for the detection of recently identified novel prions with unique biochemical properties. A complementary approach to the detection of PrPSc itself is to identify alternative, “surrogate” gene or protein biomarkers indicative of disease. Biomarkers are also useful to track the progress of disease, especially important in the assessment of therapies, or to identify individuals “at risk”. In this review we provide perspective on current progress and pitfalls in the use of “omics” technologies to screen body fluids and tissues for biomarker discovery in prion diseases.

  5. State of the Art in Tumor Antigen and Biomarker Discovery

    International Nuclear Information System (INIS)

    Even-Desrumeaux, Klervi; Baty, Daniel; Chames, Patrick

    2011-01-01

    Our knowledge of tumor immunology has resulted in multiple approaches for the treatment of cancer. However, a gap between research of new tumors markers and development of immunotherapy has been established and very few markers exist that can be used for treatment. The challenge is now to discover new targets for active and passive immunotherapy. This review aims at describing recent advances in biomarkers and tumor antigen discovery in terms of antigen nature and localization, and is highlighting the most recent approaches used for their discovery including “omics” technology

  6. State of the Art in Tumor Antigen and Biomarker Discovery

    Directory of Open Access Journals (Sweden)

    Patrick Chames

    2011-06-01

    Full Text Available Our knowledge of tumor immunology has resulted in multiple approaches for the treatment of cancer. However, a gap between research of new tumors markers and development of immunotherapy has been established and very few markers exist that can be used for treatment. The challenge is now to discover new targets for active and passive immunotherapy. This review aims at describing recent advances in biomarkers and tumor antigen discovery in terms of antigen nature and localization, and is highlighting the most recent approaches used for their discovery including “omics” technology.

  7. Maximizing biomarker discovery by minimizing gene signatures

    Directory of Open Access Journals (Sweden)

    Chang Chang

    2011-12-01

    Full Text Available Abstract Background The use of gene signatures can potentially be of considerable value in the field of clinical diagnosis. However, gene signatures defined with different methods can be quite various even when applied the same disease and the same endpoint. Previous studies have shown that the correct selection of subsets of genes from microarray data is key for the accurate classification of disease phenotypes, and a number of methods have been proposed for the purpose. However, these methods refine the subsets by only considering each single feature, and they do not confirm the association between the genes identified in each gene signature and the phenotype of the disease. We proposed an innovative new method termed Minimize Feature's Size (MFS based on multiple level similarity analyses and association between the genes and disease for breast cancer endpoints by comparing classifier models generated from the second phase of MicroArray Quality Control (MAQC-II, trying to develop effective meta-analysis strategies to transform the MAQC-II signatures into a robust and reliable set of biomarker for clinical applications. Results We analyzed the similarity of the multiple gene signatures in an endpoint and between the two endpoints of breast cancer at probe and gene levels, the results indicate that disease-related genes can be preferably selected as the components of gene signature, and that the gene signatures for the two endpoints could be interchangeable. The minimized signatures were built at probe level by using MFS for each endpoint. By applying the approach, we generated a much smaller set of gene signature with the similar predictive power compared with those gene signatures from MAQC-II. Conclusions Our results indicate that gene signatures of both large and small sizes could perform equally well in clinical applications. Besides, consistency and biological significances can be detected among different gene signatures, reflecting the

  8. Discovery of novel biomarkers and phenotypes by semantic technologies.

    Science.gov (United States)

    Trugenberger, Carlo A; Wälti, Christoph; Peregrim, David; Sharp, Mark E; Bureeva, Svetlana

    2013-02-13

    Biomarkers and target-specific phenotypes are important to targeted drug design and individualized medicine, thus constituting an important aspect of modern pharmaceutical research and development. More and more, the discovery of relevant biomarkers is aided by in silico techniques based on applying data mining and computational chemistry on large molecular databases. However, there is an even larger source of valuable information available that can potentially be tapped for such discoveries: repositories constituted by research documents. This paper reports on a pilot experiment to discover potential novel biomarkers and phenotypes for diabetes and obesity by self-organized text mining of about 120,000 PubMed abstracts, public clinical trial summaries, and internal Merck research documents. These documents were directly analyzed by the InfoCodex semantic engine, without prior human manipulations such as parsing. Recall and precision against established, but different benchmarks lie in ranges up to 30% and 50% respectively. Retrieval of known entities missed by other traditional approaches could be demonstrated. Finally, the InfoCodex semantic engine was shown to discover new diabetes and obesity biomarkers and phenotypes. Amongst these were many interesting candidates with a high potential, although noticeable noise (uninteresting or obvious terms) was generated. The reported approach of employing autonomous self-organising semantic engines to aid biomarker discovery, supplemented by appropriate manual curation processes, shows promise and has potential to impact, conservatively, a faster alternative to vocabulary processes dependent on humans having to read and analyze all the texts. More optimistically, it could impact pharmaceutical research, for example to shorten time-to-market of novel drugs, or speed up early recognition of dead ends and adverse reactions.

  9. Proteomics and Mass Spectrometry for Cancer Biomarker Discovery

    Science.gov (United States)

    Lu, Ming; Faull, Kym F.; Whitelegge, Julian P.; He, Jianbo; Shen, Dejun; Saxton, Romaine E.; Chang, Helena R.

    2007-01-01

    Proteomics is a rapidly advancing field not only in the field of biology but also in translational cancer research. In recent years, mass spectrometry and associated technologies have been explored to identify proteins or a set of proteins specific to a given disease, for the purpose of disease detection and diagnosis. Such biomarkers are being investigated in samples including cells, tissues, serum/plasma, and other types of body fluids. When sufficiently refined, proteomic technologies may pave the way for early detection of cancer or individualized therapy for cancer. Mass spectrometry approaches coupled with bioinformatic tools are being developed for biomarker discovery and validation. Understanding basic concepts and application of such technology by investigators in the field may accelerate the clinical application of protein biomarkers in disease management. PMID:19662217

  10. Automated Discovery of Speech Act Categories in Educational Games

    Science.gov (United States)

    Rus, Vasile; Moldovan, Cristian; Niraula, Nobal; Graesser, Arthur C.

    2012-01-01

    In this paper we address the important task of automated discovery of speech act categories in dialogue-based, multi-party educational games. Speech acts are important in dialogue-based educational systems because they help infer the student speaker's intentions (the task of speech act classification) which in turn is crucial to providing adequate…

  11. Proteomics for discovery of candidate colorectal cancer biomarkers

    Science.gov (United States)

    Álvarez-Chaver, Paula; Otero-Estévez, Olalla; Páez de la Cadena, María; Rodríguez-Berrocal, Francisco J; Martínez-Zorzano, Vicenta S

    2014-01-01

    Colorectal cancer (CRC) is the second most common cause of cancer-related deaths in Europe and other Western countries, mainly due to the lack of well-validated clinically useful biomarkers with enough sensitivity and specificity to detect this disease at early stages. Although it is well known that the pathogenesis of CRC is a progressive accumulation of mutations in multiple genes, much less is known at the proteome level. Therefore, in the last years many proteomic studies have been conducted to find new candidate protein biomarkers for diagnosis, prognosis and as therapeutic targets for this malignancy, as well as to elucidate the molecular mechanisms of colorectal carcinogenesis. An important advantage of the proteomic approaches is the capacity to look for multiple differentially expressed proteins in a single study. This review provides an overview of the recent reports describing the different proteomic tools used for the discovery of new protein markers for CRC such as two-dimensional electrophoresis methods, quantitative mass spectrometry-based techniques or protein microarrays. Additionally, we will also focus on the diverse biological samples used for CRC biomarker discovery such as tissue, serum and faeces, besides cell lines and murine models, discussing their advantages and disadvantages, and summarize the most frequently identified candidate CRC markers. PMID:24744574

  12. Role of proteomics in the discovery of autism biomarkers

    International Nuclear Information System (INIS)

    Ayadhi, L.A.; Halepoto, D.M.

    2013-01-01

    The epidemiology of autism is continuously increasing all over the world with social, behavioural and economical burdens. Autism is considered as a multi-factorial disorder, influenced by genetic, neurological, environmental and immunological aspects. Autism is still believed to be incurable disorder with little information about the role of proteins patterns in the diagnosis of the disease. Knowing the applications of proteomic tools, it is possible to identify quantitative and qualitative protein patterns in a wide variety of tissues and body fluids such as blood, urine, saliva and cerebrospinal fluid in order to establish specific diagnostic and prognostic biomarkers. The aim of this review is to provide an overview of the various protocols available for proteomics by using mass spectrometry analysis, discuss reports in which these techniques have been previously applied in biomarker discovery for the diagnosis of autism, and consider the future development of this area of research. (author)

  13. Application of nano-LC-based glycomics towards biomarker discovery.

    Science.gov (United States)

    Hua, Serenus; Lebrilla, Carlito; An, Hyun Joo

    2011-11-01

    The glycome, that is, the glycan components of a biological source, has been widely reported to change with disease states. However, mining the glycome for biomarkers is complicated by glycan structural heterogeneity. Nanoflow LC, or nano-LC, significantly addresses the problem by providing a highly sensitive and quantitative method of separating and profiling glycans. This review summarizes recent advances in analytical technology and methodology that enhance and augment the advantages offered by nano-LC. (e.g., reversed phase, hydrophilic interaction and porous graphitized carbon chromatography, as well as associated derivatization strategies), detectors (e.g., fluorescence and MS), and technology platforms (particularly chip-based nano-LC) are examined in detail, along with their application to biomarker discovery. Particular emphasis is placed on methods and technologies that allow structure-specific glycan profiling.

  14. Proteomic Biomarker Discovery in 1000 Human Plasma Samples with Mass Spectrometry.

    Science.gov (United States)

    Cominetti, Ornella; Núñez Galindo, Antonio; Corthésy, John; Oller Moreno, Sergio; Irincheeva, Irina; Valsesia, Armand; Astrup, Arne; Saris, Wim H M; Hager, Jörg; Kussmann, Martin; Dayon, Loïc

    2016-02-05

    The overall impact of proteomics on clinical research and its translation has lagged behind expectations. One recognized caveat is the limited size (subject numbers) of (pre)clinical studies performed at the discovery stage, the findings of which fail to be replicated in larger verification/validation trials. Compromised study designs and insufficient statistical power are consequences of the to-date still limited capacity of mass spectrometry (MS)-based workflows to handle large numbers of samples in a realistic time frame, while delivering comprehensive proteome coverages. We developed a highly automated proteomic biomarker discovery workflow. Herein, we have applied this approach to analyze 1000 plasma samples from the multicentered human dietary intervention study "DiOGenes". Study design, sample randomization, tracking, and logistics were the foundations of our large-scale study. We checked the quality of the MS data and provided descriptive statistics. The data set was interrogated for proteins with most stable expression levels in that set of plasma samples. We evaluated standard clinical variables that typically impact forthcoming results and assessed body mass index-associated and gender-specific proteins at two time points. We demonstrate that analyzing a large number of human plasma samples for biomarker discovery with MS using isobaric tagging is feasible, providing robust and consistent biological results.

  15. Novel automated blood separations validate whole cell biomarkers.

    Directory of Open Access Journals (Sweden)

    Douglas E Burger

    Full Text Available Progress in clinical trials in infectious disease, autoimmunity, and cancer is stymied by a dearth of successful whole cell biomarkers for peripheral blood lymphocytes (PBLs. Successful biomarkers could help to track drug effects at early time points in clinical trials to prevent costly trial failures late in development. One major obstacle is the inaccuracy of Ficoll density centrifugation, the decades-old method of separating PBLs from the abundant red blood cells (RBCs of fresh blood samples.To replace the Ficoll method, we developed and studied a novel blood-based magnetic separation method. The magnetic method strikingly surpassed Ficoll in viability, purity and yield of PBLs. To reduce labor, we developed an automated platform and compared two magnet configurations for cell separations. These more accurate and labor-saving magnet configurations allowed the lymphocytes to be tested in bioassays for rare antigen-specific T cells. The automated method succeeded at identifying 79% of patients with the rare PBLs of interest as compared with Ficoll's uniform failure. We validated improved upfront blood processing and show accurate detection of rare antigen-specific lymphocytes.Improving, automating and standardizing lymphocyte detections from whole blood may facilitate development of new cell-based biomarkers for human diseases. Improved upfront blood processes may lead to broad improvements in monitoring early trial outcome measurements in human clinical trials.

  16. Novel automated blood separations validate whole cell biomarkers.

    Science.gov (United States)

    Burger, Douglas E; Wang, Limei; Ban, Liqin; Okubo, Yoshiaki; Kühtreiber, Willem M; Leichliter, Ashley K; Faustman, Denise L

    2011-01-01

    Progress in clinical trials in infectious disease, autoimmunity, and cancer is stymied by a dearth of successful whole cell biomarkers for peripheral blood lymphocytes (PBLs). Successful biomarkers could help to track drug effects at early time points in clinical trials to prevent costly trial failures late in development. One major obstacle is the inaccuracy of Ficoll density centrifugation, the decades-old method of separating PBLs from the abundant red blood cells (RBCs) of fresh blood samples. To replace the Ficoll method, we developed and studied a novel blood-based magnetic separation method. The magnetic method strikingly surpassed Ficoll in viability, purity and yield of PBLs. To reduce labor, we developed an automated platform and compared two magnet configurations for cell separations. These more accurate and labor-saving magnet configurations allowed the lymphocytes to be tested in bioassays for rare antigen-specific T cells. The automated method succeeded at identifying 79% of patients with the rare PBLs of interest as compared with Ficoll's uniform failure. We validated improved upfront blood processing and show accurate detection of rare antigen-specific lymphocytes. Improving, automating and standardizing lymphocyte detections from whole blood may facilitate development of new cell-based biomarkers for human diseases. Improved upfront blood processes may lead to broad improvements in monitoring early trial outcome measurements in human clinical trials.

  17. Discovery of nutritional biomarkers: future directions based on omics technologies.

    Science.gov (United States)

    Odriozola, Leticia; Corrales, Fernado J

    2015-07-01

    Understanding the interactions between food and human biology is of utmost importance to facilitate the development of more efficient nutritional interventions that might improve our wellness status and future health outcomes by reducing risk factors for non-transmittable chronic diseases, such as cardiovascular diseases, cancer, obesity and metabolic syndrome. Dissection of the molecular mechanisms that mediate the physiological effects of diets and bioactive compounds is one of the main goals of current nutritional investigation and the food industry as might lead to the discovery of novel biomarkers. It is widely recognized that the availability of robust nutritional biomarkers represents a bottleneck that delays the innovation process of the food industry. In this regard, omics sciences have opened up new avenues of research and opportunities in nutrition. Advances in mass spectrometry, nuclear magnetic resonance, next generation sequencing and microarray technologies allow massive genome, gene expression, proteomic and metabolomic profiling, obtaining a global and in-depth analysis of physiological/pathological scenarios. For this reason, omics platforms are most suitable for the discovery and characterization of novel nutritional markers that will define the nutritional status of both individuals and populations in the near future, and to identify the nutritional bioactive compounds responsible for the health outcomes.

  18. Proteomics and Mass Spectrometry for Cancer Biomarker Discovery

    Directory of Open Access Journals (Sweden)

    Ming Lu

    2007-01-01

    Full Text Available Proteomics is a rapidly advancing field not only in the field of biology but also in translational cancer research. In recent years, mass spectrometry and associated technologies have been explored to identify proteins or a set of proteins specific to a given disease, for the purpose of disease detection and diagnosis. Such biomarkers are being investigated in samples including cells, tissues, serum/plasma, and other types of body fluids. When sufficiently refined, proteomic technologies may pave the way for early detection of cancer or individualized therapy for cancer. Mass spectrometry approaches coupled with bioinformatic tools are being developed for biomarker discovery and validation. Understanding basic concepts and application of such technology by investigators in the field may accelerate the clinical application of protein biomarkers in disease management.Abbreviations: 2DE: two-dimensional gel electrophoresis; ABPP: activity-based protein profiling; CEA: carcinoembryonic antigen; CI: confidence interval; ESI: electrospray ionization; FP: fluorophosphonate; HPLC: high performance liquid chromatography; ICAT: isotope coded affi nitytags; IEF: isoelectric focusing; iTRAQ: isobaric tags for relative and absolute quantification; LCMS: combined liquid chromatography-mass spectrometry; LCMSMS: liquid chromatography tandem mass spectrometry; LOD: limit of detection; m/z: mass to charge ratio; MALDI: matrix-assisted laser desorption ionization; MS: mass spectrometry; MUDPIT: multidimensional protein identification technology; NAF: nipple aspirate fluid; PMF: peptide mass fingerprinting; PSA: prostate specifi c antigen; PTMs: post-translational modifications; RPMA: reverse phase protein microarray; SELDI: surface enhanced laser desorption ionization; TOF: time-of-flight.

  19. The Use of Proteomics in Biomarker Discovery in Neurodegenerative Diseases

    Directory of Open Access Journals (Sweden)

    Pia Davidsson

    2005-01-01

    Full Text Available Biomarkers for neurodegenerative diseases should reflect the central pathogenic processes of the diseases. The field of clinical proteomics is especially well suited for discovery of biomarkers in cerebrospinal fluid (CSF, which reflects the proteins in the brain under healthy conditions as well as in several neurodegenerative diseases. Known proteins involved in the pathology of neurodegenerative diseases are, respectively, normal tau protein, β-amyloid (1-42, synaptic proteins, amyloid precursor protein (APP, apolipoprotein E (apoE, which previously have been studied by protein immunoassays. The objective of this paper was to summarize results from proteomic studies of differential protein patterns in neurodegenerative diseases with focus on Alzheimer's disease (AD. Today, discrimination of AD from controls and from other neurological diseases has been improved by simultaneous analysis of both β-amyloid (1-42, total-tau, and phosphorylated tau, where a combination of low levels of CSF-β-amyloid 1-42 and high levels of CSF-tau and CSF-phospho-tau is associated with an AD diagnosis. Detection of new biomarkers will further strengthen diagnosis and provide useful information in drug trials. The combination of immunoassays and proteomic methods show that the CSF proteins express differential protein patterns in AD, FTD, and PD patients, which reflect divergent underlying pathophysiological mechanisms and neuropathological changes in these diseases.

  20. Automated cell type discovery and classification through knowledge transfer

    Science.gov (United States)

    Lee, Hao-Chih; Kosoy, Roman; Becker, Christine E.

    2017-01-01

    Abstract Motivation: Recent advances in mass cytometry allow simultaneous measurements of up to 50 markers at single-cell resolution. However, the high dimensionality of mass cytometry data introduces computational challenges for automated data analysis and hinders translation of new biological understanding into clinical applications. Previous studies have applied machine learning to facilitate processing of mass cytometry data. However, manual inspection is still inevitable and becoming the barrier to reliable large-scale analysis. Results: We present a new algorithm called Automated Cell-type Discovery and Classification (ACDC) that fully automates the classification of canonical cell populations and highlights novel cell types in mass cytometry data. Evaluations on real-world data show ACDC provides accurate and reliable estimations compared to manual gating results. Additionally, ACDC automatically classifies previously ambiguous cell types to facilitate discovery. Our findings suggest that ACDC substantially improves both reliability and interpretability of results obtained from high-dimensional mass cytometry profiling data. Availability and Implementation: A Python package (Python 3) and analysis scripts for reproducing the results are availability on https://bitbucket.org/dudleylab/acdc. Contact: brian.kidd@mssm.edu or joel.dudley@mssm.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28158442

  1. BioPlat: a software for human cancer biomarker discovery.

    Science.gov (United States)

    Butti, Matias D; Chanfreau, Hernan; Martinez, Diego; García, Diego; Lacunza, Ezequiel; Abba, Martin C

    2014-06-15

    Development of effective tools such as oligo-microarrays and next-generation sequencing methods for monitoring gene expression on a large scale has resulted in the discovery of gene signatures with prognostic/predictive value in various malignant neoplastic diseases. However, with the exponential growth of gene expression databases, biologists are faced with the challenge of extracting useful information from these repositories. Here, we present a software package, BioPlat (Biomarkers Platform), which allows biologists to identify novel prognostic and predictive cancer biomarkers based on the data mining of gene expression signatures and gene expression profiling databases. BioPlat has been designed as an easy-to-use and flexible desktop software application, which provides a set of analytical tools related to data extraction, preprocessing, filtering, gene expression signature calculation, in silico validation, feature selection and annotation that leverage the integration and reuse of gene expression signatures in the context of follow-up data. BioPlat is a platform-independent software implemented in Java and supported on GNU/Linux and MS Windows, which is freely available for download at http://www.cancergenomics.net. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  2. Proteomic biomarker discovery in 1000 human plasma samples with mass spectrometry

    DEFF Research Database (Denmark)

    Cominetti, Ornella; Núñez Galindo, Antonio; Corthésy, John

    2016-01-01

    /validation trials. Compromised study designs and insufficient statistical power are consequences of the to-date still limited capacity of mass spectrometry (MS)-based workflows to handle large numbers of samples in a realistic time frame, while delivering comprehensive proteome coverages. We developed a highly...... automated proteomic biomarker discovery workflow. Herein, we have applied this approach to analyze 1000 plasma samples from the multicentered human dietary intervention study "DiOGenes". Study design, sample randomization, tracking, and logistics were the foundations of our large-scale study. We checked...... the quality of the MS data and provided descriptive statistics. The data set was interrogated for proteins with most stable expression levels in that set of plasma samples. We evaluated standard clinical variables that typically impact forthcoming results and assessed body mass index-associated and gender...

  3. Mass Spectrometry–Based Biomarker Discovery: Toward a Global Proteome Index of Individuality

    Science.gov (United States)

    Hawkridge, Adam M.; Muddiman, David C.

    2011-01-01

    Biomarker discovery and proteomics have become synonymous with mass spectrometry in recent years. Although this conflation is an injustice to the many essential biomolecular techniques widely used in biomarker-discovery platforms, it underscores the power and potential of contemporary mass spectrometry. Numerous novel and powerful technologies have been developed around mass spectrometry, proteomics, and biomarker discovery over the past 20 years to globally study complex proteomes (e.g., plasma). However, very few large-scale longitudinal studies have been carried out using these platforms to establish the analytical variability relative to true biological variability. The purpose of this review is not to cover exhaustively the applications of mass spectrometry to biomarker discovery, but rather to discuss the analytical methods and strategies that have been developed for mass spectrometry–based biomarker-discovery platforms and to place them in the context of the many challenges and opportunities yet to be addressed. PMID:20636062

  4. Multi-dimensional discovery of biomarker and phenotype complexes

    Directory of Open Access Journals (Sweden)

    Huang Kun

    2010-10-01

    Full Text Available Abstract Background Given the rapid growth of translational research and personalized healthcare paradigms, the ability to relate and reason upon networks of bio-molecular and phenotypic variables at various levels of granularity in order to diagnose, stage and plan treatments for disease states is highly desirable. Numerous techniques exist that can be used to develop networks of co-expressed or otherwise related genes and clinical features. Such techniques can also be used to create formalized knowledge collections based upon the information incumbent to ontologies and domain literature. However, reports of integrative approaches that bridge such networks to create systems-level models of disease or wellness are notably lacking in the contemporary literature. Results In response to the preceding gap in knowledge and practice, we report upon a prototypical series of experiments that utilize multi-modal approaches to network induction. These experiments are intended to elicit meaningful and significant biomarker-phenotype complexes spanning multiple levels of granularity. This work has been performed in the experimental context of a large-scale clinical and basic science data repository maintained by the National Cancer Institute (NCI funded Chronic Lymphocytic Leukemia Research Consortium. Conclusions Our results indicate that it is computationally tractable to link orthogonal networks of genes, clinical features, and conceptual knowledge to create multi-dimensional models of interrelated biomarkers and phenotypes. Further, our results indicate that such systems-level models contain interrelated bio-molecular and clinical markers capable of supporting hypothesis discovery and testing. Based on such findings, we propose a conceptual model intended to inform the cross-linkage of the results of such methods. This model has as its aim the identification of novel and knowledge-anchored biomarker-phenotype complexes.

  5. The application of mass-spectrometry-based protein biomarker discovery to theragnostics

    OpenAIRE

    Street, Jonathan M; Dear, James W

    2010-01-01

    Over the last decade rapid developments in mass spectrometry have allowed the identification of multiple proteins in complex biological samples. This proteomic approach has been applied to biomarker discovery in the context of clinical pharmacology (the combination of biomarker and drug now being termed ‘theragnostics’). In this review we provide a roadmap for early protein biomarker discovery studies, focusing on some key questions that regularly confront researchers.

  6. LOBSTAHS: An Adduct-Based Lipidomics Strategy for Discovery and Identification of Oxidative Stress Biomarkers.

    Science.gov (United States)

    Collins, James R; Edwards, Bethanie R; Fredricks, Helen F; Van Mooy, Benjamin A S

    2016-07-19

    Discovery and identification of molecular biomarkers in large LC/MS data sets requires significant automation without loss of accuracy in the compound screening and annotation process. Here, we describe a lipidomics workflow and open-source software package for high-throughput annotation and putative identification of lipid, oxidized lipid, and oxylipin biomarkers in high-mass-accuracy HPLC-MS data. Lipid and oxylipin biomarker screening through adduct hierarchy sequences, or LOBSTAHS, uses orthogonal screening criteria based on adduct ion formation patterns and other properties to identify thousands of compounds while providing the user with a confidence score for each assignment. Assignments are made from one of two customizable databases; the default databases contain 14 068 unique entries. To demonstrate the software's functionality, we screened more than 340 000 mass spectral features from an experiment in which hydrogen peroxide was used to induce oxidative stress in the marine diatom Phaeodactylum tricornutum. LOBSTAHS putatively identified 1969 unique parent compounds in 21 869 features that survived the multistage screening process. While P. tricornutum maintained more than 92% of its core lipidome under oxidative stress, patterns in biomarker distribution and abundance indicated remodeling was both subtle and pervasive. Treatment with 150 μM H2O2 promoted statistically significant carbon-chain elongation across lipid classes, with the strongest elongation accompanying oxidation in moieties of monogalactosyldiacylglycerol, a lipid typically localized to the chloroplast. Oxidative stress also induced a pronounced reallocation of lipidome peak area to triacylglycerols. LOBSTAHS can be used with environmental or experimental data from a variety of systems and is freely available at https://github.com/vanmooylipidomics/LOBSTAHS .

  7. Crowdsourcing Disease Biomarker Discovery Research: The IP4IC Study.

    Science.gov (United States)

    Chancellor, Michael B; Bartolone, Sarah N; Veerecke, Andrew; Lamb, Laura E

    2017-12-07

    Biomarker discovery is limited by readily assessable, cost efficient human samples available in large numbers that represent the entire heterogeneity of the disease. We developed a novel, active participation crowdsourcing method to determine BP-RS (Bladder Permeability Defect Risk Score). It is based on noninvasive urinary cytokines to discriminate patients with interstitial cystitis/bladder pain syndrome who had Hunner lesions from controls and patients with interstitial cystitis/bladder pain syndrome but without Hunner lesions. We performed a national crowdsourcing study in cooperation with ICA (Interstitial Cystitis Association). Patients answered demographic, symptom severity and urinary frequency questionnaires on a HIPAA (Health Insurance Portability and Accountability Act) compliant website. Urine samples were collected at home, stabilized with a preservative and sent to Beaumont Hospital for analysis. The expression of 3 urinary cytokines was used in a machine learning algorithm to develop BP-RS. The IP4IC study collected a total of 448 urine samples, representing 153 patients (147 females and 6 males) with interstitial cystitis/bladder pain syndrome, of whom 54 (50 females and 4 males) had Hunner lesions. A total of 159 female and 136 male controls also participated, who were age matched. A defined BP-RS was calculated to predict interstitial cystitis/bladder pain syndrome with Hunner lesions or a bladder permeability defect etiology with 89% validity. In this novel participation crowdsourcing study we obtained a large number of urine samples from 46 states, which were collected at home, shipped and stored at room temperature. Using a machine learning algorithm we developed BP-RS to quantify the risk of interstitial cystitis/bladder pain syndrome with Hunner lesions, which is indicative of a bladder permeability defect etiology. To our knowledge BP-RS is the first validated urine biomarker assay for interstitial cystitis/bladder pain syndrome and one of

  8. Automated vocabulary discovery for geo-parsing online epidemic intelligence.

    Science.gov (United States)

    Keller, Mikaela; Freifeld, Clark C; Brownstein, John S

    2009-11-24

    Automated surveillance of the Internet provides a timely and sensitive method for alerting on global emerging infectious disease threats. HealthMap is part of a new generation of online systems designed to monitor and visualize, on a real-time basis, disease outbreak alerts as reported by online news media and public health sources. HealthMap is of specific interest for national and international public health organizations and international travelers. A particular task that makes such a surveillance useful is the automated discovery of the geographic references contained in the retrieved outbreak alerts. This task is sometimes referred to as "geo-parsing". A typical approach to geo-parsing would demand an expensive training corpus of alerts manually tagged by a human. Given that human readers perform this kind of task by using both their lexical and contextual knowledge, we developed an approach which relies on a relatively small expert-built gazetteer, thus limiting the need of human input, but focuses on learning the context in which geographic references appear. We show in a set of experiments, that this approach exhibits a substantial capacity to discover geographic locations outside of its initial lexicon. The results of this analysis provide a framework for future automated global surveillance efforts that reduce manual input and improve timeliness of reporting.

  9. Automated vocabulary discovery for geo-parsing online epidemic intelligence

    Directory of Open Access Journals (Sweden)

    Freifeld Clark C

    2009-11-01

    Full Text Available Abstract Background Automated surveillance of the Internet provides a timely and sensitive method for alerting on global emerging infectious disease threats. HealthMap is part of a new generation of online systems designed to monitor and visualize, on a real-time basis, disease outbreak alerts as reported by online news media and public health sources. HealthMap is of specific interest for national and international public health organizations and international travelers. A particular task that makes such a surveillance useful is the automated discovery of the geographic references contained in the retrieved outbreak alerts. This task is sometimes referred to as "geo-parsing". A typical approach to geo-parsing would demand an expensive training corpus of alerts manually tagged by a human. Results Given that human readers perform this kind of task by using both their lexical and contextual knowledge, we developed an approach which relies on a relatively small expert-built gazetteer, thus limiting the need of human input, but focuses on learning the context in which geographic references appear. We show in a set of experiments, that this approach exhibits a substantial capacity to discover geographic locations outside of its initial lexicon. Conclusion The results of this analysis provide a framework for future automated global surveillance efforts that reduce manual input and improve timeliness of reporting.

  10. GWATCH: a web platform for automated gene association discovery analysis

    Science.gov (United States)

    2014-01-01

    Background As genome-wide sequence analyses for complex human disease determinants are expanding, it is increasingly necessary to develop strategies to promote discovery and validation of potential disease-gene associations. Findings Here we present a dynamic web-based platform – GWATCH – that automates and facilitates four steps in genetic epidemiological discovery: 1) Rapid gene association search and discovery analysis of large genome-wide datasets; 2) Expanded visual display of gene associations for genome-wide variants (SNPs, indels, CNVs), including Manhattan plots, 2D and 3D snapshots of any gene region, and a dynamic genome browser illustrating gene association chromosomal regions; 3) Real-time validation/replication of candidate or putative genes suggested from other sources, limiting Bonferroni genome-wide association study (GWAS) penalties; 4) Open data release and sharing by eliminating privacy constraints (The National Human Genome Research Institute (NHGRI) Institutional Review Board (IRB), informed consent, The Health Insurance Portability and Accountability Act (HIPAA) of 1996 etc.) on unabridged results, which allows for open access comparative and meta-analysis. Conclusions GWATCH is suitable for both GWAS and whole genome sequence association datasets. We illustrate the utility of GWATCH with three large genome-wide association studies for HIV-AIDS resistance genes screened in large multicenter cohorts; however, association datasets from any study can be uploaded and analyzed by GWATCH. PMID:25374661

  11. Integration of Proteomics, Bioinformatics, and Systems Biology in Traumatic Brain Injury Biomarker Discovery

    Science.gov (United States)

    Guingab-Cagmat, J.D.; Cagmat, E.B.; Hayes, R.L.; Anagli, J.

    2013-01-01

    Traumatic brain injury (TBI) is a major medical crisis without any FDA-approved pharmacological therapies that have been demonstrated to improve functional outcomes. It has been argued that discovery of disease-relevant biomarkers might help to guide successful clinical trials for TBI. Major advances in mass spectrometry (MS) have revolutionized the field of proteomic biomarker discovery and facilitated the identification of several candidate markers that are being further evaluated for their efficacy as TBI biomarkers. However, several hurdles have to be overcome even during the discovery phase which is only the first step in the long process of biomarker development. The high-throughput nature of MS-based proteomic experiments generates a massive amount of mass spectral data presenting great challenges in downstream interpretation. Currently, different bioinformatics platforms are available for functional analysis and data mining of MS-generated proteomic data. These tools provide a way to convert data sets to biologically interpretable results and functional outcomes. A strategy that has promise in advancing biomarker development involves the triad of proteomics, bioinformatics, and systems biology. In this review, a brief overview of how bioinformatics and systems biology tools analyze, transform, and interpret complex MS datasets into biologically relevant results is discussed. In addition, challenges and limitations of proteomics, bioinformatics, and systems biology in TBI biomarker discovery are presented. A brief survey of researches that utilized these three overlapping disciplines in TBI biomarker discovery is also presented. Finally, examples of TBI biomarkers and their applications are discussed. PMID:23750150

  12. Establishment of Protocols for Global Metabolomics by LC-MS for Biomarker Discovery.

    Directory of Open Access Journals (Sweden)

    Daisuke Saigusa

    Full Text Available Metabolomics is a promising avenue for biomarker discovery. Although the quality of metabolomic analyses, especially global metabolomics (G-Met using mass spectrometry (MS, largely depends on the instrumentation, potential bottlenecks still exist at several basic levels in the metabolomics workflow. Therefore, we established a precise protocol initially for the G-Met analyses of human blood plasma to overcome some these difficulties. In our protocol, samples are deproteinized in a 96-well plate using an automated liquid-handling system, and conducted either using a UHPLC-QTOF/MS system equipped with a reverse phase column or a LC-FTMS system equipped with a normal phase column. A normalization protocol of G-Met data was also developed to compensate for intra- and inter-batch differences, and the variations were significantly reduced along with our normalization, especially for the UHPLC-QTOF/MS data with a C18 reverse-phase column for positive ions. Secondly, we examined the changes in metabolomic profiles caused by the storage of EDTA-blood specimens to identify quality markers for the evaluation of the specimens' pre-analytical conditions. Forty quality markers, including lysophospholipids, dipeptides, fatty acids, succinic acid, amino acids, glucose, and uric acid were identified by G-Met for the evaluation of plasma sample quality and established the equation of calculating the quality score. We applied our quality markers to a small-scale study to evaluate the quality of clinical samples. The G-Met protocols and quality markers established here should prove useful for the discovery and development of biomarkers for a wider range of diseases.

  13. Approach to Cerebrospinal Fluid (CSF) Biomarker Discovery and Evaluation in HIV Infection

    Energy Technology Data Exchange (ETDEWEB)

    Price, Richard W.; Peterson, Julia; Fuchs, Dietmar; Angel, Thomas E.; Zetterberg, Henrik; Hagberg, Lars; Spudich, Serena S.; Smith, Richard D.; Jacobs, Jon M.; Brown, Joseph N.; Gisslen, Magnus

    2013-12-13

    Central nervous system (CNS) infection is a nearly universal facet of systemic HIV infection that varies in character and neurological consequences. While clinical staging and neuropsychological test performance have been helpful in evaluating patients, cerebrospinal fluid (CSF) biomarkers present a valuable and objective approach to more accurate diagnosis, assessment of treatment effects and understanding of evolving pathobiology. We review some lessons from our recent experience with CSF biomarker studies. We have used two approaches to biomarker analysis: targeted, hypothesis-driven and non-targeted exploratory discovery methods. We illustrate the first with data from a cross-sectional study of defined subject groups across the spectrum of systemic and CNS disease progression and the second with a longitudinal study of the CSF proteome in subjects initiating antiretroviral treatment. Both approaches can be useful and, indeed, complementary. The first is helpful in assessing known or hypothesized biomarkers while the second can identify novel biomarkers and point to broad interactions in pathogenesis. Common to both is the need for well-defined samples and subjects that span a spectrum of biological activity and biomarker concentrations. Previouslydefined guide biomarkers of CNS infection, inflammation and neural injury are useful in categorizing samples for analysis and providing critical biological context for biomarker discovery studies. CSF biomarkers represent an underutilized but valuable approach to understanding the interactions of HIV and the CNS and to more objective diagnosis and assessment of disease activity. Both hypothesis-based and discovery methods can be useful in advancing the definition and use of these biomarkers.

  14. Automated cell type discovery and classification through knowledge transfer.

    Science.gov (United States)

    Lee, Hao-Chih; Kosoy, Roman; Becker, Christine E; Dudley, Joel T; Kidd, Brian A

    2017-06-01

    Recent advances in mass cytometry allow simultaneous measurements of up to 50 markers at single-cell resolution. However, the high dimensionality of mass cytometry data introduces computational challenges for automated data analysis and hinders translation of new biological understanding into clinical applications. Previous studies have applied machine learning to facilitate processing of mass cytometry data. However, manual inspection is still inevitable and becoming the barrier to reliable large-scale analysis. We present a new algorithm called utomated ell-type iscovery and lassification (ACDC) that fully automates the classification of canonical cell populations and highlights novel cell types in mass cytometry data. Evaluations on real-world data show ACDC provides accurate and reliable estimations compared to manual gating results. Additionally, ACDC automatically classifies previously ambiguous cell types to facilitate discovery. Our findings suggest that ACDC substantially improves both reliability and interpretability of results obtained from high-dimensional mass cytometry profiling data. A Python package (Python 3) and analysis scripts for reproducing the results are availability on https://bitbucket.org/dudleylab/acdc . brian.kidd@mssm.edu or joel.dudley@mssm.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

  15. Biomarker discovery in subclinical mycobacterial infections of cattle.

    Directory of Open Access Journals (Sweden)

    Meetu Seth

    Full Text Available BACKGROUND: Bovine tuberculosis is a highly prevalent infectious disease of cattle worldwide; however, infection in the United States is limited to 0.01% of dairy herds. Thus detection of bovine TB is confounded by high background infection with M. avium subsp. paratuberculosis. The present study addresses variations in the circulating peptidome based on the pathogenesis of two biologically similar mycobacterial diseases of cattle. METHODOLOGY/PRINCIPAL FINDINGS: We hypothesized that serum proteomes of animals in response to either M. bovis or M. paratuberculosis infection will display several commonalities and differences. Sera prospectively collected from animals experimentally infected with either M. bovis or M. paratuberculosis were analyzed using high-resolution proteomics approaches. iTRAQ, a liquid chromatography and tandem mass spectrometry approach, was used to simultaneously identify and quantify peptides from multiple infections and contemporaneous uninfected control groups. Four comparisons were performed: 1 M. bovis infection versus uninfected controls, 2 M. bovis versus M. paratuberculosis infection, 3 early, and 4 advanced M. paratuberculosis infection versus uninfected controls. One hundred and ten differentially elevated proteins (P < or = 0.05 were identified. Vitamin D binding protein precursor (DBP, alpha-1 acid glycoprotein, alpha-1B glycoprotein, fetuin, and serine proteinase inhibitor were identified in both infections. Transthyretin, retinol binding proteins, and cathelicidin were identified exclusively in M. paratuberculosis infection, while the serum levels of alpha-1-microglobulin/bikunin precursor (AMBP protein, alpha-1 acid glycoprotein, fetuin, and alpha-1B glycoprotein were elevated exclusively in M. bovis infected animals. CONCLUSIONS/SIGNIFICANCE: The discovery of these biomarkers has significant impact on the elucidation of pathogenesis of two mycobacterial diseases at the cellular and the molecular level and

  16. Application of proteomics in biomarker discovery: a primer for the clinician.

    Science.gov (United States)

    Tambor, V; Fucíková, A; Lenco, J; Kacerovský, M; Rehácek, V; Stulík, J; Pudil, R

    2010-01-01

    Ever since proteomics was proven to be capable of characterizing a large number of differences in both protein quality and quantity, it has been applied in various areas of biomedicine, ranging from the deciphering molecular pathogenesis of diseases to the characterization of novel drug targets and the discovery of potential diagnostic biomarkers. Indeed, the biomarker discovery in human plasma is clearly one of the areas with enormous potential. However, without proper planning and implementation of specific techniques, the efforts and expectations may very easily be hampered. Numerous earlier projects aimed at clinical proteomics, characterized by exaggerated enthusiasm, often underestimated some principal obstacles of plasma biomarker discovery. Consequently, ambiguous and insignificant results soon led to a more critical view in this field. In this article, we critically review the current state of proteomic approaches for biomarker discovery and validation, in order to provide basic information and guidelines for both clinicians and researchers. These need to be closely considered prior to initiation of a project aimed at plasma biomarker discovery. We also present a short overview of recent applications of clinical proteomics in biomarker discovery.

  17. From SOMAmer-based biomarker discovery to diagnostic and clinical applications: a SOMAmer-based, streamlined multiplex proteomic assay.

    Directory of Open Access Journals (Sweden)

    Stephan Kraemer

    Full Text Available Recently, we reported a SOMAmer-based, highly multiplexed assay for the purpose of biomarker identification. To enable seamless transition from highly multiplexed biomarker discovery assays to a format suitable and convenient for diagnostic and life-science applications, we developed a streamlined, plate-based version of the assay. The plate-based version of the assay is robust, sensitive (sub-picomolar, rapid, can be highly multiplexed (upwards of 60 analytes, and fully automated. We demonstrate that quantification by microarray-based hybridization, Luminex bead-based methods, and qPCR are each compatible with our platform, further expanding the breadth of proteomic applications for a wide user community.

  18. Gaucher disease: a model disorder for biomarker discovery

    DEFF Research Database (Denmark)

    Boot, Rolf G; van Breemen, Mariëlle J; Wegdam, Wouter

    2009-01-01

    role of storage cells in the pathology, various attempts have been made to identify proteins in plasma or serum reflecting the body burden of these pathological cells. In this review, the existing data regarding biomarkers for Gaucher disease, as well as the current application of biomarkers...

  19. OMICS-driven biomarker discovery in nutrition and health.

    Science.gov (United States)

    Kussmann, Martin; Raymond, Frédéric; Affolter, Michael

    2006-08-05

    While traditional nutrition research has dealt with providing nutrients to nourish populations, it nowadays focuses on improving health of individuals through diet. Modern nutritional research is aiming at health promotion and disease prevention and on performance improvement. As a consequence of these ambitious objectives, the disciplines "nutrigenetics" and "nutrigenomics" have evolved. Nutrigenetics asks the question how individual genetic disposition, manifesting as single nucleotide polymorphisms, copy-number polymorphisms and epigenetic phenomena, affects susceptibility to diet. Nutrigenomics addresses the inverse relationship, that is how diet influences gene transcription, protein expression and metabolism. A major methodological challenge and first pre-requisite of nutrigenomics is integrating genomics (gene analysis), transcriptomics (gene expression analysis), proteomics (protein expression analysis) and metabonomics (metabolite profiling) to define a "healthy" phenotype. The long-term deliverable of nutrigenomics is personalised nutrition for maintenance of individual health and prevention of disease. Transcriptomics serves to put proteomic and metabolomic markers into a larger biological perspective and is suitable for a first "round of discovery" in regulatory networks. Metabonomics is a diagnostic tool for metabolic classification of individuals. The great asset of this platform is the quantitative, non-invasive analysis of easily accessible human body fluids like urine, blood and saliva. This feature also holds true to some extent for proteomics, with the constraint that proteomics is more complex in terms of absolute number, chemical properties and dynamic range of compounds present. Apart from addressing the most complex "-ome", proteomics represents the only platform that delivers not only markers for disposition and efficacy but also targets of intervention. The Omics disciplines applied in the context of nutrition and health have the potential

  20. Revisiting biomarker discovery by plasma proteomics

    DEFF Research Database (Denmark)

    Geyer, Philipp E; Holdt, Lesca M; Teupser, Daniel

    2017-01-01

    slow rate. As described in this review, mass spectrometry (MS)-based proteomics has become a powerful technology in biological research and it is now poised to allow the characterization of the plasma proteome in great depth. Previous "triangular strategies" aimed at discovering single biomarker......Clinical analysis of blood is the most widespread diagnostic procedure in medicine, and blood biomarkers are used to categorize patients and to support treatment decisions. However, existing biomarkers are far from comprehensive and often lack specificity and new ones are being developed at a very...

  1. Computer-aided biomarker discovery for precision medicine: data resources, models and applications.

    Science.gov (United States)

    Lin, Yuxin; Qian, Fuliang; Shen, Li; Chen, Feifei; Chen, Jiajia; Shen, Bairong

    2017-11-29

    Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  2. Mass Spectrometry-Based Proteomics in Molecular Diagnostics: Discovery of Cancer Biomarkers Using Tissue Culture

    Science.gov (United States)

    Paul, Debasish; Kumar, Avinash; Gajbhiye, Akshada; Santra, Manas K.; Srikanth, Rapole

    2013-01-01

    Accurate diagnosis and proper monitoring of cancer patients remain a key obstacle for successful cancer treatment and prevention. Therein comes the need for biomarker discovery, which is crucial to the current oncological and other clinical practices having the potential to impact the diagnosis and prognosis. In fact, most of the biomarkers have been discovered utilizing the proteomics-based approaches. Although high-throughput mass spectrometry-based proteomic approaches like SILAC, 2D-DIGE, and iTRAQ are filling up the pitfalls of the conventional techniques, still serum proteomics importunately poses hurdle in overcoming a wide range of protein concentrations, and also the availability of patient tissue samples is a limitation for the biomarker discovery. Thus, researchers have looked for alternatives, and profiling of candidate biomarkers through tissue culture of tumor cell lines comes up as a promising option. It is a rich source of tumor cell-derived proteins, thereby, representing a wide array of potential biomarkers. Interestingly, most of the clinical biomarkers in use today (CA 125, CA 15.3, CA 19.9, and PSA) were discovered through tissue culture-based system and tissue extracts. This paper tries to emphasize the tissue culture-based discovery of candidate biomarkers through various mass spectrometry-based proteomic approaches. PMID:23586059

  3. Mass Spectrometry-Based Proteomics in Molecular Diagnostics: Discovery of Cancer Biomarkers Using Tissue Culture

    Directory of Open Access Journals (Sweden)

    Debasish Paul

    2013-01-01

    Full Text Available Accurate diagnosis and proper monitoring of cancer patients remain a key obstacle for successful cancer treatment and prevention. Therein comes the need for biomarker discovery, which is crucial to the current oncological and other clinical practices having the potential to impact the diagnosis and prognosis. In fact, most of the biomarkers have been discovered utilizing the proteomics-based approaches. Although high-throughput mass spectrometry-based proteomic approaches like SILAC, 2D-DIGE, and iTRAQ are filling up the pitfalls of the conventional techniques, still serum proteomics importunately poses hurdle in overcoming a wide range of protein concentrations, and also the availability of patient tissue samples is a limitation for the biomarker discovery. Thus, researchers have looked for alternatives, and profiling of candidate biomarkers through tissue culture of tumor cell lines comes up as a promising option. It is a rich source of tumor cell-derived proteins, thereby, representing a wide array of potential biomarkers. Interestingly, most of the clinical biomarkers in use today (CA 125, CA 15.3, CA 19.9, and PSA were discovered through tissue culture-based system and tissue extracts. This paper tries to emphasize the tissue culture-based discovery of candidate biomarkers through various mass spectrometry-based proteomic approaches.

  4. The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Hui; Shi, Tujin; Qian, Wei-Jun; Liu, Tao; Kagan, Jacob; Srivastava, Sudhir; Smith, Richard D.; Rodland, Karin D.; Camp, David G.

    2015-12-04

    Mass spectrometry-based proteomics has become an indispensable tool in biomedical research with broad applications ranging from fundamental biology, systems biology, and biomarker discovery. Recent advances in LC-MS have made it become a major technology in clinical applications, especially in cancer biomarker discovery and verification. To overcome the challenges associated with the analysis of clinical samples, such as extremely wide dynamic range of protein concentrations in biofluids and the need to perform high throughput and accurate quantification, significant efforts have been devoted to improve the overall performance of LC-MS bases clinical proteomics. In this review, we summarize the recent advances in LC-MS in the aspect of cancer biomarker discovery and quantification, and discuss its potentials, limitations, and future perspectives.

  5. Discovery of biomarkers for oxidative stress based on cellular metabolomics.

    Science.gov (United States)

    Wang, Ningli; Wei, Jianteng; Liu, Yewei; Pei, Dong; Hu, Qingping; Wang, Yu; Di, Duolong

    2016-07-01

    Oxidative stress has a close relationship with various pathologic physiology phenomena and the potential biomarkers of oxidative stress may provide evidence for clinical diagnosis or disease prevention. Metabolomics was employed to identify the potential biomarkers of oxidative stress. High-performance liquid chromatography-diode array detector, mass spectrometry and partial least squares discriminate analysis were used in this study. The 10, 15 and 13 metabolites were considered to discriminate the model group, vitamin E-treated group and l-glutathione-treated group, respectively. Some of them have been identified, namely, malic acid, vitamin C, reduced glutathione and tryptophan. Identification of other potential biomarkers should be conducted and their physiological significance also needs to be elaborated.

  6. Comprehensive mass spectrometry based biomarker discovery and validation platform as applied to diabetic kidney disease

    Directory of Open Access Journals (Sweden)

    Scott D. Bringans

    2017-03-01

    Full Text Available A protein biomarker discovery workflow was applied to plasma samples from patients at different stages of diabetic kidney disease. The proteomics platform produced a panel of significant plasma biomarkers that were statistically scrutinised against the current gold standard tests on an analysis of 572 patients. Five proteins were significantly associated with diabetic kidney disease defined by albuminuria, renal impairment (eGFR and chronic kidney disease staging (CKD Stage ≥1, ROC curve of 0.77. The results prove the suitability and efficacy of the process used, and introduce a biomarker panel with the potential to improve diagnosis of diabetic kidney disease.

  7. The Knowledge-Integrated Network Biomarkers Discovery for Major Adverse Cardiac Events

    Science.gov (United States)

    Jin, Guangxu; Zhou, Xiaobo; Wang, Honghui; Zhao, Hong; Cui, Kemi; Zhang, Xiang-Sun; Chen, Luonan; Hazen, Stanley L.; Li, King; Wong, Stephen T. C.

    2010-01-01

    The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein–protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein–protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction. PMID:18665624

  8. Biomarker discovery in asthma and COPD: Application of proteomics techniques in human and mice

    Directory of Open Access Journals (Sweden)

    Steven Haenen

    2014-09-01

    Full Text Available The use of advanced proteomics approaches in the search for biomarkers in chronic lung diseases, such as asthma and COPD, is rather limited. Asthma and COPD are complex disorders, which can be subdivided into several phenotypes. This results in a heterogeneity of differential expressed biological molecules. Furthermore, genetic differences between animals and humans make ‘translation’ of possible biomarkers challenging. Yet, the improved sensitivity and high throughput of proteomic techniques could be an important asset for (new protein biomarker discovery in either human or animal models. We have reviewed the literature that reported the use of different proteomics approaches performed on samples obtained from humans and murine models in asthma and COPD research for the discovery of new biomarkers of diseases, biomarkers of sensitization or for the refinement of treatment. There is an increasing trend in the use of proteomics to explore new biomarkers of asthma or COPD. Although several murine models have been developed to study these lung diseases, and proteomics studies have been performed, ‘translation’ of identified candidate biomarkers into clinical studies is often lacking.

  9. Common Subcluster Mining in Microarray Data for Molecular Biomarker Discovery.

    Science.gov (United States)

    Sadhu, Arnab; Bhattacharyya, Balaram

    2017-10-11

    Molecular biomarkers can be potential facilitators for detection of cancer at early stage which is otherwise difficult through conventional biomarkers. Gene expression data from microarray experiments on both normal and diseased cell samples provide enormous scope to explore genetic relations of disease using computational techniques. Varied patterns of expressions of thousands of genes at different cell conditions along with inherent experimental error make the task of isolating disease related genes challenging. In this paper, we present a data mining method, common subcluster mining (CSM), to discover highly perturbed genes under diseased condition from differential expression patterns. The method builds heap through superposing near centroid clusters from gene expression data of normal samples and extracts its core part. It, thus, isolates genes exhibiting the most stable state across normal samples and constitute a reference set for each centroid. It performs the same operation on datasets from corresponding diseased samples and isolates the genes showing drastic changes in their expression patterns. The method thus finds the disease-sensitive genesets when applied to datasets of lung cancer, prostrate cancer, pancreatic cancer, breast cancer, leukemia and pulmonary arterial hypertension. In majority of the cases, few new genes are found over and above some previously reported ones. Genes with distinct deviations in diseased samples are prospective candidates for molecular biomarkers of the respective disease.

  10. Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies.

    Science.gov (United States)

    Skates, Steven J; Gillette, Michael A; LaBaer, Joshua; Carr, Steven A; Anderson, Leigh; Liebler, Daniel C; Ransohoff, David; Rifai, Nader; Kondratovich, Marina; Težak, Živana; Mansfield, Elizabeth; Oberg, Ann L; Wright, Ian; Barnes, Grady; Gail, Mitchell; Mesri, Mehdi; Kinsinger, Christopher R; Rodriguez, Henry; Boja, Emily S

    2013-12-06

    Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor, and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung, and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC) with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step toward building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research.

  11. A Critical Assessment of Feature Selection Methods for Biomarker Discovery in Clinical Proteomics

    NARCIS (Netherlands)

    Christin, Christin; Hoefsloot, Huub C. J.; Smilde, Age K.; Hoekman, B.; Suits, Frank; Bischoff, Rainer; Horvatovich, Peter

    In this paper, we compare the performance of six different feature selection methods for LC-MS-based proteomics and metabolomics biomarker discovery-t test, the Mann-Whitney-Wilcoxon test (mww test), nearest shrunken centroid (NSC), linear support vector machine-recursive features elimination

  12. Biomarker discovery by proteomics : challenges not only for the analytical chemist

    NARCIS (Netherlands)

    Horvatovich, Peter; Govorukhina, Natalia; Bischoff, Rainer

    2006-01-01

    This forum article outlines some of the major challenges in present day biomarker discovery research. Notably the dilemma of reaching sufficient concentration sensitivity versus the required analysis time per sample is highlighted using a model calculation. A number of possible developments and

  13. Discovery of Predictive Biomarkers for Litter Size in Boar Spermatozoa*

    Science.gov (United States)

    Kwon, Woo-Sung; Rahman, Md Saidur; Lee, June-Sub; Yoon, Sung-Jae; Park, Yoo-Jin; Pang, Myung-Geol

    2015-01-01

    Conventional semen analysis has been used for prognosis and diagnosis of male fertility. Although this tool is essential for providing initial quantitative information about semen, it remains a subject of debate. Therefore, development of new methods for the prognosis and diagnosis of male fertility should be seriously considered for animal species of economic importance as well as for humans. In the present study, we applied a comprehensive proteomic approach to identify global protein biomarkers in boar spermatozoa in order to increase the precision of male fertility prognoses and diagnoses. We determined that l-amino acid oxidase, mitochondrial malate dehydrogenase 2, NAD (MDH2), cytosolic 5′-nucleotidase 1B, lysozyme-like protein 4, and calmodulin (CALM) were significantly and abundantly expressed in high-litter size spermatozoa. We also found that equatorin, spermadhesin AWN, triosephosphate isomerase (TPI), Ras-related protein Rab-2A (RAB2A), spermadhesin AQN-3, and NADH dehydrogenase [ubiquinone] iron-sulfur protein 2 (NDUFS2) were significantly and abundantly expressed in low-litter size spermatozoa (>3-fold). Moreover, RAB2A, TPI, and NDUFS2 were negatively correlated with litter size, whereas CALM and MDH2 were positively correlated. This study provides novel biomarkers for the prediction of male fertility. To the best of our knowledge, this is the first work that shows significantly increased litter size using male fertility biomarkers in a field trial. Moreover, these protein markers may provide new developmental tools for the selection of superior sires as well as for the prognosis and diagnosis of male fertility. PMID:25693803

  14. Urinary Proteomics Pilot Study for Biomarker Discovery and Diagnosis in Heart Failure with Reduced Ejection Fraction

    DEFF Research Database (Denmark)

    Rossing, Kasper; Bosselmann, Helle Skovmand; Gustafsson, Finn

    2016-01-01

    and Results Urine samples were analyzed by on-line capillary electrophoresis coupled to electrospray ionization micro time-of-flight mass spectrometry (CE-MS) to generate individual urinary proteome profiles. In an initial biomarker discovery cohort, analysis of urinary proteome profiles from 33 HFr......Background Biomarker discovery and new insights into the pathophysiology of heart failure with reduced ejection fraction (HFrEF) may emerge from recent advances in high-throughput urinary proteomics. This could lead to improved diagnosis, risk stratification and management of HFrEF. Methods.......6%) in individuals with diastolic left ventricular dysfunction (N = 176). The HFrEF-related peptide biomarkers mainly included fragments of fibrillar type I and III collagen but also, e.g., of fibrinogen beta and alpha-1-antitrypsin. Conclusion CE-MS based urine proteome analysis served as a sensitive tool...

  15. Enhancement of MS Signal Processing For Improved Cancer Biomarker Discovery

    Science.gov (United States)

    Si, Qian

    Technological advances in proteomics have shown great potential in detecting cancer at the earliest stages. One way is to use the time of flight mass spectroscopy to identify biomarkers, or early disease indicators related to the cancer. Pattern analysis of time of flight mass spectra data from blood and tissue samples gives great hope for the identification of potential biomarkers among the complex mixture of biological and chemical samples for the early cancer detection. One of the keys issues is the pre-processing of raw mass spectra data. A lot of challenges need to be addressed: unknown noise character associated with the large volume of data, high variability in the mass spectroscopy measurements, and poorly understood signal background and so on. This dissertation focuses on developing statistical algorithms and creating data mining tools for computationally improved signal processing for mass spectrometry data. I have introduced an advanced accurate estimate of the noise model and a half-supervised method of mass spectrum data processing which requires little knowledge about the data.

  16. Biomarker discovery in neurological diseases: a metabolomic approach

    Directory of Open Access Journals (Sweden)

    Afaf El-Ansary

    2009-12-01

    Full Text Available Afaf El-Ansary, Nouf Al-Afaleg, Yousra Al-YafaeeBiochemistry Department, Science College, King Saud University, Riyadh, Saudi ArabiaAbstract: Biomarkers are pharmacological and physiological measurements or specific biochemicals in the body that have a particular molecular feature that makes them useful for measuring the progress of disease or the effects of treatment. Due to the complexity of neurological disorders, it is very difficult to have perfect markers. Brain diseases require plenty of markers to reflect the metabolic impairment of different brain cells. The recent introduction of the metabolomic approach helps the study of neurological diseases based on profiling a multitude of biochemical components related to brain metabolism. This review is a trial to elucidate the possibility to use this approach to identify plasma metabolic markers related to neurological disorders. Previous trials using different metabolomic analyses including nuclear magnetic resonance spectroscopy, gas chromatography combined with mass spectrometry, liquid chromatography combined with mass spectrometry, and capillary electrophoresis will be traced.Keywords: metabolic biomarkers, neurological disorders. metabolome, nuclear magnetic resonance, mass spectrometry, chromatography

  17. A probabilistic approach for automated discovery of perturbed genes using expression data from microarray or RNA-Seq.

    Science.gov (United States)

    Sundaramurthy, Gopinath; Eghbalnia, Hamid R

    2015-12-01

    In complex diseases, alterations of multiple molecular and cellular components in response to perturbations are indicative of disease physiology. While expression level of genes from high-throughput analysis can vary among patients, the common path among disease progression suggests that the underlying cellular sub-processes involving associated genes follow similar fates. Motivated by the interconnected nature of sub-processes, we have developed an automated methodology that combines ideas from biological networks, statistical models, and game theory, to probe connected cellular processes. The core concept in our approach uses probability of change (POC) to indicate the probability that a gene's expression level has changed between two conditions. POC facilitates the definition of change at the neighborhood, pathway, and network levels and enables evaluation of the influence of diseases on the expression. The 'connected' disease-related genes (DRG) identified display coherent and concomitant differential expression levels along paths. RNA-Seq and microarray breast cancer subtyping expression data sets were used to identify DRG between subtypes. A machine-learning algorithm was trained for subtype discrimination using the DRG, and the training yielded a set of biomarkers. The discriminative power of the biomarkers was tested using an unseen data set. Biomarkers identified overlaps with disease-specific identified genes, and we were able to classify disease subtypes with 100% and 80% agreement with PAM50, for microarray and RNA-Seq data set respectively. We present an automated probabilistic approach that offers unbiased and reproducible results, thus complementing existing methods in DRG and biomarker discovery for complex diseases. Copyright © 2015. Published by Elsevier Ltd.

  18. Comprehensive Phenotyping in Multiple Sclerosis: Discovery Based Proteomics and the Current Understanding of Putative Biomarkers

    Directory of Open Access Journals (Sweden)

    Kevin C. O’Connor

    2006-01-01

    Full Text Available Currently, there is no single test for multiple sclerosis (MS. Diagnosis is confirmed through clinical evaluation, abnormalities revealed by magnetic resonance imaging (MRI, and analysis of cerebrospinal fluid (CSF chemistry. The early and accurate diagnosis of the disease, monitoring of progression, and gauging of therapeutic intervention are important but elusive elements of patient care. Moreover, a deeper understanding of the disease pathology is needed, including discovery of accurate biomarkers for MS. Herein we review putative biomarkers of MS relating to neurodegeneration and contributions to neuropathology, with particular focus on autoimmunity. In addition, novel assessments of biomarkers not driven by hypotheses are discussed, featuring our application of advanced proteomics and metabolomics for comprehensive phenotyping of CSF and blood. This strategy allows comparison of component expression levels in CSF and serum between MS and control groups. Examination of these preliminary data suggests that several CSF proteins in MS are differentially expressed, and thus, represent putative biomarkers deserving of further evaluation.

  19. Random glycopeptide bead libraries for seromic biomarker discovery

    DEFF Research Database (Denmark)

    Kracun, Stjepan Kresimir; Cló, Emiliano; Clausen, Henrik

    2010-01-01

    glycopeptides were resynthesized at the preparative scale by automated parallel peptide synthesis and printed on microarrays for validation and broader analysis with larger sets of sera. We further showed that chemical synthesis of the monosaccharide O-glycopeptide library (Tn-glycoform) could be diversified...... have developed a random glycopeptide bead library screening platform for detection of autoantibodies and other binding proteins. Libraries were build on biocompatible PEGA beads including a safety-catch C-terminal amide linker (SCAL) that allowed mild cleavage conditions (I(2)/NaBH(4) and TFA......) for release of glycopeptides and sequence determination by ESI-Orbitrap-MS(n). As proof-of-principle, tumor -specific glycopeptide reporter epitopes were built-in into the libraries and were detected by tumor-specific monoclonal antibodies and autoantibodies from cancer patients. Sequenced and identified...

  20. Mass spectrometry based biomarker discovery, verification, and validation--quality assurance and control of protein biomarker assays.

