The Experts below are selected from a list of 305166 Experts worldwide ranked by ideXlab platform

Tracey J Woodruff - One of the best experts on this subject based on the ideXlab platform.

  • Suspect screening of maternal serum to identify new Environmental Chemical biomonitoring targets using liquid chromatography–quadrupole time-of-flight mass spectrometry
    Journal of Exposure Science & Environmental Epidemiology, 2018
    Co-Authors: Roy R Gerona, Jackie M Schwartz, Janet Pan, Matthew M Friesen, Thomas Lin, Tracey J Woodruff
    Abstract:

    The use and advantages of high-resolution mass spectrometry (MS) as a discovery tool for Environmental Chemical monitoring has been demonstrated for Environmental samples but not for biological samples. We developed a method using liquid chromatography–quadrupole time-of-flight MS (LC–QTOF/MS) for discovery of previously unmeasured Environmental Chemicals in human serum. Using non-targeted data acquisition (full scan MS analysis) we were able to screen for Environmental organic acids (EOAs) in 20 serum samples from second trimester pregnant women. We define EOAs as Environmental organic compounds with at least one dissociable proton which are utilized in commerce. EOAs include Environmental phenols, phthalate metabolites, perfluorinated compounds, phenolic metabolites of polybrominated diphenyl ethers and polychlorinated biphenyls, and acidic pesticides and/or predicted acidic pesticide metabolites. Our validated method used solid phase extraction, reversed-phase chromatography in a C18 column with gradient elution, electrospray ionization in negative polarity and automated tandem MS (MS/MS) data acquisition to maximize true positive rates. We identified “suspect EOAs” using Agilent MassHunter Qualitative Analysis software, to match Chemical formulas generated from each sample run with molecular formulas in our unique database of 693 EOAs assembled from multiple Environmental literature sources. We found potential matches for 282 (41%) of the EOAs in our database. Sixty-five of these suspect EOAs were detected in at least 75% of the samples; only 19 of these compounds are currently biomonitored in National Health and Nutrition Examination Survey. We confirmed two of three suspect EOAs by LC–QTOF/MS using a targeted method developed through LC–MS/MS, reporting the first confirmation of benzophenone-1 and bisphenol S in pregnant women’s sera. Our suspect screening workflow provides an approach to comprehensively scan Environmental Chemical exposures in humans. This can provide a better source of exposure information to help improve exposure and risk evaluation of industrial Chemicals.

  • cumulative risk and impact modeling on Environmental Chemical and social stressors
    Current Environmental Health Reports, 2018
    Co-Authors: Hongtai Huang, Aolin Wang, Rachel Morellofrosch, Marina Sirota, Amy Padula, Tracey J Woodruff
    Abstract:

    Purpose of Review The goal of this review is to identify cumulative modeling methods used to evaluate combined effects of exposures to Environmental Chemicals and social stressors. The specific review question is: What are the existing quantitative methods used to examine the cumulative impacts of exposures to Environmental Chemical and social stressors on health?

  • Cumulative Risk and Impact Modeling on Environmental Chemical and Social Stressors
    Current environmental health reports, 2018
    Co-Authors: Hongtai Huang, Aolin Wang, Marina Sirota, Amy Padula, Rachel Morello-frosch, Juleen Lam, Tracey J Woodruff
    Abstract:

    The goal of this review is to identify cumulative modeling methods used to evaluate combined effects of exposures to Environmental Chemicals and social stressors. The specific review question is: What are the existing quantitative methods used to examine the cumulative impacts of exposures to Environmental Chemical and social stressors on health? There has been an increase in literature that evaluates combined effects of exposures to Environmental Chemicals and social stressors on health using regression models; very few studies applied other data mining and machine learning techniques to this problem. The majority of studies we identified used regression models to evaluate combined effects of multiple Environmental and social stressors. With proper study design and appropriate modeling assumptions, additional data mining methods may be useful to examine combined effects of Environmental and social stressors.

Atul J Butte - One of the best experts on this subject based on the ideXlab platform.

  • Predicting Environmental Chemical factors associated with disease-related gene expression data
    BMC Medical Genomics, 2010
    Co-Authors: Chirag J Patel, Atul J Butte
    Abstract:

    Background Many common diseases arise from an interaction between Environmental and genetic factors. Our knowledge regarding environment and gene interactions is growing, but frameworks to build an association between gene-environment interactions and disease using preexisting, publicly available data has been lacking. Integrating freely-available environment-gene interaction and disease phenotype data would allow hypothesis generation for potential Environmental associations to disease. Methods We integrated publicly available disease-specific gene expression microarray data and curated Chemical-gene interaction data to systematically predict Environmental Chemicals associated with disease. We derived Chemical-gene signatures for 1,338 Chemical/Environmental Chemicals from the Comparative Toxicogenomics Database (CTD). We associated these Chemical-gene signatures with differentially expressed genes from datasets found in the Gene Expression Omnibus (GEO) through an enrichment test. Results We were able to verify our analytic method by accurately identifying Chemicals applied to samples and cell lines. Furthermore, we were able to predict known and novel Environmental associations with prostate, lung, and breast cancers, such as estradiol and bisphenol A. Conclusions We have developed a scalable and statistical method to identify possible Environmental associations with disease using publicly available data and have validated some of the associations in the literature.