    Science.gov (United States)

    Parker, Carol E; Borchers, Christoph H

    2014-06-01

    In its early years, mass spectrometry (MS)-based proteomics focused on the cataloging of proteins found in different species or different tissues. By 2005, proteomics was being used for protein quantitation, typically based on "proteotypic" peptides which act as surrogates for the parent proteins. Biomarker discovery is usually done by non-targeted "shotgun" proteomics, using relative quantitation methods to determine protein expression changes that correlate with disease (output given as "up-or-down regulation" or "fold-increases"). MS-based techniques can also perform "absolute" quantitation which is required for clinical applications (output given as protein concentrations). Here we describe the differences between these methods, factors that affect the precision and accuracy of the results, and some examples of recent studies using MS-based proteomics to verify cancer-related biomarkers. Copyright © 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

  1. Emerging concepts in biomarker discovery; The US-Japan workshop on immunological molecular markers in oncology

    Directory of Open Access Journals (Sweden)

    Rivoltini Licia

    2009-06-01

    Full Text Available Abstract Supported by the Office of International Affairs, National Cancer Institute (NCI, the "US-Japan Workshop on Immunological Biomarkers in Oncology" was held in March 2009. The workshop was related to a task force launched by the International Society for the Biological Therapy of Cancer (iSBTc and the United States Food and Drug Administration (FDA to identify strategies for biomarker discovery and validation in the field of biotherapy. The effort will culminate on October 28th 2009 in the "iSBTc-FDA-NCI Workshop on Prognostic and Predictive Immunologic Biomarkers in Cancer", which will be held in Washington DC in association with the Annual Meeting. The purposes of the US-Japan workshop were a to discuss novel approaches to enhance the discovery of predictive and/or prognostic markers in cancer immunotherapy; b to define the state of the science in biomarker discovery and validation. The participation of Japanese and US scientists provided the opportunity to identify shared or discordant themes across the distinct immune genetic background and the diverse prevalence of disease between the two Nations. Converging concepts were identified: enhanced knowledge of interferon-related pathways was found to be central to the understanding of immune-mediated tissue-specific destruction (TSD of which tumor rejection is a representative facet. Although the expression of interferon-stimulated genes (ISGs likely mediates the inflammatory process leading to tumor rejection, it is insufficient by itself and the associated mechanisms need to be identified. It is likely that adaptive immune responses play a broader role in tumor rejection than those strictly related to their antigen-specificity; likely, their primary role is to trigger an acute and tissue-specific inflammatory response at the tumor site that leads to rejection upon recruitment of additional innate and adaptive immune mechanisms. Other candidate systemic and/or tissue-specific biomarkers

  2. Automated Device for Asynchronous Extraction of RNA, DNA, or Protein Biomarkers from Surrogate Patient Samples.

    Science.gov (United States)

    Bitting, Anna L; Bordelon, Hali; Baglia, Mark L; Davis, Keersten M; Creecy, Amy E; Short, Philip A; Albert, Laura E; Karhade, Aditya V; Wright, David W; Haselton, Frederick R; Adams, Nicholas M

    2016-12-01

    Many biomarker-based diagnostic methods are inhibited by nontarget molecules in patient samples, necessitating biomarker extraction before detection. We have developed a simple device that purifies RNA, DNA, or protein biomarkers from complex biological samples without robotics or fluid pumping. The device design is based on functionalized magnetic beads, which capture biomarkers and remove background biomolecules by magnetically transferring the beads through processing solutions arrayed within small-diameter tubing. The process was automated by wrapping the tubing around a disc-like cassette and rotating it past a magnet using a programmable motor. This device recovered biomarkers at ~80% of the operator-dependent extraction method published previously. The device was validated by extracting biomarkers from a panel of surrogate patient samples containing clinically relevant concentrations of (1) influenza A RNA in nasal swabs, (2) Escherichia coli DNA in urine, (3) Mycobacterium tuberculosis DNA in sputum, and (4) Plasmodium falciparum protein and DNA in blood. The device successfully extracted each biomarker type from samples representing low levels of clinically relevant infectivity (i.e., 7.3 copies/µL of influenza A RNA, 405 copies/µL of E. coli DNA, 0.22 copies/µL of TB DNA, 167 copies/µL of malaria parasite DNA, and 2.7 pM of malaria parasite protein). © 2015 Society for Laboratory Automation and Screening.

  3. AMD, an Automated Motif Discovery Tool Using Stepwise Refinement of Gapped Consensuses

    OpenAIRE

    Shi, Jiantao; Yang, Wentao; Chen, Mingjie; Du, Yanzhi; Zhang, Ji; Wang, Kankan

    2011-01-01

    Motif discovery is essential for deciphering regulatory codes from high throughput genomic data, such as those from ChIP-chip/seq experiments. However, there remains a lack of effective and efficient methods for the identification of long and gapped motifs in many relevant tools reported to date. We describe here an automated tool that allows for de novo discovery of transcription factor binding sites, regardless of whether the motifs are long or short, gapped or contiguous.

  4. Discovery of sexual dimorphisms in metabolic and genetic biomarkers.

    Directory of Open Access Journals (Sweden)

    Kirstin Mittelstrass

    2011-08-01

    Full Text Available Metabolomic profiling and the integration of whole-genome genetic association data has proven to be a powerful tool to comprehensively explore gene regulatory networks and to investigate the effects of genetic variation at the molecular level. Serum metabolite concentrations allow a direct readout of biological processes, and association of specific metabolomic signatures with complex diseases such as Alzheimer's disease and cardiovascular and metabolic disorders has been shown. There are well-known correlations between sex and the incidence, prevalence, age of onset, symptoms, and severity of a disease, as well as the reaction to drugs. However, most of the studies published so far did not consider the role of sexual dimorphism and did not analyse their data stratified by gender. This study investigated sex-specific differences of serum metabolite concentrations and their underlying genetic determination. For discovery and replication we used more than 3,300 independent individuals from KORA F3 and F4 with metabolite measurements of 131 metabolites, including amino acids, phosphatidylcholines, sphingomyelins, acylcarnitines, and C6-sugars. A linear regression approach revealed significant concentration differences between males and females for 102 out of 131 metabolites (p-values<3.8×10(-4; Bonferroni-corrected threshold. Sex-specific genome-wide association studies (GWAS showed genome-wide significant differences in beta-estimates for SNPs in the CPS1 locus (carbamoyl-phosphate synthase 1, significance level: p<3.8×10(-10; Bonferroni-corrected threshold for glycine. We showed that the metabolite profiles of males and females are significantly different and, furthermore, that specific genetic variants in metabolism-related genes depict sexual dimorphism. Our study provides new important insights into sex-specific differences of cell regulatory processes and underscores that studies should consider sex-specific effects in design and

  5. Informatics-guided procurement of patient samples for biomarker discovery projects in cancer research.

    Science.gov (United States)

    Suh, K Stephen; Remache, Yvonne K; Patel, Jalpa S; Chen, Steve H; Haystrand, Russell; Ford, Peggy; Shaikh, Anadil M; Wang, Jian; Goy, Andre H

    2009-02-01

    Modern cancer research for biomarker discovery program requires solving several tasks that are directly involved with patient sample procurement. One requirement is to construct a highly efficient workflow on the clinical side for the procurement to generate a consistent supply of high quality samples for research. This undertaking needs a network of interdepartmental collaborations and participations at various levels, including physical human interactions, information technology implementations and a bioinformatics tool that is highly effective and user-friendly to busy clinicians and researchers associated with the sample procurement. Collegial participation that is sequential but continual from one department to another demands dedicated bioinformatics software coordinating between the institutional clinic and the tissue repository facility. Participants in the process include admissions, consenting process, phlebotomy, surgery center and pathology. During this multiple step procedures, clinical data are collected for detailed analytical endpoints to supplement logistics of defining and validating the discovery of biomarkers.

  6. Biomarker discovery by proteomics: challenges not only for the analytical chemist.

    Science.gov (United States)

    Horvatovich, Peter; Govorukhina, Natalia; Bischoff, Rainer

    2006-11-01

    This forum article outlines some of the major challenges in present day biomarker discovery research. Notably the dilemma of reaching sufficient concentration sensitivity versus the required analysis time per sample is highlighted using a model calculation. A number of possible developments and recent research findings are considered to show possible ways out of this dilemma. Finally, the challenge of processing large, megavariate datasets prior to statistical analysis is presented.

  7. Automation on an Open-Access Platform of Alzheimer's Disease Biomarker Immunoassays.

    Science.gov (United States)

    Gille, Benjamin; Dedeene, Lieselot; Stoops, Erik; Demeyer, Leentje; Francois, Cindy; Lefever, Stefanie; De Schaepdryver, Maxim; Brix, Britta; Vandenberghe, Rik; Tournoy, Jos; Vanderstichele, Hugo; Poesen, Koen

    2018-04-01

    The lack of (inter-)laboratory standardization has hampered the application of universal cutoff values for Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers and their transfer to general clinical practice. The automation of the AD biomarker immunoassays is suggested to generate more robust results than using manual testing. Open-access platforms will facilitate the integration of automation for novel biomarkers, allowing the introduction of the protein profiling concept. A feasibility study was performed on an automated open-access platform of the commercial immunoassays for the 42-amino-acid isoform of amyloid-β (Aβ 1-42 ), Aβ 1-40 , and total tau in CSF. Automated Aβ 1-42 , Aβ 1-40 , and tau immunoassays were performed within predefined acceptance criteria for bias and imprecision. Similar accuracy was obtained for ready-to-use calibrators as for reconstituted lyophilized kit calibrators. When compared with the addition of a standard curve in each test run, the use of a master calibrator curve, determined before and applied to each batch analysis as the standard curve, yielded an acceptable overall bias of -2.6% and -0.9% for Aβ 1-42 and Aβ 1-40 , respectively, with an imprecision profile of 6.2% and 8.4%, respectively. Our findings show that transfer of commercial manual immunoassays to fully automated open-access platforms is feasible, as it performs according to universal acceptance criteria.

  8. Improving feature ranking for biomarker discovery in proteomics mass spectrometry data using genetic programming

    Science.gov (United States)

    Ahmed, Soha; Zhang, Mengjie; Peng, Lifeng

    2014-07-01

    Feature selection on mass spectrometry (MS) data is essential for improving classification performance and biomarker discovery. The number of MS samples is typically very small compared with the high dimensionality of the samples, which makes the problem of biomarker discovery very hard. In this paper, we propose the use of genetic programming for biomarker detection and classification of MS data. The proposed approach is composed of two phases: in the first phase, feature selection and ranking are performed. In the second phase, classification is performed. The results show that the proposed method can achieve better classification performance and biomarker detection rate than the information gain- (IG) based and the RELIEF feature selection methods. Meanwhile, four classifiers, Naive Bayes, J48 decision tree, random forest and support vector machines, are also used to further test the performance of the top ranked features. The results show that the four classifiers using the top ranked features from the proposed method achieve better performance than the IG and the RELIEF methods. Furthermore, GP also outperforms a genetic algorithm approach on most of the used data sets.

  9. Towards Discovery and Targeted Peptide Biomarker Detection Using nanoESI-TIMS-TOF MS

    Science.gov (United States)

    Garabedian, Alyssa; Benigni, Paolo; Ramirez, Cesar E.; Baker, Erin S.; Liu, Tao; Smith, Richard D.; Fernandez-Lima, Francisco

    2017-09-01

    In the present work, the potential of trapped ion mobility spectrometry coupled to TOF mass spectrometry (TIMS-TOF MS) for discovery and targeted monitoring of peptide biomarkers from human-in-mouse xenograft tumor tissue was evaluated. In particular, a TIMS-MS workflow was developed for the detection and quantification of peptide biomarkers using internal heavy analogs, taking advantage of the high mobility resolution (R = 150-250) prior to mass analysis. Five peptide biomarkers were separated, identified, and quantified using offline nanoESI-TIMS-CID-TOF MS; the results were in good agreement with measurements using a traditional LC-ESI-MS/MS proteomics workflow. The TIMS-TOF MS analysis permitted peptide biomarker detection based on accurate mobility, mass measurements, and high sequence coverage for concentrations in the 10-200 nM range, while simultaneously achieving discovery measurements of not initially targeted peptides as markers from the same proteins and, eventually, other proteins. [Figure not available: see fulltext.

  10. INDEED: Integrated differential expression and differential network analysis of omic data for biomarker discovery.

    Science.gov (United States)

    Zuo, Yiming; Cui, Yi; Di Poto, Cristina; Varghese, Rency S; Yu, Guoqiang; Li, Ruijiang; Ressom, Habtom W

    2016-12-01

    Differential expression (DE) analysis is commonly used to identify biomarker candidates that have significant changes in their expression levels between distinct biological groups. One drawback of DE analysis is that it only considers the changes on single biomolecule level. Recently, differential network (DN) analysis has become popular due to its capability to measure the changes on biomolecular pair level. In DN analysis, network is typically built based on correlation and biomarker candidates are selected by investigating the network topology. However, correlation tends to generate over-complicated networks and the selection of biomarker candidates purely based on network topology ignores the changes on single biomolecule level. In this paper, we propose a novel approach, INDEED, that builds sparse differential network based on partial correlation and integrates DE and DN analyses for biomarker discovery. We applied this approach on real proteomic and glycomic data generated by liquid chromatography coupled with mass spectrometry for hepatocellular carcinoma (HCC) biomarker discovery study. For each omic data, we used one dataset to select biomarker candidates, built a disease classifier and evaluated the performance of the classifier on an independent dataset. The biomarker candidates, selected by INDEED, were more reproducible across independent datasets, and led to a higher classification accuracy in predicting HCC cases and cirrhotic controls compared with those selected by separate DE and DN analyses. INDEED also identified some candidates previously reported to be relevant to HCC, such as intercellular adhesion molecule 2 (ICAM2) and c4b-binding protein alpha chain (C4BPA), which were missed by both DE and DN analyses. In addition, we applied INDEED for survival time prediction based on transcriptomic data acquired by analysis of samples from breast cancer patients. We selected biomarker candidates and built a regression model for survival time prediction

  11. The Proteomics Big Challenge for Biomarkers and New Drug-Targets Discovery

    Science.gov (United States)

    Savino, Rocco; Paduano, Sergio; Preianò, Mariaimmacolata; Terracciano, Rosa

    2012-01-01

    In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets. PMID:23203042

  12. Biomarker discovery for colon cancer using a 761 gene RT-PCR assay

    Directory of Open Access Journals (Sweden)

    Hackett James R

    2007-08-01

    Full Text Available Abstract Background Reverse transcription PCR (RT-PCR is widely recognized to be the gold standard method for quantifying gene expression. Studies using RT-PCR technology as a discovery tool have historically been limited to relatively small gene sets compared to other gene expression platforms such as microarrays. We have recently shown that TaqMan® RT-PCR can be scaled up to profile expression for 192 genes in fixed paraffin-embedded (FPE clinical study tumor specimens. This technology has also been used to develop and commercialize a widely used clinical test for breast cancer prognosis and prediction, the Onco typeDX™ assay. A similar need exists in colon cancer for a test that provides information on the likelihood of disease recurrence in colon cancer (prognosis and the likelihood of tumor response to standard chemotherapy regimens (prediction. We have now scaled our RT-PCR assay to efficiently screen 761 biomarkers across hundreds of patient samples and applied this process to biomarker discovery in colon cancer. This screening strategy remains attractive due to the inherent advantages of maintaining platform consistency from discovery through clinical application. Results RNA was extracted from formalin fixed paraffin embedded (FPE tissue, as old as 28 years, from 354 patients enrolled in NSABP C-01 and C-02 colon cancer studies. Multiplexed reverse transcription reactions were performed using a gene specific primer pool containing 761 unique primers. PCR was performed as independent TaqMan® reactions for each candidate gene. Hierarchal clustering demonstrates that genes expected to co-express form obvious, distinct and in certain cases very tightly correlated clusters, validating the reliability of this technical approach to biomarker discovery. Conclusion We have developed a high throughput, quantitatively precise multi-analyte gene expression platform for biomarker discovery that approaches low density DNA arrays in numbers of

  13. Systems Biology Modeling of the Radiation Sensitivity Network: A Biomarker Discovery Platform

    International Nuclear Information System (INIS)

    Eschrich, Steven; Zhang Hongling; Zhao Haiyan; Boulware, David; Lee, Ji-Hyun; Bloom, Gregory; Torres-Roca, Javier F.

    2009-01-01

    Purpose: The discovery of effective biomarkers is a fundamental goal of molecular medicine. Developing a systems-biology understanding of radiosensitivity can enhance our ability of identifying radiation-specific biomarkers. Methods and Materials: Radiosensitivity, as represented by the survival fraction at 2 Gy was modeled in 48 human cancer cell lines. We applied a linear regression algorithm that integrates gene expression with biological variables, including ras status (mut/wt), tissue of origin and p53 status (mut/wt). Results: The biomarker discovery platform is a network representation of the top 500 genes identified by linear regression analysis. This network was reduced to a 10-hub network that includes c-Jun, HDAC1, RELA (p65 subunit of NFKB), PKC-beta, SUMO-1, c-Abl, STAT1, AR, CDK1, and IRF1. Nine targets associated with radiosensitization drugs are linked to the network, demonstrating clinical relevance. Furthermore, the model identified four significant radiosensitivity clusters of terms and genes. Ras was a dominant variable in the analysis, as was the tissue of origin, and their interaction with gene expression but not p53. Overrepresented biological pathways differed between clusters but included DNA repair, cell cycle, apoptosis, and metabolism. The c-Jun network hub was validated using a knockdown approach in 8 human cell lines representing lung, colon, and breast cancers. Conclusion: We have developed a novel radiation-biomarker discovery platform using a systems biology modeling approach. We believe this platform will play a central role in the integration of biology into clinical radiation oncology practice.

  14. Plasma biomarker discovery in preeclampsia using a novel differential isolation technology for circulating extracellular vesicles.

    Science.gov (United States)

    Tan, Kok Hian; Tan, Soon Sim; Sze, Siu Kwan; Lee, Wai Kheong Ryan; Ng, Mor Jack; Lim, Sai Kiang

    2014-10-01

    To circumvent the complex protein milieu of plasma and discover robust predictive biomarkers for preeclampsia (PE), we investigate if phospholipid-binding ligands can reduce the milieu complexity by extracting plasma extracellular vesicles for biomarker discovery. Cholera toxin B chain (CTB) and annexin V (AV) which respectively binds GM1 ganglioside and phosphatidylserine were used to isolate extracellular vesicles from plasma of PE patients and healthy pregnant women. The proteins in the vesicles were identified using enzyme-linked immunosorbent assay, antibody array, and mass spectrometry. CTB and AV were found to bind 2 distinct groups of extracellular vesicles. Antibody array and enzyme-linked immunosorbent assay revealed that PE patients had elevated levels of CD105, interleukin-6, placental growth factor, tissue inhibitor of metallopeptidase 1, and atrial natriuretic peptide in cholera toxin B- but not AV-vesicles, and elevated levels of plasminogen activator inhibitor-1, pro-calcitonin, S100b, tumor growth factor β, vascular endothelial growth factor receptor 1, brain natriuretic peptide, and placental growth factor in both cholera toxin B- and AV-vesicles. CD9 level was elevated in cholera toxin B-vesicles but reduced in AV vesicles of PE patients. Proteome analysis revealed that in cholera toxin B-vesicles, 87 and 222 proteins were present only in PE patients and healthy pregnant women respectively while in AV-vesicles, 104 and 157 proteins were present only in PE and healthy pregnant women, respectively. This study demonstrated for the first time that CTB and AV bind unique extracellular vesicles, and their protein cargo reflects the disease state of the patient. The successful use of these 2 ligands to isolate circulating plasma extracellular vesicles for biomarker discovery in PE represents a novel technology for biomarker discovery that can be applied to other specialties. Copyright © 2014 Elsevier Inc. All rights reserved.

  15. Variable importance analysis based on rank aggregation with applications in metabolomics for biomarker discovery.

    Science.gov (United States)

    Yun, Yong-Huan; Deng, Bai-Chuan; Cao, Dong-Sheng; Wang, Wei-Ting; Liang, Yi-Zeng

    2016-03-10

    Biomarker discovery is one important goal in metabolomics, which is typically modeled as selecting the most discriminating metabolites for classification and often referred to as variable importance analysis or variable selection. Until now, a number of variable importance analysis methods to discover biomarkers in the metabolomics studies have been proposed. However, different methods are mostly likely to generate different variable ranking results due to their different principles. Each method generates a variable ranking list just as an expert presents an opinion. The problem of inconsistency between different variable ranking methods is often ignored. To address this problem, a simple and ideal solution is that every ranking should be taken into account. In this study, a strategy, called rank aggregation, was employed. It is an indispensable tool for merging individual ranking lists into a single "super"-list reflective of the overall preference or importance within the population. This "super"-list is regarded as the final ranking for biomarker discovery. Finally, it was used for biomarkers discovery and selecting the best variable subset with the highest predictive classification accuracy. Nine methods were used, including three univariate filtering and six multivariate methods. When applied to two metabolic datasets (Childhood overweight dataset and Tubulointerstitial lesions dataset), the results show that the performance of rank aggregation has improved greatly with higher prediction accuracy compared with using all variables. Moreover, it is also better than penalized method, least absolute shrinkage and selectionator operator (LASSO), with higher prediction accuracy or less number of selected variables which are more interpretable. Copyright © 2016 Elsevier B.V. All rights reserved.

  16. NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review

    International Nuclear Information System (INIS)

    Smolinska, Agnieszka; Blanchet, Lionel; Buydens, Lutgarde M.C.; Wijmenga, Sybren S.

    2012-01-01

    Highlights: ► Procedures for acquisition of different biofluids by NMR. ► Recent developments in metabolic profiling of different biofluids by NMR are presented. ► The crucial steps involved in data preprocessing and multivariate chemometric analysis are reviewed. ► Emphasis is given on recent findings on Multiple Sclerosis via NMR and pattern recognition methods. - Abstract: Metabolomics is the discipline where endogenous and exogenous metabolites are assessed, identified and quantified in different biological samples. Metabolites are crucial components of biological system and highly informative about its functional state, due to their closeness to functional endpoints and to the organism's phenotypes. Nuclear Magnetic Resonance (NMR) spectroscopy, next to Mass Spectrometry (MS), is one of the main metabolomics analytical platforms. The technological developments in the field of NMR spectroscopy have enabled the identification and quantitative measurement of the many metabolites in a single sample of biofluids in a non-targeted and non-destructive manner. Combination of NMR spectra of biofluids and pattern recognition methods has driven forward the application of metabolomics in the field of biomarker discovery. The importance of metabolomics in diagnostics, e.g. in identifying biomarkers or defining pathological status, has been growing exponentially as evidenced by the number of published papers. In this review, we describe the developments in data acquisition and multivariate analysis of NMR-based metabolomics data, with particular emphasis on the metabolomics of Cerebrospinal Fluid (CSF) and biomarker discovery in Multiple Sclerosis (MScl).

  17. At the cross-roads of participatory research and biomarker discovery in autism: the need for empirical data.

    Science.gov (United States)

    Yusuf, Afiqah; Elsabbagh, Mayada

    2015-12-15

    Identifying biomarkers for autism can improve outcomes for those affected by autism. Engaging the diverse stakeholders in the research process using community-based participatory research (CBPR) can accelerate biomarker discovery into clinical applications. However, there are limited examples of stakeholder involvement in autism research, possibly due to conceptual and practical concerns. We evaluate the applicability of CBPR principles to biomarker discovery in autism and critically review empirical studies adopting these principles. Using a scoping review methodology, we identified and evaluated seven studies using CBPR principles in biomarker discovery. The limited number of studies in biomarker discovery adopting CBPR principles coupled with their methodological limitations suggests that such applications are feasible but challenging. These studies illustrate three CBPR themes: community assessment, setting global priorities, and collaboration in research design. We propose that further research using participatory principles would be useful in accelerating the pace of discovery and the development of clinically meaningful biomarkers. For this goal to be successful we advocate for increased attention to previously identified conceptual and methodological challenges to participatory approaches in health research, including improving scientific rigor and developing long-term partnerships among stakeholders.

  18. Banking on the future: biobanking for "omics" approaches to biomarker discovery for Opisthorchis-induced cholangiocarcinoma in Thailand.

    Science.gov (United States)

    Mulvenna, Jason; Yonglitthipagon, Ponlapat; Sripa, Banchob; Brindley, Paul J; Loukas, Alex; Bethony, Jeffrey M

    2012-03-01

    Cholangiocarcinoma (CCA)--bile duct cancer--is associated with late presentation, poses challenges for diagnosis, and has high mortality. These features t highlight the desperate need for biomarkers than can be measured early and in accessible body fluids such as plasma of people at risk for developing this lethal cancer. In this manuscript, we address previous limitations in the discovery stage of biomarker(s) for CCA and indicate how new generation of "omics" technologies could be used for biomarker discovery in Thailand. A key factor in the success of this biomarker program for CCA is the combination of cutting edge technology with strategic sample acquisition by a biorepositories. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  19. Progress and Potential of Imaging Mass Spectrometry Applied to Biomarker Discovery.

    Science.gov (United States)

    Quanico, Jusal; Franck, Julien; Wisztorski, Maxence; Salzet, Michel; Fournier, Isabelle

    2017-01-01

    Mapping provides a direct means to assess the impact of protein biomarkers and puts into context their relevance in the type of cancer being examined. To this end, mass spectrometry imaging (MSI) was developed to provide the needed spatial information which is missing in traditional liquid-based mass spectrometric proteomics approaches. Aptly described as a "molecular histology" technique, MSI gives an additional dimension in characterizing tumor biopsies, allowing for mapping of hundreds of molecules in a single analysis. A decade of developments focused on improving and standardizing MSI so that the technique can be translated into the clinical setting. This review describes the progress made in addressing the technological development that allows to bridge local protein detection by MSI to its identification and to illustrate its potential in studying various aspects of cancer biomarker discovery.

  20. Semi-automated knowledge discovery: identifying and profiling human trafficking

    Science.gov (United States)

    Poelmans, Jonas; Elzinga, Paul; Ignatov, Dmitry I.; Kuznetsov, Sergei O.

    2012-11-01

    We propose an iterative and human-centred knowledge discovery methodology based on formal concept analysis. The proposed approach recognizes the important role of the domain expert in mining real-world enterprise applications and makes use of specific domain knowledge, including human intelligence and domain-specific constraints. Our approach was empirically validated at the Amsterdam-Amstelland police to identify suspects and victims of human trafficking in 266,157 suspicious activity reports. Based on guidelines of the Attorney Generals of the Netherlands, we first defined multiple early warning indicators that were used to index the police reports. Using concept lattices, we revealed numerous unknown human trafficking and loverboy suspects. In-depth investigation by the police resulted in a confirmation of their involvement in illegal activities resulting in actual arrestments been made. Our human-centred approach was embedded into operational policing practice and is now successfully used on a daily basis to cope with the vastly growing amount of unstructured information.

  1. Urinary Proteomics Pilot Study for Biomarker Discovery and Diagnosis in Heart Failure with Reduced Ejection Fraction.

    Directory of Open Access Journals (Sweden)

    Kasper Rossing

    Full Text Available Biomarker discovery and new insights into the pathophysiology of heart failure with reduced ejection fraction (HFrEF may emerge from recent advances in high-throughput urinary proteomics. This could lead to improved diagnosis, risk stratification and management of HFrEF.Urine samples were analyzed by on-line capillary electrophoresis coupled to electrospray ionization micro time-of-flight mass spectrometry (CE-MS to generate individual urinary proteome profiles. In an initial biomarker discovery cohort, analysis of urinary proteome profiles from 33 HFrEF patients and 29 age- and sex-matched individuals without HFrEF resulted in identification of 103 peptides that were significantly differentially excreted in HFrEF. These 103 peptides were used to establish the support vector machine-based HFrEF classifier HFrEF103. In a subsequent validation cohort, HFrEF103 very accurately (area under the curve, AUC = 0.972 discriminated between HFrEF patients (N = 94, sensitivity = 93.6% and control individuals with and without impaired renal function and hypertension (N = 552, specificity = 92.9%. Interestingly, HFrEF103 showed low sensitivity (12.6% in individuals with diastolic left ventricular dysfunction (N = 176. The HFrEF-related peptide biomarkers mainly included fragments of fibrillar type I and III collagen but also, e.g., of fibrinogen beta and alpha-1-antitrypsin.CE-MS based urine proteome analysis served as a sensitive tool to determine a vast array of HFrEF-related urinary peptide biomarkers which might help improving our understanding and diagnosis of heart failure.

  2. Automated discovery of functional generality of human gene expression programs.

    Directory of Open Access Journals (Sweden)

    Georg K Gerber

    2007-08-01

    Full Text Available An important research problem in computational biology is the identification of expression programs, sets of co-expressed genes orchestrating normal or pathological processes, and the characterization of the functional breadth of these programs. The use of human expression data compendia for discovery of such programs presents several challenges including cellular inhomogeneity within samples, genetic and environmental variation across samples, uncertainty in the numbers of programs and sample populations, and temporal behavior. We developed GeneProgram, a new unsupervised computational framework based on Hierarchical Dirichlet Processes that addresses each of the above challenges. GeneProgram uses expression data to simultaneously organize tissues into groups and genes into overlapping programs with consistent temporal behavior, to produce maps of expression programs, which are sorted by generality scores that exploit the automatically learned groupings. Using synthetic and real gene expression data, we showed that GeneProgram outperformed several popular expression analysis methods. We applied GeneProgram to a compendium of 62 short time-series gene expression datasets exploring the responses of human cells to infectious agents and immune-modulating molecules. GeneProgram produced a map of 104 expression programs, a substantial number of which were significantly enriched for genes involved in key signaling pathways and/or bound by NF-kappaB transcription factors in genome-wide experiments. Further, GeneProgram discovered expression programs that appear to implicate surprising signaling pathways or receptor types in the response to infection, including Wnt signaling and neurotransmitter receptors. We believe the discovered map of expression programs involved in the response to infection will be useful for guiding future biological experiments; genes from programs with low generality scores might serve as new drug targets that exhibit minimal

  3. Reproducible cancer biomarker discovery in SELDI-TOF MS using different pre-processing algorithms.

    Directory of Open Access Journals (Sweden)

    Jinfeng Zou

    Full Text Available BACKGROUND: There has been much interest in differentiating diseased and normal samples using biomarkers derived from mass spectrometry (MS studies. However, biomarker identification for specific diseases has been hindered by irreproducibility. Specifically, a peak profile extracted from a dataset for biomarker identification depends on a data pre-processing algorithm. Until now, no widely accepted agreement has been reached. RESULTS: In this paper, we investigated the consistency of biomarker identification using differentially expressed (DE peaks from peak profiles produced by three widely used average spectrum-dependent pre-processing algorithms based on SELDI-TOF MS data for prostate and breast cancers. Our results revealed two important factors that affect the consistency of DE peak identification using different algorithms. One factor is that some DE peaks selected from one peak profile were not detected as peaks in other profiles, and the second factor is that the statistical power of identifying DE peaks in large peak profiles with many peaks may be low due to the large scale of the tests and small number of samples. Furthermore, we demonstrated that the DE peak detection power in large profiles could be improved by the stratified false discovery rate (FDR control approach and that the reproducibility of DE peak detection could thereby be increased. CONCLUSIONS: Comparing and evaluating pre-processing algorithms in terms of reproducibility can elucidate the relationship among different algorithms and also help in selecting a pre-processing algorithm. The DE peaks selected from small peak profiles with few peaks for a dataset tend to be reproducibly detected in large peak profiles, which suggests that a suitable pre-processing algorithm should be able to produce peaks sufficient for identifying useful and reproducible biomarkers.

  4. High Throughput Light Absorber Discovery, Part 1: An Algorithm for Automated Tauc Analysis.

    Science.gov (United States)

    Suram, Santosh K; Newhouse, Paul F; Gregoire, John M

    2016-11-14

    High-throughput experimentation provides efficient mapping of composition-property relationships, and its implementation for the discovery of optical materials enables advancements in solar energy and other technologies. In a high throughput pipeline, automated data processing algorithms are often required to match experimental throughput, and we present an automated Tauc analysis algorithm for estimating band gap energies from optical spectroscopy data. The algorithm mimics the judgment of an expert scientist, which is demonstrated through its application to a variety of high throughput spectroscopy data, including the identification of indirect or direct band gaps in Fe 2 O 3 , Cu 2 V 2 O 7 , and BiVO 4 . The applicability of the algorithm to estimate a range of band gap energies for various materials is demonstrated by a comparison of direct-allowed band gaps estimated by expert scientists and by automated algorithm for 60 optical spectra.

  5. Sample preparation and fractionation for proteome analysis and cancer biomarker discovery by mass spectrometry.

    Science.gov (United States)

    Ahmed, Farid E

    2009-03-01

    Sample preparation and fractionation technologies are one of the most crucial processes in proteomic analysis and biomarker discovery in solubilized samples. Chromatographic or electrophoretic proteomic technologies are also available for separation of cellular protein components. There are, however, considerable limitations in currently available proteomic technologies as none of them allows for the analysis of the entire proteome in a simple step because of the large number of peptides, and because of the wide concentration dynamic range of the proteome in clinical blood samples. The results of any undertaken experiment depend on the condition of the starting material. Therefore, proper experimental design and pertinent sample preparation is essential to obtain meaningful results, particularly in comparative clinical proteomics in which one is looking for minor differences between experimental (diseased) and control (nondiseased) samples. This review discusses problems associated with general and specialized strategies of sample preparation and fractionation, dealing with samples that are solution or suspension, in a frozen tissue state, or formalin-preserved tissue archival samples, and illustrates how sample processing might influence detection with mass spectrometric techniques. Strategies that dramatically improve the potential for cancer biomarker discovery in minimally invasive, blood-collected human samples are also presented.

  6. Lung Cancer Serum Biomarker Discovery Using Label Free LC-MS/MS

    Science.gov (United States)

    Zeng, Xuemei; Hood, Brian L.; Zhao, Ting; Conrads, Thomas P.; Sun, Mai; Gopalakrishnan, Vanathi; Grover, Himanshu; Day, Roger S.; Weissfeld, Joel L.; Wilson, David O.; Siegfried, Jill M.; Bigbee, William L.

    2011-01-01

    Introduction Lung cancer remains the leading cause of cancer-related death with poor survival due to the late stage at which lung cancer is typically diagnosed. Given the clinical burden from lung cancer, and the relatively favorable survival associated with early stage lung cancer, biomarkers for early detection of lung cancer are of important potential clinical benefit. Methods We performed a global lung cancer serum biomarker discovery study using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a set of pooled non-small cell lung cancer (NSCLC) case sera and matched controls. Immunoaffinity subtraction was used to deplete the top most abundant serum proteins; the remaining serum proteins were subjected to trypsin digestion and analyzed in triplicate by LC-MS/MS. The tandem mass spectrum data were searched against the human proteome database and the resultant spectral counting data were used to estimate the relative abundance of proteins across the case/control serum pools. The spectral counting derived abundances of some candidate biomarker proteins were confirmed with multiple reaction monitoring MS assays. Results A list of 49 differentially abundant candidate proteins was compiled by applying a negative binomial regression model to the spectral counting data (pbiomarkers with statistically significant differential abundance across the lung cancer case/control pools which, when validated, could improve lung cancer early detection. PMID:21304412

  7. Clinical proteomics for liver disease: a promising approach for discovery of novel biomarkers

    Directory of Open Access Journals (Sweden)

    Tsubouchi Hirohito

    2010-12-01

    Full Text Available Abstract Hepatocellular carcinoma (HCC is the fifth most common cancer and advanced hepatic fibrosis is a major risk factor for HCC. Hepatic fibrosis including liver cirrhosis and HCC are mainly induced by persistent hepatitis B or C virus infection, with approximately 500 million people infected with hepatitis B or C virus worldwide. Furthermore, the number of patients with non-alcoholic fatty liver disease (NAFLD has recently increased and NAFLD can progress to cirrhosis and HCC. These chronic liver diseases are major causes of morbidity and mortality, and the identification of non-invasive biomarkers is important for early diagnosis. Recent advancements in quantitative and large-scale proteomic methods could be used to optimize the clinical application of biomarkers. Early diagnosis of HCC and assessment of the stage of hepatic fibrosis or NAFLD can also contribute to more effective therapeutic interventions and an improve prognosis. Furthermore, advancements of proteomic techniques contribute not only to the discovery of clinically useful biomarkers, but also in clarifying the molecular mechanisms of disease pathogenesis by using body fluids, such as serum, and tissue samples and cultured cells. In this review, we report recent advances in quantitative proteomics and several findings focused on liver diseases, including HCC, NAFLD, hepatic fibrosis and hepatitis B or C virus infections.

  8. Schizophrenia genomics and proteomics: are we any closer to biomarker discovery?

    Directory of Open Access Journals (Sweden)

    Kramer Alon

    2009-01-01

    Full Text Available Abstract The field of proteomics has made leaps and bounds in the last 10 years particularly in the fields of oncology and cardiovascular medicine. In comparison, neuroproteomics is still playing catch up mainly due to the relative complexity of neurological disorders. Schizophrenia is one such disorder, believed to be the results of multiple factors both genetic and environmental. Affecting over 2 million people in the US alone, it has become a major clinical and public health concern worldwide. This paper gives an update of schizophrenia biomarker research as reviewed by Lakhan in 2006 and gives us a rundown of the progress made during the last two years. Several studies demonstrate the potential of cerebrospinal fluid as a source of neuro-specific biomarkers. Genetic association studies are making headway in identifying candidate genes for schizophrenia. In addition, metabonomics, bioinformatics, and neuroimaging techniques are aiming to complete the picture by filling in knowledge gaps. International cooperation in the form of genomics and protein databases and brain banks is facilitating research efforts. While none of the recent developments described here in qualifies as biomarker discovery, many are likely to be stepping stones towards that goal.

  9. NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review

    Energy Technology Data Exchange (ETDEWEB)

    Smolinska, Agnieszka, E-mail: A.Smolinska@science.ru.nl [Institute for Molecules and Materials, Radboud University Nijmegen, Nijmegen (Netherlands); Blanchet, Lionel [Institute for Molecules and Materials, Radboud University Nijmegen, Nijmegen (Netherlands); Department of Biochemistry, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Nijmegen (Netherlands); Buydens, Lutgarde M.C.; Wijmenga, Sybren S. [Institute for Molecules and Materials, Radboud University Nijmegen, Nijmegen (Netherlands)

    2012-10-31

    Highlights: Black-Right-Pointing-Pointer Procedures for acquisition of different biofluids by NMR. Black-Right-Pointing-Pointer Recent developments in metabolic profiling of different biofluids by NMR are presented. Black-Right-Pointing-Pointer The crucial steps involved in data preprocessing and multivariate chemometric analysis are reviewed. Black-Right-Pointing-Pointer Emphasis is given on recent findings on Multiple Sclerosis via NMR and pattern recognition methods. - Abstract: Metabolomics is the discipline where endogenous and exogenous metabolites are assessed, identified and quantified in different biological samples. Metabolites are crucial components of biological system and highly informative about its functional state, due to their closeness to functional endpoints and to the organism's phenotypes. Nuclear Magnetic Resonance (NMR) spectroscopy, next to Mass Spectrometry (MS), is one of the main metabolomics analytical platforms. The technological developments in the field of NMR spectroscopy have enabled the identification and quantitative measurement of the many metabolites in a single sample of biofluids in a non-targeted and non-destructive manner. Combination of NMR spectra of biofluids and pattern recognition methods has driven forward the application of metabolomics in the field of biomarker discovery. The importance of metabolomics in diagnostics, e.g. in identifying biomarkers or defining pathological status, has been growing exponentially as evidenced by the number of published papers. In this review, we describe the developments in data acquisition and multivariate analysis of NMR-based metabolomics data, with particular emphasis on the metabolomics of Cerebrospinal Fluid (CSF) and biomarker discovery in Multiple Sclerosis (MScl).

  10. Statistical interpretation of machine learning-based feature importance scores for biomarker discovery.

    Science.gov (United States)

    Huynh-Thu, Vân Anh; Saeys, Yvan; Wehenkel, Louis; Geurts, Pierre

    2012-07-01

    Univariate statistical tests are widely used for biomarker discovery in bioinformatics. These procedures are simple, fast and their output is easily interpretable by biologists but they can only identify variables that provide a significant amount of information in isolation from the other variables. As biological processes are expected to involve complex interactions between variables, univariate methods thus potentially miss some informative biomarkers. Variable relevance scores provided by machine learning techniques, however, are potentially able to highlight multivariate interacting effects, but unlike the p-values returned by univariate tests, these relevance scores are usually not statistically interpretable. This lack of interpretability hampers the determination of a relevance threshold for extracting a feature subset from the rankings and also prevents the wide adoption of these methods by practicians. We evaluated several, existing and novel, procedures that extract relevant features from rankings derived from machine learning approaches. These procedures replace the relevance scores with measures that can be interpreted in a statistical way, such as p-values, false discovery rates, or family wise error rates, for which it is easier to determine a significance level. Experiments were performed on several artificial problems as well as on real microarray datasets. Although the methods differ in terms of computing times and the tradeoff, they achieve in terms of false positives and false negatives, some of them greatly help in the extraction of truly relevant biomarkers and should thus be of great practical interest for biologists and physicians. As a side conclusion, our experiments also clearly highlight that using model performance as a criterion for feature selection is often counter-productive. Python source codes of all tested methods, as well as the MATLAB scripts used for data simulation, can be found in the Supplementary Material.

  11. SITC/iSBTc Cancer Immunotherapy Biomarkers Resource Document: Online resources and useful tools - a compass in the land of biomarker discovery

    Directory of Open Access Journals (Sweden)

    Disis Mary L

    2011-09-01

    Full Text Available Abstract Recent positive clinical results in cancer immunotherapy point to the potential of immune-based strategies to provide effective treatment of a variety of cancers. In some patients, the responses to cancer immunotherapy are durable, dramatically extending survival. Extensive research efforts are being made to identify and validate biomarkers that can help identify subsets of cancer patients that will benefit most from these novel immunotherapies. In addition to the clear advantage of such predictive biomarkers, immune biomarkers are playing an important role in the development, clinical evaluation and monitoring of cancer immunotherapies. This Cancer Immunotherapy Resource Document, prepared by the Society for Immunotherapy of Cancer (SITC, formerly the International Society for Biological Therapy of Cancer, iSBTc, provides key references and online resources relevant to the discovery, evaluation and clinical application of immune biomarkers. These key resources were identified by experts in the field who are actively pursuing research in biomarker identification and validation. This organized collection of the most useful references, online resources and tools serves as a compass to guide discovery of biomarkers essential to advancing novel cancer immunotherapies.

  12. Antibody validation of immunohistochemistry for biomarker discovery: Recommendations of a consortium of academic and pharmaceutical based histopathology researchers

    OpenAIRE

    Howat, W.J.; Lewis, A.; Jones, P.; Kampf, C.; Pontén, F.; C.M., van der Loos; Gray, N.; Womack, C.; Warford, A.

    2014-01-01

    As biomarker discovery takes centre-stage, the role of immunohistochemistry within that process is increasing. At the same time, the number of antibodies being produced for ‘‘research use’’ continues to rise and it is important that antibodies to be used as biomarkers are validated for specificity and sensitivity before use. This guideline seeks to provide a stepwise approach for the validation of an antibody for immunohistochemical assays, reflecting the views of a consortium of academic and...

  13. Discovery of serum biomarkers predicting development of a subsequent depressive episode in social anxiety disorder.

    Science.gov (United States)

    Gottschalk, M G; Cooper, J D; Chan, M K; Bot, M; Penninx, B W J H; Bahn, S

    2015-08-01

    Although social anxiety disorder (SAD) is strongly associated with the subsequent development of a depressive disorder (major depressive disorder or dysthymia), no underlying biological risk factors are known. We aimed to identify biomarkers which predict depressive episodes in SAD patients over a 2-year follow-up period. One hundred sixty-five multiplexed immunoassay analytes were investigated in blood serum of 143 SAD patients without co-morbid depressive disorders, recruited within the Netherlands Study of Depression and Anxiety (NESDA). Predictive performance of identified biomarkers, clinical variables and self-report inventories was assessed using receiver operating characteristics curves (ROC) and represented by the area under the ROC curve (AUC). Stepwise logistic regression resulted in the selection of four serum analytes (AXL receptor tyrosine kinase, vascular cell adhesion molecule 1, vitronectin, collagen IV) and four additional variables (Inventory of Depressive Symptomatology, Beck Anxiety Inventory somatic subscale, depressive disorder lifetime diagnosis, BMI) as optimal set of patient parameters. When combined, an AUC of 0.86 was achieved for the identification of SAD individuals who later developed a depressive disorder. Throughout our analyses, biomarkers yielded superior discriminative performance compared to clinical variables and self-report inventories alone. We report the discovery of a serum marker panel with good predictive performance to identify SAD individuals prone to develop subsequent depressive episodes in a naturalistic cohort design. Furthermore, we emphasise the importance to combine biological markers, clinical variables and self-report inventories for disease course predictions in psychiatry. Following replication in independent cohorts, validated biomarkers could help to identify SAD patients at risk of developing a depressive disorder, thus facilitating early intervention. Copyright © 2015 Elsevier Inc. All rights reserved.

  14. Early identification of hERG liability in drug discovery programs by automated patch clamp.

    Science.gov (United States)

    Danker, Timm; Möller, Clemens

    2014-01-01

    Blockade of the cardiac ion channel coded by human ether-à-gogo-related gene (hERG) can lead to cardiac arrhythmia, which has become a major concern in drug discovery and development. Automated electrophysiological patch clamp allows assessment of hERG channel effects early in drug development to aid medicinal chemistry programs and has become routine in pharmaceutical companies. However, a number of potential sources of errors in setting up hERG channel assays by automated patch clamp can lead to misinterpretation of data or false effects being reported. This article describes protocols for automated electrophysiology screening of compound effects on the hERG channel current. Protocol details and the translation of criteria known from manual patch clamp experiments to automated patch clamp experiments to achieve good quality data are emphasized. Typical pitfalls and artifacts that may lead to misinterpretation of data are discussed. While this article focuses on hERG channel recordings using the QPatch (Sophion A/S, Copenhagen, Denmark) technology, many of the assay and protocol details given in this article can be transferred for setting up different ion channel assays by automated patch clamp and are similar on other planar patch clamp platforms.

  15. AutoDrug: fully automated macromolecular crystallography workflows for fragment-based drug discovery

    International Nuclear Information System (INIS)

    Tsai, Yingssu; McPhillips, Scott E.; González, Ana; McPhillips, Timothy M.; Zinn, Daniel; Cohen, Aina E.; Feese, Michael D.; Bushnell, David; Tiefenbrunn, Theresa; Stout, C. David; Ludaescher, Bertram; Hedman, Britt; Hodgson, Keith O.; Soltis, S. Michael

    2013-01-01

    New software has been developed for automating the experimental and data-processing stages of fragment-based drug discovery at a macromolecular crystallography beamline. A new workflow-automation framework orchestrates beamline-control and data-analysis software while organizing results from multiple samples. AutoDrug is software based upon the scientific workflow paradigm that integrates the Stanford Synchrotron Radiation Lightsource macromolecular crystallography beamlines and third-party processing software to automate the crystallography steps of the fragment-based drug-discovery process. AutoDrug screens a cassette of fragment-soaked crystals, selects crystals for data collection based on screening results and user-specified criteria and determines optimal data-collection strategies. It then collects and processes diffraction data, performs molecular replacement using provided models and detects electron density that is likely to arise from bound fragments. All processes are fully automated, i.e. are performed without user interaction or supervision. Samples can be screened in groups corresponding to particular proteins, crystal forms and/or soaking conditions. A single AutoDrug run is only limited by the capacity of the sample-storage dewar at the beamline: currently 288 samples. AutoDrug was developed in conjunction with RestFlow, a new scientific workflow-automation framework. RestFlow simplifies the design of AutoDrug by managing the flow of data and the organization of results and by orchestrating the execution of computational pipeline steps. It also simplifies the execution and interaction of third-party programs and the beamline-control system. Modeling AutoDrug as a scientific workflow enables multiple variants that meet the requirements of different user groups to be developed and supported. A workflow tailored to mimic the crystallography stages comprising the drug-discovery pipeline of CoCrystal Discovery Inc. has been deployed and successfully

  16. 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

    Biomarkers are biometric measurements that provide critical quantitative information about the biological condition of the animal or individual being tested. In drug safety studies, established toxicity biomarkers are used along with other conventional study data to determine dose-limiting organ toxicity, and to define species sensitivity for new chemical entities intended for possible use as human medicines. A continuing goal of drug safety scientists in the pharmaceutical industry is to discover and develop better trans-species biomarkers that can be used to determine target organ toxicities for preclinical species in short-term studies at dose levels that are some multiple of the intended human dose and again later in full development for monitoring clinical trials at lower therapeutic doses. Of particular value are early, predictive, noninvasive biomarkers that have in vitro, in vivo, and clinical transferability. Such translational biomarkers bridge animal testing used in preclinical science and human studies that are part of subsequent clinical testing. Although suitable for in vivo preclinical regulatory studies, conventional hepatic safety biomarkers are basically confirmatory markers because they signal organ toxicity after some pathological damage has occurred, and are therefore not well-suited for short-term, predictive screening assays early in the discovery-to-development progression of new chemical entities (NCEs) available in limited quantities. Efforts between regulatory agencies and the pharmaceutical industry are underway for the coordinated discovery, qualification, verification and validation of early predictive toxicity biomarkers. Early predictive safety biomarkers are those that are detectable and quantifiable prior to the onset of irreversible tissue injury and which are associated with a mechanism of action relevant to a specific type of potential hepatic injury. Potential drug toxicity biomarkers are typically endogenous macromolecules in

  17. Malignant Mesothelioma Biomarkers: From Discovery to Use in Clinical Practice for Diagnosis, Monitoring, Screening, and Treatment.

    Science.gov (United States)

    Creaney, Jenette; Robinson, Bruce W S

    2017-07-01

    Malignant pleural mesothelioma is a highly aggressive tumor associated with asbestos exposure. There are few effective treatment options for mesothelioma, and patients have a very poor prognosis with a median survival of mesothelioma biomarker has been ongoing for the last 30 years. Many traditional soluble (glyco)protein biomarkers have been evaluated over this time, and an ever-increasing list of new biomarkers, including messenger RNA, DNA, microRNA, and antibodies, is being reported from biomarker discovery projects. To date, soluble mesothelin is the only tumor biomarker to receive US Food and Drug Administration approval for clinical use in mesothelioma. Mesothelin is a glycoprotein normally expressed on the surface of mesothelial cells, and in the cancerous state it can be present in circulation. Mesothelin has a limited expression on normal, nonmalignant tissue and is thus an attractive therapeutic target for mesothelin-positive tumors. In this review we will focus on the discovery and clinical usages of mesothelin and provide an update on other mesothelioma biomarkers and show how such biomarker studies might impact on the management of this deadly tumor in the future. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  18. A Standardized and Reproducible Urine Preparation Protocol for Cancer Biomarkers Discovery

    Directory of Open Access Journals (Sweden)

    Julia Beretov

    2014-01-01

    Full Text Available A suitable and standardized protein purification technique is essential to maintain consistency and to allow data comparison between proteomic studies for urine biomarker discovery. Ultimately, efforts should be made to standardize urine preparation protocols. The aim of this study was to develop an optimal analytical protocol to achieve maximal protein yield and to ensure that this method was applicable to examine urine protein patterns that distinguish disease and disease-free states. In this pilot study, we compared seven different urine sample preparation methods to remove salts, and to precipitate and isolate urinary proteins. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE profiles showed that the sequential preparation of urinary proteins by combining acetone and trichloroacetic acid (TCA alongside high speed centrifugation (HSC provided the best separation, and retained the most urinary proteins. Therefore, this approach is the preferred method for all further urine protein analysis.

  19. Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm.

    Science.gov (United States)

    Martinez, Emmanuel; Alvarez, Mario Moises; Trevino, Victor

    2010-08-01

    Biomarker discovery is a typical application from functional genomics. Due to the large number of genes studied simultaneously in microarray data, feature selection is a key step. Swarm intelligence has emerged as a solution for the feature selection problem. However, swarm intelligence settings for feature selection fail to select small features subsets. We have proposed a swarm intelligence feature selection algorithm based on the initialization and update of only a subset of particles in the swarm. In this study, we tested our algorithm in 11 microarray datasets for brain, leukemia, lung, prostate, and others. We show that the proposed swarm intelligence algorithm successfully increase the classification accuracy and decrease the number of selected features compared to other swarm intelligence methods. Copyright © 2010 Elsevier Ltd. All rights reserved.

  20. Association of SNCA with Parkinson: replication in the Harvard NeuroDiscovery Center Biomarker Study

    Science.gov (United States)

    Ding, Hongliu; Sarokhan, Alison K.; Roderick, Sarah S.; Bakshi, Rachit; Maher, Nancy E.; Ashourian, Paymon; Kan, Caroline G.; Chang, Sunny; Santarlasci, Andrea; Swords, Kyleen E.; Ravina, Bernard M.; Hayes, Michael T.; Sohur, U. Shivraj; Wills, Anne-Marie; Flaherty, Alice W.; Unni, Vivek K.; Hung, Albert Y.; Selkoe, Dennis J.; Schwarzschild, Michael A.; Schlossmacher, Michael G.; Sudarsky, Lewis R.; Growdon, John H.; Ivinson, Adrian J.; Hyman, Bradley T.; Scherzer, Clemens R.

    2011-01-01

    Background Mutations in the α-synuclein gene (SNCA) cause autosomal dominant forms of Parkinson’s disease, but the substantial risk conferred by this locus to the common sporadic disease has only recently emerged from genome-wide association studies. Methods Here we genotyped a prioritized non-coding variant in SNCA intron-4 in 344 patients with Parkinson’s and 275 controls from the longitudinal Harvard NeuroDiscovery Center Biomarker Study. Results The common minor allele of rs2736990 was associated with elevated disease susceptibility (odds ratio = 1.40, P value = 0.0032). Conclusions This result increases confidence in the notion that in many clinically well-characterized patients genetic variation in SNCA contributes to “sporadic” disease. PMID:21953863

  1. DIGE and iTRAQ as biomarker discovery tools in aquatic toxicology.

    Science.gov (United States)

    Martyniuk, Christopher J; Alvarez, Sophie; Denslow, Nancy D

    2012-02-01

    Molecular approaches in ecotoxicology have greatly enhanced mechanistic understanding of the impact of aquatic pollutants in organisms. These methods have included high throughput Omics technologies, including quantitative proteomics methods such as 2D differential in-gel electrophoresis (DIGE) and isobaric tagging for relative and absolute quantitation (iTRAQ). These methods are becoming more widely used in ecotoxicology studies to identify and characterize protein bioindicators of adverse effect. In teleost fish, iTRAQ has been used successfully in different fish species (e.g. fathead minnow, goldfish, largemouth bass) and tissues (e.g. hypothalamus and liver) to quantify relative protein abundance. Of interest for ecotoxicology is that many proteins commonly utilized as bioindicators of toxicity or stress are quantifiable using iTRAQ on a larger scale, providing a global baseline of biological effect from which to assess changes in the proteome. This review highlights the successes to date for high throughput quantitative proteomics using DIGE and iTRAQ in aquatic toxicology. Current challenges for the iTRAQ method for biomarker discovery in fish are the high cost and the lack of complete annotated genomes for teleosts. However, the use of protein homology from teleost fishes in protein databases and the introduction of hybrid LTQ-FT (Linear ion trap-Fourier transform) mass spectrometers with high resolution, increased sensitivity, and high mass accuracy are able to improve significantly the protein identification rates. Despite these challenges, initial studies utilizing iTRAQ for ecotoxicoproteomics have exceeded expectations and it is anticipated that the use of non-gel based quantitative proteomics will increase for protein biomarker discovery and for characterization of chemical mode of action. Copyright © 2011 Elsevier Inc. All rights reserved.

  2. Automated search-model discovery and preparation for structure solution by molecular replacement.

    Science.gov (United States)

    Keegan, Ronan M; Winn, Martyn D

    2007-04-01

    A novel automation pipeline for macromolecular structure solution by molecular replacement is described. There is a special emphasis on the discovery and preparation of a large number of search models, all of which can be passed to the core molecular-replacement programs. For routine molecular-replacement problems, the pipeline automates what a crystallographer might do and its value is simply one of convenience. For more difficult cases, the pipeline aims to discover the particular template structure and model edits required to produce a viable search model and may succeed in finding an efficacious combination that would be missed otherwise. The pipeline is described in detail and a number of examples are given. The examples are chosen to illustrate successes in real crystallography problems and also particular features of the pipeline. It is concluded that exploring a range of search models automatically can be valuable in many cases.

  3. AutoDrug: fully automated macromolecular crystallography workflows for fragment-based drug discovery.

    Science.gov (United States)

    Tsai, Yingssu; McPhillips, Scott E; González, Ana; McPhillips, Timothy M; Zinn, Daniel; Cohen, Aina E; Feese, Michael D; Bushnell, David; Tiefenbrunn, Theresa; Stout, C David; Ludaescher, Bertram; Hedman, Britt; Hodgson, Keith O; Soltis, S Michael

    2013-05-01

    AutoDrug is software based upon the scientific workflow paradigm that integrates the Stanford Synchrotron Radiation Lightsource macromolecular crystallography beamlines and third-party processing software to automate the crystallography steps of the fragment-based drug-discovery process. AutoDrug screens a cassette of fragment-soaked crystals, selects crystals for data collection based on screening results and user-specified criteria and determines optimal data-collection strategies. It then collects and processes diffraction data, performs molecular replacement using provided models and detects electron density that is likely to arise from bound fragments. All processes are fully automated, i.e. are performed without user interaction or supervision. Samples can be screened in groups corresponding to particular proteins, crystal forms and/or soaking conditions. A single AutoDrug run is only limited by the capacity of the sample-storage dewar at the beamline: currently 288 samples. AutoDrug was developed in conjunction with RestFlow, a new scientific workflow-automation framework. RestFlow simplifies the design of AutoDrug by managing the flow of data and the organization of results and by orchestrating the execution of computational pipeline steps. It also simplifies the execution and interaction of third-party programs and the beamline-control system. Modeling AutoDrug as a scientific workflow enables multiple variants that meet the requirements of different user groups to be developed and supported. A workflow tailored to mimic the crystallography stages comprising the drug-discovery pipeline of CoCrystal Discovery Inc. has been deployed and successfully demonstrated. This workflow was run once on the same 96 samples that the group had examined manually and the workflow cycled successfully through all of the samples, collected data from the same samples that were selected manually and located the same peaks of unmodeled density in the resulting difference Fourier

  4. Recent mass spectrometry-based proteomics for biomarker discovery in lung cancer, COPD, and asthma.

    Science.gov (United States)

    Fujii, Kiyonaga; Nakamura, Haruhiko; Nishimura, Toshihide

    2017-04-01

    Lung cancer and related diseases have been one of the most common causes of deaths worldwide. Genomic-based biomarkers may hardly reflect the underlying dynamic molecular mechanism of functional protein interactions, which is the center of a disease. Recent developments in mass spectrometry (MS) have made it possible to analyze disease-relevant proteins expressed in clinical specimens by proteomic challenges. Areas covered: To understand the molecular mechanisms of lung cancer and its subtypes, chronic obstructive pulmonary disease (COPD), asthma and others, great efforts have been taken to identify numerous relevant proteins by MS-based clinical proteomic approaches. Since lung cancer is a multifactorial disease that is biologically associated with asthma and COPD among various lung diseases, this study focused on proteomic studies on biomarker discovery using various clinical specimens for lung cancer, COPD, and asthma. Expert commentary: MS-based exploratory proteomics utilizing clinical specimens, which can incorporate both experimental and bioinformatic analysis of protein-protein interaction and also can adopt proteogenomic approaches, makes it possible to reveal molecular networks that are relevant to a disease subgroup and that could differentiate between drug responders and non-responders, good and poor prognoses, drug resistance, and so on.

  5. Discovery and Longitudinal Evaluation of Candidate Protein Biomarkers for Disease Recurrence in Prostate Cancer.

    Science.gov (United States)

    Tonry, Claire L; Doherty, Darren; O'Shea, Carmel; Morrissey, Brian; Staunton, Lisa; Flatley, Brian; Shannon, Aoife; Armstrong, John; Pennington, Stephen R

    2015-07-02

    When compared with hormonal therapy alone, treatment with combined hormone and radiation therapy (CHRT) gives improved disease-specific survival outcomes for patients with prostate cancer; however, a significant number of CHRT patients still succumb to recurrent disease. The purpose of this study was to use longitudinal patient samples obtained as part of an ongoing noninterventional clinical trial (ICORG06-15) to identify and evaluate a potential serum protein signature of disease recurrence. Label-free LC-MS/MS based protein discovery was undertaken on depleted serum samples from CHRT patients who showed evidence of disease recurrence (n = 3) and time-matched patient controls (n = 3). A total of 104 proteins showed a significant change between these two groups. Multiple reaction monitoring (MRM) assays were designed for a subset of these proteins as part of a panel of putative prostate cancer biomarkers (41 proteins) for evaluation in longitudinal serum samples. These data revealed significant interpatient variability in individual protein expression between time of diagnosis, disease recurrence, and beyond and serve to highlight the importance of longitudinal patient samples for evaluating the use of candidate protein biomarkers in disease monitoring.

  6. Averaged differential expression for the discovery of biomarkers in the blood of patients with prostate cancer.

    Directory of Open Access Journals (Sweden)

    V Uma Bai

    Full Text Available The identification of a blood-based diagnostic marker is a goal in many areas of medicine, including the early diagnosis of prostate cancer. We describe the use of averaged differential display as an efficient mechanism for biomarker discovery in whole blood RNA. The process of averaging reduces the problem of clinical heterogeneity while simultaneously minimizing sample handling.RNA was isolated from the blood of prostate cancer patients and healthy controls. Samples were pooled and subjected to the averaged differential display process. Transcripts present at different levels between patients and controls were purified and sequenced for identification. Transcript levels in the blood of prostate cancer patients and controls were verified by quantitative RT-PCR. Means were compared using a t-test and a receiver-operating curve was generated. The Ring finger protein 19A (RNF19A transcript was identified as having higher levels in prostate cancer patients compared to healthy men through the averaged differential display process. Quantitative RT-PCR analysis confirmed a more than 2-fold higher level of RNF19A mRNA levels in the blood of patients with prostate cancer than in healthy controls (p = 0.0066. The accuracy of distinguishing cancer patients from healthy men using RNF19A mRNA levels in blood as determined by the area under the receiving operator curve was 0.727.Averaged differential display offers a simplified approach for the comprehensive screening of body fluids, such as blood, to identify biomarkers in patients with prostate cancer. Furthermore, this proof-of-concept study warrants further analysis of RNF19A as a clinically relevant biomarker for prostate cancer detection.

  7. Data processing pipelines for comprehensive profiling of proteomics samples by label-free LC MS for biomarker discovery

    NARCIS (Netherlands)

    Christin, Christin; Bischoff, Rainer; Horvatovich, Peter

    2011-01-01

    Label-free quantitative LC-MS profiling of complex body fluids has become an important analytical tool for biomarker and biological knowledge discovery in the past decade. Accurate processing, statistical analysis and validation of acquired data diversified by the different types of mass

  8. Application in pesticide analysis: Liquid chromatography - A review of the state of science for biomarker discovery and identification

    Science.gov (United States)

    Book Chapter 18, titled Application in pesticide analysis: Liquid chromatography - A review of the state of science for biomarker discovery and identification, will be published in the book titled High Performance Liquid Chromatography in Pesticide Residue Analysis (Part of the C...

  9. Automated Discovery of Elementary Chemical Reaction Steps Using Freezing String and Berny Optimization Methods.

    Science.gov (United States)

    Suleimanov, Yury V; Green, William H

    2015-09-08

    We present a simple protocol which allows fully automated discovery of elementary chemical reaction steps using in cooperation double- and single-ended transition-state optimization algorithms--the freezing string and Berny optimization methods, respectively. To demonstrate the utility of the proposed approach, the reactivity of several single-molecule systems of combustion and atmospheric chemistry importance is investigated. The proposed algorithm allowed us to detect without any human intervention not only "known" reaction pathways, manually detected in the previous studies, but also new, previously "unknown", reaction pathways which involve significant atom rearrangements. We believe that applying such a systematic approach to elementary reaction path finding will greatly accelerate the discovery of new chemistry and will lead to more accurate computer simulations of various chemical processes.

  10. Translational bioinformatics in mental health: open access data sources and computational biomarker discovery.

    Science.gov (United States)

    Tenenbaum, Jessica D; Bhuvaneshwar, Krithika; Gagliardi, Jane P; Fultz Hollis, Kate; Jia, Peilin; Ma, Liang; Nagarajan, Radhakrishnan; Rakesh, Gopalkumar; Subbian, Vignesh; Visweswaran, Shyam; Zhao, Zhongming; Rozenblit, Leon

    2017-11-27

    Mental illness is increasingly recognized as both a significant cost to society and a significant area of opportunity for biological breakthrough. As -omics and imaging technologies enable researchers to probe molecular and physiological underpinnings of multiple diseases, opportunities arise to explore the biological basis for behavioral health and disease. From individual investigators to large international consortia, researchers have generated rich data sets in the area of mental health, including genomic, transcriptomic, metabolomic, proteomic, clinical and imaging resources. General data repositories such as the Gene Expression Omnibus (GEO) and Database of Genotypes and Phenotypes (dbGaP) and mental health (MH)-specific initiatives, such as the Psychiatric Genomics Consortium, MH Research Network and PsychENCODE represent a wealth of information yet to be gleaned. At the same time, novel approaches to integrate and analyze data sets are enabling important discoveries in the area of mental and behavioral health. This review will discuss and catalog into an organizing framework the increasingly diverse set of MH data resources available, using schizophrenia as a focus area, and will describe novel and integrative approaches to molecular biomarker discovery that make use of mental health data. © The Author 2017. Published by Oxford University Press.

  11. Topic model-based mass spectrometric data analysis in cancer biomarker discovery studies.

    Science.gov (United States)

    Wang, Minkun; Tsai, Tsung-Heng; Di Poto, Cristina; Ferrarini, Alessia; Yu, Guoqiang; Ressom, Habtom W

    2016-08-18

    A fundamental challenge in quantitation of biomolecules for cancer biomarker discovery is owing to the heterogeneous nature of human biospecimens. Although this issue has been a subject of discussion in cancer genomic studies, it has not yet been rigorously investigated in mass spectrometry based proteomic and metabolomic studies. Purification of mass spectometric data is highly desired prior to subsequent analysis, e.g., quantitative comparison of the abundance of biomolecules in biological samples. We investigated topic models to computationally analyze mass spectrometric data considering both integrated peak intensities and scan-level features, i.e., extracted ion chromatograms (EICs). Probabilistic generative models enable flexible representation in data structure and infer sample-specific pure resources. Scan-level modeling helps alleviate information loss during data preprocessing. We evaluated the capability of the proposed models in capturing mixture proportions of contaminants and cancer profiles on LC-MS based serum proteomic and GC-MS based tissue metabolomic datasets acquired from patients with hepatocellular carcinoma (HCC) and liver cirrhosis as well as synthetic data we generated based on the serum proteomic data. The results we obtained by analysis of the synthetic data demonstrated that both intensity-level and scan-level purification models can accurately infer the mixture proportions and the underlying true cancerous sources with small average error ratios (data, we found more proteins and metabolites with significant changes between HCC cases and cirrhotic controls. Candidate biomarkers selected after purification yielded biologically meaningful pathway analysis results and improved disease discrimination power in terms of the area under ROC curve compared to the results found prior to purification. We investigated topic model-based inference methods to computationally address the heterogeneity issue in samples analyzed by LC/GC-MS. We observed

  12. Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data.

    Science.gov (United States)

    Grissa, Dhouha; Pétéra, Mélanie; Brandolini, Marion; Napoli, Amedeo; Comte, Blandine; Pujos-Guillot, Estelle

    2016-01-01

    Untargeted metabolomics is a powerful phenotyping tool for better understanding biological mechanisms involved in human pathology development and identifying early predictive biomarkers. This approach, based on multiple analytical platforms, such as mass spectrometry (MS), chemometrics and bioinformatics, generates massive and complex data that need appropriate analyses to extract the biologically meaningful information. Despite various tools available, it is still a challenge to handle such large and noisy datasets with limited number of individuals without risking overfitting. Moreover, when the objective is focused on the identification of early predictive markers of clinical outcome, few years before occurrence, it becomes essential to use the appropriate algorithms and workflow to be able to discover subtle effects among this large amount of data. In this context, this work consists in studying a workflow describing the general feature selection process, using knowledge discovery and data mining methodologies to propose advanced solutions for predictive biomarker discovery. The strategy was focused on evaluating a combination of numeric-symbolic approaches for feature selection with the objective of obtaining the best combination of metabolites producing an effective and accurate predictive model. Relying first on numerical approaches, and especially on machine learning methods (SVM-RFE, RF, RF-RFE) and on univariate statistical analyses (ANOVA), a comparative study was performed on an original metabolomic dataset and reduced subsets. As resampling method, LOOCV was applied to minimize the risk of overfitting. The best k-features obtained with different scores of importance from the combination of these different approaches were compared and allowed determining the variable stabilities using Formal Concept Analysis. The results revealed the interest of RF-Gini combined with ANOVA for feature selection as these two complementary methods allowed selecting the 48

  13. Automated recognition of cell phenotypes in histology images based on membrane- and nuclei-targeting biomarkers

    International Nuclear Information System (INIS)

    Karaçalı, Bilge; Vamvakidou, Alexandra P; Tözeren, Aydın

    2007-01-01

    Three-dimensional in vitro culture of cancer cells are used to predict the effects of prospective anti-cancer drugs in vivo. In this study, we present an automated image analysis protocol for detailed morphological protein marker profiling of tumoroid cross section images. Histologic cross sections of breast tumoroids developed in co-culture suspensions of breast cancer cell lines, stained for E-cadherin and progesterone receptor, were digitized and pixels in these images were classified into five categories using k-means clustering. Automated segmentation was used to identify image regions composed of cells expressing a given biomarker. Synthesized images were created to check the accuracy of the image processing system. Accuracy of automated segmentation was over 95% in identifying regions of interest in synthesized images. Image analysis of adjacent histology slides stained, respectively, for Ecad and PR, accurately predicted regions of different cell phenotypes. Image analysis of tumoroid cross sections from different tumoroids obtained under the same co-culture conditions indicated the variation of cellular composition from one tumoroid to another. Variations in the compositions of cross sections obtained from the same tumoroid were established by parallel analysis of Ecad and PR-stained cross section images. Proposed image analysis methods offer standardized high throughput profiling of molecular anatomy of tumoroids based on both membrane and nuclei markers that is suitable to rapid large scale investigations of anti-cancer compounds for drug development

  14. Solid-state nanopores: A new platform for DNA biomarker discovery

    Science.gov (United States)

    Marshall, Michael M.

    Solid-state (SS) nanopores emerged as a molecular detection platform in 2001, offering many advantages over their biological counterparts, α-hemolysin nanopores (α-HL). These advantages include better chemical, electrical, mechanical, and thermal stability, as well as size tunability and device integration. In addition, the size of α-HL restricts its application to translocations of single-stranded polynucleotides (ssDNA and ssRNA). This research project focused on developing a SS-nanopore platform for biomarker detection, based on differentiating ssDNA and double-stranded DNA (dsDNA) at the single-molecule scale. Reported dsDNA translocation measurements result in an average residence time of ~ 30 ns/bp, so the temporal resolution required for detection of small DNA duplexes can exceed available bandwidth limitations. To address this issue, several system parameters were explored in order to slow down translocation speed, thereby increasing temporal resolution and signal-to-noise ratio. These parameters included: applied voltage, pH, pore geometry, DNA binding agents, salt composition and concentration, and temperature. Experimental findings showed that SS-nanopores can be precisely fabricated using a controlled helium ion milling technique, acidic conditions cause DNA depurination that results in slower translocation durations, and single-stranded binding proteins (SSBs) bind preferentially to ssDNA, forming complexes with distinct translocation characteristics that permit large (> 7 kb) ds- and ssDNA to be effectively distinguished. Together, these data show that SS-nanopores can serve as a tool to electronically detect the presence and relative concentration of target DNA molecules with ultrahigh sensitivity, thus demonstrating their potential utility as a biomarker discovery platform in both biomedical and environmental applications.

  15. Predicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune Cells

    KAUST Repository

    Triantafillou, Sofia

    2017-03-31

    Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.

  16. Facilitating drug discovery: an automated high-content inflammation assay in zebrafish.

    Science.gov (United States)

    Wittmann, Christine; Reischl, Markus; Shah, Asmi H; Mikut, Ralf; Liebel, Urban; Grabher, Clemens

    2012-07-16

    Zebrafish larvae are particularly amenable to whole animal small molecule screens due to their small size and relative ease of manipulation and observation, as well as the fact that compounds can simply be added to the bathing water and are readily absorbed when administered in a Introduction of the chemically induced inflammation (ChIn) assay eliminated these obstacles. Since wounding is inflicted chemically the number of embryos that can be treated simultaneously is virtually unlimited. Temporary treatment of zebrafish larvae with copper sulfate selectively induces cell death in hair cells of the lateral line system and results in rapid granulocyte recruitment to injured neuromasts. The inflammatory response can be followed in real-time by using compound transgenic cldnB::GFP/lysC::DsRED2 zebrafish larvae that express a green fluorescent protein in neuromast cells, as well as a red fluorescent protein labeling granulocytes. In order to devise a screening strategy that would allow both high-content and high-throughput analyses we introduced robotic liquid handling and combined automated microscopy with a custom developed software script. This script enables automated quantification of the inflammatory response by scoring the percent area occupied by red fluorescent leukocytes within an empirically defined area surrounding injured green fluorescent neuromasts. Furthermore, we automated data processing, handling, visualization, and storage all based on custom developed MATLAB and Python scripts. In brief, we introduce an automated HC/HT screen that allows testing of chemical compounds for their effect on initiation, progression or resolution of a granulocytic inflammatory response. This protocol serves a good starting point for more in-depth analyses of drug mechanisms and pathways involved in the orchestration of an innate immune response. In the future, it may help identifying intolerable toxic or off-target effects at earlier phases of drug discovery and thereby

  17. Mass spectrometry in biomarker applications: from untargeted discovery to targeted verification, and implications for platform convergence and clinical application

    Energy Technology Data Exchange (ETDEWEB)

    Smith, Richard D.

    2012-03-01

    It is really only in the last ten years that mass spectrometry (MS) has had a truly significant (but still small) impact on biomedical research. Much of this impact can be attributed to proteomics and its more basic applications. Early biomedical applications have included a number of efforts aimed at developing new biomarkers; however, the success of these endeavors to date have been quite modest - essentially confined to preclinical applications - and have often suffered from combinations of immature technology and hubris. Now that MS-based proteomics is reaching adolescence, it is appropriate to ask if and when biomarker-related applications will extend to the clinical realm, and what developments will be essential for this transition. Biomarker development can be described as a multistage process consisting of discovery, qualification, verification, research assay optimization, validation, and commercialization (1). From a MS perspective, it is possible to 'bin' measurements into 1 of 2 categories - those aimed at discovering potential protein biomarkers and those seeking to verify and validate biomarkers. Approaches in both categories generally involve digesting proteins (e.g., with trypsin) as a first step to yield peptides that can be effectively detected and identified with MS. Discovery-based approaches use broad 'unbiased' or 'undirected' measurements that attempt to cover as many proteins as possible in the hope of revealing promising biomarker candidates. A key challenge with this approach stems from the extremely large dynamic range (i.e., relative stoichiometry) of proteins of potential interest in biofluids such as plasma and the expectation that biomarker proteins of the greatest clinical value for many diseases may very well be present at low relative abundances (2). Protein concentrations in plasma extend from approximately 10{sup 10} pg/mL for albumin to approximately 10 pg/mL and below for interleukins and other

  18. Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry.

    Science.gov (United States)

    Keshishian, Hasmik; Burgess, Michael W; Specht, Harrison; Wallace, Luke; Clauser, Karl R; Gillette, Michael A; Carr, Steven A

    2017-08-01

    Proteomic characterization of blood plasma is of central importance to clinical proteomics and particularly to biomarker discovery studies. The vast dynamic range and high complexity of the plasma proteome have, however, proven to be serious challenges and have often led to unacceptable tradeoffs between depth of coverage and sample throughput. We present an optimized sample-processing pipeline for analysis of the human plasma proteome that provides greatly increased depth of detection, improved quantitative precision and much higher sample analysis throughput as compared with prior methods. The process includes abundant protein depletion, isobaric labeling at the peptide level for multiplexed relative quantification and ultra-high-performance liquid chromatography coupled to accurate-mass, high-resolution tandem mass spectrometry analysis of peptides fractionated off-line by basic pH reversed-phase (bRP) chromatography. The overall reproducibility of the process, including immunoaffinity depletion, is high, with a process replicate coefficient of variation (CV) of 4,500 proteins are detected and quantified per patient sample on average, with two or more peptides per protein and starting from as little as 200 μl of plasma. The approach can be multiplexed up to 10-plex using tandem mass tags (TMT) reagents, further increasing throughput, albeit with some decrease in the number of proteins quantified. In addition, we provide a rapid protocol for analysis of nonfractionated depleted plasma samples analyzed in 10-plex. This provides ∼600 quantified proteins for each of the ten samples in ∼5 h of instrument time.

  19. The use of time-resolved fluorescence in gel-based proteomics for improved biomarker discovery

    Science.gov (United States)

    Sandberg, AnnSofi; Buschmann, Volker; Kapusta, Peter; Erdmann, Rainer; Wheelock, Åsa M.

    2010-02-01

    This paper describes a new platform for quantitative intact proteomics, entitled Cumulative Time-resolved Emission 2-Dimensional Gel Electrophoresis (CuTEDGE). The CuTEDGE technology utilizes differences in fluorescent lifetimes to subtract the confounding background fluorescence during in-gel detection and quantification of proteins, resulting in a drastic improvement in both sensitivity and dynamic range compared to existing technology. The platform is primarily designed for image acquisition in 2-dimensional gel electrophoresis (2-DE), but is also applicable to 1-dimensional gel electrophoresis (1-DE), and proteins electroblotted to membranes. In a set of proof-of-principle measurements, we have evaluated the performance of the novel technology using the MicroTime 100 instrument (PicoQuant GmbH) in conjunction with the CyDye minimal labeling fluorochromes (GE Healthcare, Uppsala, Sweden) to perform differential gel electrophoresis (DIGE) analyses. The results indicate that the CuTEDGE technology provides an improvement in the dynamic range and sensitivity of detection of 3 orders of magnitude as compared to current state-of-the-art image acquisition instrumentation available for 2-DE (Typhoon 9410, GE Healthcare). Given the potential dynamic range of 7-8 orders of magnitude and sensitivities in the attomol range, the described invention represents a technological leap in detection of low abundance cellular proteins, which is desperately needed in the field of biomarker discovery.

  20. Automated Antibody De Novo Sequencing and Its Utility in Biopharmaceutical Discovery

    Science.gov (United States)

    Sen, K. Ilker; Tang, Wilfred H.; Nayak, Shruti; Kil, Yong J.; Bern, Marshall; Ozoglu, Berk; Ueberheide, Beatrix; Davis, Darryl; Becker, Christopher

    2017-05-01

    Applications of antibody de novo sequencing in the biopharmaceutical industry range from the discovery of new antibody drug candidates to identifying reagents for research and determining the primary structure of innovator products for biosimilar development. When murine, phage display, or patient-derived monoclonal antibodies against a target of interest are available, but the cDNA or the original cell line is not, de novo protein sequencing is required to humanize and recombinantly express these antibodies, followed by in vitro and in vivo testing for functional validation. Availability of fully automated software tools for monoclonal antibody de novo sequencing enables efficient and routine analysis. Here, we present a novel method to automatically de novo sequence antibodies using mass spectrometry and the Supernovo software. The robustness of the algorithm is demonstrated through a series of stress tests.

  1. Automated discovery of safety and efficacy concerns for joint & muscle pain relief treatments from online reviews.

    Science.gov (United States)

    Adams, David Z; Gruss, Richard; Abrahams, Alan S

    2017-04-01

    Product issues can cost companies millions in lawsuits and have devastating effects on a firm's sales, image and goodwill, especially in the era of social media. The ability for a system to detect the presence of safety and efficacy (S&E) concerns early on could not only protect consumers from injuries due to safety hazards, but could also mitigate financial damage to the manufacturer. Prior studies in the field of automated defect discovery have found industry-specific techniques appropriate to the automotive, consumer electronics, home appliance, and toy industries, but have not investigated pain relief medicines and medical devices. In this study, we focus specifically on automated discovery of S&E concerns in over-the-counter (OTC) joint and muscle pain relief remedies and devices. We select a dataset of over 32,000 records for three categories of Joint & Muscle Pain Relief treatments from Amazon's online product reviews, and train "smoke word" dictionaries which we use to score holdout reviews, for the presence of safety and efficacy issues. We also score using conventional sentiment analysis techniques. Compared to traditional sentiment analysis techniques, we found that smoke term dictionaries were better suited to detect product concerns from online consumer reviews, and significantly outperformed the sentiment analysis techniques in uncovering both efficacy and safety concerns, across all product subcategories. Our research can be applied to the healthcare and pharmaceutical industry in order to detect safety and efficacy concerns, reducing risks that consumers face using these products. These findings can be highly beneficial to improving quality assurance and management in joint and muscle pain relief. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. Early detection of pharmacovigilance signals with automated methods based on false discovery rates: a comparative study.

    Science.gov (United States)

    Ahmed, Ismaïl; Thiessard, Frantz; Miremont-Salamé, Ghada; Haramburu, Françoise; Kreft-Jais, Carmen; Bégaud, Bernard; Tubert-Bitter, Pascale

    2012-06-01

    Improving the detection of drug safety signals has led several pharmacovigilance regulatory agencies to incorporate automated quantitative methods into their spontaneous reporting management systems. The three largest worldwide pharmacovigilance databases are routinely screened by the lower bound of the 95% confidence interval of proportional reporting ratio (PRR₀₂.₅), the 2.5% quantile of the Information Component (IC₀₂.₅) or the 5% quantile of the Gamma Poisson Shrinker (GPS₀₅). More recently, Bayesian and non-Bayesian False Discovery Rate (FDR)-based methods were proposed that address the arbitrariness of thresholds and allow for a built-in estimate of the FDR. These methods were also shown through simulation studies to be interesting alternatives to the currently used methods. The objective of this work was twofold. Based on an extensive retrospective study, we compared PRR₀₂.₅, GPS₀₅ and IC₀₂.₅ with two FDR-based methods derived from the Fisher's exact test and the GPS model (GPS(pH0) [posterior probability of the null hypothesis H₀ calculated from the Gamma Poisson Shrinker model]). Secondly, restricting the analysis to GPS(pH0), we aimed to evaluate the added value of using automated signal detection tools compared with 'traditional' methods, i.e. non-automated surveillance operated by pharmacovigilance experts. The analysis was performed sequentially, i.e. every month, and retrospectively on the whole French pharmacovigilance database over the period 1 January 1996-1 July 2002. Evaluation was based on a list of 243 reference signals (RSs) corresponding to investigations launched by the French Pharmacovigilance Technical Committee (PhVTC) during the same period. The comparison of detection methods was made on the basis of the number of RSs detected as well as the time to detection. Results comparing the five automated quantitative methods were in favour of GPS(pH0) in terms of both number of detections of true signals and

  3. Using MALDI-IMS and MRM to stablish a pipeline for discovery and validation of tumor neovasculature biomarker candidates. — EDRN Public Portal

    Science.gov (United States)

    In an effort to circumvent the limitations associated with biomarker discovery workflows involving cell lines and cell cultures, histology-directed MALDI protein profiling and imaging mass spectrometry will be used for identification of vascular endothelial biomarkers suitable for early prostate cancer detection by CEUS targeted molecular imaging

  4. SWATHtoMRM: Development of High-Coverage Targeted Metabolomics Method Using SWATH Technology for Biomarker Discovery.

    Science.gov (United States)

    Zha, Haihong; Cai, Yuping; Yin, Yandong; Wang, Zhuozhong; Li, Kang; Zhu, Zheng-Jiang

    2018-03-02

    The complexity of metabolome presents a great analytical challenge for quantitative metabolite profiling, and restricts the application of metabolomics in biomarker discovery. Targeted metabolomics using multiple-reaction monitoring (MRM) technique has excellent capability for quantitative analysis, but suffers from the limited metabolite coverage. To address this challenge, we developed a new strategy, namely, SWATHtoMRM, which utilizes the broad coverage of SWATH-MS technology to develop high-coverage targeted metabolomics method. Specifically, SWATH-MS technique was first utilized to untargeted profile one pooled biological sample and to acquire the MS 2 spectra for all metabolites. Then, SWATHtoMRM was used to extract the large-scale MRM transitions for targeted analysis with coverage as high as 1000-2000 metabolites. Then, we demonstrated the advantages of SWATHtoMRM method in quantitative analysis such as coverage, reproducibility, sensitivity, and dynamic range. Finally, we applied our SWATHtoMRM approach to discover potential metabolite biomarkers for colorectal cancer (CRC) diagnosis. A high-coverage targeted metabolomics method with 1303 metabolites in one injection was developed to profile colorectal cancer tissues from CRC patients. A total of 20 potential metabolite biomarkers were discovered and validated for CRC diagnosis. In plasma samples from CRC patients, 17 out of 20 potential biomarkers were further validated to be associated with tumor resection, which may have a great potential in assessing the prognosis of CRC patients after tumor resection. Together, the SWATHtoMRM strategy provides a new way to develop high-coverage targeted metabolomics method, and facilitates the application of targeted metabolomics in disease biomarker discovery. The SWATHtoMRM program is freely available on the Internet ( http://www.zhulab.cn/software.php ).

  5. Advancing Urinary Protein Biomarker Discovery by Data-Independent Acquisition on a Quadrupole-Orbitrap Mass Spectrometer.

    Science.gov (United States)

    Muntel, Jan; Xuan, Yue; Berger, Sebastian T; Reiter, Lukas; Bachur, Richard; Kentsis, Alex; Steen, Hanno

    2015-11-06

    The promises of data-independent acquisition (DIA) strategies are a comprehensive and reproducible digital qualitative and quantitative record of the proteins present in a sample. We developed a fast and robust DIA method for comprehensive mapping of the urinary proteome that enables large scale urine proteomics studies. Compared to a data-dependent acquisition (DDA) experiments, our DIA assay doubled the number of identified peptides and proteins per sample at half the coefficients of variation observed for DDA data (DIA = ∼8%; DDA = ∼16%). We also tested different spectral libraries and their effects on overall protein and peptide identifications and their reproducibilities, which provided clear evidence that sample type-specific spectral libraries are preferred for reliable data analysis. To show applicability for biomarker discovery experiments, we analyzed a sample set of 87 urine samples from children seen in the emergency department with abdominal pain. The whole set was analyzed with high proteome coverage (∼1300 proteins/sample) in less than 4 days. The data set revealed excellent biomarker candidates for ovarian cyst and urinary tract infection. The improved throughput and quantitative performance of our optimized DIA workflow allow for the efficient simultaneous discovery and verification of biomarker candidates without the requirement for an early bias toward selected proteins.

  6. The Semantic Automated Discovery and Integration (SADI) Web service Design-Pattern, API and Reference Implementation.

    Science.gov (United States)

    Wilkinson, Mark D; Vandervalk, Benjamin; McCarthy, Luke

    2011-10-24

    The complexity and inter-related nature of biological data poses a difficult challenge for data and tool integration. There has been a proliferation of interoperability standards and projects over the past decade, none of which has been widely adopted by the bioinformatics community. Recent attempts have focused on the use of semantics to assist integration, and Semantic Web technologies are being welcomed by this community. SADI - Semantic Automated Discovery and Integration - is a lightweight set of fully standards-compliant Semantic Web service design patterns that simplify the publication of services of the type commonly found in bioinformatics and other scientific domains. Using Semantic Web technologies at every level of the Web services "stack", SADI services consume and produce instances of OWL Classes following a small number of very straightforward best-practices. In addition, we provide codebases that support these best-practices, and plug-in tools to popular developer and client software that dramatically simplify deployment of services by providers, and the discovery and utilization of those services by their consumers. SADI Services are fully compliant with, and utilize only foundational Web standards; are simple to create and maintain for service providers; and can be discovered and utilized in a very intuitive way by biologist end-users. In addition, the SADI design patterns significantly improve the ability of software to automatically discover appropriate services based on user-needs, and automatically chain these into complex analytical workflows. We show that, when resources are exposed through SADI, data compliant with a given ontological model can be automatically gathered, or generated, from these distributed, non-coordinating resources - a behaviour we have not observed in any other Semantic system. Finally, we show that, using SADI, data dynamically generated from Web services can be explored in a manner very similar to data housed in

  7. The Semantic Automated Discovery and Integration (SADI Web service Design-Pattern, API and Reference Implementation

    Directory of Open Access Journals (Sweden)

    Wilkinson Mark D

    2011-10-01

    Full Text Available Abstract Background The complexity and inter-related nature of biological data poses a difficult challenge for data and tool integration. There has been a proliferation of interoperability standards and projects over the past decade, none of which has been widely adopted by the bioinformatics community. Recent attempts have focused on the use of semantics to assist integration, and Semantic Web technologies are being welcomed by this community. Description SADI - Semantic Automated Discovery and Integration - is a lightweight set of fully standards-compliant Semantic Web service design patterns that simplify the publication of services of the type commonly found in bioinformatics and other scientific domains. Using Semantic Web technologies at every level of the Web services "stack", SADI services consume and produce instances of OWL Classes following a small number of very straightforward best-practices. In addition, we provide codebases that support these best-practices, and plug-in tools to popular developer and client software that dramatically simplify deployment of services by providers, and the discovery and utilization of those services by their consumers. Conclusions SADI Services are fully compliant with, and utilize only foundational Web standards; are simple to create and maintain for service providers; and can be discovered and utilized in a very intuitive way by biologist end-users. In addition, the SADI design patterns significantly improve the ability of software to automatically discover appropriate services based on user-needs, and automatically chain these into complex analytical workflows. We show that, when resources are exposed through SADI, data compliant with a given ontological model can be automatically gathered, or generated, from these distributed, non-coordinating resources - a behaviour we have not observed in any other Semantic system. Finally, we show that, using SADI, data dynamically generated from Web services

  8. Probing the O-glycoproteome of Gastric Cancer Cell Lines for Biomarker Discovery

    DEFF Research Database (Denmark)

    Vieira Campos, Diana Alexandra; Freitas, Daniela; Gomes, Joana

    2015-01-01

    Circulating O-glycoproteins shed from cancer cells represent important serum biomarkers for diagnostic and prognostic purposes. We have recently shown that selective detection of cancer-associated aberrant glycoforms of circulating O-glycoprotein biomarkers can increase specificity of cancer biom...

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

    DEFF Research Database (Denmark)

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

    2010-01-01

    -cancer agents. The consortium focuses on the identification of reliable predictive biomarkers to approved agents with anti-angiogenic activity for which no reliable predictive biomarkers exist: sunitinib, a multi-targeted tyrosine kinase inhibitor and everolimus, a mammalian target of rapamycin (mTOR) pathway...

  10. Upscaling and automation of electrophysiology: toward high throughput screening in ion channel drug discovery

    DEFF Research Database (Denmark)

    Asmild, Margit; Oswald, Nicholas; Krzywkowski, Karen M

    2003-01-01

    by developing two lines of automated patch clamp products, a traditional pipette-based system called Apatchi-1, and a silicon chip-based system QPatch. The degree of automation spans from semi-automation (Apatchi-1) where a trained technician interacts with the system in a limited way, to a complete automation...

  11. Pairwise protein expression classifier for candidate biomarker discovery for early detection of human disease prognosis

    Directory of Open Access Journals (Sweden)

    Kaur Parminder

    2012-08-01

    Full Text Available Abstract Background An approach to molecular classification based on the comparative expression of protein pairs is presented. The method overcomes some of the present limitations in using peptide intensity data for class prediction for problems such as the detection of a disease, disease prognosis, or for predicting treatment response. Data analysis is particularly challenging in these situations due to sample size (typically tens being much smaller than the large number of peptides (typically thousands. Methods based upon high dimensional statistical models, machine learning or other complex classifiers generate decisions which may be very accurate but can be complex and difficult to interpret in simple or biologically meaningful terms. A classification scheme, called ProtPair, is presented that generates simple decision rules leading to accurate classification which is based on measurement of very few proteins and requires only relative expression values, providing specific targeted hypotheses suitable for straightforward validation. Results ProtPair has been tested against clinical data from 21 patients following a bone marrow transplant, 13 of which progress to idiopathic pneumonia syndrome (IPS. The approach combines multiple peptide pairs originating from the same set of proteins, with each unique peptide pair providing an independent measure of discriminatory power. The prediction rate of the ProtPair for IPS study as measured by leave-one-out CV is 69.1%, which can be very beneficial for clinical diagnosis as it may flag patients in need of closer monitoring. The “top ranked” proteins provided by ProtPair are known to be associated with the biological processes and pathways intimately associated with known IPS biology based on mouse models. Conclusions An approach to biomarker discovery, called ProtPair, is presented. ProtPair is based on the differential expression of pairs of peptides and the associated proteins. Using mass

  12. Antibody validation of immunohistochemistry for biomarker discovery: recommendations of a consortium of academic and pharmaceutical based histopathology researchers.

    Science.gov (United States)

    Howat, William J; Lewis, Arthur; Jones, Phillipa; Kampf, Caroline; Pontén, Fredrik; van der Loos, Chris M; Gray, Neil; Womack, Chris; Warford, Anthony

    2014-11-01

    As biomarker discovery takes centre-stage, the role of immunohistochemistry within that process is increasing. At the same time, the number of antibodies being produced for "research use" continues to rise and it is important that antibodies to be used as biomarkers are validated for specificity and sensitivity before use. This guideline seeks to provide a stepwise approach for the validation of an antibody for immunohistochemical assays, reflecting the views of a consortium of academic and pharmaceutical based histopathology researchers. We propose that antibodies are placed into a tier system, level 1-3, based on evidence of their usage in immunohistochemistry, and that the degree of validation required is proportionate to their place on that tier. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  13. molSimplify: A toolkit for automating discovery in inorganic chemistry.

    Science.gov (United States)

    Ioannidis, Efthymios I; Gani, Terry Z H; Kulik, Heather J

    2016-08-15

    We present an automated, open source toolkit for the first-principles screening and discovery of new inorganic molecules and intermolecular complexes. Challenges remain in the automatic generation of candidate inorganic molecule structures due to the high variability in coordination and bonding, which we overcome through a divide-and-conquer tactic that flexibly combines force-field preoptimization of organic fragments with alignment to first-principles-trained metal-ligand distances. Exploration of chemical space is enabled through random generation of ligands and intermolecular complexes from large chemical databases. We validate the generated structures with the root mean squared (RMS) gradients evaluated from density functional theory (DFT), which are around 0.02 Ha/au across a large 150 molecule test set. Comparison of molSimplify results to full optimization with the universal force field reveals that RMS DFT gradients are improved by 40%. Seamless generation of input files, preparation and execution of electronic structure calculations, and post-processing for each generated structure aids interpretation of underlying chemical and energetic trends. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  14. Metabolomics as a Tool for Discovery of Biomarkers of Autism Spectrum Disorder in the Blood Plasma of Children

    Science.gov (United States)

    West, Paul R.; Amaral, David G.; Bais, Preeti; Smith, Alan M.; Egnash, Laura A.; Ross, Mark E.; Palmer, Jessica A.; Fontaine, Burr R.; Conard, Kevin R.; Corbett, Blythe A.; Cezar, Gabriela G.; Donley, Elizabeth L. R.; Burrier, Robert E.

    2014-01-01

    Background The diagnosis of autism spectrum disorder (ASD) at the earliest age possible is important for initiating optimally effective intervention. In the United States the average age of diagnosis is 4 years. Identifying metabolic biomarker signatures of ASD from blood samples offers an opportunity for development of diagnostic tests for detection of ASD at an early age. Objectives To discover metabolic features present in plasma samples that can discriminate children with ASD from typically developing (TD) children. The ultimate goal is to identify and develop blood-based ASD biomarkers that can be validated in larger clinical trials and deployed to guide individualized therapy and treatment. Methods Blood plasma was obtained from children aged 4 to 6, 52 with ASD and 30 age-matched TD children. Samples were analyzed using 5 mass spectrometry-based methods designed to orthogonally measure a broad range of metabolites. Univariate, multivariate and machine learning methods were used to develop models to rank the importance of features that could distinguish ASD from TD. Results A set of 179 statistically significant features resulting from univariate analysis were used for multivariate modeling. Subsets of these features properly classified the ASD and TD samples in the 61-sample training set with average accuracies of 84% and 86%, and with a maximum accuracy of 81% in an independent 21-sample validation set. Conclusions This analysis of blood plasma metabolites resulted in the discovery of biomarkers that may be valuable in the diagnosis of young children with ASD. The results will form the basis for additional discovery and validation research for 1) determining biomarkers to develop diagnostic tests to detect ASD earlier and improve patient outcomes, 2) gaining new insight into the biochemical mechanisms of various subtypes of ASD 3) identifying biomolecular targets for new modes of therapy, and 4) providing the basis for individualized treatment

  15. Discovery and identification of potential biomarkers for alcohol-induced oxidative stress based on cellular metabolomics.

    Science.gov (United States)

    Hu, Qingping; Wei, Jianteng; Liu, Yewei; Fei, Xiulan; Hao, Yuwei; Pei, Dong; Di, Duolong

    2017-07-01

    Biomarkers involved in alcohol-induced oxidative stress play an important role in alcoholic liver disease prevention and diagnosis. Alcohol-induced oxidative stress in human liver L-02 cells was used to discover the potential biomarkers. Metabolites from L-02 cells induced by alcohol were measured by high-performance liquid chromatography and mass spectrometry. Fourteen metabolites that allowed discrimination between control and model groups were discovered by multivariate statistical data analysis (i.e. principal components analysis, orthogonal partial least-squares discriminate analysis). Based on the retention time, UV spectrum and LC-MS findings of the samples and compared with the authentic standards, eight biomarkers involved in alcohol-induced oxidative stress, namely, malic acid, oxidized glutathione, γ-glutamyl-cysteinyl-glycine, adenosine triphosphate, phenylalanine, adenosine monophosphate, nitrotyrosine and tryptophan, were identified. These biomarkers offered important targets for disease diagnosis and other researches. Copyright © 2016 John Wiley & Sons, Ltd.

  16. Discovery of safety biomarkers for atorvastatin in rat urine using mass spectrometry based metabolomics combined with global and targeted approach

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, Bhowmik Salil [Bioanalysis and Biotransformation Research Center, Korea Institute of Science and Technology, P.O. Box 131, Cheongryang, Seoul 130-650 (Korea, Republic of); University of Science and Technology, (305-333) 113 Gwahangno, Yuseong-gu, Daejeon (Korea, Republic of); Lee, Young-Joo; Yi, Hong Jae [College of Pharmacy, Kyung Hee University, Hoegi-dong, Dongdaemun-gu, Seoul 130-791 (Korea, Republic of); Chung, Bong Chul [Bioanalysis and Biotransformation Research Center, Korea Institute of Science and Technology, P.O. Box 131, Cheongryang, Seoul 130-650 (Korea, Republic of); Jung, Byung Hwa, E-mail: jbhluck@kist.re.kr [Bioanalysis and Biotransformation Research Center, Korea Institute of Science and Technology, P.O. Box 131, Cheongryang, Seoul 130-650 (Korea, Republic of); University of Science and Technology, (305-333) 113 Gwahangno, Yuseong-gu, Daejeon (Korea, Republic of)

    2010-02-19

    In order to develop a safety biomarker for atorvastatin, this drug was orally administrated to hyperlipidemic rats, and a metabolomic study was performed. Atorvastatin was given in doses of either 70 mg kg{sup -1} day{sup -1} or 250 mg kg{sup -1} day{sup -1} for a period of 7 days (n = 4 for each group). To evaluate any abnormal effects of the drug, physiological and plasma biochemical parameters were measured and histopathological tests were carried out. Safety biomarkers were derived by comparing these parameters and using both global and targeted metabolic profiling. Global metabolic profiling was performed using liquid chromatography/time of flight/mass spectrometry (LC/TOF/MS) with multivariate data analysis. Several safety biomarker candidates that included various steroids and amino acids were discovered as a result of global metabolic profiling, and they were also confirmed by targeted metabolic profiling using gas chromatography/mass spectrometry (GC/MS) and capillary electrophoresis/mass spectrometry (CE/MS). Serum biochemical and histopathological tests were used to detect abnormal drug reactions in the liver after repeating oral administration of atorvastatin. The metabolic differences between control and the drug-treated groups were compared using PLS-DA score plots. These results were compared with the physiological and plasma biochemical parameters and the results of a histopathological test. Estrone, cortisone, proline, cystine, 3-ureidopropionic acid and histidine were proposed as potential safety biomarkers related with the liver toxicity of atorvastatin. These results indicate that the combined application of global and targeted metabolic profiling could be a useful tool for the discovery of drug safety biomarkers.

  17. Discovery of safety biomarkers for atorvastatin in rat urine using mass spectrometry based metabolomics combined with global and targeted approach

    International Nuclear Information System (INIS)

    Kumar, Bhowmik Salil; Lee, Young-Joo; Yi, Hong Jae; Chung, Bong Chul; Jung, Byung Hwa

    2010-01-01

    In order to develop a safety biomarker for atorvastatin, this drug was orally administrated to hyperlipidemic rats, and a metabolomic study was performed. Atorvastatin was given in doses of either 70 mg kg -1 day -1 or 250 mg kg -1 day -1 for a period of 7 days (n = 4 for each group). To evaluate any abnormal effects of the drug, physiological and plasma biochemical parameters were measured and histopathological tests were carried out. Safety biomarkers were derived by comparing these parameters and using both global and targeted metabolic profiling. Global metabolic profiling was performed using liquid chromatography/time of flight/mass spectrometry (LC/TOF/MS) with multivariate data analysis. Several safety biomarker candidates that included various steroids and amino acids were discovered as a result of global metabolic profiling, and they were also confirmed by targeted metabolic profiling using gas chromatography/mass spectrometry (GC/MS) and capillary electrophoresis/mass spectrometry (CE/MS). Serum biochemical and histopathological tests were used to detect abnormal drug reactions in the liver after repeating oral administration of atorvastatin. The metabolic differences between control and the drug-treated groups were compared using PLS-DA score plots. These results were compared with the physiological and plasma biochemical parameters and the results of a histopathological test. Estrone, cortisone, proline, cystine, 3-ureidopropionic acid and histidine were proposed as potential safety biomarkers related with the liver toxicity of atorvastatin. These results indicate that the combined application of global and targeted metabolic profiling could be a useful tool for the discovery of drug safety biomarkers.

  18. In Silico discovery of transcription factors as potential diagnostic biomarkers of ovarian cancer

    KAUST Repository

    Kaur, Mandeep

    2011-09-19

    Background: Our study focuses on identifying potential biomarkers for diagnosis and early detection of ovarian cancer (OC) through the study of transcription regulation of genes affected by estrogen hormone.Results: The results are based on a set of 323 experimentally validated OC-associated genes compiled from several databases, and their subset controlled by estrogen. For these two gene sets we computationally determined transcription factors (TFs) that putatively regulate transcription initiation. We ranked these TFs based on the number of genes they are likely to control. In this way, we selected 17 top-ranked TFs as potential key regulators and thus possible biomarkers for a set of 323 OC-associated genes. For 77 estrogen controlled genes from this set we identified three unique TFs as potential biomarkers.Conclusions: We introduced a new methodology to identify potential diagnostic biomarkers for OC. This report is the first bioinformatics study that explores multiple transcriptional regulators of OC-associated genes as potential diagnostic biomarkers in connection with estrogen responsiveness. We show that 64% of TF biomarkers identified in our study are validated based on real-time data from microarray expression studies. As an illustration, our method could identify CP2 that in combination with CA125 has been reported to be sensitive in diagnosing ovarian tumors. 2011 Kaur et al; licensee BioMed Central Ltd.

  19. Automated microfluidic platform of bead-based electrochemical immunosensor integrated with bioreactor for continual monitoring of cell secreted biomarkers

    Science.gov (United States)

    Riahi, Reza; Shaegh, Seyed Ali Mousavi; Ghaderi, Masoumeh; Zhang, Yu Shrike; Shin, Su Ryon; Aleman, Julio; Massa, Solange; Kim, Duckjin; Dokmeci, Mehmet Remzi; Khademhosseini, Ali

    2016-04-01

    There is an increasing interest in developing microfluidic bioreactors and organs-on-a-chip platforms combined with sensing capabilities for continual monitoring of cell-secreted biomarkers. Conventional approaches such as ELISA and mass spectroscopy cannot satisfy the needs of continual monitoring as they are labor-intensive and not easily integrable with low-volume bioreactors. This paper reports on the development of an automated microfluidic bead-based electrochemical immunosensor for in-line measurement of cell-secreted biomarkers. For the operation of the multi-use immunosensor, disposable magnetic microbeads were used to immobilize biomarker-recognition molecules. Microvalves were further integrated in the microfluidic immunosensor chip to achieve programmable operations of the immunoassay including bead loading and unloading, binding, washing, and electrochemical sensing. The platform allowed convenient integration of the immunosensor with liver-on-chips to carry out continual quantification of biomarkers secreted from hepatocytes. Transferrin and albumin productions were monitored during a 5-day hepatotoxicity assessment in which human primary hepatocytes cultured in the bioreactor were treated with acetaminophen. Taken together, our unique microfluidic immunosensor provides a new platform for in-line detection of biomarkers in low volumes and long-term in vitro assessments of cellular functions in microfluidic bioreactors and organs-on-chips.

  20. Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells.

    Science.gov (United States)

    Triantafillou, Sofia; Lagani, Vincenzo; Heinze-Deml, Christina; Schmidt, Angelika; Tegner, Jesper; Tsamardinos, Ioannis

    2017-10-05

    Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated  causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.

  1. Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells

    KAUST Repository

    Triantafillou, Sofia

    2017-09-29

    Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated  causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.

  2. Comprehensive serum profiling for the discovery of epithelial ovarian cancer biomarkers.

    Directory of Open Access Journals (Sweden)

    Ping Yip

    Full Text Available FDA-cleared ovarian cancer biomarkers are limited to CA-125 and HE4 for monitoring and recurrence and OVA1, a multivariate panel consisting of CA-125 and four additional biomarkers, for referring patients to a specialist. Due to relatively poor performance of these tests, more accurate and broadly applicable biomarkers are needed. We evaluated the dysregulation of 259 candidate cancer markers in serum samples from 499 patients. Sera were collected prospectively at 11 monitored sites under a single well-defined protocol. All stages of ovarian cancer and common benign gynecological conditions were represented. To ensure consistency and comparability of biomarker comparisons, all measurements were performed on a single platform, at a single site, using a panel of rigorously calibrated, qualified, high-throughput, multiplexed immunoassays and all analyses were conducted using the same software. Each marker was evaluated independently for its ability to differentiate ovarian cancer from benign conditions. A total of 175 markers were dysregulated in the cancer samples. HE4 (AUC=0.933 and CA-125 (AUC=0.907 were the most informative biomarkers, followed by IL-2 receptor α, α1-antitrypsin, C-reactive protein, YKL-40, cellular fibronectin, CA-72-4 and prostasin (AUC>0.800. To improve the discrimination between cancer and benign conditions, a simple multivariate combination of markers was explored using logistic regression. When combined into a single panel, the nine most informative individual biomarkers yielded an AUC value of 0.950, significantly higher than obtained when combining the markers in the OVA1 panel (AUC 0.912. Additionally, at a threshold sensitivity of 90%, the combination of the top 9 markers gave 88.9% specificity compared to 63.4% specificity for the OVA1 markers. Although a blinded validation study has not yet been performed, these results indicate that alternative biomarker combinations might lead to significant improvements in the

  3. A systematic approach to biomarker discovery; Preamble to "the iSBTc-FDA taskforce on immunotherapy biomarkers"

    Directory of Open Access Journals (Sweden)

    Schendel Dolores

    2008-12-01

    Full Text Available Abstract The International Society for the Biological Therapy of Cancer (iSBTc has initiated in collaboration with the United States Food and Drug Administration (FDA a programmatic look at innovative avenues for the identification of relevant parameters to assist clinical and basic scientists who study the natural course of host/tumor interactions or their response to immune manipulation. The task force has two primary goals: 1 identify best practices of standardized and validated immune monitoring procedures and assays to promote inter-trial comparisons and 2 develop strategies for the identification of novel biomarkers that may enhance our understating of principles governing human cancer immune biology and, consequently, implement their clinical application. Two working groups were created that will report the developed best practices at an NCI/FDA/iSBTc sponsored workshop tied to the annual meeting of the iSBTc to be held in Washington DC in the Fall of 2009. This foreword provides an overview of the task force and invites feedback from readers that might be incorporated in the discussions and in the final document.

  4. A Comprehensive Workflow of Mass Spectrometry-Based Untargeted Metabolomics in Cancer Metabolic Biomarker Discovery Using Human Plasma and Urine

    Directory of Open Access Journals (Sweden)

    Jianwen She

    2013-09-01

    Full Text Available Current available biomarkers lack sensitivity and/or specificity for early detection of cancer. To address this challenge, a robust and complete workflow for metabolic profiling and data mining is described in details. Three independent and complementary analytical techniques for metabolic profiling are applied: hydrophilic interaction liquid chromatography (HILIC–LC, reversed-phase liquid chromatography (RP–LC, and gas chromatography (GC. All three techniques are coupled to a mass spectrometer (MS in the full scan acquisition mode, and both unsupervised and supervised methods are used for data mining. The univariate and multivariate feature selection are used to determine subsets of potentially discriminative predictors. These predictors are further identified by obtaining accurate masses and isotopic ratios using selected ion monitoring (SIM and data-dependent MS/MS and/or accurate mass MSn ion tree scans utilizing high resolution MS. A list combining all of the identified potential biomarkers generated from different platforms and algorithms is used for pathway analysis. Such a workflow combining comprehensive metabolic profiling and advanced data mining techniques may provide a powerful approach for metabolic pathway analysis and biomarker discovery in cancer research. Two case studies with previous published data are adapted and included in the context to elucidate the application of the workflow.

  5. Quantitative label-free proteomics for discovery of biomarkers in cerebrospinal fluid: assessment of technical and inter-individual variation.

    Directory of Open Access Journals (Sweden)

    Richard J Perrin

    Full Text Available Biomarkers are required for pre-symptomatic diagnosis, treatment, and monitoring of neurodegenerative diseases such as Alzheimer's disease. Cerebrospinal fluid (CSF is a favored source because its proteome reflects the composition of the brain. Ideal biomarkers have low technical and inter-individual variability (subject variance among control subjects to minimize overlaps between clinical groups. This study evaluates a process of multi-affinity fractionation (MAF and quantitative label-free liquid chromatography tandem mass spectrometry (LC-MS/MS for CSF biomarker discovery by (1 identifying reparable sources of technical variability, (2 assessing subject variance and residual technical variability for numerous CSF proteins, and (3 testing its ability to segregate samples on the basis of desired biomarker characteristics.Fourteen aliquots of pooled CSF and two aliquots from six cognitively normal individuals were randomized, enriched for low-abundance proteins by MAF, digested endoproteolytically, randomized again, and analyzed by nano-LC-MS. Nano-LC-MS data were time and m/z aligned across samples for relative peptide quantification. Among 11,433 aligned charge groups, 1360 relatively abundant ones were annotated by MS2, yielding 823 unique peptides. Analyses, including Pearson correlations of annotated LC-MS ion chromatograms, performed for all pairwise sample comparisons, identified several sources of technical variability: i incomplete MAF and keratins; ii globally- or segmentally-decreased ion current in isolated LC-MS analyses; and iii oxidized methionine-containing peptides. Exclusion of these sources yielded 609 peptides representing 81 proteins. Most of these proteins showed very low coefficients of variation (CV<5% whether they were quantified from the mean of all or only the 2 most-abundant peptides. Unsupervised clustering, using only 24 proteins selected for high subject variance, yielded perfect segregation of pooled and

  6. Discovery of serum protein biomarkers in drug-free patients with major depressive disorder.

    Science.gov (United States)

    Lee, Min Young; Kim, Eun Young; Kim, Se Hyun; Cho, Kyung-Cho; Ha, Kyooseob; Kim, Kwang Pyo; Ahn, Yong Min

    2016-08-01

    Major depressive disorder (MDD) is a systemic and multifactorial disorder involving complex interactions between genetic predisposition and disturbances of various molecular pathways. Its underlying molecular pathophysiology remains unclear, and no valid and objective diagnostic tools for the condition are available. We performed large-scale proteomic profiling to identify novel peripheral biomarkers implicated in the pathophysiology of MDD in 25 drug-free female MDD patients and 25 healthy controls. First, quantitative serum proteome profiles were obtained and analyzed by liquid chromatography-tandem mass spectrometry using serum samples from 10 MDD patients and 10 healthy controls. Next, candidate biomarker sets, including differentially expressed proteins from the profiling experiment and those identified in the literature, were verified using multiple-reaction monitoring in 25 patients and 25 healthy controls. The final panel of potential biomarkers was selected using multiparametric statistical analysis. We identified a serum biomarker panel consisting of six proteins: apolipoprotein D, apolipoprotein B, vitamin D-binding protein, ceruloplasmin, hornerin, and profilin 1, which could be used to distinguish MDD patients from controls with 68% diagnostic accuracy. Our results suggest that modulation of the immune and inflammatory systems and lipid metabolism are involved in the pathophysiology of MDD. Our findings of functional proteomic changes in the peripheral blood of patients with MDD further clarify the molecular biological pathway underlying depression. Further studies using larger, independent cohorts are needed to verify the role of these candidate biomarkers for the diagnosis of MDD. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. Nutrition and biomarkers in psychiatry: research on micronutrient deficiencies in schizophrenia, the role of the intestine in the hyperserotonemia of autism, and a method for non-hypothesis driven discovery of biomarkers in urine

    OpenAIRE

    Kemperman, Ramses Franciscus Jacobus

    2007-01-01

    This thesis describes the study of markers of nutrition and intestinal motility in mental disorders with a focus on schizophrenia and autism, and the development, evaluation and application of a biomarker discovery method for urine. The aim of the thesis is to investigate the role of long-chain polyunsaturated fatty acids (LCPUFA), B-vitamins and platelet (PLT) serotonin (5-HT) in schizophrenia and autism. The thesis proposes also that biomarker research in psychiatric disease is of great rel...

  8. Discovery and characterization of potential prognostic biomarkers for dengue hemorrhagic fever.

    Science.gov (United States)

    Poole-Smith, B Katherine; Gilbert, Alexa; Gonzalez, Andrea L; Beltran, Manuela; Tomashek, Kay M; Ward, Brian J; Hunsperger, Elizabeth A; Ndao, Momar

    2014-12-01

    Half a million patients are hospitalized with severe dengue every year, many of whom would die without timely, appropriate clinical intervention. The majority of dengue cases are uncomplicated; however, 2-5% progress to severe dengue. Severe dengue cases have been reported with increasing frequency over the last 30 years. To discover biomarkers for severe dengue, we used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to analyze dengue virus positive serum samples from the acute phase of infection. Using this method, 16 proteins were identified as candidate biomarkers for severe dengue. From these 16 biomarkers, three candidates were selected for confirmation by enzyme-linked immunosorbent assay and Western blot: vitronectin (Vtn, 55.1 kDa), hemopexin (Hx, 52.4 kDa), and serotransferrin (Tf, 79.2 kDa). Vitronectin, Hx, and Tf best differentiated between dengue and severe dengue. © The American Society of Tropical Medicine and Hygiene.

  9. Discovery and validation of molecular biomarkers for colorectal adenomas and cancer with application to blood testing.

    Directory of Open Access Journals (Sweden)

    Lawrence C LaPointe

    Full Text Available BACKGROUND & AIMS: Colorectal cancer incidence and deaths are reduced by the detection and removal of early-stage, treatable neoplasia but we lack proven biomarkers sensitive for both cancer and pre-invasive adenomas. The aims of this study were to determine if adenomas and cancers exhibit characteristic patterns of biomarker expression and to explore whether a tissue-discovered (and validated biomarker is differentially expressed in the plasma of patients with colorectal adenomas or cancer. METHODS: Candidate RNA biomarkers were identified by oligonucleotide microarray analysis of colorectal specimens (222 normal, 29 adenoma, 161 adenocarcinoma and 50 colitis and validated in a previously untested cohort of 68 colorectal specimens using a custom-designed oligonucleotide microarray. One validated biomarker, KIAA1199, was assayed using qRT-PCR on plasma extracted RNA from 20 colonoscopy-confirmed healthy controls, 20 patients with adenoma, and 20 with cancer. RESULTS: Genome-wide analysis uncovered reproducible gene expression signatures for both adenomas and cancers compared to controls. 386/489 (79% of the adenoma and 439/529 (83% of the adenocarcinoma biomarkers were validated in independent tissues. We also identified genes differentially expressed in adenomas compared to cancer. KIAA1199 was selected for further analysis based on consistent up-regulation in neoplasia, previous studies and its interest as an uncharacterized gene. Plasma KIAA1199 RNA levels were significantly higher in patients with either cancer or adenoma (31/40 compared to neoplasia-free controls (6/20. CONCLUSIONS: Colorectal neoplasia exhibits characteristic patterns of gene expression. KIAA1199 is differentially expressed in neoplastic tissues and KIAA1199 transcripts are more abundant in the plasma of patients with either cancer or adenoma compared to controls.

  10. Use of biomarkers in the discovery of novel anti-schizophrenia drugs

    DEFF Research Database (Denmark)

    Mikkelsen, Jens D.; Thomsen, Morten S.; Hansen, Henrik

    2010-01-01

    Schizophrenia is characterized by a diverse symptomatology that often includes positive, cognitive and negative symptoms. Current anti-schizophrenic drugs act at multiple receptors, but little is known about how each of these receptors contributes to their mechanisms of action. Screening of novel...... anti-schizophrenic drug candidates targeting single receptors will be based on biomarker assays that measure signalling pathways, transcriptional factors, epigenetic mechanisms and synaptic function and translate these effects to behavioural effects in animals and humans. This review discusses current...... states of the validity of biomarkers in the identification of novel anti-schizophrenic drug candidates....

  11. Mass Spectrometry-Based Serum Proteomics for Biomarker Discovery and Validation.

    Science.gov (United States)

    Bhosale, Santosh D; Moulder, Robert; Kouvonen, Petri; Lahesmaa, Riitta; Goodlett, David R

    2017-01-01

    Blood protein measurements are used frequently in the clinic in the assessment of patient health. Nevertheless, there remains the need for new biomarkers with better diagnostic specificities. With the advent of improved technology for bioanalysis and the growth of biobanks including collections from specific disease risk cohorts, the plasma proteome has remained a target of proteomics research toward the characterization of disease-related biomarkers. The following protocol presents a workflow for serum/plasma proteomics including details of sample preparation both with and without immunoaffinity depletion of the most abundant plasma proteins and methodology for selected reaction monitoring mass spectrometry validation.

  12. Discovery of serum biomarkers predicting development of a subsequent depressive episode in social anxiety disorder

    NARCIS (Netherlands)

    Gottschalk, M.G.; Cooper, J.D.; Chan, M.K.; Bot, M.; Penninx, B.W.J.H.; Bann, S.

    2015-01-01

    Although social anxiety disorder (SAD) is strongly associated with the subsequent development of a depressive disorder (major depressive disorder or dysthymia), no underlying biological risk factors are known. We aimed to identify biomarkers which predict depressive episodes in SAD patients over a

  13. Discovery of novel biomarker candidates for liver fibrosis in hepatitis C patients: a preliminary study.

    Directory of Open Access Journals (Sweden)

    Bevin Gangadharan

    Full Text Available Liver biopsy is the reference standard for assessing liver fibrosis and no reliable non-invasive diagnostic approach is available to discriminate between the intermediate stages of fibrosis. Therefore suitable serological biomarkers of liver fibrosis are urgently needed. We used proteomics to identify novel fibrosis biomarkers in hepatitis C patients with different degrees of liver fibrosis.Proteins in plasma samples from healthy control individuals and patients with hepatitis C virus (HCV induced cirrhosis were analysed using a proteomics technique: two dimensional gel electrophoresis (2-DE. This technique separated the proteins in plasma samples of control and cirrhotic patients and by visualizing the separated proteins we were able to identify proteins which were increasing or decreasing in hepatic cirrhosis. Identified markers were validated across all Ishak fibrosis stages and compared to the markers used in FibroTest, Enhanced Liver Fibrosis (ELF test, Hepascore and FIBROSpect by Western blotting. Forty four candidate biomarkers for hepatic fibrosis were identified of which 20 were novel biomarkers of liver fibrosis. Western blot validation of all candidate markers using plasma samples from patients across all Ishak fibrosis scores showed that the markers which changed with increasing fibrosis most consistently included lipid transfer inhibitor protein, complement C3d, corticosteroid-binding globulin, apolipoprotein J and apolipoprotein L1. These five novel fibrosis markers which are secreted in blood showed a promising consistent change with increasing fibrosis stage when compared to the markers used for the FibroTest, ELF test, Hepascore and FIBROSpect. These markers will be further validated using a large clinical cohort.This study identifies 20 novel fibrosis biomarker candidates. The proteins identified may help to assess hepatic fibrosis and eliminate the need for invasive liver biopsies.

  14. MRM screening/biomarker discovery with linear ion trap MS: a library of human cancer-specific peptides

    International Nuclear Information System (INIS)

    Yang, Xu; Lazar, Iulia M

    2009-01-01

    The discovery of novel protein biomarkers is essential in the clinical setting to enable early disease diagnosis and increase survivability rates. To facilitate differential expression analysis and biomarker discovery, a variety of tandem mass spectrometry (MS/MS)-based protein profiling techniques have been developed. For achieving sensitive detection and accurate quantitation, targeted MS screening approaches, such as multiple reaction monitoring (MRM), have been implemented. MCF-7 breast cancer protein cellular extracts were analyzed by 2D-strong cation exchange (SCX)/reversed phase liquid chromatography (RPLC) separations interfaced to linear ion trap MS detection. MS data were interpreted with the Sequest-based Bioworks software (Thermo Electron). In-house developed Perl-scripts were used to calculate the spectral counts and the representative fragment ions for each peptide. In this work, we report on the generation of a library of 9,677 peptides (p < 0.001), representing ~1,572 proteins from human breast cancer cells, that can be used for MRM/MS-based biomarker screening studies. For each protein, the library provides the number and sequence of detectable peptides, the charge state, the spectral count, the molecular weight, the parameters that characterize the quality of the tandem mass spectrum (p-value, DeltaM, Xcorr, DeltaCn, Sp, no. of matching a, b, y ions in the spectrum), the retention time, and the top 10 most intense product ions that correspond to a given peptide. Only proteins identified by at least two spectral counts are listed. The experimental distribution of protein frequencies, as a function of molecular weight, closely matched the theoretical distribution of proteins in the human proteome, as provided in the SwissProt database. The amino acid sequence coverage of the identified proteins ranged from 0.04% to 98.3%. The highest-abundance proteins in the cellular extract had a molecular weight (MW)<50,000. Preliminary experiments have

  15. TOFwave: reproducibility in biomarker discovery from time-of-flight mass spectrometry data.

    Science.gov (United States)

    Chierici, Marco; Albanese, Davide; Franceschi, Pietro; Furlanello, Cesare

    2012-11-01

    Many are the sources of variability that can affect reproducibility of disease biomarkers from time-of-flight (TOF) Mass Spectrometry (MS) data. Here we present TOFwave, a complete software pipeline for TOF-MS biomarker identification, that limits the impact of parameter tuning along the whole chain of preprocessing and model selection modules. Peak profiles are obtained by a preprocessing based on Continuous Wavelet Transform (CWT), coupled with a machine learning protocol aimed at avoiding selection bias effects. Only two parameters (minimum peak width and a signal to noise cutoff) have to be explicitly set. The TOFwave pipeline is built on top of the mlpy Python package. Examples on Matrix-Assisted Laser Desorption and Ionization (MALDI) TOF datasets are presented. Software prototype, datasets and details to replicate results in this paper can be found at http://mlpy.sf.net/tofwave/.

  16. Use of biomarkers in the discovery of novel anti-schizophrenia drugs

    DEFF Research Database (Denmark)

    Mikkelsen, Jens D; Thomsen, Morten S; Hansen, Henrik H

    2010-01-01

    Schizophrenia is characterized by a diverse symptomatology that often includes positive, cognitive and negative symptoms. Current anti-schizophrenic drugs act at multiple receptors, but little is known about how each of these receptors contributes to their mechanisms of action. Screening of novel...... anti-schizophrenic drug candidates targeting single receptors will be based on biomarker assays that measure signalling pathways, transcriptional factors, epigenetic mechanisms and synaptic function and translate these effects to behavioural effects in animals and humans. This review discusses current...

  17. Efficient high-throughput discovery of large peptidic hormones and biomarkers.

    Science.gov (United States)

    Taylor, Steven W; Andon, Nancy L; Bilakovics, James M; Lowe, Carolyn; Hanley, Michael R; Pittner, Richard; Ghosh, Soumitra S

    2006-07-01

    A novel approach is presented for the simultaneous identification and relative quantification of secreted peptides, particularly those that have been historically difficult to analyze in a concerted manner. Peptides exceeding 60 residues with various degrees of post-translational modification were identified on a liquid chromatographic time scale. The approach demonstrates high efficiency pattern-based recognition analysis of complex neuroendocrine peptide sets and enables rapid identification of biomarkers from biological material.

  18. A Comprehensive Tool and Analytical Pathway for Differential Molecular Profiling and Biomarker Discovery

    Science.gov (United States)

    2014-10-20

    Approved for public release; distribution unlimited. 88ABW-2015-1753; Cleared 07 April 2015 coupled to a Waters QToF ® hybrid tandem quadrupole/time of...profiling low level kidney biomarkers in the F344 rat model. Importation of the raw QToF MS data files and subsequent analysis of the aligned and...background level previously established by our group for the QToF Micro). 27 Distribution A. Approved for public release; distribution unlimited. 88ABW

  19. Isolation and characterization of urinary extracellular vesicles: implications for biomarker discovery

    NARCIS (Netherlands)

    Merchant, M.L.; Rood, I.M.; Deegens, J.K.J.; Klein, J.B.

    2017-01-01

    Urine is a valuable diagnostic medium and, with the discovery of urinary extracellular vesicles, is viewed as a dynamic bioactive fluid. Extracellular vesicles are lipid-enclosed structures that can be classified into three categories: exosomes, microvesicles (or ectosomes) and apoptotic bodies.

  20. Urinary candidate biomarker discovery in a rat unilateral ureteral obstruction model.

    Science.gov (United States)

    Yuan, Yuan; Zhang, Fanshuang; Wu, Jianqiang; Shao, Chen; Gao, Youhe

    2015-03-20

    Urine has the potential to become a better source of biomarkers. Urinary proteins are affected by many factors; therefore, differentiating between the variables associated with any particular pathophysiological condition in clinical samples is challenging. To circumvent these problems, simpler systems, such as animal models, should be used to establish a direct relationship between disease progression and urine changes. In this study, a unilateral ureteral obstruction (UUO) model was used to observe tubular injury and the eventual development of renal fibrosis, as well as to identify differential urinary proteins in this process. Urine samples were collected from the residuary ureter linked to the kidney at 1 and 3 weeks after UUO. Five hundred proteins were identified and quantified by LC-MS/MS, out of which 7 and 19 significantly changed in the UUO 1- and 3-week groups, respectively, compared with the sham-operation group. Validation by western blot showed increased levels of Alpha-actinin-1 and Moesin in the UUO 1-week group, indicating that they may serve as candidate biomarkers of renal tubular injury, and significantly increased levels of Vimentin, Annexin A1 and Clusterin in the UUO 3-week group, indicating that they may serve as candidate biomarkers of interstitial fibrosis.

  1. Semi-automated Biopanning of Bacterial Display Libraries for Peptide Affinity Reagent Discovery and Analysis of Resulting Isolates.

    Science.gov (United States)

    Sarkes, Deborah A; Jahnke, Justin P; Stratis-Cullum, Dimitra N

    2017-12-06

    Biopanning bacterial display libraries is a proven technique for peptide affinity reagent discovery for recognition of both biotic and abiotic targets. Peptide affinity reagents can be used for similar applications to antibodies, including sensing and therapeutics, but are more robust and able to perform in more extreme environments. Specific enrichment of peptide capture agents to a protein target of interest is enhanced using semi-automated sorting methods which improve binding and wash steps and therefore decrease the occurrence of false positive binders. A semi-automated sorting method is described herein for use with a commercial automated magnetic-activated cell sorting device with an unconstrained bacterial display sorting library expressing random 15-mer peptides. With slight modifications, these methods are extendable to other automated devices, other sorting libraries, and other organisms. A primary goal of this work is to provide a comprehensive methodology and expound the thought process applied in analyzing and minimizing the resulting pool of candidates. These techniques include analysis of on-cell binding using fluorescence-activated cell sorting (FACS), to assess affinity and specificity during sorting and in comparing individual candidates, and the analysis of peptide sequences to identify trends and consensus sequences for understanding and potentially improving the affinity to and specificity for the target of interest.

  2. Discovery and identification of potential biomarkers of pediatric Acute Lymphoblastic Leukemia

    Directory of Open Access Journals (Sweden)

    Cui Ziyou

    2009-03-01

    Full Text Available Abstract Background Acute lymphoblastic leukemia (ALL is a common form of cancer in children. Currently, bone marrow biopsy is used for diagnosis. Noninvasive biomarkers for the early diagnosis of pediatric ALL are urgently needed. The aim of this study was to discover potential protein biomarkers for pediatric ALL. Methods Ninety-four pediatric ALL patients and 84 controls were randomly divided into a "training" set (45 ALL patients, 34 healthy controls and a test set (49 ALL patients, 30 healthy controls and 30 pediatric acute myeloid leukemia (AML patients. Serum proteomic profiles were measured using surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy (SELDI-TOF-MS. A classification model was established by Biomarker Pattern Software (BPS. Candidate protein biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays. Results A total of 7 protein peaks (9290 m/z, 7769 m/z, 15110 m/z, 7564 m/z, 4469 m/z, 8937 m/z, 8137 m/z were found with differential expression levels in the sera of pediatric ALL patients and controls using SELDI-TOF-MS and then analyzed by BPS to construct a classification model in the "training" set. The sensitivity and specificity of the model were found to be 91.8%, and 90.0%, respectively, in the test set. Two candidate protein peaks (7769 and 9290 m/z were found to be down-regulated in ALL patients, where these were identified as platelet factor 4 (PF4 and pro-platelet basic protein precursor (PBP. Two other candidate protein peaks (8137 and 8937 m/z were found up-regulated in the sera of ALL patients, and these were identified as fragments of the complement component 3a (C3a. Conclusion Platelet factor (PF4, connective tissue activating peptide III (CTAP-III and two fragments of C3a may be potential protein biomarkers of pediatric ALL and used to distinguish pediatric ALL patients from healthy controls and pediatric AML patients. Further studies with

  3. Nutrition and biomarkers in psychiatry : research on micronutrient deficiencies in schizophrenia, the role of the intestine in the hyperserotonemia of autism, and a method for non-hypothesis driven discovery of biomarkers in urine

    NARCIS (Netherlands)

    Kemperman, Ramses Franciscus Jacobus

    2007-01-01

    This thesis describes the study of markers of nutrition and intestinal motility in mental disorders with a focus on schizophrenia and autism, and the development, evaluation and application of a biomarker discovery method for urine. The aim of the thesis is to investigate the role of long-chain

  4. Systematic biomarker discovery and coordinative validation for different primary nephrotic syndromes using gas chromatography-mass spectrometry.

    Science.gov (United States)

    Lee, Jung-Eun; Lee, Yu Ho; Kim, Se-Yun; Kim, Yang Gyun; Moon, Ju-Young; Jeong, Kyung-Hwan; Lee, Tae Won; Ihm, Chun-Gyoo; Kim, Sooah; Kim, Kyoung Heon; Kim, Dong Ki; Kim, Yon Su; Kim, Chan-Duck; Park, Cheol Whee; Lee, Do Yup; Lee, Sang-Ho

    2016-07-01

    The goal of this study is to identify systematic biomarker panel for primary nephrotic syndromes from urine samples by applying a non-target metabolite profiling, and to validate their utility in independent sampling and analysis by multiplex statistical approaches. Nephrotic syndrome (NS) is a nonspecific kidney disorder, which is mostly represented by minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), and membranous glomerulonephritis (MGN). Since urine metabolites may mirror disease-specific functional perturbations in kidney injury, we examined urine samples for distinctive metabolic changes to identify biomarkers for clinical applications. We developed unbiased multi-component covarianced models from a discovery set with 48 samples (12 healthy controls, 12 MCD, 12 FSGS, and 12 MGN). To extensively validate their diagnostic potential, new batch from 54 patients with primary NS were independently examined a year after. In the independent validation set, the model including citric acid, pyruvic acid, fructose, ethanolamine, and cysteine effectively discriminated each NS using receiver operating characteristic (ROC) analysis except MCD-MGN comparison; nonetheless an additional metabolite multi-composite greatly improved the discrimination power between MCD and MGN. Finally, we proposed the re-constructed metabolic network distinctively dysregulated by the different NSs that may deepen comprehensive understanding of the disease mechanistic, and help the enhanced identification of NS and therapeutic plans for future. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. Protein biomarker discovery and fast monitoring for the identification and detection of Anisakids by parallel reaction monitoring (PRM) mass spectrometry.

    Science.gov (United States)

    Carrera, Mónica; Gallardo, José M; Pascual, Santiago; González, Ángel F; Medina, Isabel

    2016-06-16

    Anisakids are fish-borne parasites that are responsible for a large number of human infections and allergic reactions around the world. World health organizations and food safety authorities aim to control and prevent this emerging health problem. In the present work, a new method for the fast monitoring of these parasites is described. The strategy is divided in three steps: (i) purification of thermostable proteins from fish-borne parasites (Anisakids), (ii) in-solution HIFU trypsin digestion and (iii) monitoring of several peptide markers by parallel reaction monitoring (PRM) mass spectrometry. This methodology allows the fast detection of Anisakids in Biomarker Discovery and the Fast Monitoring for the identification and detection of Anisakids in fishery products. The strategy is based on the purification of thermostable proteins, the use of accelerated in-solution trypsin digestions under an ultrasonic field provided by High-Intensity Focused Ultrasound (HIFU) and the monitoring of several peptide biomarkers by Parallel Reaction Monitoring (PRM) Mass Spectrometry in a linear ion trap mass spectrometer. The workflow allows the unequivocal detection of Anisakids, in <2h. The present strategy constitutes the fastest method for Anisakids detection, whose application in the food quality control area, could provide to the authorities an effective and rapid method to guarantee the safety to the consumers. Copyright © 2016 Elsevier B.V. All rights reserved.

  6. Metabolic phenotyping for discovery of urinary biomarkers of diet, xenobiotics and blood pressure in the INTERMAP Study: an overview.

    Science.gov (United States)

    Chan, Queenie; Loo, Ruey Leng; Ebbels, Timothy M D; Van Horn, Linda; Daviglus, Martha L; Stamler, Jeremiah; Nicholson, Jeremy K; Holmes, Elaine; Elliott, Paul

    2017-04-01

    The etiopathogenesis of cardiovascular diseases (CVDs) is multifactorial. Adverse blood pressure (BP) is a major independent risk factor for epidemic CVD affecting ~40% of the adult population worldwide and resulting in significant morbidity and mortality. Metabolic phenotyping of biological fluids has proven its application in characterizing low-molecular-weight metabolites providing novel insights into gene-environmental-gut microbiome interaction in relation to a disease state. In this review, we synthesize key results from the INTERnational study of MAcro/micronutrients and blood Pressure (INTERMAP) Study, a cross-sectional epidemiologic study of 4680 men and women aged 40-59 years from Japan, the People's Republic of China, the United Kingdom and the United States. We describe the advancements we have made regarding the following: (1) analytical techniques for high-throughput metabolic phenotyping; (2) statistical analyses for biomarker identification; (3) discovery of unique food-specific biomarkers; and (4) application of metabolome-wide association studies to gain a better understanding into the molecular mechanisms of cross-cultural and regional BP differences.

  7. Multiple reaction monitoring (MRM)-profiling for biomarker discovery applied to human polycystic ovarian syndrome.

    Science.gov (United States)

    Cordeiro, Fernanda B; Ferreira, Christina R; Sobreira, Tiago Jose P; Yannell, Karen E; Jarmusch, Alan K; Cedenho, Agnaldo P; Lo Turco, Edson G; Cooks, R Graham

    2017-09-15

    We describe multiple reaction monitoring (MRM)-profiling, which provides accelerated discovery of discriminating molecular features, and its application to human polycystic ovary syndrome (PCOS) diagnosis. The discovery phase of the MRM-profiling seeks molecular features based on some prior knowledge of the chemical functional groups likely to be present in the sample. It does this through use of a limited number of pre-chosen and chemically specific neutral loss and/or precursor ion MS/MS scans. The output of the discovery phase is a set of precursor/product transitions. In the screening phase these MRM transitions are used to interrogate multiple samples (hence the name MRM-profiling). MRM-profiling was applied to follicular fluid samples of 22 controls and 29 clinically diagnosed PCOS patients. Representative samples were delivered by flow injection to a triple quadrupole mass spectrometer set to perform a number of pre-chosen and chemically specific neutral loss and/or precursor ion MS/MS scans. The output of this discovery phase was a set of 1012 precursor/product transitions. In the screening phase each individual sample was interrogated for these MRM transitions. Principal component analysis (PCA) and receiver operating characteristic (ROC) curves were used for statistical analysis. To evaluate the method's performance, half the samples were used to build a classification model (testing set) and half were blinded (validation set). Twenty transitions were used for the classification of the blind samples, most of them (N = 19) showed lower abundances in the PCOS group and corresponded to phosphatidylethanolamine (PE) and phosphatidylserine (PS) lipids. Agreement of 73% with clinical diagnosis was found when classifying the 26 blind samples. MRM-profiling is a supervised method characterized by its simplicity, speed and the absence of chromatographic separation. It can be used to rapidly isolate discriminating molecules in healthy/disease conditions by

  8. Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach

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    Parida Shreemanta K

    2010-01-01

    Full Text Available Abstract Background For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues. Results Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach. Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available. Conclusions The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in

  9. Discovery of Metabolic Biomarkers for Duchenne Muscular Dystrophy within a Natural History Study.

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    Simina M Boca

    Full Text Available Serum metabolite profiling in Duchenne muscular dystrophy (DMD may enable discovery of valuable molecular markers for disease progression and treatment response. Serum samples from 51 DMD patients from a natural history study and 22 age-matched healthy volunteers were profiled using liquid chromatography coupled to mass spectrometry (LC-MS for discovery of novel circulating serum metabolites associated with DMD. Fourteen metabolites were found significantly altered (1% false discovery rate in their levels between DMD patients and healthy controls while adjusting for age and study site and allowing for an interaction between disease status and age. Increased metabolites included arginine, creatine and unknown compounds at m/z of 357 and 312 while decreased metabolites included creatinine, androgen derivatives and other unknown yet to be identified compounds. Furthermore, the creatine to creatinine ratio is significantly associated with disease progression in DMD patients. This ratio sharply increased with age in DMD patients while it decreased with age in healthy controls. Overall, this study yielded promising metabolic signatures that could prove useful to monitor DMD disease progression and response to therapies in the future.

  10. In vitro biomarker discovery in the parasitic flatworm Fasciola hepatica for monitoring chemotherapeutic treatment

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    Russell M. Morphew

    2014-06-01

    Full Text Available The parasitic flatworm Fasciola hepatica is a global food security risk. With no vaccines, the sustainability of triclabendazole (TCBZ is threatened by emerging resistance. F. hepatica excretory/secretory (ES products can be detected in host faeces and used to estimate TCBZ success and failure. However, there are no faecal based molecular diagnostics dedicated to assessing drug failure or resistance to TCBZ in the field. Utilising in vitro maintenance and sub-proteomic approaches two TCBZ stress ES protein response fingerprints were identified: markers of non-killing and lethal doses. This study provides candidate protein/peptide biomarkers to validate for detection of TCBZ failure and resistance.

  11. Profiling of circulating microRNAs for prostate cancer biomarker discovery

    DEFF Research Database (Denmark)

    Haldrup, Christa; Kosaka, Nobuyoshi; Ochiya, Takahiro

    2014-01-01

    Prostate cancer (PC) is the most frequent cancer in men in the Western world. Currently, serum prostate-specific antigen levels and digital rectal examinations are used to indicate the need for diagnostic prostate biopsy, but lack in specificity and sensitivity. Thus, many men undergo unnecessary...... performed genome-wide miRNA profiling of serum samples from 13 benign prostatic hyperplasia (BPH) control patients and 31 PC patients. Furthermore, we carefully reviewed the literature on circulating miRNA biomarkers for PC. Our results confirmed the de-regulation of miR-141 and miR-375, two of the most...

  12. Biomarker discovery for early detection of hepatocellular carcinoma in hepatitis C-infected patients.

    Science.gov (United States)

    Mustafa, Mehnaz G; Petersen, John R; Ju, Hyunsu; Cicalese, Luca; Snyder, Ned; Haidacher, Sigmund J; Denner, Larry; Elferink, Cornelis

    2013-12-01

    Chronic hepatic disease damages the liver, and the resulting wound-healing process leads to liver fibrosis and the subsequent development of cirrhosis. The leading cause of hepatic fibrosis and cirrhosis is infection with hepatitis C virus (HCV), and of the patients with HCV-induced cirrhosis, 2% to 5% develop hepatocellular carcinoma (HCC), with a survival rate of 7%. HCC is one of the leading causes of cancer-related death worldwide, and the poor survival rate is largely due to late-stage diagnosis, which makes successful intervention difficult, if not impossible. The lack of sensitive and specific diagnostic tools and the urgent need for early-stage diagnosis prompted us to discover new candidate biomarkers for HCV and HCC. We used aptamer-based fractionation technology to reduce serum complexity, differentially labeled samples (six HCV and six HCC) with fluorescent dyes, and resolved proteins in pairwise two-dimensional difference gel electrophoresis. DeCyder software was used to identify differentially expressed proteins and spots picked, and MALDI-MS/MS was used to determine that ApoA1 was down-regulated by 22% (p Differential expression quantified via two-dimensional difference gel electrophoresis was confirmed by means of (18)O/(16)O stable isotope differential labeling with LC-MS/MS zoom scans. Technically independent confirmation was demonstrated by triple quadrupole LC-MS/MS selected reaction monitoring (SRM) assays with three peptides specific to human ApoA1 (DLATVYVDVLK, WQEEMELYR, and VSFLSALEEYTK) using (18)O/(16)O-labeled samples and further verified with AQUA peptides as internal standards for quantification. In 50 patient samples (24 HCV and 26 HCC), all three SRM assays yielded highly similar differential expression of ApoA1 in HCC and HCV patients. These results validated the SRM assays, which were independently confirmed by Western blotting. Thus, ApoA1 is a candidate member of an SRM biomarker panel for early diagnosis, prognosis, and

  13. Discovery of potential protein biomarkers of lung adenocarcinoma in bronchoalveolar lavage fluid by SWATH MS data-independent acquisition and targeted data extraction.

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    Ortea, I; Rodríguez-Ariza, A; Chicano-Gálvez, E; Arenas Vacas, M S; Jurado Gámez, B

    2016-04-14

    Lung cancer currently ranks as the neoplasia with the highest global mortality rate. Although some improvements have been introduced in recent years, new advances in diagnosis are required in order to increase survival rates. New mildly invasive endoscopy-based diagnostic techniques include the collection of bronchoalveolar lavage fluid (BALF), which is discarded after using a portion of the fluid for standard pathological procedures. BALF proteomic analysis can contribute to clinical practice with more sensitive biomarkers, and can complement cytohistological studies by aiding in the diagnosis, prognosis, and subtyping of lung cancer, as well as the monitoring of treatment response. The range of quantitative proteomics methodologies used for biomarker discovery is currently being broadened with the introduction of data-independent acquisition (DIA) analysis-related approaches that address the massive quantitation of the components of a proteome. Here we report for the first time a DIA-based quantitative proteomics study using BALF as the source for the discovery of potential lung cancer biomarkers. The results have been encouraging in terms of the number of identified and quantified proteins. A panel of candidate protein biomarkers for adenocarcinoma in BALF is reported; this points to the activation of the complement network as being strongly over-represented and suggests this pathway as a potential target for lung cancer research. In addition, the results reported for haptoglobin, complement C4-A, and glutathione S-transferase pi are consistent with previous studies, which indicates that these proteins deserve further consideration as potential lung cancer biomarkers in BALF. Our study demonstrates that the analysis of BALF proteins by liquid chromatography-tandem mass spectrometry (LC-MS/MS), combining a simple sample pre-treatment and SWATH DIA MS, is a useful method for the discovery of potential lung cancer biomarkers. Bronchoalveolar lavage fluid (BALF

  14. GENPLAT: an automated platform for biomass enzyme discovery and cocktail optimization.

    Science.gov (United States)

    Walton, Jonathan; Banerjee, Goutami; Car, Suzana

    2011-10-24

    The high cost of enzymes for biomass deconstruction is a major impediment to the economic conversion of lignocellulosic feedstocks to liquid transportation fuels such as ethanol. We have developed an integrated high throughput platform, called GENPLAT, for the discovery and development of novel enzymes and enzyme cocktails for the release of sugars from diverse pretreatment/biomass combinations. GENPLAT comprises four elements: individual pure enzymes, statistical design of experiments, robotic pipeting of biomass slurries and enzymes, and automated colorimeteric determination of released Glc and Xyl. Individual enzymes are produced by expression in Pichia pastoris or Trichoderma reesei, or by chromatographic purification from commercial cocktails or from extracts of novel microorganisms. Simplex lattice (fractional factorial) mixture models are designed using commercial Design of Experiment statistical software. Enzyme mixtures of high complexity are constructed using robotic pipeting into a 96-well format. The measurement of released Glc and Xyl is automated using enzyme-linked colorimetric assays. Optimized enzyme mixtures containing as many as 16 components have been tested on a variety of feedstock and pretreatment combinations. GENPLAT is adaptable to mixtures of pure enzymes, mixtures of commercial products (e.g., Accellerase 1000 and Novozyme 188), extracts of novel microbes, or combinations thereof. To make and test mixtures of ˜10 pure enzymes requires less than 100 μg of each protein and fewer than 100 total reactions, when operated at a final total loading of 15 mg protein/g glucan. We use enzymes from several sources. Enzymes can be purified from natural sources such as fungal cultures (e.g., Aspergillus niger, Cochliobolus carbonum, and Galerina marginata), or they can be made by expression of the encoding genes (obtained from the increasing number of microbial genome sequences) in hosts such as E. coli, Pichia pastoris, or a filamentous fungus such

  15. “Omics”-Informed Drug and Biomarker Discovery: Opportunities, Challenges and Future Perspectives

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    Holly Matthews

    2016-09-01

    Full Text Available The pharmaceutical industry faces unsustainable program failure despite significant increases in investment. Dwindling discovery pipelines, rapidly expanding R&D budgets and increasing regulatory control, predict significant gaps in the future drug markets. The cumulative duration of discovery from concept to commercialisation is unacceptably lengthy, and adds to the deepening crisis. Existing animal models predicting clinical translations are simplistic, highly reductionist and, therefore, not fit for purpose. The catastrophic consequences of ever-increasing attrition rates are most likely to be felt in the developing world, where resistance acquisition by killer diseases like malaria, tuberculosis and HIV have paced far ahead of new drug discovery. The coming of age of Omics-based applications makes available a formidable technological resource to further expand our knowledge of the complexities of human disease. The standardisation, analysis and comprehensive collation of the “data-heavy” outputs of these sciences are indeed challenging. A renewed focus on increasing reproducibility by understanding inherent biological, methodological, technical and analytical variables is crucial if reliable and useful inferences with potential for translation are to be achieved. The individual Omics sciences—genomics, transcriptomics, proteomics and metabolomics—have the singular advantage of being complimentary for cross validation, and together could potentially enable a much-needed systems biology perspective of the perturbations underlying disease processes. If current adverse trends are to be reversed, it is imperative that a shift in the R&D focus from speed to quality is achieved. In this review, we discuss the potential implications of recent Omics-based advances for the drug development process.

  16. Metabolomics in critical care medicine: a new approach to biomarker discovery.

    Science.gov (United States)

    Banoei, Mohammad M; Donnelly, Sarah J; Mickiewicz, Beata; Weljie, Aalim; Vogel, Hans J; Winston, Brent W

    2014-12-01

    To present an overview and comparison of the main metabolomics techniques (1H NMR, GC-MS, and LC-MS) and their current and potential use in critical care medicine. This is a focused review, not a systematic review, using the PubMed database as the predominant source of references to compare metabolomics techniques. 1H NMR, GC-MS, and LC-MS are complementary techniques that can be used on a variety of biofluids for metabolomics analysis of patients in the Intensive Care Unit (ICU). These techniques have been successfully used for diagnosis and prognosis in the ICU and other clinical settings; for example, in patients with septic shock and community-acquired pneumonia. Metabolomics is a powerful tool that has strong potential to impact diagnosis and prognosis and to examine responses to treatment in critical care medicine through diagnostic and prognostic biomarker and biopattern identification.

  17. Proteomics and metabolomics for mechanistic insights and biomarker discovery in cardiovascular disease.

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    Barallobre-Barreiro, Javier; Chung, Yuen-Li; Mayr, Manuel

    2013-08-01

    In the last decade, proteomics and metabolomics have contributed substantially to our understanding of cardiovascular diseases. The unbiased assessment of pathophysiological processes without a priori assumptions complements other molecular biology techniques that are currently used in a reductionist approach. In this review, we highlight some of the "omics" methods used to assess protein and metabolite changes in cardiovascular disease. A discrete biological function is very rarely attributed to a single molecule; more often it is the combined input of many proteins. In contrast to the reductionist approach, in which molecules are studied individually, "omics" platforms allow the study of more complex interactions in biological systems. Combining proteomics and metabolomics to quantify changes in metabolites and their corresponding enzymes will advance our understanding of pathophysiological mechanisms and aid the identification of novel biomarkers for cardiovascular disease. Copyright © 2013 Sociedad Española de Cardiología. Published by Elsevier Espana. All rights reserved.

  18. Adipokines: a treasure trove for the discovery of biomarkers for metabolic disorders.

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    Lehr, Stefan; Hartwig, Sonja; Sell, Henrike

    2012-01-01

    Adipose tissue is a major endocrine organ, releasing signaling and mediator proteins, termed adipokines, via which adipose tissue communicates with other organs. Expansion of adipose tissue in obesity alters adipokine secretion which may contribute to the development of metabolic diseases. Consequently, this correlation has emphasized the importance to further characterize the adipocyte secretion profile, and several attempts have been made to characterize the complex nature of the adipose tissue secretome by utilizing diverse proteomic profiling approaches. Although the entirety of human adipokines is still incompletely characterized, to date more than 600 potentially secretory proteins were identified providing a rich source to identify putative novel biomarkers associated with metabolic diseases. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  19. Blood diagnostic biomarkers for major depressive disorder using multiplex DNA methylation profiles: discovery and validation.

    Science.gov (United States)

    Numata, Shusuke; Ishii, Kazuo; Tajima, Atsushi; Iga, Jun-ichi; Kinoshita, Makoto; Watanabe, Shinya; Umehara, Hidehiro; Fuchikami, Manabu; Okada, Satoshi; Boku, Shuken; Hishimoto, Akitoyo; Shimodera, Shinji; Imoto, Issei; Morinobu, Shigeru; Ohmori, Tetsuro

    2015-01-01

    Aberrant DNA methylation in the blood of patients with major depressive disorder (MDD) has been reported in several previous studies. However, no comprehensive studies using medication-free subjects with MDD have been conducted. Furthermore, the majority of these previous studies has been limited to the analysis of the CpG sites in CpG islands (CGIs) in the gene promoter regions. The main aim of the present study is to identify DNA methylation markers that distinguish patients with MDD from non-psychiatric controls. Genome-wide DNA methylation profiling of peripheral leukocytes was conducted in two set of samples, a discovery set (20 medication-free patients with MDD and 19 controls) and a replication set (12 medication-free patients with MDD and 12 controls), using Infinium HumanMethylation450 BeadChips. Significant diagnostic differences in DNA methylation were observed at 363 CpG sites in the discovery set. All of these loci demonstrated lower DNA methylation in patients with MDD than in the controls, and most of them (85.7%) were located in the CGIs in the gene promoter regions. We were able to distinguish patients with MDD from the control subjects with high accuracy in the discriminant analysis using the top DNA methylation markers. We also validated these selected DNA methylation markers in the replication set. Our results indicate that multiplex DNA methylation markers may be useful for distinguishing patients with MDD from non-psychiatric controls.

  20. Low molecular weight protein enrichment on mesoporous silica thin films for biomarker discovery.

    Science.gov (United States)

    Fan, Jia; Gallagher, James W; Wu, Hung-Jen; Landry, Matthew G; Sakamoto, Jason; Ferrari, Mauro; Hu, Ye

    2012-04-17

    The identification of circulating biomarkers holds great potential for non invasive approaches in early diagnosis and prognosis, as well as for the monitoring of therapeutic efficiency.(1-3) The circulating low molecular weight proteome (LMWP) composed of small proteins shed from tissues and cells or peptide fragments derived from the proteolytic degradation of larger proteins, has been associated with the pathological condition in patients and likely reflects the state of disease.(4,5) Despite these potential clinical applications, the use of Mass Spectrometry (MS) to profile the LMWP from biological fluids has proven to be very challenging due to the large dynamic range of protein and peptide concentrations in serum.(6) Without sample pre-treatment, some of the more highly abundant proteins obscure the detection of low-abundance species in serum/plasma. Current proteomic-based approaches, such as two-dimensional polyacrylamide gel-electrophoresis (2D-PAGE) and shotgun proteomics methods are labor-intensive, low throughput and offer limited suitability for clinical applications.(7-9) Therefore, a more effective strategy is needed to isolate LMWP from blood and allow the high throughput screening of clinical samples. Here, we present a fast, efficient and reliable multi-fractionation system based on mesoporous silica chips to specifically target and enrich LMWP.(10,11) Mesoporous silica (MPS) thin films with tunable features at the nanoscale were fabricated using the triblock copolymer template pathway. Using different polymer templates and polymer concentrations in the precursor solution, various pore size distributions, pore structures, connectivity and surface properties were determined and applied for selective recovery of low mass proteins. The selective parsing of the enriched peptides into different subclasses according to their physicochemical properties will enhance the efficiency of recovery and detection of low abundance species. In combination with mass

  1. 2D-DIGE as a proteomic biomarker discovery tool in environmental studies with Procambarus clarkii.

    Science.gov (United States)

    Fernández-Cisnal, Ricardo; García-Sevillano, Miguel A; Gómez-Ariza, José L; Pueyo, Carmen; López-Barea, Juan; Abril, Nieves

    2017-04-15

    A 2D-DIGE/MS approach was used to assess protein abundance differences in the red swamp crayfish Procambarus clarkii from polluted aquatic ecosystems of Doñana National Park and surrounding areas with different pollution loads. Procambarus clarkii accumulated metals in the digestive glands and gills reflecting sediment concentrations. We first stated that, probably related to elements accumulation, pollution increased oxidative damage in P. clarkii tissues, as shown by the thiol oxidation status of proteins and MDA levels. In these animals, the altered redox status might be responsible for the deregulated abundance of proteins involved in cellular responses to oxidative stress including protein folding, mitochondrial imbalance and inflammatory processes. Interestingly, polluted P. clarkii crayfish also displayed a metabolic shift to enhanced aerobic glycolysis, most likely aimed at generating ATP and reduction equivalents in an oxidative stress situation that alters mitochondrial integrity. The deregulated proteins define the physiological processes affected by pollutants in DNP and its surrounding areas and may help us to unravel the molecular mechanisms underlying the toxicity of environmental pollutants. In addition, these proteins might be used as exposure biomarkers in environmental risk assessment. The results obtained might be extrapolated to many other locations all over the world and have the added value of providing information about the molecular responses of this environmentally and economically interesting animal. Metal content in digestive gland and gills of P. clarkii crayfish reflects their contents in sediments at sites of Doñana National Park and its surroundings. Accumulation of essential and toxic transition metals is paralleled by clear signs of oxidative stress to lipids and proteins and by significant deregulation of many proteins involved in protein folding, mitochondrial respiratory imbalance and inflammatory response. These results

  2. microRNA Biomarker Discovery and High-Throughput DNA Sequencing Are Possible Using Long-term Archived Serum Samples.

    Science.gov (United States)

    Rounge, Trine B; Lauritzen, Marianne; Langseth, Hilde; Enerly, Espen; Lyle, Robert; Gislefoss, Randi E

    2015-09-01

    The impacts of long-term storage and varying preanalytical factors on the quality and quantity of DNA and miRNA from archived serum have not been fully assessed. Preanalytical and analytical variations and degradation may introduce bias in representation of DNA and miRNA and may result in loss or corruption of quantitative data. We have evaluated DNA and miRNA quantity, quality, and variability in samples stored up to 40 years using one of the oldest prospective serum collections in the world, the Janus Serumbank, a biorepository dedicated to cancer research. miRNAs are present and stable in archived serum samples frozen at -25°C for at least 40 years. Long-time storage did not reduce miRNA yields; however, varying preanalytical conditions had a significant effect and should be taken into consideration during project design. Of note, 500 μL serum yielded sufficient miRNA for qPCR and small RNA sequencing and on average 650 unique miRNAs were detected in samples from presumably healthy donors. Of note, 500 μL serum yielded sufficient DNA for whole-genome sequencing and subsequent SNP calling, giving a uniform representation of the genomes. DNA and miRNA are stable during long-term storage, making large prospectively collected serum repositories an invaluable source for miRNA and DNA biomarker discovery. Large-scale biomarker studies with long follow-up time are possible utilizing biorepositories with archived serum and state-of-the-art technology. ©2015 American Association for Cancer Research.

  3. Cell surface profiling using high-throughput flow cytometry: a platform for biomarker discovery and analysis of cellular heterogeneity.

    Directory of Open Access Journals (Sweden)

    Craig A Gedye

    Full Text Available Cell surface proteins have a wide range of biological functions, and are often used as lineage-specific markers. Antibodies that recognize cell surface antigens are widely used as research tools, diagnostic markers, and even therapeutic agents. The ability to obtain broad cell surface protein profiles would thus be of great value in a wide range of fields. There are however currently few available methods for high-throughput analysis of large numbers of cell surface proteins. We describe here a high-throughput flow cytometry (HT-FC platform for rapid analysis of 363 cell surface antigens. Here we demonstrate that HT-FC provides reproducible results, and use the platform to identify cell surface antigens that are influenced by common cell preparation methods. We show that multiple populations within complex samples such as primary tumors can be simultaneously analyzed by co-staining of cells with lineage-specific antibodies, allowing unprecedented depth of analysis of heterogeneous cell populations. Furthermore, standard informatics methods can be used to visualize, cluster and downsample HT-FC data to reveal novel signatures and biomarkers. We show that the cell surface profile provides sufficient molecular information to classify samples from different cancers and tissue types into biologically relevant clusters using unsupervised hierarchical clustering. Finally, we describe the identification of a candidate lineage marker and its subsequent validation. In summary, HT-FC combines the advantages of a high-throughput screen with a detection method that is sensitive, quantitative, highly reproducible, and allows in-depth analysis of heterogeneous samples. The use of commercially available antibodies means that high quality reagents are immediately available for follow-up studies. HT-FC has a wide range of applications, including biomarker discovery, molecular classification of cancers, or identification of novel lineage specific or stem cell

  4. Cell surface profiling using high-throughput flow cytometry: a platform for biomarker discovery and analysis of cellular heterogeneity.

    Science.gov (United States)

    Gedye, Craig A; Hussain, Ali; Paterson, Joshua; Smrke, Alannah; Saini, Harleen; Sirskyj, Danylo; Pereira, Keira; Lobo, Nazleen; Stewart, Jocelyn; Go, Christopher; Ho, Jenny; Medrano, Mauricio; Hyatt, Elzbieta; Yuan, Julie; Lauriault, Stevan; Meyer, Mona; Kondratyev, Maria; van den Beucken, Twan; Jewett, Michael; Dirks, Peter; Guidos, Cynthia J; Danska, Jayne; Wang, Jean; Wouters, Bradly; Neel, Benjamin; Rottapel, Robert; Ailles, Laurie E

    2014-01-01

    Cell surface proteins have a wide range of biological functions, and are often used as lineage-specific markers. Antibodies that recognize cell surface antigens are widely used as research tools, diagnostic markers, and even therapeutic agents. The ability to obtain broad cell surface protein profiles would thus be of great value in a wide range of fields. There are however currently few available methods for high-throughput analysis of large numbers of cell surface proteins. We describe here a high-throughput flow cytometry (HT-FC) platform for rapid analysis of 363 cell surface antigens. Here we demonstrate that HT-FC provides reproducible results, and use the platform to identify cell surface antigens that are influenced by common cell preparation methods. We show that multiple populations within complex samples such as primary tumors can be simultaneously analyzed by co-staining of cells with lineage-specific antibodies, allowing unprecedented depth of analysis of heterogeneous cell populations. Furthermore, standard informatics methods can be used to visualize, cluster and downsample HT-FC data to reveal novel signatures and biomarkers. We show that the cell surface profile provides sufficient molecular information to classify samples from different cancers and tissue types into biologically relevant clusters using unsupervised hierarchical clustering. Finally, we describe the identification of a candidate lineage marker and its subsequent validation. In summary, HT-FC combines the advantages of a high-throughput screen with a detection method that is sensitive, quantitative, highly reproducible, and allows in-depth analysis of heterogeneous samples. The use of commercially available antibodies means that high quality reagents are immediately available for follow-up studies. HT-FC has a wide range of applications, including biomarker discovery, molecular classification of cancers, or identification of novel lineage specific or stem cell markers.

  5. Multivariate models from RNA-Seq SNVs yield candidate molecular targets for biomarker discovery: SNV-DA.

    Science.gov (United States)

    Paul, Matt R; Levitt, Nicholas P; Moore, David E; Watson, Patricia M; Wilson, Robert C; Denlinger, Chadrick E; Watson, Dennis K; Anderson, Paul E

    2016-03-31

    It has recently been shown that significant and accurate single nucleotide variants (SNVs) can be reliably called from RNA-Seq data. These may provide another source of features for multivariate predictive modeling of disease phenotype for the prioritization of candidate biomarkers. The continuous nature of SNV allele fraction features allows the concurrent investigation of several genomic phenomena, including allele specific expression, clonal expansion and/or deletion, and copy number variation. The proposed software pipeline and package, SNV Discriminant Analysis (SNV-DA), was applied on two RNA-Seq datasets with varying sample sizes sequenced at different depths: a dataset containing primary tumors from twenty patients with different disease outcomes in lung adenocarcinoma and a larger dataset of primary tumors representing two major breast cancer subtypes, estrogen receptor positive and triple negative. Predictive models were generated using the machine learning algorithm, sparse projections to latent structures discriminant analysis. Training sets composed of RNA-Seq SNV features limited to genomic regions of origin (e.g. exonic or intronic) and/or RNA-editing sites were shown to produce models with accurate predictive performances, were discriminant towards true label groupings, and were able to produce SNV rankings significantly different from than univariate tests. Furthermore, the utility of the proposed methodology is supported by its comparable performance to traditional models as well as the enrichment of selected SNVs located in genes previously associated with cancer and genes showing allele-specific expression. As proof of concept, we highlight the discovery of a previously unannotated intergenic locus that is associated with epigenetic regulatory marks in cancer and whose significant allele-specific expression is correlated with ER+ status; hereafter named ER+ associated hotspot (ERPAHS). The use of models from RNA-Seq SNVs to identify and

  6. Emerging field of metabolomics: big promise for cancer biomarker identification and drug discovery.

    Science.gov (United States)

    Patel, Seema; Ahmed, Shadab

    2015-03-25

    Most cancers are lethal and metabolic alterations are considered a hallmark of this deadly disease. Genomics and proteomics have contributed vastly to understand cancer biology. Still there are missing links as downstream to them molecular divergence occurs. Metabolomics, the omic science that furnishes a dynamic portrait of metabolic profile is expected to bridge these gaps and boost cancer research. Metabolites being the end products are more stable than mRNAs or proteins. Previous studies have shown the efficacy of metabolomics in identifying biomarkers associated with diagnosis, prognosis and treatment of cancer. Metabolites are highly informative about the functional status of the biological system, owing to their proximity to organismal phenotypes. Scores of publications have reported about high-throughput data generation by cutting-edge analytic platforms (mass spectrometry and nuclear magnetic resonance). Further sophisticated statistical softwares (chemometrics) have enabled meaningful information extraction from the metabolomic data. Metabolomics studies have demonstrated the perturbation in glycolysis, tricarboxylic acid cycle, choline and fatty acid metabolism as traits of cancer cells. This review discusses the latest progress in this field, the future trends and the deficiencies to be surmounted for optimally implementation in oncology. The authors scoured through the most recent, high-impact papers archived in Pubmed, ScienceDirect, Wiley and Springer databases to compile this review to pique the interest of researchers towards cancer metabolomics. Copyright © 2014 Elsevier B.V. All rights reserved.

  7. VirusDetect: An automated pipeline for efficient virus discovery using deep sequencing of small RNAs

    Science.gov (United States)

    Accurate detection of viruses in plants and animals is critical for agriculture production and human health. Deep sequencing and assembly of virus-derived siRNAs has proven to be a highly efficient approach for virus discovery. However, to date no computational tools specifically designed for both k...

  8. voomDDA: discovery of diagnostic biomarkers and classification of RNA-seq data

    Directory of Open Access Journals (Sweden)

    Gokmen Zararsiz

    2017-10-01

    Full Text Available RNA-Seq is a recent and efficient technique that uses the capabilities of next-generation sequencing technology for characterizing and quantifying transcriptomes. One important task using gene-expression data is to identify a small subset of genes that can be used to build diagnostic classifiers particularly for cancer diseases. Microarray based classifiers are not directly applicable to RNA-Seq data due to its discrete nature. Overdispersion is another problem that requires careful modeling of mean and variance relationship of the RNA-Seq data. In this study, we present voomDDA classifiers: variance modeling at the observational level (voom extensions of the nearest shrunken centroids (NSC and the diagonal discriminant classifiers. VoomNSC is one of these classifiers and brings voom and NSC approaches together for the purpose of gene-expression based classification. For this purpose, we propose weighted statistics and put these weighted statistics into the NSC algorithm. The VoomNSC is a sparse classifier that models the mean-variance relationship using the voom method and incorporates voom’s precision weights into the NSC classifier via weighted statistics. A comprehensive simulation study was designed and four real datasets are used for performance assessment. The overall results indicate that voomNSC performs as the sparsest classifier. It also provides the most accurate results together with power-transformed Poisson linear discriminant analysis, rlog transformed support vector machines and random forests algorithms. In addition to prediction purposes, the voomNSC classifier can be used to identify the potential diagnostic biomarkers for a condition of interest. Through this work, statistical learning methods proposed for microarrays can be reused for RNA-Seq data. An interactive web application is freely available at http://www.biosoft.hacettepe.edu.tr/voomDDA/.

  9. voomDDA: discovery of diagnostic biomarkers and classification of RNA-seq data.

    Science.gov (United States)

    Zararsiz, Gokmen; Goksuluk, Dincer; Klaus, Bernd; Korkmaz, Selcuk; Eldem, Vahap; Karabulut, Erdem; Ozturk, Ahmet

    2017-01-01

    RNA-Seq is a recent and efficient technique that uses the capabilities of next-generation sequencing technology for characterizing and quantifying transcriptomes. One important task using gene-expression data is to identify a small subset of genes that can be used to build diagnostic classifiers particularly for cancer diseases. Microarray based classifiers are not directly applicable to RNA-Seq data due to its discrete nature. Overdispersion is another problem that requires careful modeling of mean and variance relationship of the RNA-Seq data. In this study, we present voomDDA classifiers: variance modeling at the observational level (voom) extensions of the nearest shrunken centroids (NSC) and the diagonal discriminant classifiers. VoomNSC is one of these classifiers and brings voom and NSC approaches together for the purpose of gene-expression based classification. For this purpose, we propose weighted statistics and put these weighted statistics into the NSC algorithm. The VoomNSC is a sparse classifier that models the mean-variance relationship using the voom method and incorporates voom's precision weights into the NSC classifier via weighted statistics. A comprehensive simulation study was designed and four real datasets are used for performance assessment. The overall results indicate that voomNSC performs as the sparsest classifier. It also provides the most accurate results together with power-transformed Poisson linear discriminant analysis, rlog transformed support vector machines and random forests algorithms. In addition to prediction purposes, the voomNSC classifier can be used to identify the potential diagnostic biomarkers for a condition of interest. Through this work, statistical learning methods proposed for microarrays can be reused for RNA-Seq data. An interactive web application is freely available at http://www.biosoft.hacettepe.edu.tr/voomDDA/.

  10. Isolation and characterization of urinary extracellular vesicles: implications for biomarker discovery.

    Science.gov (United States)

    Merchant, Michael L; Rood, Ilse M; Deegens, Jeroen K J; Klein, Jon B

    2017-12-01

    Urine is a valuable diagnostic medium and, with the discovery of urinary extracellular vesicles, is viewed as a dynamic bioactive fluid. Extracellular vesicles are lipid-enclosed structures that can be classified into three categories: exosomes, microvesicles (or ectosomes) and apoptotic bodies. This classification is based on the mechanisms by which membrane vesicles are formed: fusion of multivesicular bodies with the plasma membranes (exosomes), budding of vesicles directly from the plasma membrane (microvesicles) or those shed from dying cells (apoptotic bodies). During their formation, urinary extracellular vesicles incorporate various cell-specific components (proteins, lipids and nucleic acids) that can be transferred to target cells. The rigour needed for comparative studies has fueled the search for optimal approaches for their isolation, purification, and characterization. RNA, the newest extracellular vesicle component to be discovered, has received substantial attention as an extracellular vesicle therapeutic, and compelling evidence suggests that ex vivo manipulation of microRNA composition may have uses in the treatment of kidney disorders. The results of these studies are building the case that urinary extracellular vesicles act as mediators of renal pathophysiology. As the field of extracellular vesicle studies is burgeoning, this Review focuses on primary data obtained from studies of human urine rather than on data from studies of laboratory animals or cultured immortalized cells.

  11. Top-down proteomics with mass spectrometry imaging: a pilot study towards discovery of biomarkers for neurodevelopmental disorders.

    Directory of Open Access Journals (Sweden)

    Hui Ye

    Full Text Available In the developing mammalian brain, inhibition of NMDA receptor can induce widespread neuroapoptosis, inhibit neurogenesis and cause impairment of learning and memory. Although some mechanistic insights into adverse neurological actions of these NMDA receptor antagonists exist, our understanding of the full spectrum of developmental events affected by early exposure to these chemical agents in the brain is still limited. Here we attempt to gain insights into the impact of pharmacologically induced excitatory/inhibitory imbalance in infancy on the brain proteome using mass spectrometric imaging (MSI. Our goal was to study changes in protein expression in postnatal day 10 (P10 rat brains following neonatal exposure to the NMDA receptor antagonist dizocilpine (MK801. Analysis of rat brains exposed to vehicle or MK801 and comparison of their MALDI MS images revealed differential relative abundances of several proteins. We then identified these markers such as ubiquitin, purkinje cell protein 4 (PEP-19, cytochrome c oxidase subunits and calmodulin, by a combination of reversed-phase (RP HPLC fractionation and top-down tandem MS platform. More in-depth large scale study along with validation experiments will be carried out in the future. Overall, our findings indicate that a brief neonatal exposure to a compound that alters excitatory/inhibitory balance in the brain has a long term effect on protein expression patterns during subsequent development, highlighting the utility of MALDI-MSI as a discovery tool for potential biomarkers.

  12. Custom database development and biomarker discovery methods for MALDI-TOF mass spectrometry-based identification of high-consequence bacterial pathogens.

    Science.gov (United States)

    Tracz, Dobryan M; Tyler, Andrea D; Cunningham, Ian; Antonation, Kym S; Corbett, Cindi R

    2017-03-01

    A high-quality custom database of MALDI-TOF mass spectral profiles was developed with the goal of improving clinical diagnostic identification of high-consequence bacterial pathogens. A biomarker discovery method is presented for identifying and evaluating MALDI-TOF MS spectra to potentially differentiate biothreat bacteria from less-pathogenic near-neighbour species. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

  13. Application of a high throughput method of biomarker discovery to improvement of the EarlyCDT(®-Lung Test.

    Directory of Open Access Journals (Sweden)

    Isabel K Macdonald

    Full Text Available BACKGROUND: The National Lung Screening Trial showed that CT screening for lung cancer led to a 20% reduction in mortality. However, CT screening has a number of disadvantages including low specificity. A validated autoantibody assay is available commercially (EarlyCDT®-Lung to aid in the early detection of lung cancer and risk stratification in patients with pulmonary nodules detected by CT. Recent advances in high throughput (HTP cloning and expression methods have been developed into a discovery pipeline to identify biomarkers that detect autoantibodies. The aim of this study was to demonstrate the successful clinical application of this strategy to add to the EarlyCDT-Lung panel in order to improve its sensitivity and specificity (and hence positive predictive value, (PPV. METHODS AND FINDINGS: Serum from two matched independent cohorts of lung cancer patients were used (n = 100 and n = 165. Sixty nine proteins were initially screened on an abridged HTP version of the autoantibody ELISA using protein prepared on small scale by a HTP expression and purification screen. Promising leads were produced in shake flask culture and tested on the full assay. These results were analyzed in combination with those from the EarlyCDT-Lung panel in order to provide a set of re-optimized cut-offs. Five proteins that still displayed cancer/normal differentiation were tested for reproducibility and validation on a second batch of protein and a separate patient cohort. Addition of these proteins resulted in an improvement in the sensitivity and specificity of the test from 38% and 86% to 49% and 93% respectively (PPV improvement from 1 in 16 to 1 in 7. CONCLUSION: This is a practical example of the value of investing resources to develop a HTP technology. Such technology may lead to improvement in the clinical utility of the EarlyCDT--Lung test, and so further aid the early detection of lung cancer.

  14. Discovery of Overcoating Metal Oxides on Photoelectrode for Water Splitting by Automated Screening.

    Science.gov (United States)

    Saito, Rie; Miseki, Yugo; Nini, Wang; Sayama, Kazuhiro

    2015-10-12

    We applied an automated semiconductor synthesis and screen system to discover overcoating film materials and optimize coating conditions on the BiVO4/WO3 composite photoelectrode to enhance stability and photocurrent. Thirteen metallic elements for overcoating oxides were examined with various coating amounts. The stability of the BiVO4/WO3 photoelectrode in a highly concentrated carbonate electrolyte aqueous solution was significantly improved by overcoating with Ta2O5 film, which was amorphous and porous when calcined at 550 °C. The photocurrent for the water oxidation reaction was only minimally inhibited by the presence of the Ta2O5 film on the BiVO4/WO3 photoelectrode.

  15. Guidelines for Biomarker of Food Intake Reviews (BFIRev): how to conduct an extensive literature search for biomarker of food intake discovery.

    Science.gov (United States)

    Praticò, Giulia; Gao, Qian; Scalbert, Augustin; Vergères, Guy; Kolehmainen, Marjukka; Manach, Claudine; Brennan, Lorraine; Pedapati, Sri Harsha; Afman, Lydia A; Wishart, David S; Vázquez-Fresno, Rosa; Lacueva, Cristina Andres; Garcia-Aloy, Mar; Verhagen, Hans; Feskens, Edith J M; Dragsted, Lars O

    2018-01-01

    Identification of new biomarkers of food and nutrient intake has developed fast over the past two decades and could potentially provide important new tools for compliance monitoring and dietary intake assessment in nutrition and health science. In recent years, metabolomics has played an important role in identifying a large number of putative biomarkers of food intake (BFIs). However, the large body of scientific literature on potential BFIs outside the metabolomics area should also be taken into account. In particular, we believe that extensive literature reviews should be conducted and that the quality of all suggested biomarkers should be systematically evaluated. In order to cover the literature on BFIs in the most appropriate and consistent manner, there is a need for appropriate guidelines on this topic. These guidelines should build upon guidelines in related areas of science while targeting the special needs of biomarker methodology. This document provides a guideline for conducting an extensive literature search on BFIs, which will provide the basis to systematically validate BFIs. This procedure will help to prioritize future work on the identification of new potential biomarkers and on validating these as well as other biomarker candidates, thereby providing better tools for future studies in nutrition and health.

  16. Guidelines for Biomarker of Food Intake Reviews (BFIRev: how to conduct an extensive literature search for biomarker of food intake discovery

    Directory of Open Access Journals (Sweden)

    Giulia Praticò

    2018-02-01

    Full Text Available Abstract Identification of new biomarkers of food and nutrient intake has developed fast over the past two decades and could potentially provide important new tools for compliance monitoring and dietary intake assessment in nutrition and health science. In recent years, metabolomics has played an important role in identifying a large number of putative biomarkers of food intake (BFIs. However, the large body of scientific literature on potential BFIs outside the metabolomics area should also be taken into account. In particular, we believe that extensive literature reviews should be conducted and that the quality of all suggested biomarkers should be systematically evaluated. In order to cover the literature on BFIs in the most appropriate and consistent manner, there is a need for appropriate guidelines on this topic. These guidelines should build upon guidelines in related areas of science while targeting the special needs of biomarker methodology. This document provides a guideline for conducting an extensive literature search on BFIs, which will provide the basis to systematically validate BFIs. This procedure will help to prioritize future work on the identification of new potential biomarkers and on validating these as well as other biomarker candidates, thereby providing better tools for future studies in nutrition and health.

  17. It's in your blood: spectral biomarker candidates for urinary bladder cancer from automated FTIR spectroscopy.

    Science.gov (United States)

    Ollesch, Julian; Heinze, Margot; Heise, H Michael; Behrens, Thomas; Brüning, Thomas; Gerwert, Klaus

    2014-04-01

    Blood samples of urinary bladder cancer (UBC) patients and patients with urinary tract infection were analysed with advanced automated high throughput Fourier transform infrared (HT-FTIR)-spectroscopy. Thin dried film samples were robotically prepared on multi-well titer plates (MTP) for absorbance measurements in transmission mode. Within the absorbance, 1st and 2nd derivative spectra of serum and two plasma preparations, discriminative patterns were identified and validated using bioinformatic tools. The optimal spectral resolution for data acquisition was determined. An accurate discrimination of the patient groups was achieved with three different independent spectral variable sets. The HT-FTIR blood test may support future clinical diagnostics. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. Whole animal automated platform for drug discovery against multi-drug resistant Staphylococcus aureus.

    Directory of Open Access Journals (Sweden)

    Rajmohan Rajamuthiah

    Full Text Available Staphylococcus aureus, the leading cause of hospital-acquired infections in the United States, is also pathogenic to the model nematode Caenorhabditis elegans. The C. elegans-S. aureus infection model was previously carried out on solid agar plates where the bacteriovorous C. elegans feeds on a lawn of S. aureus. However, agar-based assays are not amenable to large scale screens for antibacterial compounds. We have developed a high throughput liquid screening assay that uses robotic instrumentation to dispense a precise amount of methicillin resistant S. aureus (MRSA and worms in 384-well assay plates, followed by automated microscopy and image analysis. In validation of the liquid assay, an MRSA cell wall defective mutant, MW2ΔtarO, which is attenuated for killing in the agar-based assay, was found to be less virulent in the liquid assay. This robust assay with a Z'-factor consistently greater than 0.5 was utilized to screen the Biomol 4 compound library consisting of 640 small molecules with well characterized bioactivities. As proof of principle, 27 of the 30 clinically used antibiotics present in the library conferred increased C. elegans survival and were identified as hits in the screen. Surprisingly, the antihelminthic drug closantel was also identified as a hit in the screen. In further studies, we confirmed the anti-staphylococcal activity of closantel against vancomycin-resistant S. aureus isolates and other Gram-positive bacteria. The liquid C. elegans-S. aureus assay described here allows screening for anti-staphylococcal compounds that are not toxic to the host.

  19. Whole animal automated platform for drug discovery against multi-drug resistant Staphylococcus aureus.

    Science.gov (United States)

    Rajamuthiah, Rajmohan; Fuchs, Beth Burgwyn; Jayamani, Elamparithi; Kim, Younghoon; Larkins-Ford, Jonah; Conery, Annie; Ausubel, Frederick M; Mylonakis, Eleftherios

    2014-01-01

    Staphylococcus aureus, the leading cause of hospital-acquired infections in the United States, is also pathogenic to the model nematode Caenorhabditis elegans. The C. elegans-S. aureus infection model was previously carried out on solid agar plates where the bacteriovorous C. elegans feeds on a lawn of S. aureus. However, agar-based assays are not amenable to large scale screens for antibacterial compounds. We have developed a high throughput liquid screening assay that uses robotic instrumentation to dispense a precise amount of methicillin resistant S. aureus (MRSA) and worms in 384-well assay plates, followed by automated microscopy and image analysis. In validation of the liquid assay, an MRSA cell wall defective mutant, MW2ΔtarO, which is attenuated for killing in the agar-based assay, was found to be less virulent in the liquid assay. This robust assay with a Z'-factor consistently greater than 0.5 was utilized to screen the Biomol 4 compound library consisting of 640 small molecules with well characterized bioactivities. As proof of principle, 27 of the 30 clinically used antibiotics present in the library conferred increased C. elegans survival and were identified as hits in the screen. Surprisingly, the antihelminthic drug closantel was also identified as a hit in the screen. In further studies, we confirmed the anti-staphylococcal activity of closantel against vancomycin-resistant S. aureus isolates and other Gram-positive bacteria. The liquid C. elegans-S. aureus assay described here allows screening for anti-staphylococcal compounds that are not toxic to the host.

  20. Release of Tissue-specific Proteins into Coronary Perfusate as a Model for Biomarker Discovery in Myocardial Ischemia/Reperfusion Injury

    DEFF Research Database (Denmark)

    Cordwell, Stuart; Edwards, Alistair; Liddy, Kiersten

    2012-01-01

    of 60 min reperfusion following brief, reversible ischemia (15 min; 15I/60R) for comparison with irreversible I/R (60I/60R). Perfusate proteins were separated using two-dimensional gel electrophoresis (2-DE) and identified by mass spectrometry (MS), revealing 26 tissue-specific proteins released during...... reperfusion post-15I. Proteins released during irreversible I/R (60I/60R) were profiled using gel-based (2-DE and one-dimensional gel electrophoresis coupled to liquid chromatography and tandem mass spectrometry; geLC–MS) and gel-free (LC–MS/MS) methods. A total of 192 tissue-specific proteins were identified......Diagnosis of acute coronary syndromes is based on protein biomarkers, such as the cardiac troponins (cTnI/cTnT) and creatine kinase (CK-MB) that are released into the circulation. Biomarker discovery is focused on identifying very low abundance tissue-derived analytes from within albumin...

  1. Biomarkers in Veterinary Medicine.

    Science.gov (United States)

    Myers, Michael J; Smith, Emily R; Turfle, Phillip G

    2017-02-08

    This article summarizes the relevant definitions related to biomarkers; reviews the general processes related to biomarker discovery and ultimate acceptance and use; and finally summarizes and reviews, to the extent possible, examples of the types of biomarkers used in animal species within veterinary clinical practice and human and veterinary drug development. We highlight opportunities for collaboration and coordination of research within the veterinary community and leveraging of resources from human medicine to support biomarker discovery and validation efforts for veterinary medicine.

  2. The use of multiplexed MRM for the discovery of biomarkers to differentiate iron-deficiency anemia from anemia of inflammation.

    Science.gov (United States)

    Domanski, Dominik; Cohen Freue, Gabriela V; Sojo, Luis; Kuzyk, Michael A; Ratkay, Leslie; Parker, Carol E; Goldberg, Y Paul; Borchers, Christoph H

    2012-06-27

    In this study we demonstrate the use of a multiplexed MRM-based assay to distinguish among normal (NL) and iron-metabolism disorder mouse models, particularly, iron-deficiency anemia (IDA), inflammation (INFL), and inflammation and anemia (INFL+IDA). Our initial panel of potential biomarkers was based on the analysis of 14 proteins expressed by candidate genes involved in iron transport and metabolism. Based on this study, we were able to identify a panel of 8 biomarker proteins: apolipoprotein A4 (APO4), transferrin, transferrin receptor 1, ceruloplasmin, haptoglobin, lactoferrin, hemopexin, and matrix metalloproteinase-8 (MMP8) that clearly distinguish among the normal and disease models. Within this set of proteins, transferrin showed the best individual classification accuracy over all samples (72%) and within the NL group (94%). Compared to the best single-protein biomarker, transferrin, the use of the composite 8-protein biomarker panel improved the classification accuracy from 94% to 100% in the NL group, from 50% to 72% in the INFL group, from 66% to 96% in the IDA group, and from 79% to 83% in the INFL+IDA group. Based on these findings, validation of the utility of this potentially important biomarker panel in human samples in an effort to differentiate IDA, inflammation, and combinations thereof, is now warranted. This article is part of a Special Section entitled: Understanding genome regulation and genetic diversity by mass spectrometry. Copyright © 2011 Elsevier B.V. All rights reserved.

  3. Sparse multi-block PLSR for biomarker discovery when integrating data from LC-MS and NMR metabolomics

    DEFF Research Database (Denmark)

    Karaman, Ibrahim; Nørskov, Natalja; Yde, Christian Clement

    2015-01-01

    The objective of this study was to implement a multivariate method which analyzes multi-block metabolomics data and performs variable selection in order to discover potential biomarkers, simultaneously. We call this method sparse multi-block partial least squares regression (Sparse MBPLSR...... and stability of the selected variables. The performance of the method was evaluated using a simulated data set and a multi-block data set from a dietary intervention study with pigs used as model for humans. The objective of the study was to investigate the biochemical effects in plasma after dietary...... intervention with breads varying in types of dietary fiber and to identify potential biomarkers. By introducing Sparse MBPLSR, we aimed at identifying the biomarkers where data from LC–MS and NMR instruments were analyzed simultaneously and therefore in addition we intended to explore the relationships among...

  4. The Discovery of Novel Genomic, Transcriptomic, and Proteomic Biomarkers in Cardiovascular and Peripheral Vascular Disease: The State of the Art

    Directory of Open Access Journals (Sweden)

    Stefano de Franciscis

    2016-01-01

    Full Text Available Cardiovascular disease (CD and peripheral vascular disease (PVD are leading causes of mortality and morbidity in western countries and also responsible of a huge burden in terms of disability, functional decline, and healthcare costs. Biomarkers are measurable biological elements that reflect particular physiological or pathological states or predisposition towards diseases and they are currently widely studied in medicine and especially in CD. In this context, biomarkers can also be used to assess the severity or the evolution of several diseases, as well as the effectiveness of particular therapies. Genomics, transcriptomics, and proteomics have opened new windows on disease phenomena and may permit in the next future an effective development of novel diagnostic and prognostic medicine in order to better prevent or treat CD. This review will consider the current evidence of novel biomarkers with clear implications in the improvement of risk assessment, prevention strategies, and medical decision making in the field of CD.

  5. Biomarker discovery with SELDI-TOF MS in human urine associated with early renal injury: evaluation with computational analytical tools.

    NARCIS (Netherlands)

    Vanhoutte, K.J.A.; Laarakkers, C.M.; Marchiori, E.; Pickkers, P.; Wetzels, J.F.M.; Willems, J.L.; Heuvel, L.P.W.J. van den; Russel, F.G.M.; Masereeuw, R.

    2007-01-01

    BACKGROUND: Urine proteomics is one of the key emerging technologies to discover new biomarkers for renal disease, which may be used in the early diagnosis, prognosis and treatment of patients. In the present study, we validated surface-enhanced laser desorption/ionization time-of-flight mass

  6. Comparison of self-reported dietary intakes from the Automated Self-Administered 24-h recall, 4-d food records, and food-frequency questionnaires against recovery biomarkers.

    Science.gov (United States)

    Park, Yikyung; Dodd, Kevin W; Kipnis, Victor; Thompson, Frances E; Potischman, Nancy; Schoeller, Dale A; Baer, David J; Midthune, Douglas; Troiano, Richard P; Bowles, Heather; Subar, Amy F

    2018-01-01

    A limited number of studies have evaluated self-reported dietary intakes against objective recovery biomarkers. The aim was to compare dietary intakes of multiple Automated Self-Administered 24-h recalls (ASA24s), 4-d food records (4DFRs), and food-frequency questionnaires (FFQs) against recovery biomarkers and to estimate the prevalence of under- and overreporting. Over 12 mo, 530 men and 545 women, aged 50-74 y, were asked to complete 6 ASA24s (2011 version), 2 unweighed 4DFRs, 2 FFQs, two 24-h urine collections (biomarkers for protein, potassium, and sodium intakes), and 1 administration of doubly labeled water (biomarker for energy intake). Absolute and density-based energy-adjusted nutrient intakes were calculated. The prevalence of under- and overreporting of self-report against biomarkers was estimated. Ninety-two percent of men and 87% of women completed ≥3 ASA24s (mean ASA24s completed: 5.4 and 5.1 for men and women, respectively). Absolute intakes of energy, protein, potassium, and sodium assessed by all self-reported instruments were systematically lower than those from recovery biomarkers, with underreporting greater for energy than for other nutrients. On average, compared with the energy biomarker, intake was underestimated by 15-17% on ASA24s, 18-21% on 4DFRs, and 29-34% on FFQs. Underreporting was more prevalent on FFQs than on ASA24s and 4DFRs and among obese individuals. Mean protein and sodium densities on ASA24s, 4DFRs, and FFQs were similar to biomarker values, but potassium density on FFQs was 26-40% higher, leading to a substantial increase in the prevalence of overreporting compared with absolute potassium intake. Although misreporting is present in all self-report dietary assessment tools, multiple ASA24s and a 4DFR provided the best estimates of absolute dietary intakes for these few nutrients and outperformed FFQs. Energy adjustment improved estimates from FFQs for protein and sodium but not for potassium. The ASA24, which now can be

  7. Biomarker candidate discovery in Atlantic cod (Gadus morhua) continuously exposed to North Sea produced water from egg to fry

    DEFF Research Database (Denmark)

    Bohne-Kjersem, Anneli; Bache, Nicolai; Meier, Sonnich

    2010-01-01

    were able to compare the induced changes by PW to the mode of action of oestrogens. Changes in the proteome in response to exposure in whole cod fry (approximately 80 days post-hatching, dph) were detected by two-dimensional gel electrophoresis and image analysis and identified by MALDI-ToF-ToF mass...... spectrometry, using a newly developed cod EST database and the NCBI database. Many of the protein changes occurred at low levels (0.01% and 0.1% PW) of exposure, indicating putative biological responses at lower levels than previously detected. Using discriminant analysis, we identified a set of protein...... changes that may be useful as biomarker candidates of produced water (PW) and oestradiol exposure in Atlantic cod fry. The biomarker candidates discovered in this study may, following validation, prove effective as diagnostic tools in monitoring exposure and effects of discharges from the petroleum...

  8. Nonylphenol Toxicity Evaluation and Discovery of Biomarkers in Rat Urine by a Metabolomics Strategy through HPLC-QTOF-MS

    Directory of Open Access Journals (Sweden)

    Yan-Xin Zhang

    2016-05-01

    Full Text Available Nonylphenol (NP was quantified using liquid chromatography tandem mass spectrometry (LC-MS/MS in the urine and plasma of rats treated with 0, 50, and 250 mg/kg/day of NP for four consecutive days. A urinary metabolomic strategy was originally implemented by high performance liquid chromatography time of flight mass spectrometry (HPLC-QTOF-MS to explore the toxicological effects of NP and determine the overall alterations in the metabolite profiles so as to find potential biomarkers. It is essential to point out that from the observation, the metabolic data were clearly clustered and separated for the three groups. To further identify differentiated metabolites, multivariate analysis, including principal component analysis (PCA, orthogonal partial least-squares discriminant analysis (OPLS-DA, high-resolution MS/MS analysis, as well as searches of Metlin and Massbank databases, were conducted on a series of metabolites between the control and dose groups. Finally, five metabolites, including glycine, glycerophosphocholine, 5-hydroxytryptamine, malonaldehyde (showing an upward trend, and tryptophan (showing a downward trend, were identified as the potential urinary biomarkers of NP-induced toxicity. In order to validate the reliability of these potential biomarkers, an independent validation was performed by using the multiple reaction monitoring (MRM-based targeted approach. The oxidative stress reflected by urinary 8-oxo-deoxyguanosine (8-oxodG levels was elevated in individuals highly exposed to NP, supporting the hypothesis that mitochondrial dysfunction was a result of xenoestrogen accumulation. This study reveals a promising approach to find biomarkers to assist researchers in monitoring NP.

  9. Nonylphenol Toxicity Evaluation and Discovery of Biomarkers in Rat Urine by a Metabolomics Strategy through HPLC-QTOF-MS

    Science.gov (United States)

    Zhang, Yan-Xin; Yang, Xin; Zou, Pan; Du, Peng-Fei; Wang, Jing; Jin, Fen; Jin, Mao-Jun; She, Yong-Xin

    2016-01-01

    Nonylphenol (NP) was quantified using liquid chromatography tandem mass spectrometry (LC-MS/MS) in the urine and plasma of rats treated with 0, 50, and 250 mg/kg/day of NP for four consecutive days. A urinary metabolomic strategy was originally implemented by high performance liquid chromatography time of flight mass spectrometry (HPLC-QTOF-MS) to explore the toxicological effects of NP and determine the overall alterations in the metabolite profiles so as to find potential biomarkers. It is essential to point out that from the observation, the metabolic data were clearly clustered and separated for the three groups. To further identify differentiated metabolites, multivariate analysis, including principal component analysis (PCA), orthogonal partial least-squares discriminant analysis (OPLS-DA), high-resolution MS/MS analysis, as well as searches of Metlin and Massbank databases, were conducted on a series of metabolites between the control and dose groups. Finally, five metabolites, including glycine, glycerophosphocholine, 5-hydroxytryptamine, malonaldehyde (showing an upward trend), and tryptophan (showing a downward trend), were identified as the potential urinary biomarkers of NP-induced toxicity. In order to validate the reliability of these potential biomarkers, an independent validation was performed by using the multiple reaction monitoring (MRM)-based targeted approach. The oxidative stress reflected by urinary 8-oxo-deoxyguanosine (8-oxodG) levels was elevated in individuals highly exposed to NP, supporting the hypothesis that mitochondrial dysfunction was a result of xenoestrogen accumulation. This study reveals a promising approach to find biomarkers to assist researchers in monitoring NP. PMID:27187439

  10. Metabolic profiling of yeast culture using gas chromatography coupled with orthogonal acceleration accurate mass time-of-flight mass spectrometry: application to biomarker discovery.

    Science.gov (United States)

    Kondo, Elsuida; Marriott, Philip J; Parker, Rhiannon M; Kouremenos, Konstantinos A; Morrison, Paul; Adams, Mike

    2014-01-07

    Yeast and yeast cultures are frequently used as additives in diets of dairy cows. Beneficial effects from the inclusion of yeast culture in diets for dairy mammals have been reported, and the aim of this study was to develop a comprehensive analytical method for the accurate mass identification of the 'global' metabolites in order to differentiate a variety of yeasts at varying growth stages (Diamond V XP, Yea-Sacc and Levucell). Microwave-assisted derivatization for metabolic profiling is demonstrated through the analysis of differing yeast samples developed for cattle feed, which include a wide range of metabolites of interest covering a large range of compound classes. Accurate identification of the components was undertaken using GC-oa-ToFMS (gas chromatography-orthogonal acceleration-time-of-flight mass spectrometry), followed by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) for data reduction and biomarker discovery. Semi-quantification (fold changes in relative peak areas) was reported for metabolites identified as possible discriminative biomarkers (p-value 2), including D-ribose (four fold decrease), myo-inositol (five fold increase), L-phenylalanine (three fold increase), glucopyranoside (two fold increase), fructose (three fold increase) and threitol (three fold increase) respectively. Copyright © 2013 Elsevier B.V. All rights reserved.

  11. Discovery of coding genetic variants influencing diabetes-related serum biomarkers and their impact on risk of type 2 diabetes.

    Science.gov (United States)

    Ahluwalia, Tarunveer Singh; Allin, Kristine Højgaard; Sandholt, Camilla Helene; Sparsø, Thomas Hempel; Jørgensen, Marit Eika; Rowe, Michael; Christensen, Cramer; Brandslund, Ivan; Lauritzen, Torsten; Linneberg, Allan; Husemoen, Lise Lotte; Jørgensen, Torben; Hansen, Torben; Grarup, Niels; Pedersen, Oluf

    2015-04-01

    Type 2 diabetes (T2D) prevalence is spiraling globally, and knowledge of its pathophysiological signatures is crucial for a better understanding and treatment of the disease. We aimed to discover underlying coding genetic variants influencing fasting serum levels of nine biomarkers associated with T2D: adiponectin, C-reactive protein, ferritin, heat shock 70-kDa protein 1B, IGF binding protein 1 and IGF binding protein 2, IL-18, IL-2 receptor-α, and leptin. A population-based sample of 6215 adult Danes was genotyped for 16 340 coding single-nucleotide polymorphisms and were tested for association with each biomarker. Identified loci were tested for association with T2D through a large-scale meta-analysis involving up to 17 024 T2D cases and up to 64 186 controls. We discovered 11 associations between single-nucleotide polymorphisms and five distinct biomarkers at a study-wide P < 3.4 × 10(-7). Nine associations were novel: IL18: BIRC6, RAD17, MARVELD2; ferritin: F5; IGF binding protein 1: SERPING1, KLKB, GCKR, CELSR2, and heat shock 70-kDa protein 1B: CFH. Three of the identified loci (CELSR2, HNF1A, and GCKR) were significantly associated with T2D, of which the association with the CELSR2 locus has not been shown previously. The identified loci influence processes related to insulin signaling, cell communication, immune function, apoptosis, DNA repair, and oxidative stress, all of which could provide a rationale for novel diabetes therapeutic strategies.

  12. Phospholipid fatty acid biomarkers in a freshwater periphyton community exposed to uranium: discovery by non-linear statistical learning

    Energy Technology Data Exchange (ETDEWEB)

    Webb-Robertson, Bobbie-Jo M.; Bunn, Amoret L.; Bailey, Vanessa L.

    2011-01-01

    Phospholipid fatty acids (PLFA) have been widely used to characterize environmental microbial communities, generating community profiles that can distinguish phylogenetic or functional groups within the community. The poor specificity of organism groups with fatty acid biomarkers in the classic PLFA-microorganism associations is a confounding factor in many of the statistical classification/clustering approaches traditionally used to interpret PLFA profiles. In this paper we demonstrate that non-linear statistical learning methods, such as a support vector machine (SVM), can more accurately find patterns related to uranyl nitrate exposure in a freshwater periphyton community than linear methods, such as partial least squares discriminant analysis. In addition, probabilistic models of exposure can be derived from the identified lipid biomarkers to demonstrate the potential model-based approach that could be used in remediation. The SVM probability model separates dose groups at accuracies of ~87.0%, ~71.4%, ~87.5%, and 100% for the four groups; Control (non-amended system), low-dose (amended at 10 µg U L-1), medium dose (amended at 100 µg U L-1), and high dose (500 µg U L-1). The SVM model achieved an overall cross-validated classification accuracy of ~87% in contrast to ~59% for the best linear classifier.

  13. Biomarker Discovery Based on Hybrid Optimization Algorithm and Artificial Neural Networks on Microarray Data for Cancer Classification.

    Science.gov (United States)

    Moteghaed, Niloofar Yousefi; Maghooli, Keivan; Pirhadi, Shiva; Garshasbi, Masoud

    2015-01-01

    The improvement of high-through-put gene profiling based microarrays technology has provided monitoring the expression value of thousands of genes simultaneously. Detailed examination of changes in expression levels of genes can help physicians to have efficient diagnosing, classification of tumors and cancer's types as well as effective treatments. Finding genes that can classify the group of cancers correctly based on hybrid optimization algorithms is the main purpose of this paper. In this paper, a hybrid particle swarm optimization and genetic algorithm method are used for gene selection and also artificial neural network (ANN) is adopted as the classifier. In this work, we have improved the ability of the algorithm for the classification problem by finding small group of biomarkers and also best parameters of the classifier. The proposed approach is tested on three benchmark gene expression data sets: Blood (acute myeloid leukemia, acute lymphoblastic leukemia), colon and breast datasets. We used 10-fold cross-validation to achieve accuracy and also decision tree algorithm to find the relation between the biomarkers for biological point of view. To test the ability of the trained ANN models to categorize the cancers, we analyzed additional blinded samples that were not previously used for the training procedure. Experimental results show that the proposed method can reduce the dimension of the data set and confirm the most informative gene subset and improve classification accuracy with best parameters based on datasets.

  14. Discovery and validation of plasma biomarkers for major depressive disorder classification based on liquid chromatography-mass spectrometry.

    Science.gov (United States)

    Liu, Xinyu; Zheng, Peng; Zhao, Xinjie; Zhang, Yuqing; Hu, Chunxiu; Li, Jia; Zhao, Jieyu; Zhou, Jingjing; Xie, Peng; Xu, Guowang

    2015-05-01

    Major depressive disorder (MDD) is a debilitating mental disease with a pronounced impact on the quality of life of many people; however, it is still difficult to diagnose MDD accurately. In this study, a nontargeted metabolomics approach based on ultra-high-performance liquid chromatography equipped with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) was used to find the differential metabolites in plasma samples from patients with MDD and healthy controls. Furthermore, a validation analysis focusing on the differential metabolites was performed in another batch of samples using a targeted approach based on the dynamic multiple reactions monitoring method. Levels of acyl carnitines, ether lipids, and tryptophan pronouncedly decreased, whereas LPCs, LPEs, and PEs markedly increased in MDD subjects as compared with the healthy controls. Disturbed pathways, mainly located in acyl carnitine metabolism, lipid metabolism, and tryptophan metabolism, were clearly brought to light in MDD subjects. The binary logistic regression result showed that carnitine C10:1, PE-O 36:5, LPE 18:1 sn-2, and tryptophan can be used as a combinational biomarker to distinguish not only moderate but also severe MDD from healthy control with good sensitivity and specificity. Our findings, on one hand, provide critical insight into the pathological mechanism of MDD and, on the other hand, supply a combinational biomarker to aid the diagnosis of MDD in clinical usage.

  15. Semi-automated high-throughput fluorescent intercalator displacement-based discovery of cytotoxic DNA binding agents from a large compound library.

    Science.gov (United States)

    Glass, Lateca S; Bapat, Aditi; Kelley, Mark R; Georgiadis, Millie M; Long, Eric C

    2010-03-01

    High-throughput fluorescent intercalator displacement (HT-FID) was adapted to the semi-automated screening of a commercial compound library containing 60,000 molecules resulting in the discovery of cytotoxic DNA-targeted agents. Although commercial libraries are routinely screened in drug discovery efforts, the DNA binding potential of the compounds they contain has largely been overlooked. HT-FID led to the rapid identification of a number of compounds for which DNA binding properties were validated through demonstration of concentration-dependent DNA binding and increased thermal melting of A/T- or G/C-rich DNA sequences. Selected compounds were assayed further for cell proliferation inhibition in glioblastoma cells. Seven distinct compounds emerged from this screening procedure that represent structures unknown previously to be capable of targeting DNA leading to cell death. These agents may represent structures worthy of further modification to optimally explore their potential as cytotoxic anti-cancer agents. In addition, the general screening strategy described may find broader impact toward the rapid discovery of DNA targeted agents with biological activity. Copyright 2010 Elsevier Ltd. All rights reserved.

  16. Metabolomic study of lipids in serum for biomarker discovery in Alzheimer's disease using direct infusion mass spectrometry.

    Science.gov (United States)

    González-Domínguez, R; García-Barrera, T; Gómez-Ariza, J L

    2014-09-01

    In this study, we demonstrated the potential of direct infusion mass spectrometry for the lipidomic characterization of Alzheimer's disease. Serum samples were extracted for lipids recovery, and directly analyzed using an electrospray source. Metabolomic fingerprints were subjected to multivariate analysis in order to discriminate between groups of patients and healthy controls, and then some key-compounds were identified as possible markers of Alzheimer's disease. Major differences were found in lipids, although some low molecular weight metabolites also showed significant changes. Thus, important metabolic pathways involved in neurodegeneration could be studied on the basis of these perturbations, such as membrane breakdown (phospholipids and diacylglycerols), oxidative stress (prostaglandins, imidazole and histidine), alterations in neurotransmission systems (oleamide and putrescine) and hyperammonaemia (guanidine and arginine). Moreover, it is noteworthy that some of these potential biomarkers have not been previously described for Alzheimer's disease. Copyright © 2014 Elsevier B.V. All rights reserved.

  17. The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set

    Science.gov (United States)

    Milioli, Heloisa Helena; Vimieiro, Renato; Riveros, Carlos; Tishchenko, Inna; Berretta, Regina; Moscato, Pablo

    2015-01-01

    Background The prediction of breast cancer intrinsic subtypes has been introduced as a valuable strategy to determine patient diagnosis and prognosis, and therapy response. The PAM50 method, based on the expression levels of 50 genes, uses a single sample predictor model to assign subtype labels to samples. Intrinsic errors reported within this assay demonstrate the challenge of identifying and understanding the breast cancer groups. In this study, we aim to: a) identify novel biomarkers for subtype individuation by exploring the competence of a newly proposed method named CM1 score, and b) apply an ensemble learning, as opposed to the use of a single classifier, for sample subtype assignment. The overarching objective is to improve class prediction. Methods and Findings The microarray transcriptome data sets used in this study are: the METABRIC breast cancer data recorded for over 2000 patients, and the public integrated source from ROCK database with 1570 samples. We first computed the CM1 score to identify the probes with highly discriminative patterns of expression across samples of each intrinsic subtype. We further assessed the ability of 42 selected probes on assigning correct subtype labels using 24 different classifiers from the Weka software suite. For comparison, the same method was applied on the list of 50 genes from the PAM50 method. Conclusions The CM1 score portrayed 30 novel biomarkers for predicting breast cancer subtypes, with the confirmation of the role of 12 well-established genes. Intrinsic subtypes assigned using the CM1 list and the ensemble of classifiers are more consistent and homogeneous than the original PAM50 labels. The new subtypes show accurate distributions of current clinical markers ER, PR and HER2, and survival curves in the METABRIC and ROCK data sets. Remarkably, the paradoxical attribution of the original labels reinforces the limitations of employing a single sample classifiers to predict breast cancer intrinsic subtypes

  18. SELDI-TOF MS-based discovery of a biomarker in Cucumis sativus seeds exposed to CuO nanoparticles.

    Science.gov (United States)

    Moon, Young-Sun; Park, Eun-Sil; Kim, Tae-Oh; Lee, Hoi-Seon; Lee, Sung-Eun

    2014-11-01

    Metal oxide nanoparticles (NPs) can inhibit plant seed germination and root elongation via the release of metal ions. In the present study, two acute phytotoxicity tests, seed germination and root elongation tests, were conducted on cucumber seeds (Cucumis sativus) treated with bulk copper oxide (CuO) and CuO NPs. Two concentrations of bulk CuO and CuO NPs, 200 and 600ppm, were used to test the inhibition rate of root germination; both concentrations of bulk CuO weakly inhibited seed germination, whereas CuO NPs significantly inhibited germination, showing a low germination rate of 23.3% at 600ppm. Root elongation tests demonstrated that CuO NPs were much stronger inhibitors than bulk CuO. SELDI-TOF MS analysis showed that 34 proteins were differentially expressed in cucumber seeds after exposure to CuO NPs, with the expression patterns of at least 9 proteins highly differing from those in seeds treated with bulk CuO and in control plants. Therefore, these 9 proteins were used to identify CuO NP-specific biomarkers in cucumber plants exposed to CuO NPs. A 5977-m/z protein was the most distinguishable biomarker for determining phytotoxicity by CuO NPs. Principal component analysis (PCA) of the SELDI-TOF MS results showed variability in the modes of inhibitory action on cucumber seeds and roots. To our knowledge, this is the first study to demonstrate that the phytotoxic effect of metal oxide NPs on plants is not caused by the same mode of action as other toxins. Copyright © 2014 Elsevier B.V. All rights reserved.

  19. Biomarkers of safety and immune protection for genetically modified live attenuated leishmania vaccines against visceral leishmaniasis - discovery and implications.

    Science.gov (United States)

    Gannavaram, Sreenivas; Dey, Ranadhir; Avishek, Kumar; Selvapandiyan, Angamuthu; Salotra, Poonam; Nakhasi, Hira L

    2014-01-01

    Despite intense efforts there is no safe and efficacious vaccine against visceral leishmaniasis, which is fatal and endemic in many tropical countries. A major shortcoming in the vaccine development against blood-borne parasitic agents such as Leishmania is the inadequate predictive power of the early immune responses mounted in the host against the experimental vaccines. Often immune correlates derived from in-bred animal models do not yield immune markers of protection that can be readily extrapolated to humans. The limited efficacy of vaccines based on DNA, subunit, heat killed parasites has led to the realization that acquisition of durable immunity against the protozoan parasites requires a controlled infection with a live attenuated organism. Recent success of irradiated malaria parasites as a vaccine candidate further strengthens this approach to vaccination. We developed several gene deletion mutants in Leishmania donovani as potential live attenuated vaccines and reported extensively on the immunogenicity of LdCentrin1 deleted mutant in mice, hamsters, and dogs. Additional limited studies using genetically modified live attenuated Leishmania parasites as vaccine candidates have been reported. However, for the live attenuated parasite vaccines, the primary barrier against widespread use remains the absence of clear biomarkers associated with protection and safety. Recent studies in evaluation of vaccines, e.g., influenza and yellow fever vaccines, using systems biology tools demonstrated the power of such strategies in understanding the immunological mechanisms that underpin a protective phenotype. Applying similar tools in isolated human tissues such as PBMCs from healthy individuals infected with live attenuated parasites such as LdCen(-/-) in vitro followed by human microarray hybridization experiments will enable us to understand how early vaccine-induced gene expression profiles and the associated immune responses are coordinately regulated in normal

  20. Biomarkers of Safety and Immune Protection for Genetically Modified Live Attenuated Leishmania Vaccines Against Visceral Leishmaniasis – Discovery and Implications

    Science.gov (United States)

    Gannavaram, Sreenivas; Dey, Ranadhir; Avishek, Kumar; Selvapandiyan, Angamuthu; Salotra, Poonam; Nakhasi, Hira L.

    2014-01-01

    Despite intense efforts there is no safe and efficacious vaccine against visceral leishmaniasis, which is fatal and endemic in many tropical countries. A major shortcoming in the vaccine development against blood-borne parasitic agents such as Leishmania is the inadequate predictive power of the early immune responses mounted in the host against the experimental vaccines. Often immune correlates derived from in-bred animal models do not yield immune markers of protection that can be readily extrapolated to humans. The limited efficacy of vaccines based on DNA, subunit, heat killed parasites has led to the realization that acquisition of durable immunity against the protozoan parasites requires a controlled infection with a live attenuated organism. Recent success of irradiated malaria parasites as a vaccine candidate further strengthens this approach to vaccination. We developed several gene deletion mutants in Leishmania donovani as potential live attenuated vaccines and reported extensively on the immunogenicity of LdCentrin1 deleted mutant in mice, hamsters, and dogs. Additional limited studies using genetically modified live attenuated Leishmania parasites as vaccine candidates have been reported. However, for the live attenuated parasite vaccines, the primary barrier against widespread use remains the absence of clear biomarkers associated with protection and safety. Recent studies in evaluation of vaccines, e.g., influenza and yellow fever vaccines, using systems biology tools demonstrated the power of such strategies in understanding the immunological mechanisms that underpin a protective phenotype. Applying similar tools in isolated human tissues such as PBMCs from healthy individuals infected with live attenuated parasites such as LdCen−/− in vitro followed by human microarray hybridization experiments will enable us to understand how early vaccine-induced gene expression profiles and the associated immune responses are coordinately regulated in normal

  1. Discovery proteomics and nonparametric modeling pipeline in the development of a candidate biomarker panel for dengue hemorrhagic fever.

    Science.gov (United States)

    Brasier, Allan R; Garcia, Josefina; Wiktorowicz, John E; Spratt, Heidi M; Comach, Guillermo; Ju, Hyunsu; Recinos, Adrian; Soman, Kizhake; Forshey, Brett M; Halsey, Eric S; Blair, Patrick J; Rocha, Claudio; Bazan, Isabel; Victor, Sundar S; Wu, Zheng; Stafford, Susan; Watts, Douglas; Morrison, Amy C; Scott, Thomas W; Kochel, Tadeusz J

    2012-02-01

    Secondary dengue viral infection can produce capillary leakage associated with increased mortality known as dengue hemorrhagic fever (DHF). Because the mortality of DHF can be reduced by early detection and intensive support, improved methods for its detection are needed. We applied multidimensional protein profiling to predict outcomes in a prospective dengue surveillance study in South America. Plasma samples taken from initial clinical presentation of acute dengue infection were subjected to proteomics analyses using ELISA and a recently developed biofluid analysis platform. Demographics, clinical laboratory measurements, nine cytokines, and 419 plasma proteins collected at the time of initial presentation were compared between the DF and DHF outcomes. Here, the subject's gender, clinical parameters, two cytokines, and 42 proteins discriminated between the outcomes. These factors were reduced by multivariate adaptive regression splines (MARS) that a highly accurate classification model based on eight discriminant features with an area under the receiver operator curve (AUC) of 0.999. Model analysis indicated that the feature-outcome relationship were nonlinear. Although this DHF risk model will need validation in a larger cohort, we conclude that approaches to develop predictive biomarker models for disease outcome will need to incorporate nonparametric modeling approaches. © 2012 Wiley Periodicals, Inc.

  2. In-depth cDNA library sequencing provides quantitative gene expression profiling in cancer biomarker discovery.

    Science.gov (United States)

    Yang, Wanling; Ying, Dingge; Lau, Yu-Lung

    2009-06-01

    Quantitative gene expression analysis plays an important role in identifying differentially expressed genes in various pathological states, gene expression regulation and co-regulation, shedding light on gene functions. Although microarray is widely used as a powerful tool in this regard, it is suboptimal quantitatively and unable to detect unknown gene variants. Here we demonstrated effective detection of differential expression and co-regulation of certain genes by expressed sequence tag analysis using a selected subset of cDNA libraries. We discussed the issues of sequencing depth and library preparation, and propose that increased sequencing depth and improved preparation procedures may allow detection of many expression features for less abundant gene variants. With the reduction of sequencing cost and the emerging of new generation sequencing technology, in-depth sequencing of cDNA pools or libraries may represent a better and powerful tool in gene expression profiling and cancer biomarker detection. We also propose using sequence-specific subtraction to remove hundreds of the most abundant housekeeping genes to increase sequencing depth without affecting relative expression ratio of other genes, as transcripts from as few as 300 most abundantly expressed genes constitute about 20% of the total transcriptome. In-depth sequencing also represents a unique advantage of detecting unknown forms of transcripts, such as alternative splicing variants, fusion genes, and regulatory RNAs, as well as detecting mutations and polymorphisms that may play important roles in disease pathogenesis.

  3. Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data.

    Science.gov (United States)

    Posma, Joram M; Garcia-Perez, Isabel; Ebbels, Timothy M D; Lindon, John C; Stamler, Jeremiah; Elliott, Paul; Holmes, Elaine; Nicholson, Jeremy K

    2018-02-27

    Metabolism is altered by genetics, diet, disease status, environment, and many other factors. Modeling either one of these is often done without considering the effects of the other covariates. Attributing differences in metabolic profile to one of these factors needs to be done while controlling for the metabolic influence of the rest. We describe here a data analysis framework and novel confounder-adjustment algorithm for multivariate analysis of metabolic profiling data. Using simulated data, we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods. Covariate-adjusted projections to latent structures (CA-PLS) are exemplified here using a large-scale metabolic phenotyping study of two Chinese populations at different risks for cardiovascular disease. Using CA-PLS, we find that some previously reported differences are actually associated with external factors and discover a number of previously unreported biomarkers linked to different metabolic pathways. CA-PLS can be applied to any multivariate data where confounding may be an issue and the confounder-adjustment procedure is translatable to other multivariate regression techniques.

  4. Closing the Loop: Automated Data-Driven Cognitive Model Discoveries Lead to Improved Instruction and Learning Gains

    Science.gov (United States)

    Liu, Ran; Koedinger, Kenneth R.

    2017-01-01

    As the use of educational technology becomes more ubiquitous, an enormous amount of learning process data is being produced. Educational data mining seeks to analyze and model these data, with the ultimate goal of improving learning outcomes. The most firmly grounded and rigorous evaluation of an educational data mining discovery is whether it…

  5. CSF biomarkers of Alzheimer's disease concord with amyloid-β PET and predict clinical progression: A study of fully automated immunoassays in BioFINDER and ADNI cohorts.

    Science.gov (United States)

    Hansson, Oskar; Seibyl, John; Stomrud, Erik; Zetterberg, Henrik; Trojanowski, John Q; Bittner, Tobias; Lifke, Valeria; Corradini, Veronika; Eichenlaub, Udo; Batrla, Richard; Buck, Katharina; Zink, Katharina; Rabe, Christina; Blennow, Kaj; Shaw, Leslie M

    2018-03-01

    We studied whether fully automated Elecsys cerebrospinal fluid (CSF) immunoassay results were concordant with positron emission tomography (PET) and predicted clinical progression, even with cutoffs established in an independent cohort. Cutoffs for Elecsys amyloid-β 1-42 (Aβ), total tau/Aβ(1-42), and phosphorylated tau/Aβ(1-42) were defined against [ 18 F]flutemetamol PET in Swedish BioFINDER (n = 277) and validated against [ 18 F]florbetapir PET in Alzheimer's Disease Neuroimaging Initiative (n = 646). Clinical progression in patients with mild cognitive impairment (n = 619) was studied. CSF total tau/Aβ(1-42) and phosphorylated tau/Aβ(1-42) ratios were highly concordant with PET classification in BioFINDER (overall percent agreement: 90%; area under the curve: 94%). The CSF biomarker statuses established by predefined cutoffs were highly concordant with PET classification in Alzheimer's Disease Neuroimaging Initiative (overall percent agreement: 89%-90%; area under the curves: 96%) and predicted greater 2-year clinical decline in patients with mild cognitive impairment. Strikingly, tau/Aβ ratios were as accurate as semiquantitative PET image assessment in predicting visual read-based outcomes. Elecsys CSF biomarker assays may provide reliable alternatives to PET in Alzheimer's disease diagnosis. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

  6. Biomarkers of safety and immune protection for genetically modified live attenuated Leishmania vaccines against visceral leishmaniasis-Discovery and implications

    Directory of Open Access Journals (Sweden)

    Sreenivas eGannavaram

    2014-05-01

    Full Text Available Despite intense efforts there is no safe and efficacious vaccine against visceral leishmaniasis, which is fatal and endemic in many tropical countries. A major shortcoming in the vaccine development against blood borne parasitic agents such as Leishmania is the inadequate predictive power of the early immune responses mounted in the host against the experimental vaccines. Often immune correlates derived from in-bred animal models do not yield immune markers of protection that can be readily extrapolated to humans. The limited efficacy of vaccines based on DNA, sub-unit, heat killed parasites has led to the realization that acquisition of durable immunity against the protozoan parasites requires a controlled infection with a live attenuated organism. Recent success of irradiated malaria parasites as a vaccine candidate further strengthens this approach to vaccination. We developed several gene deletion mutants in L. donovani as potential live attenuated vaccines and reported extensively on the immunogenicity of LdCentrin1 deleted mutant in mice, hamsters and dogs. Additional limited studies using genetically modified live attenuated Leishmania parasites as vaccine candidates have been reported. However, for the live attenuated parasite vaccines, the primary barrier against widespread use remains the absence of clear biomarkers associated with protection and safety. Recent studies in evaluation of vaccines e.g., influenza and yellow fever vaccines, using systems biology tools demonstrated the power of such strategies in understanding the immunological mechanisms that underpin a protective phenotype. Applying similar tools in isolated human tissues such as PBMCs from healthy individuals infected with live attenuated parasites such as LdCen1-/- in vitro followed by human microarray hybridization experiments will enable us to understand how early vaccine-induced gene expression profiles and the associated immune responses are coordinately regulated

  7. Autoantibody profiling on human proteome microarray for biomarker discovery in cerebrospinal fluid and sera of neuropsychiatric lupus.

    Directory of Open Access Journals (Sweden)

    Chaojun Hu

    Full Text Available Autoantibodies in cerebrospinal fluid (CSF from patients with neuropsychiatric systemic lupus erythematosus (NPSLE may be potential biomarkers for prediction, diagnosis, or prognosis of NPSLE. We used a human proteome microarray with~17,000 unique full-length human proteins to investigate autoantibodies associated with NPSLE. Twenty-nine CSF specimens from 12 NPSLE, 7 non-NPSLE, and 10 control (non-systemic lupus erythematosuspatients were screened for NPSLE-associated autoantibodies with proteome microarrays. A focused autoantigen microarray of candidate NPSLE autoantigens was applied to profile a larger cohort of CSF with patient-matched sera. We identified 137 autoantigens associated with NPSLE. Ingenuity Pathway Analysis revealed that these autoantigens were enriched for functions involved in neurological diseases (score = 43.Anti-proliferating cell nuclear antigen (PCNA was found in the CSF of NPSLE and non-NPSLE patients. The positive rates of 4 autoantibodies in CSF specimens were significantly different between the SLE (i.e., NPSLE and non-NPSLE and control groups: anti-ribosomal protein RPLP0, anti-RPLP1, anti-RPLP2, and anti-TROVE2 (also known as anti-Ro/SS-A. The positive rate for anti-SS-A associated with NPSLE was higher than that for non-NPSLE (31.11% cf. 10.71%; P = 0.045.Further analysis showed that anti-SS-A in CSF specimens was related to neuropsychiatric syndromes of the central nervous system in SLE (P = 0.009. Analysis with Spearman's rank correlation coefficient indicated that the titers of anti-RPLP2 and anti-SS-A in paired CSF and serum specimens significantly correlated. Human proteome microarrays offer a powerful platform to discover novel autoantibodies in CSF samples. Anti-SS-A autoantibodies may be potential CSF markers for NPSLE.

  8. Robofurnace: A semi-automated laboratory chemical vapor deposition system for high-throughput nanomaterial synthesis and process discovery

    Science.gov (United States)

    Oliver, C. Ryan; Westrick, William; Koehler, Jeremy; Brieland-Shoultz, Anna; Anagnostopoulos-Politis, Ilias; Cruz-Gonzalez, Tizoc; Hart, A. John

    2013-11-01

    Laboratory research and development on new materials, such as nanostructured thin films, often utilizes manual equipment such as tube furnaces due to its relatively low cost and ease of setup. However, these systems can be prone to inconsistent outcomes due to variations in standard operating procedures and limitations in performance such as heating and cooling rates restrict the parameter space that can be explored. Perhaps more importantly, maximization of research throughput and the successful and efficient translation of materials processing knowledge to production-scale systems, relies on the attainment of consistent outcomes. In response to this need, we present a semi-automated lab-scale chemical vapor deposition (CVD) furnace system, called "Robofurnace." Robofurnace is an automated CVD system built around a standard tube furnace, which automates sample insertion and removal and uses motion of the furnace to achieve rapid heating and cooling. The system has a 10-sample magazine and motorized transfer arm, which isolates the samples from the lab atmosphere and enables highly repeatable placement of the sample within the tube. The system is designed to enable continuous operation of the CVD reactor, with asynchronous loading/unloading of samples. To demonstrate its performance, Robofurnace is used to develop a rapid CVD recipe for carbon nanotube (CNT) forest growth, achieving a 10-fold improvement in CNT forest mass density compared to a benchmark recipe using a manual tube furnace. In the long run, multiple systems like Robofurnace may be linked to share data among laboratories by methods such as Twitter. Our hope is Robofurnace and like automation will enable machine learning to optimize and discover relationships in complex material synthesis processes.

  9. Robofurnace: A semi-automated laboratory chemical vapor deposition system for high-throughput nanomaterial synthesis and process discovery

    International Nuclear Information System (INIS)

    Oliver, C. Ryan; Westrick, William; Koehler, Jeremy; Brieland-Shoultz, Anna; Anagnostopoulos-Politis, Ilias; Cruz-Gonzalez, Tizoc; Hart, A. John

    2013-01-01

    Laboratory research and development on new materials, such as nanostructured thin films, often utilizes manual equipment such as tube furnaces due to its relatively low cost and ease of setup. However, these systems can be prone to inconsistent outcomes due to variations in standard operating procedures and limitations in performance such as heating and cooling rates restrict the parameter space that can be explored. Perhaps more importantly, maximization of research throughput and the successful and efficient translation of materials processing knowledge to production-scale systems, relies on the attainment of consistent outcomes. In response to this need, we present a semi-automated lab-scale chemical vapor deposition (CVD) furnace system, called “Robofurnace.” Robofurnace is an automated CVD system built around a standard tube furnace, which automates sample insertion and removal and uses motion of the furnace to achieve rapid heating and cooling. The system has a 10-sample magazine and motorized transfer arm, which isolates the samples from the lab atmosphere and enables highly repeatable placement of the sample within the tube. The system is designed to enable continuous operation of the CVD reactor, with asynchronous loading/unloading of samples. To demonstrate its performance, Robofurnace is used to develop a rapid CVD recipe for carbon nanotube (CNT) forest growth, achieving a 10-fold improvement in CNT forest mass density compared to a benchmark recipe using a manual tube furnace. In the long run, multiple systems like Robofurnace may be linked to share data among laboratories by methods such as Twitter. Our hope is Robofurnace and like automation will enable machine learning to optimize and discover relationships in complex material synthesis processes

  10. Application of Fluorescence Two-Dimensional Difference In-Gel Electrophoresis as a Proteomic Biomarker Discovery Tool in Muscular Dystrophy Research

    Directory of Open Access Journals (Sweden)

    Steven Carberry

    2013-12-01

    Full Text Available In this article, we illustrate the application of difference in-gel electrophoresis for the proteomic analysis of dystrophic skeletal muscle. The mdx diaphragm was used as a tissue model of dystrophinopathy. Two-dimensional gel electrophoresis is a widely employed protein separation method in proteomic investigations. Although two-dimensional gels usually underestimate the cellular presence of very high molecular mass proteins, integral membrane proteins and low copy number proteins, this method is extremely powerful in the comprehensive analysis of contractile proteins, metabolic enzymes, structural proteins and molecular chaperones. This gives rise to two-dimensional gel electrophoretic separation as the method of choice for studying contractile tissues in health and disease. For comparative studies, fluorescence difference in-gel electrophoresis has been shown to provide an excellent biomarker discovery tool. Since aged diaphragm fibres from the mdx mouse model of Duchenne muscular dystrophy closely resemble the human pathology, we have carried out a mass spectrometry-based comparison of the naturally aged diaphragm versus the senescent dystrophic diaphragm. The proteomic comparison of wild type versus mdx diaphragm resulted in the identification of 84 altered protein species. Novel molecular insights into dystrophic changes suggest increased cellular stress, impaired calcium buffering, cytostructural alterations and disturbances of mitochondrial metabolism in dystrophin-deficient muscle tissue.

  11. Application of Fluorescence Two-Dimensional Difference In-Gel Electrophoresis as a Proteomic Biomarker Discovery Tool in Muscular Dystrophy Research

    Science.gov (United States)

    Carberry, Steven; Zweyer, Margit; Swandulla, Dieter; Ohlendieck, Kay

    2013-01-01

    In this article, we illustrate the application of difference in-gel electrophoresis for the proteomic analysis of dystrophic skeletal muscle. The mdx diaphragm was used as a tissue model of dystrophinopathy. Two-dimensional gel electrophoresis is a widely employed protein separation method in proteomic investigations. Although two-dimensional gels usually underestimate the cellular presence of very high molecular mass proteins, integral membrane proteins and low copy number proteins, this method is extremely powerful in the comprehensive analysis of contractile proteins, metabolic enzymes, structural proteins and molecular chaperones. This gives rise to two-dimensional gel electrophoretic separation as the method of choice for studying contractile tissues in health and disease. For comparative studies, fluorescence difference in-gel electrophoresis has been shown to provide an excellent biomarker discovery tool. Since aged diaphragm fibres from the mdx mouse model of Duchenne muscular dystrophy closely resemble the human pathology, we have carried out a mass spectrometry-based comparison of the naturally aged diaphragm versus the senescent dystrophic diaphragm. The proteomic comparison of wild type versus mdx diaphragm resulted in the identification of 84 altered protein species. Novel molecular insights into dystrophic changes suggest increased cellular stress, impaired calcium buffering, cytostructural alterations and disturbances of mitochondrial metabolism in dystrophin-deficient muscle tissue. PMID:24833232

  12. Study design and data analysis considerations for the discovery of prognostic molecular biomarkers: a case study of progression free survival in advanced serous ovarian cancer.

    Science.gov (United States)

    Qin, Li-Xuan; Levine, Douglas A

    2016-06-10

    Accurate discovery of molecular biomarkers that are prognostic of a clinical outcome is an important yet challenging task, partly due to the combination of the typically weak genomic signal for a clinical outcome and the frequently strong noise due to microarray handling effects. Effective strategies to resolve this challenge are in dire need. We set out to assess the use of careful study design and data normalization for the discovery of prognostic molecular biomarkers. Taking progression free survival in advanced serous ovarian cancer as an example, we conducted empirical analysis on two sets of microRNA arrays for the same set of tumor samples: arrays in one set were collected using careful study design (that is, uniform handling and randomized array-to-sample assignment) and arrays in the other set were not. We found that (1) handling effects can confound the clinical outcome under study as a result of chance even with randomization, (2) the level of confounding handling effects can be reduced by data normalization, and (3) good study design cannot be replaced by post-hoc normalization. In addition, we provided a practical approach to define positive and negative control markers for detecting handling effects and assessing the performance of a normalization method. Our work showcased the difficulty of finding prognostic biomarkers for a clinical outcome of weak genomic signals, illustrated the benefits of careful study design and data normalization, and provided a practical approach to identify handling effects and select a beneficial normalization method. Our work calls for careful study design and data analysis for the discovery of robust and translatable molecular biomarkers.

  13. Phylogenetic Conflict in Bears Identified by Automated Discovery of Transposable Element Insertions in Low-Coverage Genomes

    Science.gov (United States)

    Gallus, Susanne; Janke, Axel

    2017-01-01

    Abstract Phylogenetic reconstruction from transposable elements (TEs) offers an additional perspective to study evolutionary processes. However, detecting phylogenetically informative TE insertions requires tedious experimental work, limiting the power of phylogenetic inference. Here, we analyzed the genomes of seven bear species using high-throughput sequencing data to detect thousands of TE insertions. The newly developed pipeline for TE detection called TeddyPi (TE detection and discovery for Phylogenetic Inference) identified 150,513 high-quality TE insertions in the genomes of ursine and tremarctine bears. By integrating different TE insertion callers and using a stringent filtering approach, the TeddyPi pipeline produced highly reliable TE insertion calls, which were confirmed by extensive in vitro validation experiments. Analysis of single nucleotide substitutions in the flanking regions of the TEs shows that these substitutions correlate with the phylogenetic signal from the TE insertions. Our phylogenomic analyses show that TEs are a major driver of genomic variation in bears and enabled phylogenetic reconstruction of a well-resolved species tree, despite strong signals for incomplete lineage sorting and introgression. The analyses show that the Asiatic black, sun, and sloth bear form a monophyletic clade, in which phylogenetic incongruence originates from incomplete lineage sorting. TeddyPi is open source and can be adapted to various TE and structural variation callers. The pipeline makes it possible to confidently extract thousands of TE insertions even from low-coverage genomes (∼10×) of nonmodel organisms. This opens new possibilities for biologists to study phylogenies and evolutionary processes as well as rates and patterns of (retro-)transposition and structural variation. PMID:28985298

  14. Phylogenetic Conflict in Bears Identified by Automated Discovery of Transposable Element Insertions in Low-Coverage Genomes.

    Science.gov (United States)

    Lammers, Fritjof; Gallus, Susanne; Janke, Axel; Nilsson, Maria A

    2017-10-01

    Phylogenetic reconstruction from transposable elements (TEs) offers an additional perspective to study evolutionary processes. However, detecting phylogenetically informative TE insertions requires tedious experimental work, limiting the power of phylogenetic inference. Here, we analyzed the genomes of seven bear species using high-throughput sequencing data to detect thousands of TE insertions. The newly developed pipeline for TE detection called TeddyPi (TE detection and discovery for Phylogenetic Inference) identified 150,513 high-quality TE insertions in the genomes of ursine and tremarctine bears. By integrating different TE insertion callers and using a stringent filtering approach, the TeddyPi pipeline produced highly reliable TE insertion calls, which were confirmed by extensive in vitro validation experiments. Analysis of single nucleotide substitutions in the flanking regions of the TEs shows that these substitutions correlate with the phylogenetic signal from the TE insertions. Our phylogenomic analyses show that TEs are a major driver of genomic variation in bears and enabled phylogenetic reconstruction of a well-resolved species tree, despite strong signals for incomplete lineage sorting and introgression. The analyses show that the Asiatic black, sun, and sloth bear form a monophyletic clade, in which phylogenetic incongruence originates from incomplete lineage sorting. TeddyPi is open source and can be adapted to various TE and structural variation callers. The pipeline makes it possible to confidently extract thousands of TE insertions even from low-coverage genomes (∼10×) of nonmodel organisms. This opens new possibilities for biologists to study phylogenies and evolutionary processes as well as rates and patterns of (retro-)transposition and structural variation. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  15. Service discovery at home

    NARCIS (Netherlands)

    Sundramoorthy, V.; Scholten, Johan; Jansen, P.G.; Hartel, Pieter H.

    2003-01-01

    Service discovery is a fairly new field that kicked off since the advent of ubiquitous computing and has been found essential in the making of intelligent networks by implementing automated discovery and remote control between devices. This paper provides an overview and comparison of several

  16. Service Discovery At Home

    NARCIS (Netherlands)

    Sundramoorthy, V.; Scholten, Johan; Jansen, P.G.; Hartel, Pieter H.

    Service discovery is a fady new field that kicked off since the advent of ubiquitous computing and has been found essential in the making of intelligent networks by implementing automated discovery and remote control between deviies. This paper provides an ovewiew and comparison of several prominent

  17. Biomarker discovery in low-grade breast cancer using isobaric stable isotope tags and two-dimensional liquid chromatography-tandem mass spectrometry (iTRAQ-2DLC-MS/MS) based quantitative proteomic analysis.

    Science.gov (United States)

    Bouchal, Pavel; Roumeliotis, Theodoros; Hrstka, Roman; Nenutil, Rudolf; Vojtesek, Borivoj; Garbis, Spiros D

    2009-01-01

    The present pilot study constitutes a proof-of-principle in the use of a quantitative LC-MS/MS based proteomic method for the comparative analysis of representative low-grade breast primary tumor tissues with and without metastases and metastasis in lymph node relative to the nonmetastatic tumor type. The study method incorporated iTRAQ stable isotope labeling, two-dimensional liquid chromatography, nanoelectrospray ionization and high resolution tandem mass spectrometry using the hybrid QqTOF platform (iTRAQ-2DLC-MS/MS). The principal aims of this study were (1) to define the protein spectrum obtainable using this approach, and (2) to highlight potential candidates for verification and validation studies focused on biomarkers involved in metastatic processes in breast cancer. The study resulted in the reproducible identification of 605 nonredundant proteins (p biomarker discovery program.

  18. Qualitative and quantitative characterization of plasma proteins when incorporating traveling wave ion mobility into a liquid chromatography-mass spectrometry workflow for biomarker discovery: use of product ion quantitation as an alternative data analysis tool for label free quantitation.

    Science.gov (United States)

    Daly, Charlotte E; Ng, Leong L; Hakimi, Amirmansoor; Willingale, Richard; Jones, Donald J L

    2014-02-18

    Discovery of protein biomarkers in clinical samples necessitates significant prefractionation prior to liquid chromatography-mass spectrometry (LC-MS) analysis. Integrating traveling wave ion mobility spectrometry (TWIMS) enables in-line gas phase separation which when coupled with nanoflow liquid chromatography and data independent acquisition tandem mass spectrometry, confers significant advantages to the discovery of protein biomarkers by improving separation and inherent sensitivity. Incorporation of TWIMS leads to a packet of concentrated ions which ultimately provides a significant improvement in sensitivity. As a consequence of ion packeting, when present at high concentrations, accurate quantitation of proteins can be affected due to detector saturation effects. Human plasma was analyzed in triplicate using liquid-chromatography data independent acquisition mass spectrometry (LC-DIA-MS) and using liquid-chromatography ion-mobility data independent acquisition mass spectrometry (LC-IM-DIA-MS). The inclusion of TWIMS was assessed for the effect on sample throughput, data integrity, confidence of protein and peptide identification, and dynamic range. The number of identified proteins is significantly increased by an average of 84% while both the precursor and product mass accuracies are maintained between the modalities. Sample dynamic range is also maintained while quantitation is achieved for all but the most abundant proteins by incorporating a novel data interpretation method that allows accurate quantitation to occur. This additional separation is all achieved within a workflow with no discernible deleterious effect on throughput. Consequently, TWIMS greatly enhances proteome coverage and can be reliably used for quantification when using an alternative product ion quantification strategy. Using TWIMS in biomarker discovery in human plasma is thus recommended.

  19. Mass spectrometry for biomarker development

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Chaochao; Liu, Tao; Baker, Erin Shammel; Rodland, Karin D.; Smith, Richard D.

    2015-06-19

    Biomarkers potentially play a crucial role in early disease diagnosis, prognosis and targeted therapy. In the past decade, mass spectrometry based proteomics has become increasingly important in biomarker development due to large advances in technology and associated methods. This chapter mainly focuses on the application of broad (e.g. shotgun) proteomics in biomarker discovery and the utility of targeted proteomics in biomarker verification and validation. A range of mass spectrometry methodologies are discussed emphasizing their efficacy in the different stages in biomarker development, with a particular emphasis on blood biomarker development.

  20. Discovery, screening and evaluation of a plasma biomarker panel for subjects with psychological suboptimal health state using (1)H-NMR-based metabolomics profiles.

    Science.gov (United States)

    Tian, Jun-Sheng; Xia, Xiao-Tao; Wu, Yan-Fei; Zhao, Lei; Xiang, Huan; Du, Guan-Hua; Zhang, Xiang; Qin, Xue-Mei

    2016-09-21

    Individuals in the state of psychological suboptimal health keep increasing, only scales and questionnaires were used to diagnose in clinic under current conditions, and symptoms of high reliability and accuracy are destitute. Therefore, the noninvasive and precise laboratory diagnostic methods are needed. This study aimed to develop an objective method through screen potential biomarkers or a biomarker panel to facilitate the diagnosis in clinic using plasma metabolomics. Profiles were based on H-nuclear magnetic resonance ((1)H-NMR) metabolomics techniques combing with multivariate statistical analysis. Furthermore, methods of correlation analysis with Metaboanalyst 3.0 for selecting a biomarker panel, traditional Chinese medicine (TCM) drug intervention for validating the close relations between the biomarker panel and the state and the receiver operating characteristic curves (ROC curves) analysis for evaluation of clinical diagnosis ability were carried out. 9 endogenous metabolites containing trimethylamine oxide (TMAO), glutamine, N-acetyl-glycoproteins, citrate, tyrosine, phenylalanine, isoleucine, valine and glucose were identified and considered as potential biomarkers. Then a biomarker panel consisting of phenylalanine, glutamine, tyrosine, citrate, N-acetyl-glycoproteins and TMAO was selected, which exhibited the highest area under the curve (AUC = 0.971). This study provided critical insight into the pathological mechanism of psychological suboptimal health and would supply a novel and valuable diagnostic method.

  1. Automated Discovery of Mimicry Attacks

    National Research Council Canada - National Science Library

    Giffin, Jonathon T; Jha, Somesh; Miller, Barton P

    2006-01-01

    .... These systems are useful only if they detect actual attacks. Previous research developed manually-constructed mimicry and evasion attacks that avoided detection by hiding a malicious series of system calls within a valid sequence allowed by the model...

  2. Automated Bone Scan Index as a quantitative imaging biomarker in metastatic castration-resistant prostate cancer patients being treated with enzalutamide

    DEFF Research Database (Denmark)

    Anand, Aseem; Morris, Michael J.; Larson, Steven M

    2016-01-01

    alone (C-index 0.73), p = 0.041. Conclusions: The upgraded and analytically validated automated BSI was found to be a strong predictor of OS in mCRPC patients. Additionally, the change in automated BSI demonstrated an additive clinical value to the change in PSA in mCRPC patients being treated...

  3. Analysis of Serum Metabolic Profile by Ultra-performance Liquid Chromatography-mass Spectrometry for Biomarkers Discovery: Application in a Pilot Study to Discriminate Patients with Tuberculosis

    Directory of Open Access Journals (Sweden)

    Shuang Feng

    2015-01-01

    Full Text Available Background: Tuberculosis (TB is a chronic wasting inflammatory disease characterized by multisystem involvement, which can cause metabolic derangements in afflicted patients. Metabolic signatures have been exploited in the study of several diseases. However, the serum that is successfully used in TB diagnosis on the basis of metabolic profiling is not by much. Methods: Orthogonal partial least-squares discriminant analysis was capable of distinguishing TB patients from both healthy subjects and patients with conditions other than TB. Therefore, TB-specific metabolic profiling was established. Clusters of potential biomarkers for differentiating TB active from non-TB diseases were identified using Mann-Whitney U-test. Multiple logistic regression analysis of metabolites was calculated to determine the suitable biomarker group that allows the efficient differentiation of patients with TB active from the control subjects. Results: From among 271 participants, 12 metabolites were found to contribute to the distinction between the TB active group and the control groups. These metabolites were mainly involved in the metabolic pathways of the following three biomolecules: Fatty acids, amino acids, and lipids. The receiver operating characteristic curves of 3D, 7D, and 11D-phytanic acid, behenic acid, and threoninyl-γ-glutamate exhibited excellent efficiency with area under the curve (AUC values of 0.904 (95% confidence interval [CI]: 0863-0.944, 0.93 (95% CI: 0.893-0.966, and 0.964 (95% CI: 00.941-0.988, respectively. The largest and smallest resulting AUCs were 0.964 and 0.720, indicating that these biomarkers may be involved in the disease mechanisms. The combination of lysophosphatidylcholine (18:0, behenic acid, threoninyl-γ-glutamate, and presqualene diphosphate was used to represent the most suitable biomarker group for the differentiation of patients with TB active from the control subjects, with an AUC value of 0.991. Conclusion: The

  4. Large-Scale Glycomics of Livestock: Discovery of Highly Sensitive Serum Biomarkers Indicating an Environmental Stress Affecting Immune Responses and Productivity of Holstein Dairy Cows.

    Science.gov (United States)

    Rehan, Ibrahim F; Ueda, Koichiro; Mitani, Tomohiro; Amano, Maho; Hinou, Hiroshi; Ohashi, Tetsu; Kondo, Seiji; Nishimura, Shin-Ichiro

    2015-12-09

    Because various stresses strongly influence the food productivity of livestock, biomarkers to indicate unmeasurable environmental stress in domestic animals are of increasing importance. Thermal comfort is one of the basic principles of dairy cow welfare that enhances productivity. To discover sensitive biomarkers that monitor such environmental stresses in dairy cows, we herein performed, for the first time, large-scale glycomics on 336 lactating Holstein cow serum samples over 9 months between February and October. Glycoblotting combined with MALDI-TOF/MS and DMB/HPLC allowed for comprehensive glycomics of whole serum glycoproteins. The results obtained revealed seasonal alterations in serum N-glycan levels and their structural characteristics, such as an increase in high-mannose type N-glycans in spring, the occurrence of di/triantennary complex type N-glycans terminating with two or three Neu5Gc residues in summer and autumn, and N-glycans in winter dominantly displaying Neu5Ac. A multivariate analysis revealed a correlation between the serum expression levels of these season-specific glycoforms and productivity.

  5. Robust Selection Algorithm (RSA for Multi-Omic Biomarker Discovery; Integration with Functional Network Analysis to Identify miRNA Regulated Pathways in Multiple Cancers.

    Directory of Open Access Journals (Sweden)

    Vasudha Sehgal

    Full Text Available MicroRNAs (miRNAs play a crucial role in the maintenance of cellular homeostasis by regulating the expression of their target genes. As such, the dysregulation of miRNA expression has been frequently linked to cancer. With rapidly accumulating molecular data linked to patient outcome, the need for identification of robust multi-omic molecular markers is critical in order to provide clinical impact. While previous bioinformatic tools have been developed to identify potential biomarkers in cancer, these methods do not allow for rapid classification of oncogenes versus tumor suppressors taking into account robust differential expression, cutoffs, p-values and non-normality of the data. Here, we propose a methodology, Robust Selection Algorithm (RSA that addresses these important problems in big data omics analysis. The robustness of the survival analysis is ensured by identification of optimal cutoff values of omics expression, strengthened by p-value computed through intensive random resampling taking into account any non-normality in the data and integration into multi-omic functional networks. Here we have analyzed pan-cancer miRNA patient data to identify functional pathways involved in cancer progression that are associated with selected miRNA identified by RSA. Our approach demonstrates the way in which existing survival analysis techniques can be integrated with a functional network analysis framework to efficiently identify promising biomarkers and novel therapeutic candidates across diseases.

  6. Metabolic profiling study on potential toxicity and immunotoxicity-biomarker discovery in rats treated with cyclophosphamide using HPLC-ESI-IT-TOF-MS.

    Science.gov (United States)

    Li, Jing; Lin, Wensi; Lin, Weiwei; Xu, Peng; Zhang, Jianmei; Yang, Haisong; Ling, Xiaomei

    2015-05-01

    Despite the recent advances in understanding toxicity mechanism of cyclophosphamide (CTX), the development of biomarkers is still essential. CTX-induced immunotoxicity in rats by a metabonomics approach was investigated using high-performance liquid chromatography coupled with ion trap time-of-flight mass spectrometry (HPLC-ESI-IT-TOF-MS). The rats were orally administered CTX (30 mg/kg/day) for five consecutive days, and on the fifth day samples of urine, thymus and spleen were collected and analyzed. A significant difference in metabolic profiling was observed between the CTX-treated group and the control group by partial least squares-discriminant analysis (PLS-DA), which indicated that metabolic disturbances of immunotoxicity in CTX-treated rats had occurred. One potential biomarker in spleen, three in urine and three in thymus were identified. It is suggested that the CTX-toxicity mechanism may involve the modulation of tryptophan metabolism, phospholipid metabolism and energy metabolism. This research can help to elucidate the CTX-influenced pathways at a low dose and can further help to indicate the patients' pathological status at earlier stages of toxicological progression after drug administration. Copyright © 2014 John Wiley & Sons, Ltd.

  7. Proteomics Approaches to Identify Tumor Antigen Directed Autoantibodies as Cancer Biomarkers

    Directory of Open Access Journals (Sweden)

    Yuji Imafuku

    2004-01-01

    Full Text Available The identification of autoantibodies to tumor cell proteins by proteomics approaches has great potential impact on cancer biomarker discovery. The humoral immune response represents a form of biological amplification of signals that are otherwise weak due to very low concentrations of antigen, especially in the early stages of cancers. In addition, proteomics can detect immunoreactivity directed against protein post-translational modifications. Two-dimensional gel based Western blots, protein antigen microarrays, and multiplex ELISA reactions have been applied by our group to antigen based biomarker detection and validation. The latter two are based on liquid-phase separations that are suitable for automation. This work has resulted in the identification of numerous cancer biomarker candidates. Large clinical studies are currently planned to establish their value in early cancer diagnosis.

  8. Proteomics approaches to identify tumor antigen directed autoantibodies as cancer biomarkers.

    Science.gov (United States)

    Imafuku, Yuji; Omenn, Gilbert S; Hanash, Samir

    2004-01-01

    The identification of autoantibodies to tumor cell proteins by proteomics approaches has great potential impact on cancer biomarker discovery. The humoral immune response represents a form of biological amplification of signals that are otherwise weak due to very low concentrations of antigen, especially in the early stages of cancers. In addition, proteomics can detect immunoreactivity directed against protein post-translational modifications. Two-dimensional gel based Western blots, protein antigen microarrays, and multiplex ELISA reactions have been applied by our group to antigen based biomarker detection and validation. The latter two are based on liquid-phase separations that are suitable for automation. This work has resulted in the identification of numerous cancer biomarker candidates. Large clinical studies are currently planned to establish their value in early cancer diagnosis.

  9. Ultra-high throughput sequencing-based small RNA discovery and discrete statistical biomarker analysis in a collection of cervical tumours and matched controls

    Directory of Open Access Journals (Sweden)

    Gu Sam

    2010-05-01

    Full Text Available Abstract Background Ultra-high throughput sequencing technologies provide opportunities both for discovery of novel molecular species and for detailed comparisons of gene expression patterns. Small RNA populations are particularly well suited to this analysis, as many different small RNAs can be completely sequenced in a single instrument run. Results We prepared small RNA libraries from 29 tumour/normal pairs of human cervical tissue samples. Analysis of the resulting sequences (42 million in total defined 64 new human microRNA (miRNA genes. Both arms of the hairpin precursor were observed in twenty-three of the newly identified miRNA candidates. We tested several computational approaches for the analysis of class differences between high throughput sequencing datasets and describe a novel application of a log linear model that has provided the most effective analysis for this data. This method resulted in the identification of 67 miRNAs that were differentially-expressed between the tumour and normal samples at a false discovery rate less than 0.001. Conclusions This approach can potentially be applied to any kind of RNA sequencing data for analysing differential sequence representation between biological sample sets.

  10. Influence of PCOS in Obese vs. Non-Obese women from Mesenchymal Progenitors Stem Cells and Other Endometrial Cells: An in silico biomarker discovery.

    Science.gov (United States)

    Desai, Ashvini; Madar, Inamul Hasan; Asangani, Amjad Hussain; Ssadh, Hussain Al; Tayubi, Iftikhar Aslam

    2017-01-01

    Polycystic ovary syndrome (PCOS) is endocrine system disease which affect women ages 18 to 44 where the women's hormones are imbalance. Recently it has been reported to occur in early age. Alteration of normal gene expression in PCOS has shown negative effects on long-term health issues. PCOS has been the responsible factor for the infertility in women of reproductive age group. Early diagnosis and treatment can improve the women's health suffering from PCOS. Earlier Studies shows correlation of PCOS upon insulin resistance with significant outcome, Current study shows the linkage between PCOS with obesity and non-obese patients. Gene expression datasets has been downloaded from GEO (control and PCOS affected patients). Normalization of the datasets were performed using R based on RMA and differentially expressed gene (DEG) were selected on the basis of p-value 0.05 followed by functional annotation of selected gene using Enrich R and DAVID. The DEGs were significantly related to PCOS with obesity and other risk factors involved in disease. The Gene Enrichment Analysis suggests alteration of genes and associated pathway in case of obesity. Current study provides a productive groundwork for specific biomarkers identification for the accurate diagnosis and efficient target for the treatment of PCOS.

  11. A statistical framework for genome-wide discovery of biomarker splice variations with GeneChip Human Exon 1.0 ST Arrays.

    Science.gov (United States)

    Yoshida, Ryo; Numata, Kazuyuki; Imoto, Seiya; Nagasaki, Masao; Doi, Atsushi; Ueno, Kazuko; Miyano, Satoru

    2006-01-01

    Alternative splicing is an important regulatory mechanism that generates multiple mRNA transcripts which are transcribed into functionally diverse proteins. According to the current studies, aberrant transcripts due to splicing mutations are known to cause for 15% of genetic diseases. Therefore understanding regulatory mechanism of alternative splicing is essential for identifying potential biomarkers for several types of human diseases. Most recently, advent of GeneChip Human Exon 1.0 ST Array enables us to measure genome-wide expression profiles of over one million exons. With this new microarray platform, analysis of functional gene expressions could be extended to detect not only differentially expressed genes, but also a set of specific-splicing events that are differentially observed between one or more experimental conditions, e.g. tumor or normal control cells. In this study, we address the statistical problems to identify differentially observed splicing variations from exon expression profiles. The proposed method is organized according to the following process: (1) Data preprocessing for removing systematic biases from the probe intensities. (2) Whole transcript analysis with the analysis of variance (ANOVA) to identify a set of loci that cause the alternative splicing-related to a certain disease. We test the proposed statistical approach on exon expression profiles of colorectal carcinoma. The applicability is verified and discussed in relation to the existing biological knowledge. This paper intends to highlight the potential role of statistical analysis of all exon microarray data. Our work is an important first step toward development of more advanced statistical technology. Supplementary information and materials are available from http://bonsai.ims.u-tokyo.ac.jp/~yoshidar/IBSB2006_ExonArray.htm.

  12. Automated Phase Mapping with AgileFD and its Application to Light Absorber Discovery in the V-Mn-Nb Oxide System.

    Science.gov (United States)

    Suram, Santosh K; Xue, Yexiang; Bai, Junwen; Le Bras, Ronan; Rappazzo, Brendan; Bernstein, Richard; Bjorck, Johan; Zhou, Lan; van Dover, R Bruce; Gomes, Carla P; Gregoire, John M

    2017-01-09

    Rapid construction of phase diagrams is a central tenet of combinatorial materials science with accelerated materials discovery efforts often hampered by challenges in interpreting combinatorial X-ray diffraction data sets, which we address by developing AgileFD, an artificial intelligence algorithm that enables rapid phase mapping from a combinatorial library of X-ray diffraction patterns. AgileFD models alloying-based peak shifting through a novel expansion of convolutional nonnegative matrix factorization, which not only improves the identification of constituent phases but also maps their concentration and lattice parameter as a function of composition. By incorporating Gibbs' phase rule into the algorithm, physically meaningful phase maps are obtained with unsupervised operation, and more refined solutions are attained by injecting expert knowledge of the system. The algorithm is demonstrated through investigation of the V-Mn-Nb oxide system where decomposition of eight oxide phases, including two with substantial alloying, provides the first phase map for this pseudoternary system. This phase map enables interpretation of high-throughput band gap data, leading to the discovery of new solar light absorbers and the alloying-based tuning of the direct-allowed band gap energy of MnV 2 O 6 . The open-source family of AgileFD algorithms can be implemented into a broad range of high throughput workflows to accelerate materials discovery.

  13. Proteomic response of mussels Mytilus galloprovincialis exposed to CuO NPs and Cu²⁺: an exploratory biomarker discovery.

    Science.gov (United States)

    Gomes, Tânia; Chora, Suze; Pereira, Catarina G; Cardoso, Cátia; Bebianno, Maria João

    2014-10-01

    absence of the mussel genome precluded the identification of other proteins relevant to clarify the effects of CuO NPs in mussels' tissues, proteomics analysis provided additional knowledge of their potential effects at the protein level that after confirmation and validation can be used as putative new biomarkers in nanotoxicology. Copyright © 2014 Elsevier B.V. All rights reserved.

  14. Automated security management

    CERN Document Server

    Al-Shaer, Ehab; Xie, Geoffrey

    2013-01-01

    In this contributed volume, leading international researchers explore configuration modeling and checking, vulnerability and risk assessment, configuration analysis, and diagnostics and discovery. The authors equip readers to understand automated security management systems and techniques that increase overall network assurability and usability. These constantly changing networks defend against cyber attacks by integrating hundreds of security devices such as firewalls, IPSec gateways, IDS/IPS, authentication servers, authorization/RBAC servers, and crypto systems. Automated Security Managemen

  15. Proteomic response of mussels Mytilus galloprovincialis exposed to CuO NPs and Cu{sup 2+}: An exploratory biomarker discovery

    Energy Technology Data Exchange (ETDEWEB)

    Gomes, Tânia, E-mail: tania.gomes@niva.no; Chora, Suze; Pereira, Catarina G.; Cardoso, Cátia; Bebianno, Maria João

    2014-10-15

    the other hand, Cu{sup 2+} affected a protein associated with adhesion and mobility, precollagen-D that is associated with the detoxification mechanism of Cu{sup 2+}. Protein identification clearly showed that the toxicity of CuO NPs is not solely due to Cu{sup 2+} dissolution and can result in mitochondrial and nucleus stress-induced cell signalling cascades that can lead to apoptosis. While the absence of the mussel genome precluded the identification of other proteins relevant to clarify the effects of CuO NPs in mussels’ tissues, proteomics analysis provided additional knowledge of their potential effects at the protein level that after confirmation and validation can be used as putative new biomarkers in nanotoxicology.

  16. Application of an automated natural language processing (NLP) workflow to enable federated search of external biomedical content in drug discovery and development.

    Science.gov (United States)

    McEntire, Robin; Szalkowski, Debbie; Butler, James; Kuo, Michelle S; Chang, Meiping; Chang, Man; Freeman, Darren; McQuay, Sarah; Patel, Jagruti; McGlashen, Michael; Cornell, Wendy D; Xu, Jinghai James

    2016-05-01

    External content sources such as MEDLINE(®), National Institutes of Health (NIH) grants and conference websites provide access to the latest breaking biomedical information, which can inform pharmaceutical and biotechnology company pipeline decisions. The value of the sites for industry, however, is limited by the use of the public internet, the limited synonyms, the rarity of batch searching capability and the disconnected nature of the sites. Fortunately, many sites now offer their content for download and we have developed an automated internal workflow that uses text mining and tailored ontologies for programmatic search and knowledge extraction. We believe such an efficient and secure approach provides a competitive advantage to companies needing access to the latest information for a range of use cases and complements manually curated commercial sources. Copyright © 2016. Published by Elsevier Ltd.

  17. Automated methods of corrosion measurement

    DEFF Research Database (Denmark)

    Andersen, Jens Enevold Thaulov; Bech-Nielsen, Gregers; Reeve, John Ch

    1997-01-01

    to revise assumptions regarding the basis of the method, which sometimes leads to the discovery of as-yet unnoticed phenomena. The present selection of automated methods for corrosion measurements is not motivated simply by the fact that a certain measurement can be performed automatically. Automation...

  18. Biomarkers: Delivering on the expectation of molecularly driven, quantitative health.

    Science.gov (United States)

    Wilson, Jennifer L; Altman, Russ B

    2018-02-01

    Biomarkers are the pillars of precision medicine and are delivering on expectations of molecular, quantitative health. These features have made clinical decisions more precise and personalized, but require a high bar for validation. Biomarkers have improved health outcomes in a few areas such as cancer, pharmacogenetics, and safety. Burgeoning big data research infrastructure, the internet of things, and increased patient participation will accelerate discovery in the many areas that have not yet realized the full potential of biomarkers for precision health. Here we review themes of biomarker discovery, current implementations of biomarkers for precision health, and future opportunities and challenges for biomarker discovery. Impact statement Precision medicine evolved because of the understanding that human disease is molecularly driven and is highly variable across patients. This understanding has made biomarkers, a diverse class of biological measurements, more relevant for disease diagnosis, monitoring, and selection of treatment strategy. Biomarkers' impact on precision medicine can be seen in cancer, pharmacogenomics, and safety. The successes in these cases suggest many more applications for biomarkers and a greater impact for precision medicine across the spectrum of human disease. The authors assess the status of biomarker-guided medical practice by analyzing themes for biomarker discovery, reviewing the impact of these markers in the clinic, and highlight future and ongoing challenges for biomarker discovery. This work is timely and relevant, as the molecular, quantitative approach of precision medicine is spreading to many disease indications.

  19. Biomarkers: A Challenging Conundrum in Cardiovascular Disease.

    Science.gov (United States)

    Libby, Peter; King, Kevin

    2015-12-01

    The use of biomarkers has proven utility in cardiovascular medicine and holds great promise for future advances, but their application requires considerable rigor in thinking and methodology. Numerous confounding factors can cloud the clinical and investigative uses of biomarkers. Yet, the thoughtful and critical use of biomarkers can doubtless aid discovery of new pathogenic pathways, identify novel therapeutic targets, and provide a bridge between the laboratory and the clinic. Biomarkers can provide diagnostic and prognostic tools to the practitioner. The careful application of biomarkers can also help design and guide clinical trials required to establish the efficacy of novel interventions to improve patient outcomes. Point of care testing, technological advances, such as microfluidic and wearable devices, and the power of omics approaches all promise to elevate the potential contributions of biomarkers to discovery science, translation, clinical trials, and the practice of cardiovascular medicine. © 2015 American Heart Association, Inc.

  20. Implementation of proteomic biomarkers: making it work.

    Science.gov (United States)

    Mischak, Harald; Ioannidis, John P A; Argiles, Angel; Attwood, Teresa K; Bongcam-Rudloff, Erik; Broenstrup, Mark; Charonis, Aristidis; Chrousos, George P; Delles, Christian; Dominiczak, Anna; Dylag, Tomasz; Ehrich, Jochen; Egido, Jesus; Findeisen, Peter; Jankowski, Joachim; Johnson, Robert W; Julien, Bruce A; Lankisch, Tim; Leung, Hing Y; Maahs, David; Magni, Fulvio; Manns, Michael P; Manolis, Efthymios; Mayer, Gert; Navis, Gerjan; Novak, Jan; Ortiz, Alberto; Persson, Frederik; Peter, Karlheinz; Riese, Hans H; Rossing, Peter; Sattar, Naveed; Spasovski, Goce; Thongboonkerd, Visith; Vanholder, Raymond; Schanstra, Joost P; Vlahou, Antonia

    2012-09-01

    While large numbers of proteomic biomarkers have been described, they are generally not implemented in medical practice. We have investigated the reasons for this shortcoming, focusing on hurdles downstream of biomarker verification, and describe major obstacles and possible solutions to ease valid biomarker implementation. Some of the problems lie in suboptimal biomarker discovery and validation, especially lack of validated platforms with well-described performance characteristics to support biomarker qualification. These issues have been acknowledged and are being addressed, raising the hope that valid biomarkers may start accumulating in the foreseeable future. However, successful biomarker discovery and qualification alone does not suffice for successful implementation. Additional challenges include, among others, limited access to appropriate specimens and insufficient funding, the need to validate new biomarker utility in interventional trials, and large communication gaps between the parties involved in implementation. To address this problem, we propose an implementation roadmap. The implementation effort needs to involve a wide variety of stakeholders (clinicians, statisticians, health economists, and representatives of patient groups, health insurance, pharmaceutical companies, biobanks, and regulatory agencies). Knowledgeable panels with adequate representation of all these stakeholders may facilitate biomarker evaluation and guide implementation for the specific context of use. This approach may avoid unwarranted delays or failure to implement potentially useful biomarkers, and may expedite meaningful contributions of the biomarker community to healthcare. © 2012 The Authors. European Journal of Clinical Investigation © 2012 Stichting European Society for Clinical Investigation Journal Foundation.

  1. Volatility Discovery

    DEFF Research Database (Denmark)

    Dias, Gustavo Fruet; Scherrer, Cristina; Papailias, Fotis

    The price discovery literature investigates how homogenous securities traded on different markets incorporate information into prices. We take this literature one step further and investigate how these markets contribute to stochastic volatility (volatility discovery). We formally show...... that the realized measures from homogenous securities share a fractional stochastic trend, which is a combination of the price and volatility discovery measures. Furthermore, we show that volatility discovery is associated with the way that market participants process information arrival (market sensitivity......). Finally, we compute volatility discovery for 30 actively traded stocks in the U.S. and report that Nyse and Arca dominate Nasdaq....

  2. Discovering Biomarkers within the Genomic Landscape of Renal Cell Carcinoma

    Science.gov (United States)

    A, Sankin

    2016-01-01

    Recent advances in molecular sequencing technology have led to the discovery of numerous biomarkers in renal cell carcinoma (RCC). These biomarkers have the potential to predict clinical outcomes and aid in clinical management decisions. The following commentary is a review of the preliminary data on some of the most promising genetic biomarker candidates. PMID:27104219

  3. Discovery, detection and use of biomarkers

    Energy Technology Data Exchange (ETDEWEB)

    Swanson, Basil I.; Mukundan, Harshini; Sakamuri, Rama Murthy

    2017-12-05

    Provided herein are systems for and methods of capturing, detecting, quantifying, and characterizing target moieties that are characterized by having a lipophilic portion of sufficient size and chemical composition whereby the target moiety inserts (or partitions) into a lipid assembly. Examples of such assays employ synthetic lipid constructs such as supported bilayers which are used to capture target moieties; other example assays exploit the natural absorption of compounds into natural lipid constructs such as HDL or LDL particles or cell membranes to capture target moieties. In specific embodiments, the target moieties are bacterial pathogen associated molecular pattern (PAMP) molecules or compounds not yet identified as PAMP molecules. Also provided are methods of determining PAMP molecule fingerprints and profiles that are linked to (indicative of) bacterial infection, disease states or progression, development of antibiotic resistance, and so forth, as well as these fingerprints, profiles and methods of using them.

  4. Analysis of urinary 8-isoprostane as an oxidative stress biomarker by stable isotope dilution using automated online in-tube solid-phase microextraction coupled with liquid chromatography-tandem mass spectrometry.

    Science.gov (United States)

    Mizuno, Keisuke; Kataoka, Hiroyuki

    2015-08-10

    We have developed a simple and sensitive method for the determination of the oxidative stress biomarker 8-isoprostane (8-IP) in human urine by automated online in-tube solid-phase microextraction (SPME) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) using a Zorbax Eclipse XDB-8 column and 0.1% formic acid/methanol (25/75, v/v) as a mobile phase. Electrospray MS/MS for 8-IP was performed on an API 4000 triple quadruple mass spectrometer in negative ion mode. The optimum in-tube SPME conditions were 20 draw/eject cycles with a sample size of 40 μL using a Carboxen 1006 PLOT capillary column for the extraction. The extracted compounds were easily desorbed from the capillary by passage of the mobile phase, and no carryover was observed. Total analysis time of this method including online extraction and analysis was about 30 min for each sample. The in-tube SPME LC-MS/MS method showed good linearity in the concentration range of 20-1000 pg/mL with a correlation coefficient r = 0.9999 for 8-IP using a stable isotope-labeled internal standard, 8-IP-d4. The detection limit of 8-IP was 3.3 pg/mL and the proposed method showed 42-fold higher sensitivity than the direct injection method. The intra-day and inter-day precisions (relative standard deviations) were below 5.0% and 8.5% (n = 5), respectively. This method was applied successfully to the analysis of urine samples without pretreatment or interference peaks. The recovery rates of 8-IP spiked into urine samples were above 92%. This method is useful for assessing the effects of oxidative stress and antioxidant intake. Copyright © 2015 Elsevier B.V. All rights reserved.

  5. Beyond Discovery

    DEFF Research Database (Denmark)

    Korsgaard, Steffen; Sassmannshausen, Sean Patrick

    2017-01-01

    In this chapter we explore four alternatives to the dominant discovery view of entrepreneurship; the development view, the construction view, the evolutionary view, and the Neo-Austrian view. We outline the main critique points of the discovery presented in these four alternatives, as well as the...

  6. Hepcidin- A Burgeoning Biomarker

    Directory of Open Access Journals (Sweden)

    Hemkant Manikrao Deshmukh

    2017-10-01

    Full Text Available The discovery of hepcidin has triggered a virtual ignition of studies on iron metabolism and related disorders. The peptide hormone hepcidin is a key homeostatic regulator of iron metabolism. The synthesis of hepcidin is induced by systemic iron levels and by inflammatory stimuli. Several human diseases are associated with variations in hepcidin concentrations. The evaluation of hepcidin in biological fluids is therefore a promising device in the diagnosis and management of medical situations in which iron metabolism is affected. Thus, it made us to recapitulate role of hepcidin as biomarker.

  7. Automated Discovery of Flight Track Anomalies

    Data.gov (United States)

    National Aeronautics and Space Administration — As new technologies are developed to handle the complexities of the Next Generation Air Transportation System (NextGen), it is increasingly important to address both...

  8. The automated discovery of hybrid processes

    NARCIS (Netherlands)

    Maggi, F.M.; Slaats, T.; Reijers, H.A.

    2014-01-01

    The declarative-procedural dichotomy is highly relevant when choosing the most suitable process modeling language to represent a discovered process. Less-structured processes with a high level of variability can be described in a more compact way using a declarative language. By contrast, procedural

  9. The Automated Discovery of Hybrid Processes

    DEFF Research Database (Denmark)

    Slaats, Tijs; Reijers, Hajo; Maggi, Fabrizio Maria

    2014-01-01

    The declarative-procedural dichotomy is highly relevant when choosing the most suitable process modeling language to represent a discovered process. Less-structured processes with a high level of variability can be described in a more compact way using a declarative language. By contrast, procedu...

  10. Automating Information Discovery Within the Invisible Web

    Science.gov (United States)

    Sweeney, Edwina; Curran, Kevin; Xie, Ermai

    A Web crawler or spider crawls through the Web looking for pages to index, and when it locates a new page it passes the page on to an indexer. The indexer identifies links, keywords, and other content and stores these within its database. This database is searched by entering keywords through an interface and suitable Web pages are returned in a results page in the form of hyperlinks accompanied by short descriptions. The Web, however, is increasingly moving away from being a collection of documents to a multidimensional repository for sounds, images, audio, and other formats. This is leading to a situation where certain parts of the Web are invisible or hidden. The term known as the "Deep Web" has emerged to refer to the mass of information that can be accessed via the Web but cannot be indexed by conventional search engines. The concept of the Deep Web makes searches quite complex for search engines. Google states that the claim that conventional search engines cannot find such documents as PDFs, Word, PowerPoint, Excel, or any non-HTML page is not fully accurate and steps have been taken to address this problem by implementing procedures to search items such as academic publications, news, blogs, videos, books, and real-time information. However, Google still only provides access to a fraction of the Deep Web. This chapter explores the Deep Web and the current tools available in accessing it.

  11. Plasma proteomics to identify biomarkers - Application to cardiovascular diseases

    DEFF Research Database (Denmark)

    Beck, Hans Christian; Overgaard, Martin; Melholt Rasmussen, Lars

    2015-01-01

    There is an unmet need for new cardiovascular biomarkers. Despite this only few biomarkers for the diagnosis or screening of cardiovascular diseases have been implemented in the clinic. Thousands of proteins can be analysed in plasma by mass spectrometry-based proteomics technologies. Therefore......, this technology may therefore identify new biomarkers that previously have not been associated with cardiovascular diseases. In this review, we summarize the key challenges and considerations, including strategies, recent discoveries and clinical applications in cardiovascular proteomics that may lead...... to the discovery of novel cardiovascular biomarkers....

  12. Discovery and validation of plasma-protein biomarker panels for the detection of colorectal cancer and advanced adenoma in a Danish collection of samples from patients referred for diagnostic colonoscopy

    DEFF Research Database (Denmark)

    Blume, John E.; Wilhelmsen, Michael; Benz, Ryan W.

    2016-01-01

    and utilization of such a resource is an important step in the development of blood-based biomarker tests for colorectal cancer.Methods: We have created a subject data and biological sample resource, Endoscopy II, which is based on 4698 individuals referred for diagnostic colonoscopy in Denmark between May 2010...

  13. Biomarkers in inflammatory bowel diseases

    DEFF Research Database (Denmark)

    Bennike, Tue; Birkelund, Svend; Stensballe, Allan

    2014-01-01

    or stool later can be screened for. When considering the protein complexity encountered in intestinal biopsy-samples and the recent development within the field of mass spectrometry driven quantitative proteomics, a more thorough and accurate biomarker discovery endeavor could today be performed than ever......Unambiguous diagnosis of the two main forms of inflammatory bowel diseases (IBD): Ulcerative colitis (UC) and Crohn's disease (CD), represents a challenge in the early stages of the diseases. The diagnosis may be established several years after the debut of symptoms. Hence, protein biomarkers...... for early and accurate diagnostic could help clinicians improve treatment of the individual patients. Moreover, the biomarkers could aid physicians to predict disease courses and in this way, identify patients in need of intensive treatment. Patients with low risk of disease flares may avoid treatment...

  14. Biomarker discovery in biological specimens (plasma, hair, liver and kidney) of diabetic mice based upon metabolite profiling using ultra-performance liquid chromatography with electrospray ionization time-of-flight mass spectrometry.

    Science.gov (United States)

    Tsutsui, Haruhito; Maeda, Toshio; Min, Jun Zhe; Inagaki, Shinsuke; Higashi, Tatsuya; Kagawa, Yoshiyuki; Toyo'oka, Toshimasa

    2011-05-12

    The number of diabetic patients has recently been increasing worldwide. Diabetes is a multifactorial disorder based on environmental factors and genetic background. In many cases, diabetes is asymptomatic for a long period and the patient is not aware of the disease. Therefore, the potential biomarker(s), leading to the early detection and/or prevention of diabetes mellitus, are strongly required. However, the diagnosis of the prediabetic state in humans is a very difficult issue, because the lifestyle is variable in each person. Although the development of a diagnosis method in humans is the goal of our research, the extraction and structural identification of biomarker candidates in several biological specimens (i.e., plasma, hair, liver and kidney) of ddY strain mice, which undergo naturally occurring diabetes along with aging, were carried out based upon a metabolite profiling study. The low-molecular-mass compounds including metabolites in the biological specimens of diabetic mice (ddY-H) and normal mice (ddY-L) were globally separated by ultra-performance liquid chromatography (UPLC) using different reversed-phase columns (i.e., T3-C18 and HS-F5) and detected by electrospray ionization time-of-flight mass spectrometry (ESI-TOF-MS). The biomarker candidates related to diabetes mellitus were extracted from a multivariate statistical analysis, such as an orthogonal partial least-squares-discriminant analysis (OPLS-DA), followed by a database search, such as ChemSpider, KEGG and HMDB. Many metabolites and unknown compounds in each biological specimen were detected as the biomarker candidates related to diabetic mellitus. Among them, the elucidation of the chemical structures of several possible metabolites, including more than two biological specimens, was carried out along with the comparison of the tandem MS/MS analyses using authentic compounds. One metabolite was clearly identified as N-acetyl-L-leucine based upon the MS/MS spectra and the retention time on

  15. Bioinformatics in translational drug discovery.

    Science.gov (United States)

    Wooller, Sarah K; Benstead-Hume, Graeme; Chen, Xiangrong; Ali, Yusuf; Pearl, Frances M G

    2017-08-31

    Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications. © 2017 The Author(s).

  16. Application of magnetic sensors in automation control

    International Nuclear Information System (INIS)

    Hou Chunhong; Qian Zhenghong

    2011-01-01

    Controls in automation need speed and position feedback. The feedback device is often referred to as encoder. Feedback technology includes mechanical, optical, and magnetic, etc. All advance with new inventions and discoveries. Magnetic sensing as a feedback technology offers certain advantages over other technologies like optical one. With new discoveries like GMR (Giant Magneto-Resistance), TMR (Tunneling Magneto-Resistance) becoming feasible for commercialization, more and more applications will be using advanced magnetic sensors in automation. This paper offers a general review on encoder and applications of magnetic sensors in automation control.

  17. Discovery of biomarker combinations that predict periodontal health or disease with high accuracy from GCF samples based on high-throughput proteomic analysis and mixed-integer linear optimization.

    Science.gov (United States)

    Baliban, Richard C; Sakellari, Dimitra; Li, Zukui; Guzman, Yannis A; Garcia, Benjamin A; Floudas, Christodoulos A

    2013-02-01

    To identify optimal combination(s) of proteomic based biomarkers in gingival crevicular fluid (GCF) samples from chronic periodontitis (CP) and periodontally healthy individuals and validate the predictions through known and blind test sets. GCF samples were collected from 96 CP and periodontally healthy subjects and analysed using high-performance liquid chromatography, tandem mass spectrometry and the PILOT_PROTEIN algorithm. A mixed-integer linear optimization (MILP) model was then developed to identify the optimal combination of biomarkers which could clearly distinguish a blind subject sample as healthy or diseased. A thorough cross-validation of the MILP model capability was performed on a training set of 55 samples and greater than 99% accuracy was consistently achieved when annotating the testing set samples as healthy or diseased. The model was then trained on all 55 samples and tested on two different blind test sets, and using an optimal combination of 7 human proteins and 3 bacterial proteins, the model was able to correctly predict 40 out of 41 healthy and diseased samples. The proposed large-scale proteomic analysis and MILP model led to the identification of novel combinations of biomarkers for consistent diagnosis of periodontal status with greater than 95% predictive accuracy. © 2012 John Wiley & Sons A/S.

  18. Genomic biomarkers and genetic risk factors in amyotrophic lateral sclerosis

    NARCIS (Netherlands)

    Saris, C.G.J.

    2013-01-01

    In this thesis, we focus our biomarker discovery on gene expression in blood of an ALS mouse model and ALS patients to find ALS specific gene activity. 1. To explore the potential of blood or muscle transcriptome changes as a diagnostic biomarker. We hypothesized that blood gene expression reflects

  19. Data Discovery

    Directory of Open Access Journals (Sweden)

    Gerhard Weikum

    2013-07-01

    Full Text Available Discovery of documents, data sources, facts, and opinions is at the very heart of digital information and knowledge services. Being able to search, discover, compile, and analyse relevant information for a user’s specific tasks is of utmost importance in science (e.g., computational life sciences, digital humanities, etc., business (e.g., market and media analytics, customer relationship management, etc. , and society at large (e.g., consumer information, traffic logistics, health discussions, etc..

  20. Cosmic Discovery

    Science.gov (United States)

    Harwit, Martin

    1984-04-01

    In the remarkable opening section of this book, a well-known Cornell astronomer gives precise thumbnail histories of the 43 basic cosmic discoveries - stars, planets, novae, pulsars, comets, gamma-ray bursts, and the like - that form the core of our knowledge of the universe. Many of them, he points out, were made accidentally and outside the mainstream of astronomical research and funding. This observation leads him to speculate on how many more major phenomena there might be and how they might be most effectively sought out in afield now dominated by large instruments and complex investigative modes and observational conditions. The book also examines discovery in terms of its political, financial, and sociological context - the role of new technologies and of industry and the military in revealing new knowledge; and methods of funding, of peer review, and of allotting time on our largest telescopes. It concludes with specific recommendations for organizing astronomy in ways that will best lead to the discovery of the many - at least sixty - phenomena that Harwit estimates are still waiting to be found.

  1. [Biomarkers in multiple sclerosis].

    Science.gov (United States)

    Fernández, Óscar; Arroyo-González, Rafael; Rodríguez-Antigüedad, Alfredo; García-Merino, Juan A; Comabella, Manuel; Villar, Luisa M; Izquierdo, Guillermo; Tintoré, Mar; Oreja-Guevara, Celia; Álvarez-Cermeño, José C; Meca-Lallana, José E; Prieto, José M; Ramió-Torrentà, Lluís; Martínez-Yélamos, Sergio; Montalban, Xavier

    2013-04-01

    Multiple sclerosis is the most frequent disabling neurological disease in young adults. Its development includes independent processes of inflammation, demyelination, neurodegeneration, gliosis and repair, which are responsible for the heterogeneity and individual variability in the expression of the disease, its prognosis and response to treatment. As part of personalised medicine, the progress made in the search for new biomarkers has identified promising candidates that may be useful for the early diagnosis of the disease, for detecting prognostic and developmental profiles of the disease, and for monitoring the response to treatment. Unfortunately, few of them have been validated adequately, which prevents them from being applied in clinical practice. In view of the latest findings, the experts recommend orienting research in another direction, not so much towards the discovery of new molecules or imaging techniques, but instead towards a clinical validation of these markers, with the aim of fostering translational research. This review offers an update on the information about the biomarkers in multiple sclerosis that have currently been validated and are thus potential candidates, as well as looking at their value in the diagnosis, prognosis, evaluation of the development of the disability caused by the disease and the response to therapy.

  2. Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment.

    Science.gov (United States)

    Phan, John H; Moffitt, Richard A; Stokes, Todd H; Liu, Jian; Young, Andrew N; Nie, Shuming; Wang, May D

    2009-06-01

    Recent advances in biomarker discovery, biocomputing and nanotechnology have raised new opportunities in the emerging fields of personalized medicine (in which disease detection, diagnosis and therapy are tailored to each individual's molecular profile) and predictive medicine (in which genetic and molecular information is used to predict disease development, progression and clinical outcome). Here, we discuss advanced biocomputing tools for cancer biomarker discovery and multiplexed nanoparticle probes for cancer biomarker profiling, in addition to the prospects for and challenges involved in correlating biomolecular signatures with clinical outcome. This bio-nano-info convergence holds great promise for molecular diagnosis and individualized therapy of cancer and other human diseases.

  3. Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment

    Science.gov (United States)

    Phan, John H.; Moffitt, Richard A.; Stokes, Todd H.; Liu, Jian; Young, Andrew N.; Nie, Shuming; Wang, May D.

    2013-01-01

    Recent advances in biomarker discovery, biocomputing, and nanotechnology have raised new opportunities for the emerging field of personalized medicine in which disease detection, diagnosis, and therapy are tailored to each individual’s molecular profile, and also for predictive medicine that uses genetic/molecular information to predict disease development, progression, and clinical outcome. Here we discuss advanced biocomputing tools for cancer biomarker discovery and multiplexed nanoparticle probes for cancer biomarker profiling, together with prospects and challenges in correlating biomolecular signatures with clinical outcome. This bio-nano-info convergence holds great promise for molecular diagnosis and individualized therapy of cancer and other human diseases. PMID:19409634

  4. DNA Methylation Biomarkers: Cancer and Beyond

    Directory of Open Access Journals (Sweden)

    Thomas Mikeska

    2014-09-01

    Full Text Available Biomarkers are naturally-occurring characteristics by which a particular pathological process or disease can be identified or monitored. They can reflect past environmental exposures, predict disease onset or course, or determine a patient’s response to therapy. Epigenetic changes are such characteristics, with most epigenetic biomarkers discovered to date based on the epigenetic mark of DNA methylation. Many tissue types are suitable for the discovery of DNA methylation biomarkers including cell-based samples such as blood and tumor material and cell-free DNA samples such as plasma. DNA methylation biomarkers with diagnostic, prognostic and predictive power are already in clinical trials or in a clinical setting for cancer. Outside cancer, strong evidence that complex disease originates in early life is opening up exciting new avenues for the detection of DNA methylation biomarkers for adverse early life environment and for estimation of future disease risk. However, there are a number of limitations to overcome before such biomarkers reach the clinic. Nevertheless, DNA methylation biomarkers have great potential to contribute to personalized medicine throughout life. We review the current state of play for DNA methylation biomarkers, discuss the barriers that must be crossed on the way to implementation in a clinical setting, and predict their future use for human disease.

  5. The discovery of how gender influences age immunological mechanisms in health and disease, and the identification of ageing gender-specific biomarkers, could lead to specifically tailored treatment and ultimately improve therapeutic success rates

    Directory of Open Access Journals (Sweden)

    Berghella Anna

    2012-11-01

    Full Text Available Abstract The control of human health and diseases in the elderly population is becoming a challenge, since mean age and life expectation are progressively increasing as well as chronic degenerative diseases. These disorders are of complex diagnosis and they are difficult to be treated, but it is hoped that the predictive medicine will lead to more specific and effective treatment by using specific markers to identify persons with high risk of developing disease, before the clinical manifestation. Peripheral blood targets and biomarkers are currently the most practical, non-invasive means of disease diagnosing, predicting prognosis and therapeutic response. Human longevity is directly correlated with the optimal functioning of the immune system. Recent findings indicate that the sexual dimorphism of T helper (Th cytokine pathways and the regulation of Th cell network homeostasis are normally present in the immune response and undergoes to adverse changes with ageing. Furthermore, immune senescence affects both men and women, but it does not affect them equally. Therefore, we hypothesize that the comprehension of the interferences between these gender specific pathways, the ageing immunological mechanism in pathological or healthy state and the current therapies, could lead to specifically tailored treatment and eventually improve the therapeutic success rates. Reaching this aim requires the identification of ageing gender-specific biomarkers that could easily reveal the above mentioned correlations.

  6. Library Automation

    OpenAIRE

    Dhakne, B. N.; Giri, V. V; Waghmode, S. S.

    2010-01-01

    New technologies library provides several new materials, media and mode of storing and communicating the information. Library Automation reduces the drudgery of repeated manual efforts in library routine. By use of library automation collection, Storage, Administration, Processing, Preservation and communication etc.

  7. Discovery Mondays

    CERN Multimedia

    2003-01-01

    Many people don't realise quite how much is going on at CERN. Would you like to gain first-hand knowledge of CERN's scientific and technological activities and their many applications? Try out some experiments for yourself, or pick the brains of the people in charge? If so, then the «Lundis Découverte» or Discovery Mondays, will be right up your street. Starting on May 5th, on every first Monday of the month you will be introduced to a different facet of the Laboratory. CERN staff, non-scientists, and members of the general public, everyone is welcome. So tell your friends and neighbours and make sure you don't miss this opportunity to satisfy your curiosity and enjoy yourself at the same time. You won't have to listen to a lecture, as the idea is to have open exchange with the expert in question and for each subject to be illustrated with experiments and demonstrations. There's no need to book, as Microcosm, CERN's interactive museum, will be open non-stop from 7.30 p.m. to 9 p.m. On the first Discovery M...

  8. Discovery of new natural products by application of X-hitting, a novel algorithm for automated comparison of full UV-spectra, combined with structural determination by NMR spectroscophy

    DEFF Research Database (Denmark)

    Larsen, Thomas Ostenfeld; Petersen, Bent O.; Duus, Jens Øllgaard

    2005-01-01

    X-hitting, a newly developed algorithm for automated comparison of UV data, has been used for the tracking of two novel spiro-quinazoline metabolites, lapatins A (1)andB(2), in a screening study targeting quinazolines. The structures of 1 and 2 were elucidated by analysis of spectroscopic data...

  9. Coronary Artery-Bypass-Graft Surgery Increases the Plasma Concentration of Exosomes Carrying a Cargo of Cardiac MicroRNAs: An Example of Exosome Trafficking Out of the Human Heart with Potential for Cardiac Biomarker Discovery.

    Directory of Open Access Journals (Sweden)

    Costanza Emanueli

    Full Text Available Exosome nanoparticles carry a composite cargo, including microRNAs (miRs. Cultured cardiovascular cells release miR-containing exosomes. The exosomal trafficking of miRNAs from the heart is largely unexplored. Working on clinical samples from coronary-artery by-pass graft (CABG surgery, we investigated if: 1 exosomes containing cardiac miRs and hence putatively released by cardiac cells increase in the circulation after surgery; 2 circulating exosomes and exosomal cardiac miRs correlate with cardiac troponin (cTn, the current "gold standard" surrogate biomarker of myocardial damage.The concentration of exosome-sized nanoparticles was determined in serial plasma samples. Cardiac-expressed (miR-1, miR-24, miR-133a/b, miR-208a/b, miR-210, non-cardiovascular (miR-122 and quality control miRs were measured in whole plasma and in plasma exosomes. Linear regression analyses were employed to establish the extent to which the circulating individual miRs, exosomes and exosomal cardiac miR correlated with cTn-I. Cardiac-expressed miRs and the nanoparticle number increased in the plasma on completion of surgery for up to 48 hours. The exosomal concentration of cardiac miRs also increased after CABG. Cardiac miRs in the whole plasma did not correlate significantly with cTn-I. By contrast cTn-I was positively correlated with the plasma exosome level and the exosomal cardiac miRs.The plasma concentrations of exosomes and their cargo of cardiac miRs increased in patients undergoing CABG and were positively correlated with hs-cTnI. These data provide evidence that CABG induces the trafficking of exosomes from the heart to the peripheral circulation. Future studies are necessary to investigate the potential of circulating exosomes as clinical biomarkers in cardiac patients.

  10. Potential Peripheral Biomarkers for the Diagnosis of Alzheimer's Disease

    Directory of Open Access Journals (Sweden)

    Seema Patel

    2011-01-01

    Full Text Available Advances in the discovery of a peripheral biomarker for the diagnosis of Alzheimer's would provide a way to better detect the onset of this debilitating disease in a manner that is both noninvasive and universally available. This paper examines the current approaches that are being used to discover potential biomarker candidates available in the periphery. The search for a peripheral biomarker that could be utilized diagnostically has resulted in an extensive amount of studies that employ several biological approaches, including the assessment of tissues, genomics, proteomics, epigenetics, and metabolomics. Although a definitive biomarker has yet to be confirmed, advances in the understanding of the mechanisms of the disease and major susceptibility factors have been uncovered and reveal promising possibilities for the future discovery of a useful biomarker.

  11. Process automation

    International Nuclear Information System (INIS)

    Moser, D.R.

    1986-01-01

    Process automation technology has been pursued in the chemical processing industries and to a very limited extent in nuclear fuel reprocessing. Its effective use has been restricted in the past by the lack of diverse and reliable process instrumentation and the unavailability of sophisticated software designed for process control. The Integrated Equipment Test (IET) facility was developed by the Consolidated Fuel Reprocessing Program (CFRP) in part to demonstrate new concepts for control of advanced nuclear fuel reprocessing plants. A demonstration of fuel reprocessing equipment automation using advanced instrumentation and a modern, microprocessor-based control system is nearing completion in the facility. This facility provides for the synergistic testing of all chemical process features of a prototypical fuel reprocessing plant that can be attained with unirradiated uranium-bearing feed materials. The unique equipment and mission of the IET facility make it an ideal test bed for automation studies. This effort will provide for the demonstration of the plant automation concept and for the development of techniques for similar applications in a full-scale plant. A set of preliminary recommendations for implementing process automation has been compiled. Some of these concepts are not generally recognized or accepted. The automation work now under way in the IET facility should be useful to others in helping avoid costly mistakes because of the underutilization or misapplication of process automation. 6 figs

  12. OPEN DATA FOR DISCOVERY SCIENCE.

    Science.gov (United States)

    Payne, Philip R O; Huang, Kun; Shah, Nigam H; Tenenbaum, Jessica

    2017-01-01

    The modern healthcare and life sciences ecosystem is moving towards an increasingly open and data-centric approach to discovery science. This evolving paradigm is predicated on a complex set of information needs related to our collective ability to share, discover, reuse, integrate, and analyze open biological, clinical, and population level data resources of varying composition, granularity, and syntactic or semantic consistency. Such an evolution is further impacted by a concomitant growth in the size of data sets that can and should be employed for both hypothesis discovery and testing. When such open data can be accessed and employed for discovery purposes, a broad spectrum of high impact end-points is made possible. These span the spectrum from identification of de novo biomarker complexes that can inform precision medicine, to the repositioning or repurposing of extant agents for new and cost-effective therapies, to the assessment of population level influences on disease and wellness. Of note, these types of uses of open data can be either primary, wherein open data is the substantive basis for inquiry, or secondary, wherein open data is used to augment or enrich project-specific or proprietary data that is not open in and of itself. This workshop is concerned with the key challenges, opportunities, and methodological best practices whereby open data can be used to drive the advancement of discovery science in all of the aforementioned capacities.

  13. Tuberculosis: advances and challenges in development of new diagnostics and biomarkers.

    Science.gov (United States)

    Walzl, Gerhard; McNerney, Ruth; du Plessis, Nelita; Bates, Matthew; McHugh, Timothy D; Chegou, Novel N; Zumla, Alimuddin

    2018-03-23

    Tuberculosis remains the leading cause of death from an infectious disease worldwide. Early and accurate diagnosis and detection of drug-sensitive and drug-resistant tuberculosis is essential for achieving global tuberculosis control. Despite the introduction of the Xpert MTB/RIF assay as the first-line rapid tuberculosis diagnostic test, the gap between global estimates of incidence and new case notifications is 4·1 million people. More accurate, rapid, and cost-effective screening tests are needed to improve case detection. Diagnosis of extrapulmonary tuberculosis and tuberculosis in children, people living with HIV, and pregnant women remains particularly problematic. The diagnostic molecular technology landscape has continued to expand, including the development of tests for resistance to several antituberculosis drugs. Biomarkers are urgently needed to indicate progression from latent infection to clinical disease, to predict risk of reactivation after cure, and to provide accurate endpoints for drug and vaccine trials. Sophisticated bioinformatic computational tools and systems biology approaches are being applied to the discovery and validation of biomarkers, with substantial progress taking place. New data have been generated from the study of T-cell responses and T-cell function, serological studies, flow cytometric-based assays, and protein and gene expression studies. Alternative diagnostic strategies under investigation as potential screening and triaging tools include non-sputum-based detection with breath-based tests and automated digital radiography. We review developments and key achievements in the search for new tuberculosis diagnostics and biomarkers. We highlight gaps and challenges in evaluation and rollout of new diagnostics and biomarkers, and prioritise areas needing further investment, including impact assessment and cost-benefit studies. Copyright © 2018 Elsevier Ltd. All rights reserved.

  14. Towards Robot Scientists for autonomous scientific discovery.

    Science.gov (United States)

    Sparkes, Andrew; Aubrey, Wayne; Byrne, Emma; Clare, Amanda; Khan, Muhammed N; Liakata, Maria; Markham, Magdalena; Rowland, Jem; Soldatova, Larisa N; Whelan, Kenneth E; Young, Michael; King, Ross D

    2010-01-04

    We review the main components of autonomous scientific discovery, and how they lead to the concept of a Robot Scientist. This is a system which uses techniques from artificial intelligence to automate all aspects of the scientific discovery process: it generates hypotheses from a computer model of the domain, designs experiments to test these hypotheses, runs the physical experiments using robotic systems, analyses and interprets the resulting data, and repeats the cycle. We describe our two prototype Robot Scientists: Adam and Eve. Adam has recently proven the potential of such systems by identifying twelve genes responsible for catalysing specific reactions in the metabolic pathways of the yeast Saccharomyces cerevisiae. This work has been formally recorded in great detail using logic. We argue that the reporting of science needs to become fully formalised and that Robot Scientists can help achieve this. This will make scientific information more reproducible and reusable, and promote the integration of computers in scientific reasoning. We believe the greater automation of both the physical and intellectual aspects of scientific investigations to be essential to the future of science. Greater automation improves the accuracy and reliability of experiments, increases the pace of discovery and, in common with conventional laboratory automation, removes tedious and repetitive tasks from the human scientist.

  15. Advances in synthetic peptides reagent discovery

    Science.gov (United States)

    Adams, Bryn L.; Sarkes, Deborah A.; Finch, Amethist S.; Stratis-Cullum, Dimitra N.

    2013-05-01

    Bacterial display technology offers a number of advantages over competing display technologies (e.g, phage) for the rapid discovery and development of peptides with interaction targeted to materials ranging from biological hazards through inorganic metals. We have previously shown that discovery of synthetic peptide reagents utilizing bacterial display technology is relatively simple and rapid to make laboratory automation possible. This included extensive study of the protective antigen system of Bacillus anthracis, including development of discovery, characterization, and computational biology capabilities for in-silico optimization. Although the benefits towards CBD goals are evident, the impact is far-reaching due to our ability to understand and harness peptide interactions that are ultimately extendable to the hybrid biomaterials of the future. In this paper, we describe advances in peptide discovery including, new target systems (e.g. non-biological materials), advanced library development and clone analysis including integrated reporting.

  16. Automated External Defibrillator

    Science.gov (United States)

    ... To Health Topics / Automated External Defibrillator Automated External Defibrillator Also known as What Is An automated external ... in survival. Training To Use an Automated External Defibrillator Learning how to use an AED and taking ...

  17. Allergic asthma biomarkers using systems approaches

    Directory of Open Access Journals (Sweden)

    Gaurab eSircar

    2014-01-01

    Full Text Available Asthma is characterized by lung inflammation caused by complex interaction between the immune system and environmental factors such as allergens and inorganic pollutants. Recent research in this field is focused on discovering new biomarkers associated with asthma pathogenesis. This review illustrates updated research associating biomarkers of allergic asthma and their potential use in systems biology of the disease. We focus on biomolecules with altered expression, which may serve as inflammatory, diagnostic and therapeutic biomarkers of asthma discovered in human or experimental asthma model using genomic, proteomic and epigenomic approaches for gene and protein expression profiling. These include high-throughput technologies such as state of the art microarray and proteomics Mass Spectrometry (MS platforms. Emerging concepts of molecular interactions and pathways may provide new insights in searching potential clinical biomarkers. We summarized certain pathways with significant linkage to asthma pathophysiology by analyzing the compiled biomarkers. Systems approaches with this data can identify the regulating networks, which will eventually identify the key biomarkers to be used for diagnostics and drug discovery.

  18. Single Domain Antibodies as New Biomarker Detectors

    Science.gov (United States)

    Fischer, Katja; Leow, Chiuan Yee; Chuah, Candy; McCarthy, James

    2017-01-01

    Biomarkers are defined as indicators of biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention. Biomarkers have been widely used for early detection, prediction of response after treatment, and for monitoring the progression of diseases. Antibodies represent promising tools for recognition of biomarkers, and are widely deployed as analytical tools in clinical settings. For immunodiagnostics, antibodies are now exploited as binders for antigens of interest across a range of platforms. More recently, the discovery of antibody surface display and combinatorial chemistry techniques has allowed the exploration of new binders from a range of animals, for instance variable domains of new antigen receptors (VNAR) from shark and variable heavy chain domains (VHH) or nanobodies from camelids. These single domain antibodies (sdAbs) have some advantages over conventional murine immunoglobulin owing to the lack of a light chain, making them the smallest natural biomarker binders thus far identified. In this review, we will discuss several biomarkers used as a means to validate diseases progress. The potential functionality of modern singe domain antigen binders derived from phylogenetically early animals as new biomarker detectors for current diagnostic and research platforms development will be described. PMID:29039819

  19. Single Domain Antibodies as New Biomarker Detectors

    Directory of Open Access Journals (Sweden)

    Chiuan Herng Leow

    2017-10-01

    Full Text Available Biomarkers are defined as indicators of biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention. Biomarkers have been widely used for early detection, prediction of response after treatment, and for monitoring the progression of diseases. Antibodies represent promising tools for recognition of biomarkers, and are widely deployed as analytical tools in clinical settings. For immunodiagnostics, antibodies are now exploited as binders for antigens of interest across a range of platforms. More recently, the discovery of antibody surface display and combinatorial chemistry techniques has allowed the exploration of new binders from a range of animals, for instance variable domains of new antigen receptors (VNAR from shark and variable heavy chain domains (VHH or nanobodies from camelids. These single domain antibodies (sdAbs have some advantages over conventional murine immunoglobulin owing to the lack of a light chain, making them the smallest natural biomarker binders thus far identified. In this review, we will discuss several biomarkers used as a means to validate diseases progress. The potential functionality of modern singe domain antigen binders derived from phylogenetically early animals as new biomarker detectors for current diagnostic and research platforms development will be described.

  20. Mass spectrometry-based proteomic quest for diabetes biomarkers.

    Science.gov (United States)

    Shao, Shiying; Guo, Tiannan; Aebersold, Ruedi

    2015-06-01

    Diabetes mellitus (DM) is a metabolic disorder characterized by chronic hyperglycemia, which affects hundreds of millions of individuals worldwide. Early diagnosis and complication prevention of DM are helpful for disease treatment. However, currently available DM diagnostic markers fail to achieve the goals. Identification of new diabetic biomarkers assisted by mass spectrometry (MS)-based proteomics may offer solution for the clinical challenges. Here, we review the current status of biomarker discovery in DM, and describe the pressure cycling technology (PCT)-Sequential Window Acquisition of all Theoretical fragment-ion (SWATH) workflow for sample-processing, biomarker discovery and validation, which may accelerate the current quest for DM biomarkers. This article is part of a Special Issue entitled: Medical Proteomics. Copyright © 2014 Elsevier B.V. All rights reserved.

  1. Library Automation.

    Science.gov (United States)

    Husby, Ole

    1990-01-01

    The challenges and potential benefits of automating university libraries are reviewed, with special attention given to cooperative systems. Aspects discussed include database size, the role of the university computer center, storage modes, multi-institutional systems, resource sharing, cooperative system management, networking, and intelligent…

  2. Use of biomarkers in ALS drug development and clinical trials.

    Science.gov (United States)

    Bakkar, Nadine; Boehringer, Ashley; Bowser, Robert

    2015-05-14

    The past decade has seen a dramatic increase in the discovery of candidate biomarkers for ALS. These biomarkers typically can either differentiate ALS from control subjects or predict disease course (slow versus fast progression). At the same time, late-stage clinical trials for ALS have failed to generate improved drug treatments for ALS patients. Incorporation of biomarkers into the ALS drug development pipeline and the use of biologic and/or imaging biomarkers in early- and late-stage ALS clinical trials have been absent and only recently pursued in early-phase clinical trials. Further clinical research studies are needed to validate biomarkers for disease progression and develop biomarkers that can help determine that a drug has reached its target within the central nervous system. In this review we summarize recent progress in biomarkers across ALS model systems and patient population, and highlight continued research directions for biomarkers that stratify the patient population to enrich for patients that may best respond to a drug candidate, monitor disease progression and track drug responses in clinical trials. It is crucial that we further develop and validate ALS biomarkers and incorporate these biomarkers into the ALS drug development process. This article is part of a Special Issue entitled ALS complex pathogenesis. Copyright © 2014 Elsevier B.V. All rights reserved.

  3. Automated DBS microsampling, microscale automation and microflow LC-MS for therapeutic protein PK.

    Science.gov (United States)

    Zhang, Qian; Tomazela, Daniela; Vasicek, Lisa A; Spellman, Daniel S; Beaumont, Maribel; Shyong, BaoJen; Kenny, Jacqueline; Fauty, Scott; Fillgrove, Kerry; Harrelson, Jane; Bateman, Kevin P

    2016-04-01

    Reduce animal usage for discovery-stage PK studies for biologics programs using microsampling-based approaches and microscale LC-MS. We report the development of an automated DBS-based serial microsampling approach for studying the PK of therapeutic proteins in mice. Automated sample preparation and microflow LC-MS were used to enable assay miniaturization and improve overall assay throughput. Serial sampling of mice was possible over the full 21-day study period with the first six time points over 24 h being collected using automated DBS sample collection. Overall, this approach demonstrated comparable data to a previous study using single mice per time point liquid samples while reducing animal and compound requirements by 14-fold. Reduction in animals and drug material is enabled by the use of automated serial DBS microsampling for mice studies in discovery-stage studies of protein therapeutics.

  4. Automated Motivic Analysis

    DEFF Research Database (Denmark)

    Lartillot, Olivier

    2016-01-01

    Motivic analysis provides very detailed understanding of musical composi- tions, but is also particularly difficult to formalize and systematize. A computational automation of the discovery of motivic patterns cannot be reduced to a mere extraction of all possible sequences of descriptions....... The systematic approach inexorably leads to a proliferation of redundant structures that needs to be addressed properly. Global filtering techniques cause a drastic elimination of interesting structures that damages the quality of the analysis. On the other hand, a selection of closed patterns allows...... for lossless compression. The structural complexity resulting from successive repetitions of patterns can be controlled through a simple modelling of cycles. Generally, motivic patterns cannot always be defined solely as sequences of descriptions in a fixed set of dimensions: throughout the descriptions...

  5. Advances in biomarkers of major depressive disorder.

    Science.gov (United States)

    Huang, Tiao-Lai; Lin, Chin-Chuen

    2015-01-01

    Major depressive disorder (MDD) is characterized by mood, vegetative, cognitive, and even psychotic symptoms and signs that can cause substantial impairments in quality of life and functioning. Biomarkers are measurable indicators that could help diagnosing MDD or predicting treatment response. In this chapter, lipid profiles, immune/inflammation, and neurotrophic factor pathways that have long been implicated in the pathogenesis of MDD are discussed. Then, pharmacogenetics and epigenetics of serotonin transport and its metabolism pathway, brain-derived neurotrophic factor, and abnormality of hypothalamo-pituitary-adrenocortical axis also revealed new biomarkers. Lastly, new techniques, such as proteomics and metabolomics, which allow researchers to approach the studying of MDD with new directions and make new discoveries are addressed. In the future, more data are needed regarding pathophysiology of MDD, including protein levels, single nucleotide polymorphism, epigenetic regulation, and clinical data in order to better identify reliable and consistent biomarkers for diagnosis, treatment choice, and outcome prediction. © 2015 Elsevier Inc. All rights reserved.

  6. Differential membrane proteomics using 18O-labeling to identify biomarkers for cholangiocarcinoma

    DEFF Research Database (Denmark)

    Kristiansen, Troels Zakarias; Harsha, H C; Grønborg, Mads

    2008-01-01

    Quantitative proteomic methodologies allow profiling of hundreds to thousands of proteins in a high-throughput fashion. This approach is increasingly applied to cancer biomarker discovery to identify proteins that are differentially regulated in cancers. Fractionation of protein samples based...

  7. Combination of biomarkers

    DEFF Research Database (Denmark)

    Thurfjell, Lennart; Lötjönen, Jyrki; Lundqvist, Roger

    2012-01-01

    The New National Institute on Aging-Alzheimer's Association diagnostic guidelines for Alzheimer's disease (AD) incorporate biomarkers in the diagnostic criteria and suggest division of biomarkers into two categories: Aβ accumulation and neuronal degeneration or injury.......The New National Institute on Aging-Alzheimer's Association diagnostic guidelines for Alzheimer's disease (AD) incorporate biomarkers in the diagnostic criteria and suggest division of biomarkers into two categories: Aβ accumulation and neuronal degeneration or injury....

  8. Target discovery from data mining approaches.

    Science.gov (United States)

    Yang, Yongliang; Adelstein, S James; Kassis, Amin I

    2012-02-01

    Data mining of available biomedical data and information has greatly boosted target discovery in the 'omics' era. Target discovery is the key step in the biomarker and drug discovery pipeline to diagnose and fight human diseases. In biomedical science, the 'target' is a broad concept ranging from molecular entities (such as genes, proteins and miRNAs) to biological phenomena (such as molecular functions, pathways and phenotypes). Within the context of biomedical science, data mining refers to a bioinformatics approach that combines biological concepts with computer tools or statistical methods that are mainly used to discover, select and prioritize targets. In response to the huge demand of data mining for target discovery in the 'omics' era, this review explicates various data mining approaches and their applications to target discovery with emphasis on text and microarray data analysis. Two emerging data mining approaches, chemogenomic data mining and proteomic data mining, are briefly introduced. Also discussed are the limitations of various data mining approaches found in the level of database integration, the quality of data annotation, sample heterogeneity and the performance of analytical and mining tools. Tentative strategies of integrating different data sources for target discovery, such as integrated text mining with high-throughput data analysis and integrated mining with pathway databases, are introduced. Published by Elsevier Ltd.

  9. Blood based proteomic biomarkers of Alzheimer’s disease pathology

    Directory of Open Access Journals (Sweden)

    Alison Louise Baird

    2015-11-01

    Full Text Available The complexity of Alzheimer’s disease (AD and its long prodromal phase poses challenges for early diagnosis and yet allows for the possibility of the development of disease modifying treatments for secondary prevention. It is therefore of importance to develop biomarkers, in particular in the preclinical or early phases that reflect the pathological characteristics of the disease and moreover could be of utility in triaging subjects for preventative therapeutic clinical trials. Much research has sought biomarkers for diagnostic purposes by comparing affected people to unaffected controls. However, given that AD pathology precedes disease onset, a pathology endophenotype design for biomarker discovery creates the opportunity for detection of much earlier markers of disease. Blood based biomarkers potentially provide a minimally invasive option for this purpose and research in the field has adopted various omics approaches in order to achieve this. This review will therefore examine the current literature regarding blood based proteomic biomarkers of AD and its associated pathology.

  10. Does the biomarker search paradigm need re-booting?

    Directory of Open Access Journals (Sweden)

    Hurst Robert E

    2009-02-01

    Full Text Available Abstract The clinical problem of bladder cancer is its high recurrence and progression, and that the most sensitive and specific means of monitoring is cystoscopy, which is invasive and has poor patient compliance. Biomarkers for recurrence and progression could make a great contribution, but in spite of decades of research, no biomarkers are commercially available with the requisite sensitivity and specificity. In the post-genomic age, the means to search the entire genome for biomarkers has become available, but the conventional approaches to biomarker discovery are entirely inadequate to yield results with the new technology. Finding clinically useful biomarker panels with sensitivity and specificity equal to that of cystoscopy is a problem of systems biology.

  11. New sepsis biomarkers

    Directory of Open Access Journals (Sweden)

    Dolores Limongi

    2016-06-01

    Full Text Available Sepsis remains a leading cause of death in the intensive care units and in all age groups worldwide. Early recognition and diagnosis are key to achieving improved outcomes. Therefore, novel biomarkers that might better inform clinicians treating such patients are surely needed. The main attributes of successful biomarkers would be high sensitivity, specificity, possibility of bedside monitoring and financial accessibility. A panel of sepsis biomarkers along with currently used laboratory tests will facilitate earlier diagnosis, timely treatment and improved outcome may be more effective than single biomarkers. In this review, we summarize the most recent advances on sepsis biomarkers evaluated in clinical and experimental studies.

  12. Machine Learning for Quantification of Small Vessel Disease Imaging Biomarkers

    NARCIS (Netherlands)

    Ghafoorian, M.

    2018-01-01

    This thesis is devoted to developing fully automated methods for quantification of small vessel disease imaging bio-markers, namely WMHs and lacunes, using vari- ous machine learning/deep learning and computer vision techniques. The rest of the thesis is organized as follows: Chapter 2 describes

  13. Gastric Cancer (Biomarkers Knowledgebase (GCBKB: A Curated and Fully Integrated Knowledgebase of Putative Biomarkers Related to Gastric Cancer

    Directory of Open Access Journals (Sweden)

    Bernett T.K. Lee

    2006-01-01

    Full Text Available The Gastric Cancer (Biomarkers Knowledgebase (GCBKB (http://biomarkers.bii.a-star.edu.sg/background/gastricCancerBiomarkersKb.php is a curated and fully integrated knowledgebase that provides data relating to putative biomarkers that may be used in the diagnosis and prognosis of gastric cancer. It is freely available to all users. The data contained in the knowledgebase was derived from a large literature source and the putative biomarkers therein have been annotated with data from the public domain. The knowledgebase is maintained by a curation team who update the data from a defined source. As well as mining data from the literature, the knowledgebase will also be populated with unpublished experimental data from investigators working in the gastric cancer biomarker discovery field. Users can perform searches to identify potential markers defined by experiment type, tissue type and disease state. Search results may be saved, manipulated and retrieved at a later date. As far as the authors are aware this is the first open access database dedicated to the discovery and investigation of gastric cancer biomarkers.

  14. Robust automated mass spectra interpretation and chemical formula calculation using mixed integer linear programming.

    Science.gov (United States)

    Baran, Richard; Northen, Trent R

    2013-10-15

    Untargeted metabolite profiling using liquid chromatography and mass spectrometry coupled via electrospray ionization is a powerful tool for the discovery of novel natural products, metabolic capabilities, and biomarkers. However, the elucidation of the identities of uncharacterized metabolites from spectral features remains challenging. A critical step in the metabolite identification workflow is the assignment of redundant spectral features (adducts, fragments, multimers) and calculation of the underlying chemical formula. Inspection of the data by experts using computational tools solving partial problems (e.g., chemical formula calculation for individual ions) can be performed to disambiguate alternative solutions and provide reliable results. However, manual curation is tedious and not readily scalable or standardized. Here we describe an automated procedure for the robust automated mass spectra interpretation and chemical formula calculation using mixed integer linear programming optimization (RAMSI). Chemical rules among related ions are expressed as linear constraints and both the spectra interpretation and chemical formula calculation are performed in a single optimization step. This approach is unbiased in that it does not require predefined sets of neutral losses and positive and negative polarity spectra can be combined in a single optimization. The procedure was evaluated with 30 experimental mass spectra and was found to effectively identify the protonated or deprotonated molecule ([M + H](+) or [M - H](-)) while being robust to the presence of background ions. RAMSI provides a much-needed standardized tool for interpreting ions for subsequent identification in untargeted metabolomics workflows.

  15. Autonomous Systems: Habitat Automation

    Data.gov (United States)

    National Aeronautics and Space Administration — The Habitat Automation Project Element within the Autonomous Systems Project is developing software to automate the automation of habitats and other spacecraft. This...

  16. An Automation Planning Primer.

    Science.gov (United States)

    Paynter, Marion

    1988-01-01

    This brief planning guide for library automation incorporates needs assessment and evaluation of options to meet those needs. A bibliography of materials on automation planning and software reviews, library software directories, and library automation journals is included. (CLB)

  17. Prospective of ischemic stroke biomarkers

    Directory of Open Access Journals (Sweden)

    Szewczak Krzysztof

    2017-06-01

    Full Text Available Methods currently used in brain vascular disorder diagnostics are neither fast enough nor clear-out; thus, there exists a necessity of finding new types of testing which could enlarge and complete the actual panel of diagnostics or be an alternative to current methods. The discovery of sensitive and specific biomarkers of ischemic brain stroke will improve the effects of treatment and will help to assess the progress or complications of the disease. The relevant diagnosis of ischemic stroke (IS within the first 4.5 hours after the initial symptoms allows for the initiation of treatment with recombinant tissue plasminogen activators which limits the magnitude of negative changes in the brain and which enhance the final effectiveness of therapy. The potential biomarkers which are under investigation are substances involved in the processes of coagulation and fibrinolysis, and are of molecules released from damaged vascular endothelial cells and from nerves and cardiac tissue. The analyzed substances are typical of oxidative stress, apoptosis, excitotoxicity and damage of the blood brain barrier.

  18. Integrative analysis to select cancer candidate biomarkers to targeted validation

    Science.gov (United States)

    Heberle, Henry; Domingues, Romênia R.; Granato, Daniela C.; Yokoo, Sami; Canevarolo, Rafael R.; Winck, Flavia V.; Ribeiro, Ana Carolina P.; Brandão, Thaís Bianca; Filgueiras, Paulo R.; Cruz, Karen S. P.; Barbuto, José Alexandre; Poppi, Ronei J.; Minghim, Rosane; Telles, Guilherme P.; Fonseca, Felipe Paiva; Fox, Jay W.; Santos-Silva, Alan R.; Coletta, Ricardo D.; Sherman, Nicholas E.; Paes Leme, Adriana F.

    2015-01-01

    Targeted proteomics has flourished as the method of choice for prospecting for and validating potential candidate biomarkers in many diseases. However, challenges still remain due to the lack of standardized routines that can prioritize a limited number of proteins to be further validated in human samples. To help researchers identify candidate biomarkers that best characterize their samples under study, a well-designed integrative analysis pipeline, comprising MS-based discovery, feature selection methods, clustering techniques, bioinformatic analyses and targeted approaches was performed using discovery-based proteomic data from the secretomes of three classes of human cell lines (carcinoma, melanoma and non-cancerous). Three feature selection algorithms, namely, Beta-binomial, Nearest Shrunken Centroids (NSC), and Support Vector Machine-Recursive Features Elimination (SVM-RFE), indicated a panel of 137 candidate biomarkers for carcinoma and 271 for melanoma, which were differentially abundant between the tumor classes. We further tested the strength of the pipeline in selecting candidate biomarkers by immunoblotting, human tissue microarrays, label-free targeted MS and functional experiments. In conclusion, the proposed integrative analysis was able to pre-qualify and prioritize candidate biomarkers from discovery-based proteomics to targeted MS. PMID:26540631

  19. Topology Discovery Using Cisco Discovery Protocol

    OpenAIRE

    Rodriguez, Sergio R.

    2009-01-01

    In this paper we address the problem of discovering network topology in proprietary networks. Namely, we investigate topology discovery in Cisco-based networks. Cisco devices run Cisco Discovery Protocol (CDP) which holds information about these devices. We first compare properties of topologies that can be obtained from networks deploying CDP versus Spanning Tree Protocol (STP) and Management Information Base (MIB) Forwarding Database (FDB). Then we describe a method of discovering topology ...

  20. Determination of the oxidative stress biomarker urinary 8-hydroxy-2'-deoxyguanosine by automated on-line in-tube solid-phase microextraction coupled with liquid chromatography-tandem mass spectrometry.

    Science.gov (United States)

    Kataoka, Hiroyuki; Mizuno, Keisuke; Oda, Eri; Saito, Akihiro

    2016-04-15

    A simple and sensitive method for the determination of 8-hydroxy-2'-deoxyguanosine (8-OHdG), a marker of oxidative DNA damage in human urine, was developed using automated on-line in-tube solid-phase microextraction (SPME) coupled with stable isotope-dilution liquid chromatography-tandem mass spectrometry (LC-MS/MS). Creatinine was also analyzed simultaneously to normalize urine volume by the in-tube SPME LC-MS/MS method, and 8-OHdG and creatinine were separated within 3 min using a Zorbax Eclipse XDB-C8 column. Electrospray MS/MS for these compounds was performed on an API 4000 triple quadruple mass spectrometer in the positive ion mode by multiple reaction monitoring. The optimum in-tube SPME conditions were 20 draw/eject cycles of 40 μL of sample at a flow rate of 200 μL/min using a Carboxen 1006 PLOT capillary column as an extraction device. The extracted compounds were easily desorbed from the capillary by passage of the mobile phase, and no carryover was observed. The calibration curve for 8-OHdG using its stable isotope-labeled internal standard was linear in the range of 0.05-10 ng/mL, and the detection limit was 8.3 pg/mL. The intra-day and inter-day precision (relative standard deviations) were below 3.1% and 9.6% (n=5), respectively. This method was applied successfully to the analysis of urine samples without any other pretreatment and interference peaks, with good recovery rates above 91% in spiked urine samples. The limits of quantification of 8-OHdG and creatinine in 0.1 mL urine samples were about 0.32 and 0.69 ng/mL (S/N=10), respectively. This method was utilized to assess the effects of smoking, green tea drinking and alcohol drinking on the urinary excretion of 8-OHdG. Copyright © 2015 Elsevier B.V. All rights reserved.

  1. Discovery and Validation of Proteomic Biomarkers for Radiation Exposure

    Science.gov (United States)

    2012-02-01

    Biomagnetism and Magnetic Biosystems Based on ;\\lfolecular Recognition Processes (ESF-ElvfBO Symposium), Sant Feliu de Guixols, Spain, Sept 22-27, 2007. 4. S...valve senso r arrays," European Symposium on Biomagnetism and Magnetic Biosystems Based on Molecular Recognition Processes (ESF-EMBO Symposium

  2. Computational and Experimental Approaches to Cancer Biomarker Discovery

    DEFF Research Database (Denmark)

    Krzystanek, Marcin

    at least two fundamental mechanisms responsible for DNA aberrations present in a given tumor: 1) active mutational processes caused either by endogenous or exogenous factors, for example chemical agents such as tobacco smoke or cancer cytotoxics, or by active enzymatic processes such as APOBEC related...... with a purely biological, experimental approach where the effects of treatment with cytotoxic agents or defects in DNA repair mechanisms can be individually quantified and turned into mutational signatures.In the second part of the thesis I present work towards identification and improvement of the current...... are expected.This work, together with manifold of efforts being done all over the world, is hopefully a step towards implementation of personalized medicine and better treatments for cancer patients. ...

  3. THE DISCOVERY OF BIOMARKERS OF VIRAL INFECTIVITY BY MASS SPECTROMETRY

    Science.gov (United States)

    Over the past three decades, the CDC and the U.S. EPA have collected and reported data relating to occurrences and causes of waterborne-disease outbreaks in the United States. Thirty nine outbreaks associated with drinking water were reported during 1999-2000. According to CDC'...

  4. Seasonal allergic rhinitis and systems biology-oriented biomarker discovery

    NARCIS (Netherlands)

    Baars, E.W.; Nierop, A.F.M.; Savelkoul, H.F.J.

    2015-01-01

    There is an increasing interest in science and medicine in the systems approach. Instead of the reductionist approach that focuses on the physical and chemical properties of the individual components, systems biology aims to describe, understand, and explain from the complex biological systems

  5. Analytical strategies in lipidomics and applications in disease biomarker discovery

    NARCIS (Netherlands)

    Hu, C.; Heijden, R. van der; Wang, M.; Greef, J. van der; Hankemeier, T.; Xu, G.

    2009-01-01

    Lipidomics is a lipid-targeted metabolomics approach aiming at comprehensive analysis of lipids in biological systems. Recently, lipid profiling, or so-called lipidomics research, has captured increased attention due to the well-recognized roles of lipids in numerous human diseases to which

  6. Challenges for red blood cell biomarker discovery through proteomics

    NARCIS (Netherlands)

    Barasa, B.A.|info:eu-repo/dai/nl/341538353; Slijper, M.|info:eu-repo/dai/nl/146303989

    2014-01-01

    Red blood cells are rather unique body cells, since they have lost all organelles when mature, which results in lack of potential to replace proteins that have lost their function. They maintain only a few pathways for obtaining energy and reducing power for the key functions they need to fulfill.

  7. Automated Budget System -

    Data.gov (United States)

    Department of Transportation — The Automated Budget System (ABS) automates management and planning of the Mike Monroney Aeronautical Center (MMAC) budget by providing enhanced capability to plan,...

  8. Cardiac biomarkers in Neonatology

    OpenAIRE

    Vijlbrief, D.C.

    2015-01-01

    In this thesis, the role for cardiac biomarkers in neonatology was investigated. Several clinically relevant results were reported. In term and preterm infants, hypoxia and subsequent adaptation play an important role in cardiac biomarker elevation. The elevated natriuretic peptides are indicative of abnormal function; elevated troponins are suggestive for cardiomyocyte damage. This methodology makes these biomarkers of additional value in the treatment of newborn infants, separate or as a co...

  9. Automation 2017

    CERN Document Server

    Zieliński, Cezary; Kaliczyńska, Małgorzata

    2017-01-01

    This book consists of papers presented at Automation 2017, an international conference held in Warsaw from March 15 to 17, 2017. It discusses research findings associated with the concepts behind INDUSTRY 4.0, with a focus on offering a better understanding of and promoting participation in the Fourth Industrial Revolution. Each chapter presents a detailed analysis of a specific technical problem, in most cases followed by a numerical analysis, simulation and description of the results of implementing the solution in a real-world context. The theoretical results, practical solutions and guidelines presented are valuable for both researchers working in the area of engineering sciences and practitioners looking for solutions to industrial problems. .

  10. Marketing automation

    Directory of Open Access Journals (Sweden)

    TODOR Raluca Dania

    2017-01-01

    Full Text Available The automation of the marketing process seems to be nowadays, the only solution to face the major changes brought by the fast evolution of technology and the continuous increase in supply and demand. In order to achieve the desired marketing results, businessis have to employ digital marketing and communication services. These services are efficient and measurable thanks to the marketing technology used to track, score and implement each campaign. Due to the technical progress, the marketing fragmentation, demand for customized products and services on one side and the need to achieve constructive dialogue with the customers, immediate and flexible response and the necessity to measure the investments and the results on the other side, the classical marketing approached had changed continue to improve substantially.

  11. Urine Exosomes: An Emerging Trove of Biomarkers.

    Science.gov (United States)

    Street, J M; Koritzinsky, E H; Glispie, D M; Star, R A; Yuen, P S T

    Exosomes are released by most cells and can be isolated from all biofluids including urine. Exosomes are small vesicles formed as part of the endosomal pathway that contain cellular material surrounded by a lipid bilayer that can be traced to the plasma membrane. Exosomes are potentially a more targeted source of material for biomarker discovery than unfractionated urine, and provide diagnostic and pathophysiological information without an invasive tissue biopsy. Cytoplasmic contents including protein, mRNA, miRNA, and lipids have all been studied within the exosomal fraction. Many prospective urinary exosomal biomarkers have been successfully identified for a variety of kidney or genitourinary tract conditions; detection of systemic conditions may also be possible. Isolation and analysis of exosomes can be achieved by several approaches, although many require specialized equipment or involve lengthy protocols. The need for timely analysis in the clinical setting has driven considerable innovation with several promising options recently emerging. Consensus on exosome isolation, characterization, and normalization procedures would resolve critical clinical translational bottlenecks for existing candidate exosomal biomarkers and provide a template for additional discovery studies. 2017 Published by Elsevier Inc.

  12. Polar Domain Discovery with Sparkler

    Science.gov (United States)

    Duerr, R.; Khalsa, S. J. S.; Mattmann, C. A.; Ottilingam, N. K.; Singh, K.; Lopez, L. A.

    2017-12-01

    The scientific web is vast and ever growing. It encompasses millions of textual, scientific and multimedia documents describing research in a multitude of scientific streams. Most of these documents are hidden behind forms which require user action to retrieve and thus can't be directly accessed by content crawlers. These documents are hosted on web servers across the world, most often on outdated hardware and network infrastructure. Hence it is difficult and time-consuming to aggregate documents from the scientific web, especially those relevant to a specific domain. Thus generating meaningful domain-specific insights is currently difficult. We present an automated discovery system (Figure 1) using Sparkler, an open-source, extensible, horizontally scalable crawler which facilitates high throughput and focused crawling of documents pertinent to a particular domain such as information about polar regions. With this set of highly domain relevant documents, we show that it is possible to answer analytical questions about that domain. Our domain discovery algorithm leverages prior domain knowledge to reach out to commercial/scientific search engines to generate seed URLs. Subject matter experts then annotate these seed URLs manually on a scale from highly relevant to irrelevant. We leverage this annotated dataset to train a machine learning model which predicts the `domain relevance' of a given document. We extend Sparkler with this model to focus crawling on documents relevant to that domain. Sparkler avoids disruption of service by 1) partitioning URLs by hostname such that every node gets a different host to crawl and by 2) inserting delays between subsequent requests. With an NSF-funded supercomputer Wrangler, we scaled our domain discovery pipeline to crawl about 200k polar specific documents from the scientific web, within a day.

  13. The use of mass spectrometry for analysing metabolite biomarkers in epidemiology

    DEFF Research Database (Denmark)

    Lind, Mads Vendelbo; Savolainen, Otto I; Ross, Alastair B

    2016-01-01

    measurement tools. One tool that is increasingly being used for measuring biomarkers in epidemiological cohorts is mass spectrometry (MS), because of the high specificity and sensitivity of MS-based methods and the expanding range of biomarkers that can be measured. Further, the ability of MS to quantify many...... biomarkers simultaneously is advantageously compared to single biomarker methods. However, as with all methods used to measure biomarkers, there are a number of pitfalls to consider which may have an impact on results when used in epidemiology. In this review we discuss the use of MS for biomarker analyses......, focusing on metabolites and their application and potential issues related to large-scale epidemiology studies, the use of MS "omics" approaches for biomarker discovery and how MS-based results can be used for increasing biological knowledge gained from epidemiological studies. Better understanding...

  14. Biosignature Discovery for Substance Use Disorders Using Statistical Learning.

    Science.gov (United States)

    Baurley, James W; McMahan, Christopher S; Ervin, Carolyn M; Pardamean, Bens; Bergen, Andrew W

    2018-02-01

    There are limited biomarkers for substance use disorders (SUDs). Traditional statistical approaches are identifying simple biomarkers in large samples, but clinical use cases are still being established. High-throughput clinical, imaging, and 'omic' technologies are generating data from SUD studies and may lead to more sophisticated and clinically useful models. However, analytic strategies suited for high-dimensional data are not regularly used. We review strategies for identifying biomarkers and biosignatures from high-dimensional data types. Focusing on penalized regression and Bayesian approaches, we address how to leverage evidence from existing studies and knowledge bases, using nicotine metabolism as an example. We posit that big data and machine learning approaches will considerably advance SUD biomarker discovery. However, translation to clinical practice, will require integrated scientific efforts. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. The State of the Art in Library Discovery 2010

    Science.gov (United States)

    Breeding, Marshall

    2010-01-01

    Resource discovery tops the charts as the foremost issue within the realm of library automation. As a new year commences, the author sees a more pressing need to accelerate the pace with which libraries deliver content and services in ways that users will find compelling, relevant, and convenient. The evolution of the web advances relentlessly,…

  16. Biomarkers of Diabetic Retinopathy.

    Science.gov (United States)

    Ting, Daniel Shu Wei; Tan, Kara-Anne; Phua, Val; Tan, Gavin Siew Wei; Wong, Chee Wai; Wong, Tien Yin

    2016-12-01

    Diabetic retinopathy (DR), a leading cause of acquired vision loss, is a microvascular complication of diabetes. While traditional risk factors for diabetic retinopathy including longer duration of diabetes, poor blood glucose control, and dyslipidemia are helpful in stratifying patient's risk for developing retinopathy, many patients without these traditional risk factors develop DR; furthermore, there are persons with long diabetes duration who do not develop DR. Thus, identifying biomarkers to predict DR or to determine therapeutic response is important. A biomarker can be defined as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. Incorporation of biomarkers into risk stratification of persons with diabetes would likely aid in early diagnosis and guide treatment methods for those with DR or with worsening DR. Systemic biomarkers of DR include serum measures including genomic, proteomic, and metabolomics biomarkers. Ocular biomarkers including tears and vitreous and retinal vascular structural changes have also been studied extensively to prognosticate the risk of DR development. The current studies on biomarkers are limited by the need for larger sample sizes, cross-validation in different populations and ethnic groups, and time-efficient and cost-effective analytical techniques. Future research is important to explore novel DR biomarkers that are non-invasive, rapid, economical, and accurate to help reduce the incidence and progression of DR in people with diabetes.

  17. Omics-based biomarkers: current status and potential use in the clinic

    Directory of Open Access Journals (Sweden)

    Héctor Quezada

    2017-05-01

    Full Text Available In recent years, the use of high-throughput omics technologies has led to the rapid discovery of many candidate biomarkers. However, few of them have made the transition to the clinic. In this review, the promise of omics technologies to contribute to the process of biomarker development is described. An overview of the current state in this area is presented with examples of genomics, proteomics, transcriptomics, metabolomics and microbiomics biomarkers in the field of oncology, along with some proposed strategies to accelerate their validation and translation to improve the care of patients with neoplasms. The inherent complexity underlying neoplasms combined with the requirement of developing well-designed biomarker discovery processes based on omics technologies present a challenge for the effective development of biomarkers that may be useful in guiding therapies, addressing disease risks, and predicting clinical outcomes.

  18. Both Automation and Paper.

    Science.gov (United States)

    Purcell, Royal

    1988-01-01

    Discusses the concept of a paperless society and the current situation in library automation. Various applications of automation and telecommunications are addressed, and future library automation is considered. Automation at the Monroe County Public Library in Bloomington, Indiana, is described as an example. (MES)

  19. Biomarkers of Hypoxic Ischemic Encephalopathy in Newborns

    Directory of Open Access Journals (Sweden)

    Martha V. Douglas-Escobar

    2012-11-01

    Full Text Available As neonatal intensive care has evolved, the focus has shifted from improving mortality alone to an effort to improve both mortality and morbidity. The most frequent source of neonatal brain injury occurs as a result of hypoxic-ischemic injury. Hypoxic-ischemic injury occurs in about 2 of 1,000 full-term infants and severe injured infants will have lifetime disabilities and neurodevelopmental delays. Most recently, remarkable efforts toward neuroprotection have been started with the advent of therapeutic hypothermia and a key step in the evolution of neonatal neuroprotection is the discovery of biomarkers that enable the clinician-scientist to screen infants for brain injury, monitor progression of disease, identify injured brain regions, and assess efficacy of neuroprotective clinical trials. Lastly, biomarkers offer great hope identifying when an injury occurred shedding light on the potential pathophysiology and the most effective therapy. In this article, we will review biomarkers of HIE including S100b, neuron specific enolase, umbilical cord IL-6, CK-BB, GFAP, myelin basic protein, UCHL-1, and pNF-H. We hope to contribute to the awareness, validation and clinical use of established as well as novel neonatal brain injury biomarkers.

  20. Academic Drug Discovery Centres

    DEFF Research Database (Denmark)

    Kirkegaard, Henriette Schultz; Valentin, Finn

    2014-01-01

    Academic drug discovery centres (ADDCs) are seen as one of the solutions to fill the innovation gap in early drug discovery, which has proven challenging for previous organisational models. Prior studies of ADDCs have identified the need to analyse them from the angle of their economic and organi......Academic drug discovery centres (ADDCs) are seen as one of the solutions to fill the innovation gap in early drug discovery, which has proven challenging for previous organisational models. Prior studies of ADDCs have identified the need to analyse them from the angle of their economic...... their performance....

  1. MrBUMP: an automated pipeline for molecular replacement

    OpenAIRE

    Keegan, Ronan M.; Winn, Martyn D.

    2007-01-01

    A novel automation pipeline for macromolecular structure solution by molecular replacement is described. There is a special emphasis on the discovery and preparation of a large number of search models, all of which can be passed to the core molecular-replacement programs. For routine molecular-replacement problems, the pipeline automates what a crystallographer might do and its value is simply one of convenience. For more difficult cases, the pipeline aims to discover the particular template ...

  2. "Eureka, Eureka!" Discoveries in Science

    Science.gov (United States)

    Agarwal, Pankaj

    2011-01-01

    Accidental discoveries have been of significant value in the progress of science. Although accidental discoveries are more common in pharmacology and chemistry, other branches of science have also benefited from such discoveries. While most discoveries are the result of persistent research, famous accidental discoveries provide a fascinating…

  3. COPD Exacerbation Biomarkers Validated Using Multiple Reaction Monitoring Mass Spectrometry.

    Directory of Open Access Journals (Sweden)

    Janice M Leung

    Full Text Available Acute exacerbations of chronic obstructive pulmonary disease (AECOPD result in considerable morbidity and mortality. However, there are no objective biomarkers to diagnose AECOPD.We used multiple reaction monitoring mass spectrometry to quantify 129 distinct proteins in plasma samples from patients with COPD. This analytical approach was first performed in a biomarker cohort of patients hospitalized with AECOPD (Cohort A, n = 72. Proteins differentially expressed between AECOPD and convalescent states were chosen using a false discovery rate 1.2. Protein selection and classifier building were performed using an elastic net logistic regression model. The performance of the biomarker panel was then tested in two independent AECOPD cohorts (Cohort B, n = 37, and Cohort C, n = 109 using leave-pair-out cross-validation methods.Five proteins were identified distinguishing AECOPD and convalescent states in Cohort A. Biomarker scores derived from this model were significantly higher during AECOPD than in the convalescent state in the discovery cohort (p<0.001. The receiver operating characteristic cross-validation area under the curve (CV-AUC statistic was 0.73 in Cohort A, while in the replication cohorts the CV-AUC was 0.77 for Cohort B and 0.79 for Cohort C.A panel of five biomarkers shows promise in distinguishing AECOPD from convalescence and may provide the basis for a clinical blood test to diagnose AECOPD. Further validation in larger cohorts is necessary for future clinical translation.

  4. Development and validation of quantitative methods for the analysis of clinical biomarkers of iron metabolism applying stable isotopes

    OpenAIRE

    Konz, Tobías

    2016-01-01

    The continuous discovery of new biomarkers is reflected not only in the scientific literature but also in its application in current clinical practice. The urgent need to determine the values of appropriate clinical parameters (biomarkers) in biological fluids is driving the development of new analytical methodologies capable of providing quantitative, precise, accurate and validated information on the concentration of those biomarkers, both in control individuals as well as in patients affec...

  5. Magnetic Bead-Based Biosensing on an Automated & Integrated Lab-on-a-Disc Platform

    DEFF Research Database (Denmark)

    Uddin, Rokon

    centrifugal microfluidic platform with incorporated readout units. The assays were developed through surface functionalization of micro or nano-sized magnetic beads with specific antibodies or aptamers to specifically bind with the biomarker of interest resulting in the formation of the biomarker......-bridged magnetic bead clusters and hence called ‘agglutination’ assay. The concentration of the analyte or biomarker was quantified based on the size of the clusters. The model biomarkers studied in this project were thrombin – a blood coagulation protein; C-reactive protein – an acute phase protein......-biomarker for inflammatory diseases; and mononuclear white blood cell count – a biomarker for the prognosis of different medical conditions. Furthermore, the concept of the agglutination assay was utilized for a biomarker discovery application by investigating the mechanism of action of a T2D drug - metformin through...

  6. Automated Service Discovery using Autonomous Control Technologies Project

    Data.gov (United States)

    National Aeronautics and Space Administration — With the advent of mobile commerce technologies, the realization of pervasive computing and the formation of ad-hoc networks can be leveraged to the benefit of the...

  7. Automated Atmospheric Composition Dataset Level Metadata Discovery. Difficulties and Surprises

    Science.gov (United States)

    Strub, R. F.; Falke, S. R.; Kempler, S.; Fialkowski, E.; Goussev, O.; Lynnes, C.

    2015-12-01

    The Atmospheric Composition Portal (ACP) is an aggregator and curator of information related to remotely sensed atmospheric composition data and analysis. It uses existing tools and technologies and, where needed, enhances those capabilities to provide interoperable access, tools, and contextual guidance for scientists and value-adding organizations using remotely sensed atmospheric composition data. The initial focus is on Essential Climate Variables identified by the Global Climate Observing System - CH4, CO, CO2, NO2, O3, SO2 and aerosols. This poster addresses our efforts in building the ACP Data Table, an interface to help discover and understand remotely sensed data that are related to atmospheric composition science and applications. We harvested GCMD, CWIC, GEOSS metadata catalogs using machine to machine technologies - OpenSearch, Web Services. We also manually investigated the plethora of CEOS data providers portals and other catalogs where that data might be aggregated. This poster is our experience of the excellence, variety, and challenges we encountered.Conclusions:1.The significant benefits that the major catalogs provide are their machine to machine tools like OpenSearch and Web Services rather than any GUI usability improvements due to the large amount of data in their catalog.2.There is a trend at the large catalogs towards simulating small data provider portals through advanced services. 3.Populating metadata catalogs using ISO19115 is too complex for users to do in a consistent way, difficult to parse visually or with XML libraries, and too complex for Java XML binders like CASTOR.4.The ability to search for Ids first and then for data (GCMD and ECHO) is better for machine to machine operations rather than the timeouts experienced when returning the entire metadata entry at once. 5.Metadata harvest and export activities between the major catalogs has led to a significant amount of duplication. (This is currently being addressed) 6.Most (if not all) Earth science atmospheric composition data providers store a reference to their data at GCMD.

  8. Automated Discovery of Internet Censorship by Web Crawling

    OpenAIRE

    Darer, Alexander; Farnan, Oliver; Wright, Joss

    2018-01-01

    Censorship of the Internet is widespread around the world. As access to the web becomes increasingly ubiquitous, filtering of this resource becomes more pervasive. Transparency about specific content that citizens are denied access to is atypical. To counter this, numerous techniques for maintaining URL filter lists have been proposed by various individuals and organisations that aim to empirical data on censorship for benefit of the public and wider censorship research community. We present ...

  9. Automated Service Discovery using Autonomous Control Technologies, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — With the advent of mobile commerce technologies, the realization of pervasive computing and the formation of ad-hoc networks can be leveraged to the benefit of the...

  10. Biomarkers in Airway Diseases

    Directory of Open Access Journals (Sweden)

    Janice M Leung

    2013-01-01

    Full Text Available The inherent limitations of spirometry and clinical history have prompted clinicians and scientists to search for surrogate markers of airway diseases. Although few biomarkers have been widely accepted into the clinical armamentarium, the authors explore three sources of biomarkers that have shown promise as indicators of disease severity and treatment response. In asthma, exhaled nitric oxide measurements can predict steroid responsiveness and sputum eosinophil counts have been used to titrate anti-inflammatory therapies. In chronic obstructive pulmonary disease, inflammatory plasma biomarkers, such as fibrinogen, club cell secretory protein-16 and surfactant protein D, can denote greater severity and predict the risk of exacerbations. While the multitude of disease phenotypes in respiratory medicine make biomarker development especially challenging, these three may soon play key roles in the diagnosis and management of airway diseases.

  11. amphibian_biomarker_data

    Data.gov (United States)

    U.S. Environmental Protection Agency — Amphibian metabolite data used in Snyder, M.N., Henderson, W.M., Glinski, D.G., Purucker, S. T., 2017. Biomarker analysis of american toad (Anaxyrus americanus) and...

  12. Advancing Alcohol Biomarkers Research

    OpenAIRE

    Bearer, Cynthia F.; Bailey, Shannon M.; Hoek, Jan B.

    2010-01-01

    Biomarkers to detect past alcohol use and identify alcohol-related diseases have long been pursued as important tools for research into alcohol use disorders as well as for clinical and treatment applications and other settings. The National Institute on Alcohol Abuse and Alcoholism (NIAAA) sponsored a workshop titled “Workshop on Biomarkers for Alcohol-Induced Disorders” in June 2008. The intent of this workshop was to review and discuss recent progress in the development and implementation ...

  13. Biomarkers for Ectopic Pregnancy and Pregnancy of Unknown Location

    Science.gov (United States)

    Senapati, Suneeta; Barnhart, Kurt T.

    2013-01-01

    Early pregnancy failure is the most common complication of pregnancy, and 1–2% of all pregnancies will be ectopic. As one of the leading causes of maternal morbidity and mortality, diagnosing ectopic pregnancy and determining the fate of a pregnancy of unknown location are of great clinical concern. Several serum and plasma biomarkers for ectopic pregnancy have been investigated independently and in combination. The following is a review of the state of biomarker discovery and development for ectopic pregnancy and pregnancy of unknown location. PMID:23290746

  14. Developing novel blood-based biomarkers for Alzheimer's disease

    DEFF Research Database (Denmark)

    Snyder, Heather M; Carrillo, Maria C; Grodstein, Francine

    2014-01-01

    Alzheimer's disease is the public health crisis of the 21st century. There is a clear need for a widely available, inexpensive and reliable method to diagnosis Alzheimer's disease in the earliest stages, track disease progression, and accelerate clinical development of new therapeutics. One avenue...... of research being explored is blood based biomarkers. In April 2012, the Alzheimer's Association and the Alzheimer's Drug Discovery Foundation convened top scientists from around the world to discuss the state of blood based biomarker development. This manuscript summarizes the meeting and the resultant...

  15. The Greatest Mathematical Discovery?

    Energy Technology Data Exchange (ETDEWEB)

    Bailey, David H.; Borwein, Jonathan M.

    2010-05-12

    What mathematical discovery more than 1500 years ago: (1) Is one of the greatest, if not the greatest, single discovery in the field of mathematics? (2) Involved three subtle ideas that eluded the greatest minds of antiquity, even geniuses such as Archimedes? (3) Was fiercely resisted in Europe for hundreds of years after its discovery? (4) Even today, in historical treatments of mathematics, is often dismissed with scant mention, or else is ascribed to the wrong source? Answer: Our modern system of positional decimal notation with zero, together with the basic arithmetic computational schemes, which were discovered in India about 500 CE.

  16. Biomarkers in rare diseases.

    Science.gov (United States)

    Ferlini, A; Scotton, C; Novelli, G

    2013-01-01

    Nowadays 7,000 rare diseases (RDs) have been identified with a prevalence less than 5/10,000. Despite of the enormous effort the European Union (EU) has already invested in this field, still 4,000 RDs remain orphan of genetic diagnosis and causative gene identification. The genetic definition of RDs represents a prerequisite for being diagnosed, for having a robust prevention, for entering in a specific standard of care, and ultimately, for being included in clinical trials, often via personalized medicine. It is well established that biomarkers can offer a way to speed up research by understanding the pathophysiological mechanisms of diseases. In particular, biomarkers will offer an invaluable tool for monitoring disease progression, prognosis and response to drug treatment. In this review, we summarize the different types of biomarkers and their importance as well as their translational applications in RDs. We have reviewed the current knowledge on biomarkers state-of-the-art via literature data, specific websites and EU sources regarding past, pending and current projects. Here we provide a comprehensive scenario of biomarkers research, its applications in clinical practice, with special emphasis on translational research applicable to diagnostic and clinical trials. The experience of the EU project BIO-NMD is also mentioned. Biomarkers represent key features in both diagnostics and research on rare diseases and will encounter wide exploitation in translational and personalized medicine. © 2013 S. Karger AG, Basel

  17. Human cervicovaginal fluid biomarkers to predict term and preterm labor

    Science.gov (United States)

    Heng, Yujing J.; Liong, Stella; Permezel, Michael; Rice, Gregory E.; Di Quinzio, Megan K. W.; Georgiou, Harry M.

    2015-01-01

    Preterm birth (PTB; birth before 37 completed weeks of gestation) remains the major cause of neonatal morbidity and mortality. The current generation of biomarkers predictive of PTB have limited utility. In pregnancy, the human cervicovaginal fluid (CVF) proteome is a reflection of the local biochemical milieu and is influenced by the physical changes occurring in the vagina, cervix and adjacent overlying fetal membranes. Term and preterm labor (PTL) share common pathways of cervical ripening, myometrial activation and fetal membranes rupture leading to birth. We therefore hypothesize that CVF biomarkers predictive of labor may be similar in both the term and preterm labor setting. In this review, we summarize some of the existing published literature as well as our team's breadth of work utilizing the CVF for the discovery and validation of putative CVF biomarkers predictive of human labor. Our team established an efficient method for collecting serial CVF samples for optimal 2-dimensional gel electrophoresis resolution and analysis. We first embarked on CVF biomarker discovery for the prediction of spontaneous onset of term labor using 2D-electrophoresis and solution array multiple analyte profiling. 2D-electrophoretic analyses were subsequently performed on CVF samples associated with PTB. Several proteins have been successfully validated and demonstrate that these biomarkers are associated with term and PTL and may be predictive of both term and PTL. In addition, the measurement of these putative biomarkers was found to be robust to the influences of vaginal microflora and/or semen. The future development of a multiple biomarker bed-side test would help improve the prediction of PTB and the clinical management of patients. PMID:26029118

  18. Autonomy and Automation

    Science.gov (United States)

    Shively, Jay

    2017-01-01

    A significant level of debate and confusion has surrounded the meaning of the terms autonomy and automation. Automation is a multi-dimensional concept, and we propose that Remotely Piloted Aircraft Systems (RPAS) automation should be described with reference to the specific system and task that has been automated, the context in which the automation functions, and other relevant dimensions. In this paper, we present definitions of automation, pilot in the loop, pilot on the loop and pilot out of the loop. We further propose that in future, the International Civil Aviation Organization (ICAO) RPAS Panel avoids the use of the terms autonomy and autonomous when referring to automated systems on board RPA. Work Group 7 proposes to develop, in consultation with other workgroups, a taxonomy of Levels of Automation for RPAS.

  19. An automated swimming respirometer

    DEFF Research Database (Denmark)

    STEFFENSEN, JF; JOHANSEN, K; BUSHNELL, PG

    1984-01-01

    An automated respirometer is described that can be used for computerized respirometry of trout and sharks.......An automated respirometer is described that can be used for computerized respirometry of trout and sharks....

  20. Configuration Management Automation (CMA) -

    Data.gov (United States)

    Department of Transportation — Configuration Management Automation (CMA) will provide an automated, integrated enterprise solution to support CM of FAA NAS and Non-NAS assets and investments. CMA...

  1. Context-sensitive service discovery experimental prototype and evaluation

    DEFF Research Database (Denmark)

    Balken, Robin; Haukrogh, Jesper; L. Jensen, Jens

    2007-01-01

    The amount of different networks and services available to users today are increasing. This introduces the need for a way to locate and sort out irrelevant services in the process of discovering available services to a user. This paper describes and evaluates a prototype of an automated discovery...... and selection system, which locates services relevant to a user, based on his/her context and the context of the available services. The prototype includes a multi-level, hierarchical system approach and the introduction of entities called User-nodes, Super-nodes and Root-nodes. These entities separate...... the network in domains that handle the complex distributed service discovery, which is based on dynamically changing context information. In the prototype, a method for performing context-sensitive service discovery has been realised. The service discovery part utilizes UPnP, which has been expanded in order...

  2. Fateful discovery almost forgotten

    CERN Multimedia

    1989-01-01

    "The discovery of the fission of uranium exactly half a century ago is at risk of passing unremarked because of the general ambivalence towards the consequences of this development. Can that be wise?" (4 pages)

  3. Biomarkers in the Diagnosis and Prognosis of Alzheimer's Disease.

    Science.gov (United States)

    Schaffer, Cole; Sarad, Nakia; DeCrumpe, Ashton; Goswami, Disha; Herrmann, Sara; Morales, Jose; Patel, Parth; Osborne, Jim

    2015-10-01

    Alzheimer's disease (AD) is a neurodegenerative disease that inhibits cognitive functions and has no cure. This report reviews the current diagnostic standards for AD with an emphasis on early diagnosis using the cerebrospinal fluid (CSF) biomarkers amyloid-beta, t-tau, and p-tau and fluorodeoxyglucose positron emission tomography imaging. Abnormal levels of these CSF biomarkers and decreased cerebral uptake of glucose have recently been used in the early diagnosis of AD in experimental studies. These promising biomarkers can be measured using immunoassays performed in singleplex or multiplex formats. Although presently, there are no Food and Drug Administration-approved in vitro diagnostics (IVDs) for early detection of AD, a multiplex immunoassay measuring a panel of promising AD biomarkers in CSF may be a likely IVD candidate for the clinical AD diagnostic market. Specifically, the INNO-BIA AlzBio3 immunoassay kit, performed using bead arrays on the xMAP Luminex analyzer, allows simultaneous quantification of amyloid-beta, t-tau, and p-tau biomarkers. AD biomarkers can also be screened using enzyme-linked immunosorbent assays that are offered as laboratory-developed tests. © 2014 Society for Laboratory Automation and Screening.

  4. On the antiproton discovery

    International Nuclear Information System (INIS)

    Piccioni, O.

    1989-01-01

    The author of this article describes his own role in the discovery of the antiproton. Although Segre and Chamberlain received the Nobel Prize in 1959 for its discovery, the author claims that their experimental method was his idea which he communicated to them informally in December 1954. He describes how his application for citizenship (he was Italian), and other scientists' manipulation, prevented him from being at Berkeley to work on the experiment himself. (UK)

  5. Automation in College Libraries.

    Science.gov (United States)

    Werking, Richard Hume

    1991-01-01

    Reports the results of a survey of the "Bowdoin List" group of liberal arts colleges. The survey obtained information about (1) automation modules in place and when they had been installed; (2) financing of automation and its impacts on the library budgets; and (3) library director's views on library automation and the nature of the…

  6. What is a biomarker? Research investments and lack of clinical integration necessitate a review of biomarker terminology and validation schema.

    Science.gov (United States)

    Ptolemy, Adam S; Rifai, Nader

    2010-01-01

    A continual trend of annual growth can be seen within research devoted to the discovery and validation of disease biomarkers within both the natural and clinical sciences. This expansion of intellectual endeavours was quantified through database searches of (a) research grant awards provided by the various branches of the National Institutes of Health (NIH) and (b) academic publications. A search of awards presented between 1986 and 2009 revealed a total of 28,856 grants awarded by the NIH containing the term "biomarker". The total funds for these awards in 2008 and 2009 alone were over $2.5 billion. During the same respective time frames, searches of "biomarker" and either "discovery", "genomics", "proteomics" or "metabolomics" yielded a total of 4,928 NIH grants whose combined funding exceeded $1.2 billion. The derived trend in NIH awards paralleled the annual expansion in "biomarker" literature. A PubMed search for the term, between 1990 and 2009, revealed a total of 441,510 published articles, with 38,457 published in 2008. These enormous investments and academic outputs however have not translated into the expected integration of new biomarkers for patient care. For example no proteomics derived biomarkers are currently being utilized in routine clinical management. This translational chasm necessitates a review of the previously proposed biomarker definitions and evaluation schema. A subsequent discussion of both the analytical and pre-analytical considerations for such research is also presented within. This required knowledge should aid scientists in their pursuit and validation of new biological markers of disease.

  7. The beautiful cell: high-content screening in drug discovery.

    Science.gov (United States)

    Bickle, Marc

    2010-09-01

    The term "high-content screening" has become synonymous with imaging screens using automated microscopes and automated image analysis. The term was coined a little over 10 years ago. Since then the technology has evolved considerably and has established itself firmly in the drug discovery and development industry. Both the instruments and the software controlling the instruments and analyzing the data have come to maturity, so the full benefits of high-content screening can now be realized. Those benefits are the capability of carrying out phenotypic multiparametric cellular assays in an unbiased, fully automated, and quantitative fashion. Automated microscopes and automated image analysis are being applied at all stages of the drug discovery and development pipeline. All major pharmaceutical companies have adopted the technology and it is in the process of being embraced broadly by the academic community. This review aims at describing the current capabilities and limits of the technology as well as highlighting necessary developments that are required to exploit fully the potential of high-content screening and analysis.

  8. Improving the quality of biomarker candidates in untargeted metabolomics via peak table-based alignment of comprehensive two-dimensional gas chromatography-mass spectrometry data.

    Science.gov (United States)

    Bean, Heather D; Hill, Jane E; Dimandja, Jean-Marie D

    2015-05-15

    The potential of high-resolution analytical technologies like GC×GC/TOF MS in untargeted metabolomics and biomarker discovery has been limited by the development of fully automated software that can efficiently align and extract information from multiple chromatographic data sets. In this work we report the first investigation on a peak-by-peak basis of the chromatographic factors that impact GC×GC data alignment. A representative set of 16 compounds of different chromatographic characteristics were followed through the alignment of 63 GC×GC chromatograms. We found that varying the mass spectral match parameter had a significant influence on the alignment for poorly-resolved peaks, especially those at the extremes of the detector linear range, and no influence on well-chromatographed peaks. Therefore, optimized chromatography is required for proper GC×GC data alignment. Based on these observations, a workflow is presented for the conservative selection of biomarker candidates from untargeted metabolomics analyses. Copyright © 2015 Elsevier B.V. All rights reserved.

  9. Tissue- and Serum-Associated Biomarkers of Hepatocellular Carcinoma

    Directory of Open Access Journals (Sweden)

    Ranjit Chauhan

    2016-01-01

    Full Text Available Hepatocellular carcinoma (HCC, one of the leading causes of cancer deaths in the world, is offering a challenge to human beings, with the current modes of treatment being a palliative approach. Lack of proper curative or preventive treatment methods encouraged extensive research around the world with an aim to detect a vaccine or therapeutic target biomolecule that could lead to development of a drug or vaccine against HCC. Biomarkers or biological disease markers have emerged as a potential tool as drug/vaccine targets, as they can accurately diagnose, predict, and even prevent the diseases. Biomarker expression in tissue, serum, plasma, or urine can detect tumor in very early stages of its development and monitor the cancer progression and also the effect of therapeutic interventions. Biomarker discoveries are driven by advanced techniques, such as proteomics, transcriptomics, whole genome sequencing, micro- and micro-RNA arrays, and translational clinics. In this review, an overview of the potential of tissue- and serum-associated HCC biomarkers as diagnostic, prognostic, and therapeutic targets for drug development is presented. In addition, we highlight recently developed micro-RNA, long noncoding RNA biomarkers, and single-nucleotide changes, which may be used independently or as complementary biomarkers. These active investigations going on around the world aimed at conquering HCC might show a bright light in the near future.

  10. Glial biomarkers in human central nervous system disease.

    Science.gov (United States)

    Garden, Gwenn A; Campbell, Brian M

    2016-10-01

    There is a growing understanding that aberrant GLIA function is an underlying factor in psychiatric and neurological disorders. As drug discovery efforts begin to focus on glia-related targets, a key gap in knowledge includes the availability of validated biomarkers to help determine which patients suffer from dysfunction of glial cells or who may best respond by targeting glia-related drug mechanisms. Biomarkers are biological variables with a significant relationship to parameters of disease states and can be used as surrogate markers of disease pathology, progression, and/or responses to drug treatment. For example, imaging studies of the CNS enable localization and characterization of anatomical lesions without the need to isolate tissue for biopsy. Many biomarkers of disease pathology in the CNS involve assays of glial cell function and/or response to injury. Each major glia subtype (oligodendroglia, astroglia and microglia) are connected to a number of important and useful biomarkers. Here, we describe current and emerging glial based biomarker approaches for acute CNS injury and the major categories of chronic nervous system dysfunction including neurodegenerative, neuropsychiatric, neoplastic, and autoimmune disorders of the CNS. These descriptions are highlighted in the context of how biomarkers are employed to better understand the role of glia in human CNS disease and in the development of novel therapeutic treatments. GLIA 2016;64:1755-1771. © 2016 Wiley Periodicals, Inc.

  11. Pathways to new drug discovery in neuropsychiatry.

    Science.gov (United States)

    Berk, Michael

    2012-11-29

    There is currently a crisis in drug discovery for neuropsychiatric disorders, with a profound, yet unexpected drought in new drug development across the spectrum. In this commentary, the sources of this dilemma and potential avenues to redress the issue are explored. These include a critical review of diagnostic issues and of selection of participants for clinical trials, and the mechanisms for identifying new drugs and new drug targets. Historically, the vast majority of agents have been discovered serendipitously or have been modifications of existing agents. Serendipitous discoveries, based on astute clinical observation or data mining, remain a valid option, as is illustrated by the suggestion in the paper by Wahlqvist and colleagues that treatment with sulfonylurea and metformin reduces the risk of affective disorder. However, the identification of agents targeting disorder-related biomarkers is currently proving particularly fruitful. There is considerable hope for genetics as a purist, pathophysiologically valid pathway to drug discovery; however, it is unclear whether the science is ready to meet this promise. Fruitful paradigms will require a break from the orthodoxy, and creativity and risk may well be the fingerprints of success.See related article http://www.biomedcentral.com/1741-7015/10/150.

  12. Pathways to new drug discovery in neuropsychiatry

    Directory of Open Access Journals (Sweden)

    Berk Michael

    2012-11-01

    Full Text Available Abstract There is currently a crisis in drug discovery for neuropsychiatric disorders, with a profound, yet unexpected drought in new drug development across the spectrum. In this commentary, the sources of this dilemma and potential avenues to redress the issue are explored. These include a critical review of diagnostic issues and of selection of participants for clinical trials, and the mechanisms for identifying new drugs and new drug targets. Historically, the vast majority of agents have been discovered serendipitously or have been modifications of existing agents. Serendipitous discoveries, based on astute clinical observation or data mining, remain a valid option, as is illustrated by the suggestion in the paper by Wahlqvist and colleagues that treatment with sulfonylurea and metformin reduces the risk of affective disorder. However, the identification of agents targeting disorder-related biomarkers is currently proving particularly fruitful. There is considerable hope for genetics as a purist, pathophysiologically valid pathway to drug discovery; however, it is unclear whether the science is ready to meet this promise. Fruitful paradigms will require a break from the orthodoxy, and creativity and risk may well be the fingerprints of success. See related article http://www.biomedcentral.com/1741-7015/10/150

  13. A proteomics approach to the identification of biomarkers for psoriasis utilising keratome biopsy

    DEFF Research Database (Denmark)

    Williamson, James C; Scheipers, Peter; Schwämmle, Veit

    2013-01-01

    The discovery of plasma biomarkers for psoriasis vulgaris may aid clinicians in disease grading and monitoring of treatment response. We have therefore developed a proteomics/mass spectrometry based workflow which enables biomarker discovery from psoriasis patient samples. We have utilised keratome...... [Epidermal Fatty Acid Binding Protein]) and more than 30 novel alterations. From this disease localised dataset we further assessed several proteins as potential biomarkers in the plasma of patients with psoriasis versus healthy controls utilising selected reaction monitoring mass spectrometry (SRM-MS/MS)....... skin biopsy, which results in reduced cellular complexity compared to punch biopsy. Furthermore, we applied short term ex vivo culture in order to enrich for a "secretome" sub-proteome reflective of the disease and enriched in potential biomarkers. Using these sample preparation techniques we performed...

  14. Biomarkers of sepsis

    Science.gov (United States)

    2013-01-01

    Sepsis is an unusual systemic reaction to what is sometimes an otherwise ordinary infection, and it probably represents a pattern of response by the immune system to injury. A hyper-inflammatory response is followed by an immunosuppressive phase during which multiple organ dysfunction is present and the patient is susceptible to nosocomial infection. Biomarkers to diagnose sepsis may allow early intervention which, although primarily supportive, can reduce the risk of death. Although lactate is currently the most commonly used biomarker to identify sepsis, other biomarkers may help to enhance lactate’s effectiveness; these include markers of the hyper-inflammatory phase of sepsis, such as pro-inflammatory cytokines and chemokines; proteins such as C-reactive protein and procalcitonin which are synthesized in response to infection and inflammation; and markers of neutrophil and monocyte activation. Recently, markers of the immunosuppressive phase of sepsis, such as anti-inflammatory cytokines, and alterations of the cell surface markers of monocytes and lymphocytes have been examined. Combinations of pro- and anti-inflammatory biomarkers in a multi-marker panel may help identify patients who are developing severe sepsis before organ dysfunction has advanced too far. Combined with innovative approaches to treatment that target the immunosuppressive phase, these biomarkers may help to reduce the mortality rate associated with severe sepsis which, despite advances in supportive measures, remains high. PMID:23480440

  15. Automation in Clinical Microbiology

    Science.gov (United States)

    Ledeboer, Nathan A.

    2013-01-01

    Historically, the trend toward automation in clinical pathology laboratories has largely bypassed the clinical microbiology laboratory. In this article, we review the historical impediments to automation in the microbiology laboratory and offer insight into the reasons why we believe that we are on the cusp of a dramatic change that will sweep a wave of automation into clinical microbiology laboratories. We review the currently available specimen-processing instruments as well as the total laboratory automation solutions. Lastly, we outline the types of studies that will need to be performed to fully assess the benefits of automation in microbiology laboratories. PMID:23515547

  16. Automation of industrial bioprocesses.

    Science.gov (United States)

    Beyeler, W; DaPra, E; Schneider, K

    2000-01-01

    The dramatic development of new electronic devices within the last 25 years has had a substantial influence on the control and automation of industrial bioprocesses. Within this short period of time the method of controlling industrial bioprocesses has changed completely. In this paper, the authors will use a practical approach focusing on the industrial applications of automation systems. From the early attempts to use computers for the automation of biotechnological processes up to the modern process automation systems some milestones are highlighted. Special attention is given to the influence of Standards and Guidelines on the development of automation systems.

  17. Biomarkers for sepsis.

    Science.gov (United States)

    Henriquez-Camacho, Cesar; Losa, Juan

    2014-01-01

    Bloodstream infections are a major concern because of high levels of antibiotic consumption and of the increasing prevalence of antimicrobial resistance. Bacteraemia is identified in a small percentage of patients with signs and symptoms of sepsis. Biomarkers are widely used in clinical practice and they are useful for monitoring the infectious process. Procalcitonin (PCT) and C-reactive protein (CRP) have been most widely used, but even these have limited abilities to distinguish sepsis from other inflammatory conditions or to predict outcome. PCT has been used to guide empirical antibacterial therapy in patients with respiratory infections and help to determine if antibacterial therapy can be stopped. New biomarkers such as those in this review will discuss the major types of biomarkers of bloodstream infections/sepsis, including soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), soluble urokinase-type plasminogen receptor (suPAR), proadrenomedullin (ProADM), and presepsin.

  18. Inflammatory biomarkers and cancer

    DEFF Research Database (Denmark)

    Rasmussen, Line Jee Hartmann; Schultz, Martin; Gaardsting, Anne

    2017-01-01

    In Denmark, patients with serious nonspecific symptoms and signs of cancer (NSSC) are referred to the diagnostic outpatient clinics (DOCs) where an accelerated cancer diagnostic program is initiated. Various immunological and inflammatory biomarkers have been associated with cancer, including...... soluble urokinase plasminogen activator receptor (suPAR) and the pattern recognition receptors (PRRs) pentraxin-3, mannose-binding lectin, ficolin-1, ficolin-2 and ficolin-3. We aimed to evaluate these biomarkers and compare their diagnostic ability to classical biomarkers for diagnosing cancer...... in patients with NSSC. Patients were included from the DOC, Department of Infectious Diseases, Copenhagen University Hospital Hvidovre. Patients were given a final diagnosis based on the combined results from scans, blood work and physical examination. Weight loss, Charlson score and previous cancer were...

  19. Biomarkers of the Dementia

    Directory of Open Access Journals (Sweden)

    Mikio Shoji

    2011-01-01

    Full Text Available Recent advances in biomarker studies on dementia are summarized here. CSF Aβ40, Aβ42, total tau, and phosphorylated tau are the most sensitive biomarkers for diagnosis of Alzheimer's disease (AD and prediction of onset of AD from mild cognitive impairment (MCI. Based on this progress, new diagnostic criteria for AD, MCI, and preclinical AD were proposed by National Institute of Aging (NIA and Alzheimer's Association in August 2010. In these new criteria, progress in biomarker identification and amyloid imaging studies in the past 10 years have added critical information. Huge contributions of basic and clinical studies have established clinical evidence supporting these markers. Based on this progress, essential therapy for cure of AD is urgently expected.

  20. Update on Biomarkers for the Detection of Endometriosis

    Directory of Open Access Journals (Sweden)

    Amelie Fassbender

    2015-01-01

    Full Text Available Endometriosis is histologically characterized by the displacement of endometrial tissue to extrauterine locations including the pelvic peritoneum, ovaries, and bowel. An important cause of infertility and pelvic pain, the individual and global socioeconomic burden of endometriosis is significant. Laparoscopy remains the gold standard for the diagnosis of the condition. However, the invasive nature of surgery, coupled with the lack of a laboratory biomarker for the disease, results in a mean latency of 7–11 years from onset of symptoms to definitive diagnosis. Unfortunately, the delay in diagnosis may have significant consequences in terms of disease progression. The discovery of a sufficiently sensitive and specific biomarker for the nonsurgical detection of endometriosis promises earlier diagnosis and prevention of deleterious sequelae and represents a clear research priority. In this review, we describe and discuss the current status of biomarkers of endometriosis in plasma, urine, and endometrium.

  1. Update on Biomarkers for the Detection of Endometriosis

    Science.gov (United States)

    Fassbender, Amelie; Burney, Richard O.; O, Dorien F.; D'Hooghe, Thomas; Giudice, Linda

    2015-01-01

    Endometriosis is histologically characterized by the displacement of endometrial tissue to extrauterine locations including the pelvic peritoneum, ovaries, and bowel. An important cause of infertility and pelvic pain, the individual and global socioeconomic burden of endometriosis is significant. Laparoscopy remains the gold standard for the diagnosis of the condition. However, the invasive nature of surgery, coupled with the lack of a laboratory biomarker for the disease, results in a mean latency of 7–11 years from onset of symptoms to definitive diagnosis. Unfortunately, the delay in diagnosis may have significant consequences in terms of disease progression. The discovery of a sufficiently sensitive and specific biomarker for the nonsurgical detection of endometriosis promises earlier diagnosis and prevention of deleterious sequelae and represents a clear research priority. In this review, we describe and discuss the current status of biomarkers of endometriosis in plasma, urine, and endometrium. PMID:26240814

  2. Use of palliative radiotherapy trials for clinical biomarker development.

    Science.gov (United States)

    Wan, Jonathan; Milosevic, Michael; Brade, Anthony M

    2008-09-01

    Approximately one quarter of all cancer patients will require palliative radiation treatment at some point during the course of their disease, but only a minority of these patients are entered in clinical trials. We review the literature debating the ethics of inclusion of "palliative" patients on clinical trials. We suggest that these patients provide a potentially valuable resource that can be leveraged to facilitate the discovery and validation of biomarkers predictive of radiation response and toxicity. In addition, this patient population offers valuable opportunities to test combination of radiation and targeted therapies to screen for activity, toxicity and biomarkers in a relatively safe manner. Patients undergoing palliative radiation therapy may provide new opportunities for the development and testing of predictive radiotherapy biomarkers as well as affording opportunities to test combinations of radiation and targeted therapies.

  3. The ongoing quest for biomarkers in Ankylosing Spondylitis.

    Science.gov (United States)

    Danve, Abhijeet; O'Dell, James

    2015-11-01

    Ankylosing Spondylitis poses significant challenges in terms of early diagnosis, assessment of disease activity, predicting response to the treatment and monitoring radiographic progression. With better understanding of underlying immunopathogenesis, effective targeted therapies are available which improve symptoms, quality of life and possibly slow the radiographic progression. There has been a growing interest in the discovery of biomarkers for defining various aspects of disease assessment and management in Ankylosing Spondylitis. The C-reactive protein and HLA-B27 are most commonly used biomarkers. This review describes many other newer biomarkers which have potential clinical applications in this chronic inflammatory disease. © 2015 Asia Pacific League of Associations for Rheumatology and Wiley Publishing Asia Pty Ltd.

  4. Magnetic Materials for the Selective Analysis of Peptide and Protein Biomarkers.

    Science.gov (United States)

    Piovesana, Susy; Capriotti, Anna Laura

    2017-01-01

    This mini-review article provides an overview on the use of magnetic materials for the analysis of protein biomarkers. In particular, the advantage provided by magnetic solid phase extraction will be discussed with selected examples, considering untargeted analysis for screening new biomarker proteins and targeted investigation on known and suggested new biomarkers. Aspects, such as enrichment efficiency over conventional techniques, ease of use, functionalization versatility and automation will be considered, together with quantification and deeper structure elucidation provided by coupling selective or specific enrichment to powerful characterization techniques, such as mass spectrometry. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  5. Biomarkers of Nutrition for Development—Iodine Review1234

    Science.gov (United States)

    Rohner, Fabian; Zimmermann, Michael; Jooste, Pieter; Pandav, Chandrakant; Caldwell, Kathleen; Raghavan, Ramkripa; Raiten, Daniel J.

    2014-01-01

    The objective of the Biomarkers of Nutrition for Development (BOND) project is to provide state-of-the-art information and service with regard to selection, use, and interpretation of biomarkers of nutrient exposure, status, function, and effect. Specifically, the BOND project seeks to develop consensus on accurate assessment methodologies that are applicable to researchers (laboratory/clinical/surveillance), clinicians, programmers, and policy makers (data consumers). The BOND project is also intended to develop targeted research agendas to support the discovery and development of biomarkers through improved understanding of nutrient biology within relevant biologic systems. In phase I of the BOND project, 6 nutrients (iodine, vitamin A, iron, zinc, folate, and vitamin B-12) were selected for their high public health importance because they typify the challenges faced by users in the selection, use, and interpretation of biomarkers. For each nutrient, an expert panel was constituted and charged with the development of a comprehensive review covering the respective nutrient’s biology, existing biomarkers, and specific issues of use with particular reference to the needs of the individual user groups. In addition to the publication of these reviews, materials from each will be extracted to support the BOND interactive Web site (http://www.nichd.nih.gov/global_nutrition/programs/bond/pages/index.aspx). This review represents the first in the series of reviews and covers all relevant aspects of iodine biology and biomarkers. The article is organized to provide the reader with a full appreciation of iodine’s background history as a public health issue, its biology, and an overview of available biomarkers and specific considerations for the use and interpretation of iodine biomarkers across a range of clinical and population-based uses. The review also includes a detailed research agenda to address priority gaps in our understanding of iodine biology and assessment

  6. iPTF Discoveries of Recent Core-Collapse Supernovae

    Science.gov (United States)

    Taddia, F.; Ferretti, R.; Papadogiannakis, S.; Petrushevska, T.; Fremling, C.; Karamehmetoglu, E.; Nyholm, A.; Roy, R.; Hangard, L.; Horesh, A.; Khazov, D.; Knezevic, S.; Johansson, J.; Leloudas, G.; Manulis, I.; Rubin, A.; Soumagnac, M.; Vreeswijk, P.; Yaron, O.; Bar, I.; Cao, Y.; Kulkarni, S.; Blagorodnova, N.

    2016-05-01

    The intermediate Palomar Transient Factory (ATel #4807) reports the discovery and classification of the following core-collapse SNe. Our automated candidate vetting to distinguish a real astrophysical source (1.0) from bogus artifacts (0.0) is powered by three generations of machine learning algorithms: RB2 (Brink et al. 2013MNRAS.435.1047B), RB4 (Rebbapragada et al. 2015AAS...22543402R) and RB5 (Wozniak et al. 2013AAS...22143105W).

  7. iPTF Discoveries of Recent Type Ia Supernovae

    Science.gov (United States)

    Papadogiannakis, S.; Taddia, F.; Petrushevska, T.; Ferretti, R.; Fremling, C.; Karamehmetoglu, E.; Nyholm, A.; Roy, R.; Hangard, L.; Vreeswijk, P.; Horesh, A.; Manulis, I.; Rubin, A.; Yaron, O.; Leloudas, G.; Khazov, D.; Soumagnac, M.; Knezevic, S.; Johansson, J.; Nir, G.; Cao, Y.; Blagorodnova, N.; Kulkarni, S.

    2016-05-01

    The intermediate Palomar Transient Factory (ATel #4807) reports the discovery and classification of the following Type Ia SNe. Our automated candidate vetting to distinguish a real astrophysical source (1.0) from bogus artefacts (0.0) is powered by three generations of machine learning algorithms: RB2 (Brink et al. 2013MNRAS.435.1047B), RB4 (Rebbapragada et al. 2015AAS...22543402R) and RB5 (Wozniak et al. 2013AAS...22143105W).

  8. Biomarkers of diseases in medicine

    Indian Academy of Sciences (India)

    phases (phase I, phase II and phase III), research on biomarkers has largely been guided by intui- tion and experience. In 2002, the National Can- cer Institute's 'Early Detection Research Network' developed a five-phase approach to systematic dis- covery and evaluation of biomarkers. In general, biomarker development ...

  9. Validation of New Cancer Biomarkers

    DEFF Research Database (Denmark)

    Duffy, Michael J; Sturgeon, Catherine M; Söletormos, Georg

    2015-01-01

    BACKGROUND: Biomarkers are playing increasingly important roles in the detection and management of patients with cancer. Despite an enormous number of publications on cancer biomarkers, few of these biomarkers are in widespread clinical use. CONTENT: In this review, we discuss the key steps in ad...

  10. Biomarkers for anorexia nervosa

    DEFF Research Database (Denmark)

    Sjøgren, Jan Magnus

    2017-01-01

    Biomarkers for anorexia nervosa (AN) which reflect the pathophysiology and relate to the aetiology of the disease, are warranted and could bring us one step closer to targeted treatment of AN. Some leads may be found in the biochemistry which often is found disturbed in AN, although normalization...

  11. Emerging Biomarkers in Glioblastoma

    Energy Technology Data Exchange (ETDEWEB)

    McNamara, Mairéad G.; Sahebjam, Solmaz; Mason, Warren P., E-mail: warren.mason@uhn.ca [Pencer Brain Tumor Centre, Princess Margaret Cancer Centre, 610 University Avenue, Toronto, Ontario M5G 2M9 (Canada)

    2013-08-22

    Glioblastoma, the most common primary brain tumor, has few available therapies providing significant improvement in survival. Molecular signatures associated with tumor aggressiveness as well as with disease progression and their relation to differences in signaling pathways implicated in gliomagenesis have recently been described. A number of biomarkers which have potential in diagnosis, prognosis and prediction of response to therapy have been identified and along with imaging modalities could contribute to the clinical management of GBM. Molecular biomarkers including O(6)-methlyguanine-DNA-methyltransferase (MGMT) promoter and deoxyribonucleic acid (DNA) methylation, loss of heterozygosity (LOH) of chromosomes 1p and 19q, loss of heterozygosity 10q, isocitrate dehydrogenase (IDH) mutations, epidermal growth factor receptor (EGFR), epidermal growth factor, latrophilin, and 7 transmembrane domain-containing protein 1 on chromosome 1 (ELTD1), vascular endothelial growth factor (VEGF), tumor suppressor protein p53, phosphatase and tensin homolog (PTEN), p16INK4a gene, cytochrome c oxidase (CcO), phospholipid metabolites, telomerase messenger expression (hTERT messenger ribonucleic acid [mRNA]), microRNAs (miRNAs), cancer stem cell markers and imaging modalities as potential biomarkers are discussed. Inclusion of emerging biomarkers in prospective clinical trials is warranted in an effort for more effective personalized therapy in the future.

  12. Biomarkers of cell senescence

    Science.gov (United States)

    Dirmi, Goberdhan P.; Campisi, Judith; Peacocke, Monica

    1996-01-01

    The present invention provides a biomarker system for the in vivo and in vitro assessment of cell senescence. In the method of the present invention, .beta.-galactosidase activity is utilized as a means by which cell senescence may be assessed either in in vitro cell cultures or in vivo.

  13. Biomarkers in atopic dermatitis

    NARCIS (Netherlands)

    Thijs, J.L.

    2017-01-01

    Main findings of this thesis · A meta-analysis including 222 studies showed that serum TARC level is the best biomarker for disease severity currently available (chapter 2). · Immunoglobulin free light chains have been shown to correlate with disease severity in paediatric AD. However, they do not

  14. Emerging Biomarkers in Glioblastoma

    Directory of Open Access Journals (Sweden)

    Warren P. Mason

    2013-08-01

    Full Text Available Glioblastoma, the most common primary brain tumor, has few available therapies providing significant improvement in survival. Molecular signatures associated with tumor aggressiveness as well as with disease progression and their relation to differences in signaling pathways implicated in gliomagenesis have recently been described. A number of biomarkers which have potential in diagnosis, prognosis and prediction of response to therapy have been identified and along with imaging modalities could contribute to the clinical management of GBM. Molecular biomarkers including O(6-methlyguanine-DNA-methyltransferase (MGMT promoter and deoxyribonucleic acid (DNA methylation, loss of heterozygosity (LOH of chromosomes 1p and 19q, loss of heterozygosity 10q, isocitrate dehydrogenase (IDH mutations, epidermal growth factor receptor (EGFR, epidermal growth factor, latrophilin, and 7 transmembrane domain-containing protein 1 on chromosome 1 (ELTD1, vascular endothelial growth factor (VEGF, tumor suppressor protein p53, phosphatase and tensin homolog (PTEN, p16INK4a gene, cytochrome c oxidase (CcO, phospholipid metabolites, telomerase messenger expression (hTERT messenger ribonucleic acid [mRNA], microRNAs (miRNAs, cancer stem cell markers and imaging modalities as potential biomarkers are discussed. Inclusion of emerging biomarkers in prospective clinical trials is warranted in an effort for more effective personalized therapy in the future.

  15. In search of biomarkers for autism: scientific, social and ethical challenges.

    Science.gov (United States)

    Walsh, Pat; Elsabbagh, Mayada; Bolton, Patrick; Singh, Ilina

    2011-09-20

    There is widespread hope that the discovery of valid biomarkers for autism will both reveal the causes of autism and enable earlier and more targeted methods for diagnosis and intervention. However, growing enthusiasm about recent advances in this area of autism research needs to be tempered by an awareness of the major scientific challenges and the important social and ethical concerns arising from the development of biomarkers and their clinical application. Collaborative approaches involving scientists and other stakeholders must combine the search for valid, clinically useful autism biomarkers with efforts to ensure that individuals with autism and their families are treated with respect and understanding.

  16. Biomarkers for personalized oncology: recent advances and future challenges.

    Science.gov (United States)

    Kalia, Madhu

    2015-03-01

    Cancer is a group of diseases characterized by the uncontrolled growth and spread of abnormal cells and oncology is a branch of medicine that deals with tumors. The last decade has seen significant advances in the development of biomarkers in oncology that play a critical role in understanding molecular and cellular mechanisms which drive tumor initiation, maintenance and progression. Clinical molecular diagnostics and biomarker discoveries in oncology are advancing rapidly as we begin to understand the complex mechanisms that transform a normal cell into an abnormal one. These discoveries have fueled the development of novel drug targets and new treatment strategies. The standard of care for patients with advanced-stage cancers has shifted away from an empirical treatment strategy based on the clinical-pathological profile to one where a biomarker driven treatment algorithm based on the molecular profile of the tumor is used. Recent advances in multiplex genotyping technologies and high-throughput genomic profiling by next-generation sequencing make possible the rapid and comprehensive analysis of the cancer genome of individual patients even from very little tumor biopsy material. Predictive (diagnostic) biomarkers are helpful in matching targeted therapies with patients and in preventing toxicity of standard (systemic) therapies. Prognostic biomarkers identify somatic germ line mutations, changes in DNA methylation, elevated levels of microRNA (miRNA) and circulating tumor cells (CTC) in blood. Predictive biomarkers using molecular diagnostics are currently in use in clinical practice of personalized oncotherapy for the treatment of five diseases: chronic myeloid leukemia, colon, breast, lung cancer and melanoma and these biomarkers are being used successfully to evaluate benefits that can be achieved through targeted therapy. Examples of these molecularly targeted biomarker therapies are: tyrosine kinase inhibitors in chronic myeloid leukemia and

  17. Discovery of Fullerenes

    Indian Academy of Sciences (India)

    ... Journal of Science Education; Volume 2; Issue 1. Discovery of Fullerenes Giving a New Shape to Carbon Chemistry. Rathna Ananthaiah. Research News Volume 2 Issue 1 January 1997 pp 68-73. Fulltext. Click here to view fulltext PDF. Permanent link: http://www.ias.ac.in/article/fulltext/reso/002/01/0068-0073 ...

  18. Landmark Discoveries in Neurosciences

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 17; Issue 11. Landmark Discoveries in Neurosciences. Niranjan Kambi Neeraj Jain. General Article Volume 17 Issue 11 November 2012 pp 1054-1064. Fulltext. Click here to view fulltext PDF. Permanent link:

  19. The discovery of fission

    International Nuclear Information System (INIS)

    McKay, H.A.C.

    1978-01-01

    In this article by the retired head of the Separation Processes Group of the Chemistry Division, Atomic Energy Research Establishment, Harwell, U.K., the author recalls what he terms 'an exciting drama, the unravelling of the nature of the atomic nucleus' in the years before the Second World War, including the discovery of fission. 12 references. (author)

  20. Automation systems for radioimmunoassay

    International Nuclear Information System (INIS)

    Yamasaki, Paul

    1974-01-01

    The application of automation systems for radioimmunoassay (RIA) was discussed. Automated systems could be useful in the second step, of the four basic processes in the course of RIA, i.e., preparation of sample for reaction. There were two types of instrumentation, a semi-automatic pipete, and a fully automated pipete station, both providing for fast and accurate dispensing of the reagent or for the diluting of sample with reagent. Illustrations of the instruments were shown. (Mukohata, S.)

  1. Automated stopcock actuator

    OpenAIRE

    Vandehey, N. T.; O\\'Neil, J. P.

    2015-01-01

    Introduction We have developed a low-cost stopcock valve actuator for radiochemistry automation built using a stepper motor and an Arduino, an open-source single-board microcontroller. The con-troller hardware can be programmed to run by serial communication or via two 5–24 V digital lines for simple integration into any automation control system. This valve actuator allows for automated use of a single, disposable stopcock, providing a number of advantages over stopcock manifold systems ...

  2. Automated Analysis of Accountability

    DEFF Research Database (Denmark)

    Bruni, Alessandro; Giustolisi, Rosario; Schürmann, Carsten

    2017-01-01

    that are amenable to automated verification. Our definitions are general enough to be applied to different classes of protocols and different automated security verification tools. Furthermore, we point out formally the relation between verifiability and accountability. We validate our definitions...... with the automatic verification of three protocols: a secure exam protocol, Google’s Certificate Transparency, and an improved version of Bingo Voting. We find through automated verification that all three protocols satisfy verifiability while only the first two protocols meet accountability....

  3. Assessed and Emerging Biomarkers in Stroke and Training-Mediated Stroke Recovery: State of the Art

    Directory of Open Access Journals (Sweden)

    Marialuisa Gandolfi

    2017-01-01

    Full Text Available Since the increasing update of the biomolecular scientific literature, biomarkers in stroke have reached an outstanding and remarkable revision in the very recent years. Besides the diagnostic and prognostic role of some inflammatory markers, many further molecules and biological factors have been added to the list, including tissue derived cytokines, growth factor-like molecules, hormones, and microRNAs. The literatures on brain derived growth factor and other neuroimmune mediators, bone-skeletal muscle biomarkers, cellular and immunity biomarkers, and the role of microRNAs in stroke recovery were reviewed. To date, biomarkers represent a possible challenge in the diagnostic and prognostic evaluation of stroke onset, pathogenesis, and recovery. Many molecules are still under investigation and may become promising and encouraging biomarkers. Experimental and clinical research should increase this list and promote new discoveries in this field, to improve stroke diagnosis and treatment.

  4. Assessed and Emerging Biomarkers in Stroke and Training-Mediated Stroke Recovery: State of the Art

    Science.gov (United States)

    Vella, Antonio

    2017-01-01

    Since the increasing update of the biomolecular scientific literature, biomarkers in stroke have reached an outstanding and remarkable revision in the very recent years. Besides the diagnostic and prognostic role of some inflammatory markers, many further molecules and biological factors have been added to the list, including tissue derived cytokines, growth factor-like molecules, hormones, and microRNAs. The literatures on brain derived growth factor and other neuroimmune mediators, bone-skeletal muscle biomarkers, cellular and immunity biomarkers, and the role of microRNAs in stroke recovery were reviewed. To date, biomarkers represent a possible challenge in the diagnostic and prognostic evaluation of stroke onset, pathogenesis, and recovery. Many molecules are still under investigation and may become promising and encouraging biomarkers. Experimental and clinical research should increase this list and promote new discoveries in this field, to improve stroke diagnosis and treatment. PMID:28373915

  5. Translational paradigms in pharmacology and drug discovery.

    Science.gov (United States)

    Mullane, Kevin; Winquist, Raymond J; Williams, Michael

    2014-01-01

    The translational sciences represent the core element in enabling and utilizing the output from the biomedical sciences and to improving drug discovery metrics by reducing the attrition rate as compounds move from preclinical research to clinical proof of concept. Key to understanding the basis of disease causality and to developing therapeutics is an ability to accurately diagnose the disease and to identify and develop safe and effective therapeutics for its treatment. The former requires validated biomarkers and the latter, qualified targets. Progress has been hampered by semantic issues, specifically those that define the end product, and by scientific issues that include data reliability, an overt reductionistic cultural focus and a lack of hierarchically integrated data gathering and systematic analysis. A necessary framework for these activities is represented by the discipline of pharmacology, efforts and training in which require recognition and revitalization. Copyright © 2013 Elsevier Inc. All rights reserved.

  6. ALS Biomarkers for Therapy Development: State of the Field & Future Directions

    Science.gov (United States)

    Benatar, Michael; Boylan, Kevin; Jeromin, Andreas; Rutkove, Seward B.; Berry, James; Atassi, Nazem; Bruijn, Lucie

    2015-01-01

    Biomarkers have become the focus of intense research in the field of amyotrophic lateral sclerosis (ALS), with the hope that they might aid therapy development efforts. Notwithstanding the discovery of many candidate biomarkers, none have yet emerged as validated tools for drug development. In this review we present a nuanced view of biomarkers based on the perspective of the FDA; highlight the distinction between discovery and validation; describe existing and emerging resources; review leading biological fluid-based, electrophysiological and neuroimaging candidates relevant to therapy development efforts; discuss lessons learned from biomarker initiatives in related neurodegenerative diseases; and outline specific steps that we, as a field, might take in order to hasten the development and validation of biomarkers that will prove useful in enhancing efforts to develop effective treatments for ALS patients. Most important among these perhaps is the proposal to establish a federated ALS Biomarker Consortium (ABC) in which all interested and willing stakeholders may participate with equal opportunity to contribute to the broader mission of biomarker development and validation. PMID:26574709

  7. Urinary Biomarkers for Chronic Kidney Disease with a Focus on Gene Transcript.

    Science.gov (United States)

    Lyu, Lin-Li; Feng, Ye; Liu, Bi-Cheng

    2017-09-20

    In the upcoming era of precision medicine, searching for the early, noninvasive biomarkers has been the cornerstone and major challenge in the management of chronic kidney disease (CKD). Urine contains rich biological information which could be the ideal source for noninvasive biomarkers of CKD. This review will discuss the recent advance in urinary biomarker. This review was based on data in articles published in the PubMed databases up to June 20, 2017, with the following keywords: "Chronic kidney disease", "Biomarker", and "Urine". Original articles and important reviews on urinary biomarker were selected for this review. Urinary biomarker studies of CKD mainly focused on urine sediment, supernatant, and urinary extracellular vesicles. The gene transcript (microRNA [miRNA], messenger RNA [mRNA]) biomarkers have been recently shown with diagnostic potential for CKD reflecting kidney function and histological change. However, challenges regarding technique and data analysis need to be resolved before translation to clinic. Different fractions of urine contain rich information for biomarker discovery, among which urine (extracellular vesicles) mRNA, miRNA, might represent promising biomarker for CKD.

  8. Management Planning for Workplace Automation.

    Science.gov (United States)

    McDole, Thomas L.

    Several factors must be considered when implementing office automation. Included among these are whether or not to automate at all, the effects of automation on employees, requirements imposed by automation on the physical environment, effects of automation on the total organization, and effects on clientele. The reasons behind the success or…

  9. Laboratory Automation and Middleware.

    Science.gov (United States)

    Riben, Michael

    2015-06-01

    The practice of surgical pathology is under constant pressure to deliver the highest quality of service, reduce errors, increase throughput, and decrease turnaround time while at the same time dealing with an aging workforce, increasing financial constraints, and economic uncertainty. Although not able to implement total laboratory automation, great progress continues to be made in workstation automation in all areas of the pathology laboratory. This report highlights the benefits and challenges of pathology automation, reviews middleware and its use to facilitate automation, and reviews the progress so far in the anatomic pathology laboratory. Copyright © 2015 Elsevier Inc. All rights reserved.

  10. Automated cloning methods.; TOPICAL

    International Nuclear Information System (INIS)

    Collart, F.

    2001-01-01

    Argonne has developed a series of automated protocols to generate bacterial expression clones by using a robotic system designed to be used in procedures associated with molecular biology. The system provides plate storage, temperature control from 4 to 37 C at various locations, and Biomek and Multimek pipetting stations. The automated system consists of a robot that transports sources from the active station on the automation system. Protocols for the automated generation of bacterial expression clones can be grouped into three categories (Figure 1). Fragment generation protocols are initiated on day one of the expression cloning procedure and encompass those protocols involved in generating purified coding region (PCR)

  11. Complacency and Automation Bias in the Use of Imperfect Automation.

    Science.gov (United States)

    Wickens, Christopher D; Clegg, Benjamin A; Vieane, Alex Z; Sebok, Angelia L

    2015-08-01

    We examine the effects of two different kinds of decision-aiding automation errors on human-automation interaction (HAI), occurring at the first failure following repeated exposure to correctly functioning automation. The two errors are incorrect advice, triggering the automation bias, and missing advice, reflecting complacency. Contrasts between analogous automation errors in alerting systems, rather than decision aiding, have revealed that alerting false alarms are more problematic to HAI than alerting misses are. Prior research in decision aiding, although contrasting the two aiding errors (incorrect vs. missing), has confounded error expectancy. Participants performed an environmental process control simulation with and without decision aiding. For those with the aid, automation dependence was created through several trials of perfect aiding performance, and an unexpected automation error was then imposed in which automation was either gone (one group) or wrong (a second group). A control group received no automation support. The correct aid supported faster and more accurate diagnosis and lower workload. The aid failure degraded all three variables, but "automation wrong" had a much greater effect on accuracy, reflecting the automation bias, than did "automation gone," reflecting the impact of complacency. Some complacency was manifested for automation gone, by a longer latency and more modest reduction in accuracy. Automation wrong, creating the automation bias, appears to be a more problematic form of automation error than automation gone, reflecting complacency. Decision-aiding automation should indicate its lower degree of confidence in uncertain environments to avoid the automation bias. © 2015, Human Factors and Ergonomics Society.

  12. Advances in Computer, Communication, Control and Automation

    CERN Document Server

    011 International Conference on Computer, Communication, Control and Automation

    2012-01-01

    The volume includes a set of selected papers extended and revised from the 2011 International Conference on Computer, Communication, Control and Automation (3CA 2011). 2011 International Conference on Computer, Communication, Control and Automation (3CA 2011) has been held in Zhuhai, China, November 19-20, 2011. This volume  topics covered include signal and Image processing, speech and audio Processing, video processing and analysis, artificial intelligence, computing and intelligent systems, machine learning, sensor and neural networks, knowledge discovery and data mining, fuzzy mathematics and Applications, knowledge-based systems, hybrid systems modeling and design, risk analysis and management, system modeling and simulation. We hope that researchers, graduate students and other interested readers benefit scientifically from the proceedings and also find it stimulating in the process.

  13. Cancer therapy trials employing level-of-evidence-1 disease forecast cancer biomarkers uPA and its inhibitor PAI-1

    DEFF Research Database (Denmark)

    Schmitt, Manfred; Harbeck, Nadia; Brünner, Nils

    2011-01-01

    Clinical research on cancer biomarkers is essential in understanding recent discoveries in cancer biology and heterogeneity of the cancer disease. However, there are only a few examples of clinically useful studies that have identified cancer biomarkers with clinical benefit. Urokinase-type plasm...

  14. Small sample sizes in high-throughput miRNA screens: A common pitfall for the identification of miRNA biomarkers

    NARCIS (Netherlands)

    Kok, M. G. M.; de Ronde, M. W. J.; Moerland, P. D.; Ruijter, J. M.; Creemers, E. E.; Pinto-Sietsma, S. J.

    2018-01-01

    Since the discovery of microRNAs (miRNAs), circulating miRNAs have been proposed as biomarkers for disease. Consequently, many groups have tried to identify circulating miRNA biomarkers for various types of diseases including cardiovascular disease and cancer. However, the replicability of these

  15. New developments and concepts related to biomarker application to vaccines

    Science.gov (United States)

    Ahmed, S. Sohail; Black, Steve; Ulmer, Jeffrey

    2012-01-01

    Summary This minireview will provide a perspective on new developments and concepts related to biomarker applications for vaccines. In the context of preventive vaccines, biomarkers have the potential to predict adverse events in select subjects due to differences in genetic make‐up/underlying medical conditions or to predict effectiveness (good versus poor response). When expanding them to therapeutic vaccines, their utility in identification of patients most likely to respond favourably (or avoid potentially negative effects of treatment) becomes self‐explanatory. Despite the progress made so far on dissection of various pathways of biological significance in humans, there is still plenty to unravel about the mysteries related to the quantitative and qualitative aspects of the human host response. This review will provide a focused overview of new concepts and developments in the field of vaccine biomarkers including (i) vaccine‐dependent signatures predicting subject response and safety, (ii) predicting therapeutic vaccine efficacy in chronic diseases, (iii) exploring the genetic make‐up of the host that may modulate subject‐specific adverse events or affect the quality of immune responses, and (iv) the topic of volunteer stratification as a result of biomarker screening (e.g. for therapeutic vaccines but also potentially for preventive vaccines) or as a reflection of an effort to compare select groups (e.g. vaccinated subjects versus patients recovering from infection) to enable the discovery of clinically relevant biomarkers for preventive vaccines. PMID:21895991

  16. Towards Improved Biomarker Research

    DEFF Research Database (Denmark)

    Kjeldahl, Karin

    This thesis takes a look at the data analytical challenges associated with the search for biomarkers in large-scale biological data such as transcriptomics, proteomics and metabolomics data. These studies aim to identify genes, proteins or metabolites which can be associated with e.g. a diet......, disease (e.g. cancer), drug response or physiological status. The value of these omics studies has to some extent been questioned as it is often observed that the validity of claimed biomarkers has been very difficult to verify in other studies. On the other hand, in many studies it is difficult...... is investigated and followed by some suggestions which can potentially improve the chances of a successful outcome of an omics study. A method widely applied in the analysis of omics studies is Partial Least Squares (PLS) regression which is one of the work horses within the chemometrics tool box; a method which...

  17. Novel biomarkers for sepsis

    DEFF Research Database (Denmark)

    Larsen, Frederik Fruergaard; Petersen, J Asger

    2017-01-01

    BACKGROUND: Sepsis is a prevalent condition among hospitalized patients that carries a high risk of morbidity and mortality. Rapid recognition of sepsis as the cause of deterioration is desirable, so effective treatment can be initiated rapidly. Traditionally, diagnosis was based on presence of two...... or more positive SIRS criteria due to infection. However, recently published sepsis-3 criteria put more emphasis on organ dysfunction caused by infection in the definition of sepsis. Regardless of this, no gold standard for diagnosis exist, and clinicians still rely on a number of traditional and novel...... biomarkers to discriminate between patients with and without infection, as the cause of deterioration. METHOD: Narrative review of current literature. RESULTS: A number of the most promising biomarkers for diagnoses and prognostication of sepsis are presented. CONCLUSION: Procalcitonin, presepsin, CD64, su...

  18. Salivaomics for oral diseases biomarkers detection.

    Science.gov (United States)

    Tasoulas, Jason; Patsouris, Efstratios; Giaginis, Constantinos; Theocharis, Stamatios

    2016-01-01

    The variation of saliva composition in different physiological and pathological states is well demonstrated. Several saliva constituents (enzymes, hormones, antibodies, cytokines etc.) are up- or down-regulated in respect to benign, premalignant and malignant conditions in the oral cavity, and several patterns of deregulation are associated with specific disorders. Omics technologies have contributed significantly in the identification of alterations in gene expression, transcription, protein coding and small molecules concentration, in biologic systems. In this aspect, salivaomics integrate these technologies in saliva analysis and represent a novel and holistic approach in oral disease management including diagnosis, prognosis and monitoring. This review summarizes the current research in the discovery of biomarkers and molecular signatures with diagnostic or prognostic utility for oral diseases in saliva. The review also focuses on the emerging issues of the salivaomics technology and saliva diagnostics and the translational potential.

  19. Biomarkers of Selenium Status

    Directory of Open Access Journals (Sweden)

    Gerald F. Combs, Jr.

    2015-03-01

    Full Text Available The essential trace element, selenium (Se, has multiple biological activities, which depend on the level of Se intake. Relatively low Se intakes determine the expression of selenoenzymes in which it serves as an essential constituent. Higher intakes have been shown to have anti-tumorigenic potential; and very high Se intakes can produce adverse effects. This hierarchy of biological activities calls for biomarkers informative at different levels of Se exposure. Some Se-biomarkers, such as the selenoproteins and particularly GPX3 and SEPP1, provide information about function directly and are of value in identifying nutritional Se deficiency and tracking responses of deficient individuals to Se-treatment. They are useful under conditions of Se intake within the range of regulated selenoprotein expression, e.g., for humans <55 μg/day and for animals <20 μg/kg diet. Other Se-biomarkers provide information indirectly through inferences based on Se levels of foods, tissues, urine or feces. They can indicate the likelihood of deficiency or adverse effects, but they do not provide direct evidence of either condition. Their value is in providing information about Se status over a wide range of Se intake, particularly from food forms. There is need for additional Se biomarkers particularly for assessing Se status in non-deficient individuals for whom the prospects of cancer risk reduction and adverse effects risk are the primary health considerations. This would include determining whether supranutritional intakes of Se may be required for maximal selenoprotein expression in immune surveillance cells. It would also include developing methods to determine low molecular weight Se-metabolites, i.e., selenoamino acids and methylated Se-metabolites, which to date have not been detectable in biological specimens. Recent analytical advances using tandem liquid chromatography-mass spectrometry suggest prospects for detecting these metabolites.

  20. Urinary Protein Biomarker Analysis

    Science.gov (United States)

    2017-10-01

    associated protein biomarkers were identified by transcriptomic comparison of cancer cells vs. normal luminal cells; cancer-associated stromal cells vs...analysis; (C) correction with PSA, P = 0.012); (D) ROC curve analysis. 4-1. Use of PSA levels for marker level normalization Other organs along the...Copyright: Shi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which

  1. The neutron discovery

    International Nuclear Information System (INIS)

    Six, J.

    1987-01-01

    The neutron: who had first the idea, who discovered it, who established its main properties. To these apparently simple questions, multiple answers exist. The progressive discovery of the neutron is a marvellous illustration of some characteristics of the scientific research, where the unforeseen may be combined with the expected. This discovery is replaced in the context of the 1930's scientific effervescence that succeeded the revolutionary introduction of quantum mechanics. This book describes the works of Bothe, the Joliot-Curie and Chadwick which led to the neutron in an unexpected way. A historical analysis allows to give a new interpretation on the hypothesis suggested by the Joliot-Curie. Some texts of these days will help the reader to revive this fascinating story [fr

  2. Discovery of charm

    Energy Technology Data Exchange (ETDEWEB)

    Goldhaber, G.

    1984-11-01

    In my talk I will cover the period 1973 to 1976 which saw the discoveries of the J/psi and psi' resonances and most of the Psion spectroscopy, the tau lepton and the D/sup 0/,D/sup +/ charmed meson doublet. Occasionally I will refer briefly to more recent results. Since this conference is on the history of the weak-interactions I will deal primarily with the properties of naked charm and in particular the weakly decaying doublet of charmed mesons. Most of the discoveries I will mention were made with the SLAC-LBL Magnetic Detector or MARK I which we operated at SPEAR from 1973 to 1976. 27 references.

  3. Atlas of Astronomical Discoveries

    CERN Document Server

    Schilling, Govert

    2011-01-01

    Four hundred years ago in Middelburg, in the Netherlands, the telescope was invented. The invention unleashed a revolution in the exploration of the universe. Galileo Galilei discovered mountains on the Moon, spots on the Sun, and moons around Jupiter. Christiaan Huygens saw details on Mars and rings around Saturn. William Herschel discovered a new planet and mapped binary stars and nebulae. Other astronomers determined the distances to stars, unraveled the structure of the Milky Way, and discovered the expansion of the universe. And, as telescopes became bigger and more powerful, astronomers delved deeper into the mysteries of the cosmos. In his Atlas of Astronomical Discoveries, astronomy journalist Govert Schilling tells the story of 400 years of telescopic astronomy. He looks at the 100 most important discoveries since the invention of the telescope. In his direct and accessible style, the author takes his readers on an exciting journey encompassing the highlights of four centuries of astronomy. Spectacul...

  4. Automated System Marketplace 1994.

    Science.gov (United States)

    Griffiths, Jose-Marie; Kertis, Kimberly

    1994-01-01

    Reports results of the 1994 Automated System Marketplace survey based on responses from 60 vendors. Highlights include changes in the library automation marketplace; estimated library systems revenues; minicomputer and microcomputer-based systems; marketplace trends; global markets and mergers; research needs; new purchase processes; and profiles…

  5. Automation benefits BWR customers

    International Nuclear Information System (INIS)

    Anon.

    1982-01-01

    A description is given of the increasing use of automation at General Electric's Wilmington fuel fabrication plant. Computerised systems and automated equipment perform a large number of inspections, inventory and process operations, and new advanced systems are being continuously introduced to reduce operator errors and expand product reliability margins. (U.K.)

  6. Automate functional testing

    Directory of Open Access Journals (Sweden)

    Ramesh Kalindri

    2014-06-01

    Full Text Available Currently, software engineers are increasingly turning to the option of automating functional tests, but not always have successful in this endeavor. Reasons range from low planning until over cost in the process. Some principles that can guide teams in automating these tests are described in this article.

  7. Automation in Warehouse Development

    NARCIS (Netherlands)

    Hamberg, R.; Verriet, J.

    2012-01-01

    The warehouses of the future will come in a variety of forms, but with a few common ingredients. Firstly, human operational handling of items in warehouses is increasingly being replaced by automated item handling. Extended warehouse automation counteracts the scarcity of human operators and

  8. Identity Management Processes Automation

    Directory of Open Access Journals (Sweden)

    A. Y. Lavrukhin

    2010-03-01

    Full Text Available Implementation of identity management systems consists of two main parts, consulting and automation. The consulting part includes development of a role model and identity management processes description. The automation part is based on the results of consulting part. This article describes the most important aspects of IdM implementation.

  9. Work and Programmable Automation.

    Science.gov (United States)

    DeVore, Paul W.

    A new industrial era based on electronics and the microprocessor has arrived, an era that is being called intelligent automation. Intelligent automation, in the form of robots, replaces workers, and the new products, using microelectronic devices, require significantly less labor to produce than the goods they replace. The microprocessor thus…

  10. Library Automation in Pakistan.

    Science.gov (United States)

    Haider, Syed Jalaluddin

    1998-01-01

    Examines the state of library automation in Pakistan. Discusses early developments; financial support by the Netherlands Library Development Project (Pakistan); lack of automated systems in college/university and public libraries; usage by specialist libraries; efforts by private-sector libraries and the National Library in Pakistan; commonly used…

  11. Library Automation Style Guide.

    Science.gov (United States)

    Gaylord Bros., Liverpool, NY.

    This library automation style guide lists specific terms and names often used in the library automation industry. The terms and/or acronyms are listed alphabetically and each is followed by a brief definition. The guide refers to the "Chicago Manual of Style" for general rules, and a notes section is included for the convenience of individual…

  12. Planning for Office Automation.

    Science.gov (United States)

    Sherron, Gene T.

    1982-01-01

    The steps taken toward office automation by the University of Maryland are described. Office automation is defined and some types of word processing systems are described. Policies developed in the writing of a campus plan are listed, followed by a section on procedures adopted to implement the plan. (Author/MLW)

  13. The Automated Office.

    Science.gov (United States)

    Naclerio, Nick

    1979-01-01

    Clerical personnel may be able to climb career ladders as a result of office automation and expanded job opportunities in the word processing area. Suggests opportunities in an automated office system and lists books and periodicals on word processing for counselors and teachers. (MF)

  14. Automating the Small Library.

    Science.gov (United States)

    Skapura, Robert

    1987-01-01

    Discusses the use of microcomputers for automating school libraries, both for entire systems and for specific library tasks. Highlights include available library management software, newsletters that evaluate software, constructing an evaluation matrix, steps to consider in library automation, and a brief discussion of computerized card catalogs.…

  15. Trend analysis of time-series data: A novel method for untargeted metabolite discovery

    NARCIS (Netherlands)

    Peters, S.; Janssen, H.-G.; Vivó-Truyols, G.

    2010-01-01

    A new strategy for biomarker discovery is presented that uses time-series metabolomics data. Data sets from samples analysed at different time points after an intervention are searched for compounds that show a meaningful trend following the intervention. Obviously, this requires new data-analytical

  16. Discoveries of isotopes by fission

    Indian Academy of Sciences (India)

    also contributed to the discovery of new isotopes. More recently, most of the very neutron- rich isotopes have been discovered by projectile fission. After a brief summary of the discovery of fission process itself, these production mechanisms will be discussed. The paper concludes with an outlook on future discoveries of ...

  17. Recent Discoveries and Bible Translation.

    Science.gov (United States)

    Harrelson, Walter

    1990-01-01

    Discusses recent discoveries for "Bible" translation with a focus on the "Dead Sea Scrolls." Examines recent discoveries that provide direct support for alternative reading of biblical passages and those discoveries that have contributed additional insight to knowledge of cultural practices, especially legal and religious…

  18. In Vivo Imaging Biomarkers in Mouse Models of Alzheimer's Disease: Are We Lost in Translation or Breaking Through?

    Directory of Open Access Journals (Sweden)

    Benoît Delatour

    2010-01-01

    Full Text Available Identification of biomarkers of Alzheimer's Disease (AD is a critical priority to efficiently diagnose the patients, to stage the progression of neurodegeneration in living subjects, and to assess the effects of disease-modifier treatments. This paper addresses the development and usefulness of preclinical neuroimaging biomarkers of AD. It is today possible to image in vivo the brain of small rodents at high resolution and to detect the occurrence of macroscopic/microscopic lesions in these species, as well as of functional alterations reminiscent of AD pathology. We will outline three different types of imaging biomarkers that can be used in AD mouse models: biomarkers with clear translational potential, biomarkers that can serve as in vivo readouts (in particular in the context of drug discovery exclusively for preclinical research, and finally biomarkers that constitute new tools for fundamental research on AD physiopathogeny.

  19. Fateful discovery almost forgotten

    International Nuclear Information System (INIS)

    Anon.

    1989-01-01

    The paper reviews the discovery of the fission of uranium, which took place fifty years ago. A description is given of the work of Meitner and Frisch in interpreting the Fermi data on the bombardment of uranium nuclei with neutrons, i.e. proposing fission. The historical events associated with the development and exploitation of uranium fission are described, including the Manhattan Project, Hiroshima and Nagasaki, Shippingport, and Chernobyl. (U.K.)

  20. Discovery as a process

    Energy Technology Data Exchange (ETDEWEB)

    Loehle, C.

    1994-05-01

    The three great myths, which form a sort of triumvirate of misunderstanding, are the Eureka! myth, the hypothesis myth, and the measurement myth. These myths are prevalent among scientists as well as among observers of science. The Eureka! myth asserts that discovery occurs as a flash of insight, and as such is not subject to investigation. This leads to the perception that discovery or deriving a hypothesis is a moment or event rather than a process. Events are singular and not subject to description. The hypothesis myth asserts that proper science is motivated by testing hypotheses, and that if something is not experimentally testable then it is not scientific. This myth leads to absurd posturing by some workers conducting empirical descriptive studies, who dress up their study with a ``hypothesis`` to obtain funding or get it published. Methods papers are often rejected because they do not address a specific scientific problem. The fact is that many of the great breakthroughs in silence involve methods and not hypotheses or arise from largely descriptive studies. Those captured by this myth also try to block funding for those developing methods. The third myth is the measurement myth, which holds that determining what to measure is straightforward, so one doesn`t need a lot of introspection to do science. As one ecologist put it to me ``Don`t give me any of that philosophy junk, just let me out in the field. I know what to measure.`` These myths lead to difficulties for scientists who must face peer review to obtain funding and to get published. These myths also inhibit the study of science as a process. Finally, these myths inhibit creativity and suppress innovation. In this paper I first explore these myths in more detail and then propose a new model of discovery that opens the supposedly miraculous process of discovery to doser scrutiny.

  1. Discovery of TUG-770

    DEFF Research Database (Denmark)

    Christiansen, Elisabeth; Hansen, Steffen Vissing Fahnøe; Urban, Christian

    2013-01-01

    Free fatty acid receptor 1 (FFA1 or GPR40) enhances glucose-stimulated insulin secretion from pancreatic β-cells and currently attracts high interest as a new target for the treatment of type 2 diabetes. We here report the discovery of a highly potent FFA1 agonist with favorable physicochemical...... and pharmacokinetic properties. The compound efficiently normalizes glucose tolerance in diet-induced obese mice, an effect that is fully sustained after 29 days of chronic dosing....

  2. Discovery concepts for Mars

    Science.gov (United States)

    Luhmann, J. G.; Russell, C. T.; Brace, L. H.; Nagy, A. F.; Jakosky, B. M.; Barth, C. A.; Waite, J. H.

    1992-01-01

    Two focused Mars missions that would fit within the guidelines for the proposed Discovery line are discussed. The first mission would deal with the issue of the escape of the atmosphere (Mars') to space. A complete understanding of this topic is crucial to deciphering the evolution of the atmosphere, climate change, and volatile inventories. The second mission concerns the investigation of remanent magnetization of the crust and its relationship to the ionosphere and the atmosphere.

  3. Advances in inspection automation

    Science.gov (United States)

    Weber, Walter H.; Mair, H. Douglas; Jansen, Dion; Lombardi, Luciano

    2013-01-01

    This new session at QNDE reflects the growing interest in inspection automation. Our paper describes a newly developed platform that makes the complex NDE automation possible without the need for software programmers. Inspection tasks that are tedious, error-prone or impossible for humans to perform can now be automated using a form of drag and drop visual scripting. Our work attempts to rectify the problem that NDE is not keeping pace with the rest of factory automation. Outside of NDE, robots routinely and autonomously machine parts, assemble components, weld structures and report progress to corporate databases. By contrast, components arriving in the NDT department typically require manual part handling, calibrations and analysis. The automation examples in this paper cover the development of robotic thickness gauging and the use of adaptive contour following on the NRU reactor inspection at Chalk River.

  4. Automated model building

    CERN Document Server

    Caferra, Ricardo; Peltier, Nicholas

    2004-01-01

    This is the first book on automated model building, a discipline of automated deduction that is of growing importance Although models and their construction are important per se, automated model building has appeared as a natural enrichment of automated deduction, especially in the attempt to capture the human way of reasoning The book provides an historical overview of the field of automated deduction, and presents the foundations of different existing approaches to model construction, in particular those developed by the authors Finite and infinite model building techniques are presented The main emphasis is on calculi-based methods, and relevant practical results are provided The book is of interest to researchers and graduate students in computer science, computational logic and artificial intelligence It can also be used as a textbook in advanced undergraduate courses

  5. Automation in Warehouse Development

    CERN Document Server

    Verriet, Jacques

    2012-01-01

    The warehouses of the future will come in a variety of forms, but with a few common ingredients. Firstly, human operational handling of items in warehouses is increasingly being replaced by automated item handling. Extended warehouse automation counteracts the scarcity of human operators and supports the quality of picking processes. Secondly, the development of models to simulate and analyse warehouse designs and their components facilitates the challenging task of developing warehouses that take into account each customer’s individual requirements and logistic processes. Automation in Warehouse Development addresses both types of automation from the innovative perspective of applied science. In particular, it describes the outcomes of the Falcon project, a joint endeavour by a consortium of industrial and academic partners. The results include a model-based approach to automate warehouse control design, analysis models for warehouse design, concepts for robotic item handling and computer vision, and auton...

  6. Automation in Immunohematology

    Directory of Open Access Journals (Sweden)

    Meenu Bajpai

    2012-01-01

    Full Text Available There have been rapid technological advances in blood banking in South Asian region over the past decade with an increasing emphasis on quality and safety of blood products. The conventional test tube technique has given way to newer techniques such as column agglutination technique, solid phase red cell adherence assay, and erythrocyte-magnetized technique. These new technologies are adaptable to automation and major manufacturers in this field have come up with semi and fully automated equipments for immunohematology tests in the blood bank. Automation improves the objectivity and reproducibility of tests. It reduces human errors in patient identification and transcription errors. Documentation and traceability of tests, reagents and processes and archiving of results is another major advantage of automation. Shifting from manual methods to automation is a major undertaking for any transfusion service to provide quality patient care with lesser turnaround time for their ever increasing workload. This article discusses the various issues involved in the process.

  7. The NINDS Parkinson's disease biomarkers program: The Ninds Parkinson's Disease Biomarkers Program

    Energy Technology Data Exchange (ETDEWEB)

    Rosenthal, Liana S. [Department of Neurology, Johns Hopkins University School of Medicine, Baltimore Maryland USA; Drake, Daniel [Department of Biostatistics, Columbia University, New York New York USA; Alcalay, Roy N. [Department of Neurology, Columbia University, New York New York USA; Babcock, Debra [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA; Bowman, F. DuBois [Department of Biostatistics, Columbia University, New York New York USA; Chen-Plotkin, Alice [Department of Neurology, University of Pennsylvania, Philadelphia Pennsylvania USA; Dawson, Ted M. [Department of Neurology, Johns Hopkins University School of Medicine, Baltimore Maryland USA; Neuroregeneration and Stem Cell Programs, Institute for Cell Engineering, Solomon H. Snyder Department of Neuroscience, Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore Maryland USA; Dewey, Richard B. [Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas USA; German, Dwight C. [Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas USA; Huang, Xuemei [Department of Neurology, Penn State Hershey Medical Center, Hershey Pennsylvania USA; Landin, Barry [Center for Information Technology, National Institutes of Health, Bethesda Maryland USA; McAuliffe, Matthew [Center for Information Technology, National Institutes of Health, Bethesda Maryland USA; Petyuk, Vladislav A. [Biological Sciences Division, Pacific Northwest National Laboratory, Richland Washington USA; Scherzer, Clemens R. [Department of Neurology, Brigham & Women' s Hospital, Harvard Medical School, Cambridge Massachusetts USA; Hillaire-Clarke, Coryse St. [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA; Sieber, Beth-Anne [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA; Sutherland, Margaret [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA; Tarn, Chi [Coriell Institute for Medical Research, Camden New Jersey USA; West, Andrew [Department of Neurology, University of Alabama at Birmingham, Birmingham USA; Vaillancourt, David [Department of Applied Physiology and Kinesiology, University of Florida, Gainesville Florida USA; Zhang, Jing [Department of Pathology, University of Washington, Seattle Washington USA; Gwinn, Katrina [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA

    2015-10-07

    Background: Neuroprotection for Parkinson Disease (PD) remains elusive. Biomarkers hold the promise of removing roadblocks to therapy development. The National Institute of Neurological Disorders and Stroke (NINDS) has therefore established the Parkinson’s Disease Biomarkers Program (PDBP) to promote discovery of biomarkers for use in phase II-III clinical trials in PD. Methods: The PDBP facilitates biomarker development to improve neuroprotective clinical trial design, essential for advancing therapeutics for PD. To date, eleven consortium projects in the PDBP are focused on the development of clinical and laboratory-based PD biomarkers for diagnosis, progression tracking, and/or the prediction of prognosis. Seven of these projects also provide detailed longitudinal data and biospecimens from PD patients and controls, as a resource for all PD researchers. Standardized operating procedures and pooled reference samples have been created in order to allow cross-project comparisons and assessment of batch effects. A web-based Data Management Resource facilitates rapid sharing of data and biosamples across the entire PD research community for additional biomarker projects. Results: Here we describe the PDBP, highlight standard operating procedures for the collection of biospecimens and data, and provide an interim report with quality control analysis on the first 1082 participants and 1033 samples with quality control analysis collected as of October 2014. Conclusions: By making samples and data available to academics and industry, encouraging the adoption of existing standards, and providing a resource which complements existing programs, the PDBP will accelerate the pace of PD biomarker research, with the goal of improving diagnostic methods and treatment.

  8. Biomarkers in Diabetic Retinopathy

    Science.gov (United States)

    Jenkins, Alicia J.; Joglekar, Mugdha V.; Hardikar, Anandwardhan A.; Keech, Anthony C.; O'Neal, David N.; Januszewski, Andrzej S.

    2015-01-01

    There is a global diabetes epidemic correlating with an increase in obesity. This coincidence may lead to a rise in the prevalence of type 2 diabetes. There is also an as yet unexplained increase in the incidence of type 1 diabetes, which is not related to adiposity. Whilst improved diabetes care has substantially improved diabetes outcomes, the disease remains a common cause of working age adult-onset blindness. Diabetic retinopathy is the most frequently occurring complication of diabetes; it is greatly feared by many diabetes patients. There are multiple risk factors and markers for the onset and progression of diabetic retinopathy, yet residual risk remains. Screening for diabetic retinopathy is recommended to facilitate early detection and treatment. Common biomarkers of diabetic retinopathy and its risk in clinical practice today relate to the visualization of the retinal vasculature and measures of glycemia, lipids, blood pressure, body weight, smoking, and pregnancy status. Greater knowledge of novel biomarkers and mediators of diabetic retinopathy, such as those related to inflammation and angiogenesis, has contributed to the development of additional therapeutics, in particular for late-stage retinopathy, including intra-ocular corticosteroids and intravitreal vascular endothelial growth factor inhibitors ('anti-VEGFs') agents. Unfortunately, in spite of a range of treatments (including laser photocoagulation, intraocular steroids, and anti-VEGF agents, and more recently oral fenofibrate, a PPAR-alpha agonist lipid-lowering drug), many patients with diabetic retinopathy do not respond well to current therapeutics. Therefore, more effective treatments for diabetic retinopathy are necessary. New analytical techniques, in particular those related to molecular markers, are accelerating progress in diabetic retinopathy research. Given the increasing incidence and prevalence of diabetes, and the limited capacity of healthcare systems to screen and treat

  9. Translational Prospects and Challenges in Human Induced Pluripotent Stem Cell Research in Drug Discovery

    Directory of Open Access Journals (Sweden)

    Masaki Hosoya

    2016-12-01

    Full Text Available Despite continuous efforts to improve the process of drug discovery and development, achieving success at the clinical stage remains challenging because of a persistent translational gap between the preclinical and clinical settings. Under these circumstances, the discovery of human induced pluripotent stem (iPS cells has brought new hope to the drug discovery field because they enable scientists to humanize a variety of pharmacological and toxicological models in vitro. The availability of human iPS cell-derived cells, particularly as an alternative for difficult-to-access tissues and organs, is increasing steadily; however, their use in the field of translational medicine remains challenging. Biomarkers are an essential part of the translational effort to shift new discoveries from bench to bedside as they provide a measurable indicator with which to evaluate pharmacological and toxicological effects in both the preclinical and clinical settings. In general, during the preclinical stage of the drug development process, in vitro models that are established to recapitulate human diseases are validated by using a set of biomarkers; however, their translatability to a clinical setting remains problematic. This review provides an overview of current strategies for human iPS cell-based drug discovery from the perspective of translational research, and discusses the importance of early consideration of clinically relevant biomarkers.

  10. Predictive Biomarkers for Asthma Therapy.

    Science.gov (United States)

    Medrek, Sarah K; Parulekar, Amit D; Hanania, Nicola A

    2017-09-19

    Asthma is a heterogeneous disease characterized by multiple phenotypes. Treatment of patients with severe disease can be challenging. Predictive biomarkers are measurable characteristics that reflect the underlying pathophysiology of asthma and can identify patients that are likely to respond to a given therapy. This review discusses current knowledge regarding predictive biomarkers in asthma. Recent trials evaluating biologic therapies targeting IgE, IL-5, IL-13, and IL-4 have utilized predictive biomarkers to identify patients who might benefit from treatment. Other work has suggested that using composite biomarkers may offer enhanced predictive capabilities in tailoring asthma therapy. Multiple biomarkers including sputum eosinophil count, blood eosinophil count, fractional concentration of nitric oxide in exhaled breath (FeNO), and serum periostin have been used to identify which patients will respond to targeted asthma medications. Further work is needed to integrate predictive biomarkers into clinical practice.

  11. Patient-derived tumour xenografts for breast cancer drug discovery.

    Science.gov (United States)

    Cassidy, John W; Batra, Ankita S; Greenwood, Wendy; Bruna, Alejandra

    2016-12-01

    Despite remarkable advances in our understanding of the drivers of human malignancies, new targeted therapies often fail to show sufficient efficacy in clinical trials. Indeed, the cost of bringing a new agent to market has risen substantially in the last several decades, in part fuelled by extensive reliance on preclinical models that fail to accurately reflect tumour heterogeneity. To halt unsustainable rates of attrition in the drug discovery process, we must develop a new generation of preclinical models capable of reflecting the heterogeneity of varying degrees of complexity found in human cancers. Patient-derived tumour xenograft (PDTX) models prevail as arguably the most powerful in this regard because they capture cancer's heterogeneous nature. Herein, we review current breast cancer models and their use in the drug discovery process, before discussing best practices for developing a highly annotated cohort of PDTX models. We describe the importance of extensive multidimensional molecular and functional characterisation of models and combination drug-drug screens to identify complex biomarkers of drug resistance and response. We reflect on our own experiences and propose the use of a cost-effective intermediate pharmacogenomic platform (the PDTX-PDTC platform) for breast cancer drug and biomarker discovery. We discuss the limitations and unanswered questions of PDTX models; yet, still strongly envision that their use in basic and translational research will dramatically change our understanding of breast cancer biology and how to more effectively treat it. © 2016 The authors.

  12. Systematic review automation technologies

    Science.gov (United States)

    2014-01-01

    Systematic reviews, a cornerstone of evidence-based medicine, are not produced quickly enough to support clinical practice. The cost of production, availability of the requisite expertise and timeliness are often quoted as major contributors for the delay. This detailed survey of the state of the art of information systems designed to support or automate individual tasks in the systematic review, and in particular systematic reviews of randomized controlled clinical trials, reveals trends that see the convergence of several parallel research projects. We surveyed literature describing informatics systems that support or automate the processes of systematic review or each of the tasks of the systematic review. Several projects focus on automating, simplifying and/or streamlining specific tasks of the systematic review. Some tasks are already fully automated while others are still largely manual. In this review, we describe each task and the effect that its automation would have on the entire systematic review process, summarize the existing information system support for each task, and highlight where further research is needed for realizing automation for the task. Integration of the systems that automate systematic review tasks may lead to a revised systematic review workflow. We envisage the optimized workflow will lead to system in which each systematic review is described as a computer program that automatically retrieves relevant trials, appraises them, extracts and synthesizes data, evaluates the risk of bias, performs meta-analysis calculations, and produces a report in real time. PMID:25005128

  13. On-Site School Library Automation: Automation Anywhere with Laptops.

    Science.gov (United States)

    Gunn, Holly; Oxner, June

    2000-01-01

    Four years after the Halifax Regional School Board was formed through amalgamation, over 75% of its school libraries were automated. On-site automation with laptops was a quicker, more efficient way of automating than sending a shelf list to the Technical Services Department. The Eastern Shore School Library Automation Project was a successful…

  14. GENE AND PROTEIN EXPRESSION PROFILING OF PANCREATIC TUMOURS REVEAL DYSREGULATED PATHWAYS AND NOVEL POTENTIAL BIOMARKER.

    Science.gov (United States)

    Nweke, E N; Ntwasa, M N; Brand, M B; Devar, J D; Smith, M D; Candy, G P

    2017-06-01

    Pancreatic cancer (PDAC) is a deadly type of cancer with almost an equal amount of new cases and deaths observed yearly. It accounts for about 7% of cancer-related deaths worldwide. In many multi-racial societies including South Africa, the black population has the highest incidence rate. Less than 5% of PDAC patients live up to 5 years. The lack of specific and sensitive diagnostic PDAC biomarkers is strongly responsible for this poor statistic. The discovery of differentially expressed genes and proteins associated with PDAC is crucial to elucidating this condition and may lead to biomarker finding and further understanding of the disease. Tissue samples were obtained from Black South African PDAC patients during the Whipple procedure. Using focused arrays and RNA Sequencing, we have shown differentially expressed genes and proteins between tumour and normal tissue samples of PDAC patients in the quest for potential biomarker discovery. Furthermore, we utilised multiple bioinformatics tools to identify pathways and biological processes enriched by differentially expressed genes/proteins, and to discover novel variants and novel potential PDAC biomarkers. Real-time PCR and ELISA were also employed to validate our novel potential PDAC biomarker. We have identified novel 1) potential transcriptomic and 2) proteomic biomarkers of pancreatic cancer. Our identified transcriptomic biomarker has a sensitivity and specificity of 100% and 80% respectively. Furthermore, we observed novel genetic variants and dysregulated pathways occurring during pancreatic carcinogenesis. This study has identified novel potential biomarkers which can help in the diagnosis of PDAC. Information obtained from enriched signalling pathways help in further understanding the biology of PDAC. Going forward, the identified novel potential biomarkers need to be further validated in a larger sample number using easily accessible samples like blood.

  15. Nanoslit design for ion conductivity gradient enhanced dielectrophoresis for ultrafast biomarker enrichment in physiological media.

    Science.gov (United States)

    Rohani, Ali; Varhue, Walter; Liao, Kuo-Tang; Chou, Chia-Fu; Swami, Nathan S

    2016-05-01

    Selective and rapid enrichment of biomolecules is of great interest for biomarker discovery, protein crystallization, and in biosensing for speeding assay kinetics and reducing signal interferences. The current state of the art is based on DC electrokinetics, wherein localized ion depletion at the microchannel to nanochannel interface is used to enhance electric fields, and the resulting biomarker electromigration is balanced against electro-osmosis in the microchannel to cause high degrees of biomarker enrichment. However, biomarker enrichment is not selective, and the levels fall off within physiological media of high conductivity, due to a reduction in ion concentration polarization and electro-osmosis effects. Herein, we present a methodology for coupling AC electrokinetics with ion concentration polarization effects in nanoslits under DC fields, for enabling ultrafast biomarker enrichment in physiological media. Using AC fields at the critical frequency necessary for negative dielectrophoresis of the biomarker of interest, along with a critical offset DC field to create proximal ion accumulation and depletion regions along the perm-selective region inside a nanoslit, we enhance the localized field and field gradient to enable biomarker enrichment over a wide spatial extent along the nanoslit length. While enrichment under DC electrokinetics relies solely on ion depletion to enhance fields, this AC electrokinetic mechanism utilizes ion depletion as well as ion accumulation regions to enhance the field and its gradient. Hence, biomarker enrichment continues to be substantial in spite of the steady drop in nanostructure perm-selectivity within physiological media.

  16. Response biomarkers: re-envisioning the approach to tailoring drug therapy for cancer

    International Nuclear Information System (INIS)

    Amin, Shahil; Bathe, Oliver F.

    2016-01-01

    The rapidly expanding arsenal of chemotherapeutic agents approved in the past 5 years represents significant progress in the field. However, this poses a challenge for oncologists to choose which drug or combination of drugs is best for any individual. Because only a fraction of patients respond to any drug, efforts have been made to devise strategies to personalize care. The majority of efforts have involved development of predictive biomarkers. While there are notable successes, there are no predictive biomarkers for most drugs. Moreover, predictive biomarkers enrich the cohort of individuals likely to benefit; they do not guarantee benefit. There is a need to devise alternate strategies to tailor cancer care. One alternative approach is to enhance the current adaptive approach, which involves administration of a drug and cessation of treatment once progression is documented. This currently involves radiographic tests for the most part, which are expensive, inconvenient and imperfect in their ability to categorize patients who are and are not benefiting from treatment. A biomarker approach to categorizing response may have advantages. Herein, we discuss the state of the art on treatment response assessment. While the most mature technologies for response assessment involve radiographic tests such as CT and PET, reports are emerging on biomarkers used to monitor therapeutic efficacy. Potentially, response biomarkers represent a less expensive and more convenient means of monitoring therapy, although an ideal response biomarker has not yet been described. A framework for future response biomarker discovery is described

  17. Biomarker Identification Using Text Mining

    Directory of Open Access Journals (Sweden)

    Hui Li

    2012-01-01

    Full Text Available Identifying molecular biomarkers has become one of the important tasks for scientists to assess the different phenotypic states of cells or organisms correlated to the genotypes of diseases from large-scale biological data. In this paper, we proposed a text-mining-based method to discover biomarkers from PubMed. First, we construct a database based on a dictionary, and then we used a finite state machine to identify the biomarkers. Our method of text mining provides a highly reliable approach to discover the biomarkers in the PubMed database.

  18. Automated electron microprobe

    International Nuclear Information System (INIS)

    Thompson, K.A.; Walker, L.R.

    1986-01-01

    The Plant Laboratory at the Oak Ridge Y-12 Plant has recently obtained a Cameca MBX electron microprobe with a Tracor Northern TN5500 automation system. This allows full stage and spectrometer automation and digital beam control. The capabilities of the system include qualitative and quantitative elemental microanalysis for all elements above and including boron in atomic number, high- and low-magnification imaging and processing, elemental mapping and enhancement, and particle size, shape, and composition analyses. Very low magnification, quantitative elemental mapping using stage control (which is of particular interest) has been accomplished along with automated size, shape, and composition analysis over a large relative area

  19. Operational proof of automation

    International Nuclear Information System (INIS)

    Jaerschky, R.; Reifenhaeuser, R.; Schlicht, K.

    1976-01-01

    Automation of the power plant process may imply quite a number of problems. The automation of dynamic operations requires complicated programmes often interfering in several branched areas. This reduces clarity for the operating and maintenance staff, whilst increasing the possibilities of errors. The synthesis and the organization of standardized equipment have proved very successful. The possibilities offered by this kind of automation for improving the operation of power plants will only sufficiently and correctly be turned to profit, however, if the application of these technics of equipment is further improved and if its volume is tallied with a definite etc. (orig.) [de

  20. Chef infrastructure automation cookbook

    CERN Document Server

    Marschall, Matthias

    2013-01-01

    Chef Infrastructure Automation Cookbook contains practical recipes on everything you will need to automate your infrastructure using Chef. The book is packed with illustrated code examples to automate your server and cloud infrastructure.The book first shows you the simplest way to achieve a certain task. Then it explains every step in detail, so that you can build your knowledge about how things work. Eventually, the book shows you additional things to consider for each approach. That way, you can learn step-by-step and build profound knowledge on how to go about your configuration management

  1. Opportunities and Challenges of Proteomics in Pediatric Patients: Circulating Biomarkers After Hematopoietic Stem Cell Transplantation As a Successful Example

    Science.gov (United States)

    Paczesny, Sophie; Duncan, Christine; Jacobsohn, David; Krance, Robert; Leung, Kathryn; Carpenter, Paul; Bollard, Catherine; Renbarger, Jamie; Cooke, Kenneth

    2015-01-01

    Biomarkers have the potential to improve diagnosis and prognosis, facilitate targeted treatment, and reduce health care costs. Thus, there is great hope that biomarkers will be integrated in all clinical decisions in the near future. A decade ago, the biomarker field was launched with great enthusiasm because mass spectrometry revealed that blood contains a rich library of candidate biomarkers. However, biomarker research has not yet delivered on its promise due to several limitations: (i) improper sample handling and tracking as well as limited sample availability in the pediatric population, (ii) omission of appropriate controls in original study designs, (iii) lability and low abundance of interesting biomarkers in blood, and (iv) the inability to mechanistically tie biomarker presence to disease biology. These limitations as well as successful strategies to overcome them are discussed in this review. Several advances in biomarker discovery and validation have been made in hematopoietic stem cell transplantation, the current most effective tumor immunotherapy, and these could serve as examples for other conditions. This review provides fresh optimism that biomarkers clinically relevant in pediatrics are closer to being realized based on: (i) a uniform protocol for low-volume blood collection and preservation, (ii) inclusion of well-controlled independent cohorts, (iii) novel technologies and instrumentation with low analytical sensitivity, and (iv) integrated animal models for exploring potential biomarkers and targeted therapies. PMID:25196024

  2. Biomarkers: refining diagnosis and expediting drug development - reality, aspiration and the role of open innovation.

    Science.gov (United States)

    Salter, H; Holland, R

    2014-09-01

    In the last decade, there have been intensive efforts to invent, qualify and use novel biomarkers as a means to improve success rates in drug discovery and development. The biomarkers field is maturing and this article considers whether these research efforts have brought about the expected benefits. The characteristics of a clinically useful biomarker are described and the impact this area of research has had is evaluated by reviewing a few, key examples of emerging biomarkers. There is evidence that the impact has been genuine and is increasing in both the drug and the diagnostic discovery and development processes. Beneficial impact on patient health outcomes seems relatively limited thus far, with the greatest impact in oncology (again, both in terms of novel drugs and in terms of more refined diagnoses and therefore more individualized treatment). However, the momentum of research would indicate that patient benefits are likely to increase substantially and to broaden across multiple therapeutic areas. Even though this research was originally driven by a desire to improve the drug discovery and development process, and was therefore funded with this aim in mind, it seems likely that the largest impact may actually come from more refined diagnosis. Refined diagnosis will facilitate both better allocation of healthcare resources and the use of treatment regimens which are optimized for the individual patient. This article also briefly reviews emerging technological approaches and how they relate to the challenges inherent in biomarker discovery and validation, and discusses the role of public/private partnerships in innovative biomarker research. © 2014 The Association for the Publication of the Journal of Internal Medicine.

  3. Recent advances in inkjet dispensing technologies: applications in drug discovery.

    Science.gov (United States)

    Zhu, Xiangcheng; Zheng, Qiang; Yang, Hu; Cai, Jin; Huang, Lei; Duan, Yanwen; Xu, Zhinan; Cen, Peilin

    2012-09-01

    Inkjet dispensing technology is a promising fabrication methodology widely applied in drug discovery. The automated programmable characteristics and high-throughput efficiency makes this approach potentially very useful in miniaturizing the design patterns for assays and drug screening. Various custom-made inkjet dispensing systems as well as specialized bio-ink and substrates have been developed and applied to fulfill the increasing demands of basic drug discovery studies. The incorporation of other modern technologies has further exploited the potential of inkjet dispensing technology in drug discovery and development. This paper reviews and discusses the recent developments and practical applications of inkjet dispensing technology in several areas of drug discovery and development including fundamental assays of cells and proteins, microarrays, biosensors, tissue engineering, basic biological and pharmaceutical studies. Progression in a number of areas of research including biomaterials, inkjet mechanical systems and modern analytical techniques as well as the exploration and accumulation of profound biological knowledge has enabled different inkjet dispensing technologies to be developed and adapted for high-throughput pattern fabrication and miniaturization. This in turn presents a great opportunity to propel inkjet dispensing technology into drug discovery.

  4. Digital imaging biomarkers feed machine learning for melanoma screening.

    Science.gov (United States)

    Gareau, Daniel S; Correa da Rosa, Joel; Yagerman, Sarah; Carucci, John A; Gulati, Nicholas; Hueto, Ferran; DeFazio, Jennifer L; Suárez-Fariñas, Mayte; Marghoob, Ashfaq; Krueger, James G

    2017-07-01

    We developed an automated approach for generating quantitative image analysis metrics (imaging biomarkers) that are then analysed with a set of 13 machine learning algorithms to generate an overall risk score that is called a Q-score. These methods were applied to a set of 120 "difficult" dermoscopy images of dysplastic nevi and melanomas that were subsequently excised/classified. This approach yielded 98% sensitivity and 36% specificity for melanoma detection, approaching sensitivity/specificity of expert lesion evaluation. Importantly, we found strong spectral dependence of many imaging biomarkers in blue or red colour channels, suggesting the need to optimize spectral evaluation of pigmented lesions. © 2016 The Authors. Experimental Dermatology Published by John Wiley & Sons Ltd.

  5. Automation Interface Design Development

    Data.gov (United States)

    National Aeronautics and Space Administration — Our research makes its contributions at two levels. At one level, we addressed the problems of interaction between humans and computers/automation in a particular...

  6. Automated Vehicles Symposium 2014

    CERN Document Server

    Beiker, Sven; Road Vehicle Automation 2

    2015-01-01

    This paper collection is the second volume of the LNMOB series on Road Vehicle Automation. The book contains a comprehensive review of current technical, socio-economic, and legal perspectives written by experts coming from public authorities, companies and universities in the U.S., Europe and Japan. It originates from the Automated Vehicle Symposium 2014, which was jointly organized by the Association for Unmanned Vehicle Systems International (AUVSI) and the Transportation Research Board (TRB) in Burlingame, CA, in July 2014. The contributions discuss the challenges arising from the integration of highly automated and self-driving vehicles into the transportation system, with a focus on human factors and different deployment scenarios. This book is an indispensable source of information for academic researchers, industrial engineers, and policy makers interested in the topic of road vehicle automation.

  7. Fixed automated spray technology.

    Science.gov (United States)

    2011-04-19

    This research project evaluated the construction and performance of Boschungs Fixed Automated : Spray Technology (FAST) system. The FAST system automatically sprays de-icing material on : the bridge when icing conditions are about to occur. The FA...

  8. Automated Vehicles Symposium 2015

    CERN Document Server

    Beiker, Sven

    2016-01-01

    This edited book comprises papers about the impacts, benefits and challenges of connected and automated cars. It is the third volume of the LNMOB series dealing with Road Vehicle Automation. The book comprises contributions from researchers, industry practitioners and policy makers, covering perspectives from the U.S., Europe and Japan. It is based on the Automated Vehicles Symposium 2015 which was jointly organized by the Association of Unmanned Vehicle Systems International (AUVSI) and the Transportation Research Board (TRB) in Ann Arbor, Michigan, in July 2015. The topical spectrum includes, but is not limited to, public sector activities, human factors, ethical and business aspects, energy and technological perspectives, vehicle systems and transportation infrastructure. This book is an indispensable source of information for academic researchers, industrial engineers and policy makers interested in the topic of road vehicle automation.

  9. Automation synthesis modules review

    International Nuclear Information System (INIS)

    Boschi, S.; Lodi, F.; Malizia, C.; Cicoria, G.; Marengo, M.

    2013-01-01

    The introduction of 68 Ga labelled tracers has changed the diagnostic approach to neuroendocrine tumours and the availability of a reliable, long-lived 68 Ge/ 68 Ga generator has been at the bases of the development of 68 Ga radiopharmacy. The huge increase in clinical demand, the impact of regulatory issues and a careful radioprotection of the operators have boosted for extensive automation of the production process. The development of automated systems for 68 Ga radiochemistry, different engineering and software strategies and post-processing of the eluate were discussed along with impact of automation with regulations. - Highlights: ► Generators availability and robust chemistry boosted for the huge diffusion of 68Ga radiopharmaceuticals. ► Different technological approaches for 68Ga radiopharmaceuticals will be discussed. ► Generator eluate post processing and evolution to cassette based systems were the major issues in automation. ► Impact of regulations on the technological development will be also considered

  10. Disassembly automation automated systems with cognitive abilities

    CERN Document Server

    Vongbunyong, Supachai

    2015-01-01

    This book presents a number of aspects to be considered in the development of disassembly automation, including the mechanical system, vision system and intelligent planner. The implementation of cognitive robotics increases the flexibility and degree of autonomy of the disassembly system. Disassembly, as a step in the treatment of end-of-life products, can allow the recovery of embodied value left within disposed products, as well as the appropriate separation of potentially-hazardous components. In the end-of-life treatment industry, disassembly has largely been limited to manual labor, which is expensive in developed countries. Automation is one possible solution for economic feasibility. The target audience primarily comprises researchers and experts in the field, but the book may also be beneficial for graduate students.

  11. Searching for new biomarkers in ovarian cancer patients: Rationale and design of a retrospective study under the Mermaid III project

    Directory of Open Access Journals (Sweden)

    Julie L. Hentze

    2017-12-01

    A thorough investigation of biomarkers in ovarian cancer, including large numbers of different markers, has never been done before. Besides from improving diagnosis and treatment, other outcomes could be markers for screening, knowledge of the molecular aspects of cancer and the discovery of new drugs. Moreover, biomarkers are a prerequisite for the development of precision medicine. This study will attack the ovarian cancer problem from several angles, thereby increasing the chance of successfully contributing to saving lives.

  12. Inertial Microfluidic Cell Stretcher (iMCS): Fully Automated, High-Throughput, and Near Real-Time Cell Mechanotyping.

    Science.gov (United States)

    Deng, Yanxiang; Davis, Steven P; Yang, Fan; Paulsen, Kevin S; Kumar, Maneesh; Sinnott DeVaux, Rebecca; Wang, Xianhui; Conklin, Douglas S; Oberai, Assad; Herschkowitz, Jason I; Chung, Aram J

    2017-07-01

    Mechanical biomarkers associated with cytoskeletal structures have been reported as powerful label-free cell state identifiers. In order to measure cell mechanical properties, traditional biophysical (e.g., atomic force microscopy, micropipette aspiration, optical stretchers) and microfluidic approaches were mainly employed; however, they critically suffer from low-throughput, low-sensitivity, and/or time-consuming and labor-intensive processes, not allowing techniques to be practically used for cell biology research applications. Here, a novel inertial microfluidic cell stretcher (iMCS) capable of characterizing large populations of single-cell deformability near real-time is presented. The platform inertially controls cell positions in microchannels and deforms cells upon collision at a T-junction with large strain. The cell elongation motions are recorded, and thousands of cell deformability information is visualized near real-time similar to traditional flow cytometry. With a full automation, the entire cell mechanotyping process runs without any human intervention, realizing a user friendly and robust operation. Through iMCS, distinct cell stiffness changes in breast cancer progression and epithelial mesenchymal transition are reported, and the use of the platform for rapid cancer drug discovery is shown as well. The platform returns large populations of single-cell quantitative mechanical properties (e.g., shear modulus) on-the-fly with high statistical significances, enabling actual usages in clinical and biophysical studies. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. Automated Lattice Perturbation Theory

    Energy Technology Data Exchange (ETDEWEB)

    Monahan, Christopher

    2014-11-01

    I review recent developments in automated lattice perturbation theory. Starting with an overview of lattice perturbation theory, I focus on the three automation packages currently "on the market": HiPPy/HPsrc, Pastor and PhySyCAl. I highlight some recent applications of these methods, particularly in B physics. In the final section I briefly discuss the related, but distinct, approach of numerical stochastic perturbation theory.

  14. Automated ISMS control auditability

    OpenAIRE

    Suomu, Mikko

    2015-01-01

    This thesis focuses on researching a possible reference model for automated ISMS’s (Information Security Management System) technical control auditability. The main objective was to develop a generic framework for automated compliance status monitoring of the ISO27001:2013 standard which could be re‐used in any ISMS system. The framework was tested with Proof of Concept (PoC) empirical research in a test infrastructure which simulates the framework target deployment environment. To fulfi...

  15. Marketing automation supporting sales

    OpenAIRE

    Sandell, Niko

    2016-01-01

    The past couple of decades has been a time of major changes in marketing. Digitalization has become a permanent part of marketing and at the same time enabled efficient collection of data. Personalization and customization of content are playing a crucial role in marketing when new customers are acquired. This has also created a need for automation to facilitate the distribution of targeted content. As a result of successful marketing automation more information of the customers is gathered ...

  16. Automated lattice data generation

    Directory of Open Access Journals (Sweden)

    Ayyar Venkitesh

    2018-01-01

    Full Text Available The process of generating ensembles of gauge configurations (and measuring various observables over them can be tedious and error-prone when done “by hand”. In practice, most of this procedure can be automated with the use of a workflow manager. We discuss how this automation can be accomplished using Taxi, a minimal Python-based workflow manager built for generating lattice data. We present a case study demonstrating this technology.

  17. Mapping the Stacks: Sustainability and User Experience of Animated Maps in Library Discovery Interfaces

    Science.gov (United States)

    McMillin, Bill; Gibson, Sally; MacDonald, Jean

    2016-01-01

    Animated maps of the library stacks were integrated into the catalog interface at Pratt Institute and into the EBSCO Discovery Service interface at Illinois State University. The mapping feature was developed for optimal automation of the update process to enable a range of library personnel to update maps and call-number ranges. The development…

  18. Biomarkers in Vasculitis

    Science.gov (United States)

    Monach, Paul A.

    2014-01-01

    Purpose of review Better biomarkers are needed for guiding management of patients with vasculitis. Large cohorts and technological advances had led to an increase in pre-clinical studies of potential biomarkers. Recent findings The most interesting markers described recently include a gene expression signature in CD8+ T cells that predicts tendency to relapse or remain relapse-free in ANCA-associated vasculitis, and a pair of urinary proteins that are elevated in Kawasaki disease but not other febrile illnesses. Both of these studies used “omics” technologies to generate and then test hypotheses. More conventional hypothesis-based studies have indicated that the following circulating proteins have potential to improve upon clinically available tests: pentraxin-3 in giant cell arteritis and Takayasu’s arteritis; von Willebrand factor antigen in childhood central nervous system vasculitis; eotaxin-3 and other markers related to eosinophils or Th2 immune responses in eosinophilic granulomatosis with polyangiitis (Churg-Strauss syndrome); and MMP-3, TIMP-1, and CXCL13 in ANCA-associated vasculitis. Summary New markers testable in blood and urine have the potential to assist with diagnosis, staging, assessment of current disease activity, and prognosis. However, the standards for clinical usefulness, in particular the demonstration of either very high sensitivity or very high specificity, have yet to be met for clinically relevant outcomes. PMID:24257367

  19. Automated detection of structural alerts (chemical fragments in (ecotoxicology

    Directory of Open Access Journals (Sweden)

    Ronan Bureau

    2013-02-01

    Full Text Available This mini-review describes the evolution of different algorithms dedicated to the automated discovery of chemical fragments associated to (ecotoxicological endpoints. These structural alerts correspond to one of the most interesting approach of in silico toxicology due to their direct link with specific toxicological mechanisms. A number of expert systems are already available but, since the first work in this field which considered a binomial distribution of chemical fragments between two datasets, new data miners were developed and applied with success in chemoinformatics. The frequency of a chemical fragment in a dataset is often at the core of the process for the definition of its toxicological relevance. However, recent progresses in data mining provide new insights into the automated discovery of new rules. Particularly, this review highlights the notion of Emerging Patterns that can capture contrasts between classes of data.

  20. AUTOMATED DETECTION OF STRUCTURAL ALERTS (CHEMICAL FRAGMENTS IN (ECOTOXICOLOGY

    Directory of Open Access Journals (Sweden)

    Alban Lepailleur

    2013-02-01

    Full Text Available This mini-review describes the evolution of different algorithms dedicated to the automated discovery of chemical fragments associated to (ecotoxicological endpoints. These structural alerts correspond to one of the most interesting approach of in silico toxicology due to their direct link with specific toxicological mechanisms. A number of expert systems are already available but, since the first work in this field which considered a binomial distribution of chemical fragments between two datasets, new data miners were developed and applied with success in chemoinformatics. The frequency of a chemical fragment in a dataset is often at the core of the process for the definition of its toxicological relevance. However, recent progresses in data mining provide new insights into the automated discovery of new rules. Particularly, this review highlights the notion of Emerging Patterns that can capture contrasts between classes of data.