  • predicting Environmental Chemical factors associated with disease related gene expression data
    BMC Medical Genomics, 2010
    Co-Authors: Chirag J Patel, Atul J Butte
    Abstract:

    Background Many common diseases arise from an interaction between Environmental and genetic factors. Our knowledge regarding environment and gene interactions is growing, but frameworks to build an association between gene-environment interactions and disease using preexisting, publicly available data has been lacking. Integrating freely-available environment-gene interaction and disease phenotype data would allow hypothesis generation for potential Environmental associations to disease.

Chirag J Patel - One of the best experts on this subject based on the ideXlab platform.

  • Predicting Environmental Chemical factors associated with disease-related gene expression data
    BMC Medical Genomics, 2010
    Co-Authors: Chirag J Patel, Atul J Butte
    Abstract:

    Background Many common diseases arise from an interaction between Environmental and genetic factors. Our knowledge regarding environment and gene interactions is growing, but frameworks to build an association between gene-environment interactions and disease using preexisting, publicly available data has been lacking. Integrating freely-available environment-gene interaction and disease phenotype data would allow hypothesis generation for potential Environmental associations to disease. Methods We integrated publicly available disease-specific gene expression microarray data and curated Chemical-gene interaction data to systematically predict Environmental Chemicals associated with disease. We derived Chemical-gene signatures for 1,338 Chemical/Environmental Chemicals from the Comparative Toxicogenomics Database (CTD). We associated these Chemical-gene signatures with differentially expressed genes from datasets found in the Gene Expression Omnibus (GEO) through an enrichment test. Results We were able to verify our analytic method by accurately identifying Chemicals applied to samples and cell lines. Furthermore, we were able to predict known and novel Environmental associations with prostate, lung, and breast cancers, such as estradiol and bisphenol A. Conclusions We have developed a scalable and statistical method to identify possible Environmental associations with disease using publicly available data and have validated some of the associations in the literature.

  • predicting Environmental Chemical factors associated with disease related gene expression data
    BMC Medical Genomics, 2010
    Co-Authors: Chirag J Patel, Atul J Butte
    Abstract:

    Background Many common diseases arise from an interaction between Environmental and genetic factors. Our knowledge regarding environment and gene interactions is growing, but frameworks to build an association between gene-environment interactions and disease using preexisting, publicly available data has been lacking. Integrating freely-available environment-gene interaction and disease phenotype data would allow hypothesis generation for potential Environmental associations to disease.

Hongtai Huang - One of the best experts on this subject based on the ideXlab platform.

  • cumulative risk and impact modeling on Environmental Chemical and social stressors
    Current Environmental Health Reports, 2018
    Co-Authors: Hongtai Huang, Aolin Wang, Rachel Morellofrosch, Marina Sirota, Amy Padula, Tracey J Woodruff
    Abstract:

    Purpose of Review The goal of this review is to identify cumulative modeling methods used to evaluate combined effects of exposures to Environmental Chemicals and social stressors. The specific review question is: What are the existing quantitative methods used to examine the cumulative impacts of exposures to Environmental Chemical and social stressors on health?

  • Cumulative Risk and Impact Modeling on Environmental Chemical and Social Stressors
    Current environmental health reports, 2018
    Co-Authors: Hongtai Huang, Aolin Wang, Marina Sirota, Amy Padula, Rachel Morello-frosch, Juleen Lam, Tracey J Woodruff
    Abstract:

    The goal of this review is to identify cumulative modeling methods used to evaluate combined effects of exposures to Environmental Chemicals and social stressors. The specific review question is: What are the existing quantitative methods used to examine the cumulative impacts of exposures to Environmental Chemical and social stressors on health? There has been an increase in literature that evaluates combined effects of exposures to Environmental Chemicals and social stressors on health using regression models; very few studies applied other data mining and machine learning techniques to this problem. The majority of studies we identified used regression models to evaluate combined effects of multiple Environmental and social stressors. With proper study design and appropriate modeling assumptions, additional data mining methods may be useful to examine combined effects of Environmental and social stressors.

Harold G Craighead - One of the best experts on this subject based on the ideXlab platform.

  • Micro- and nanomechanical sensors for Environmental, Chemical, and biological detection
    Lab on a Chip, 2007
    Co-Authors: Philip S. Waggoner, Harold G Craighead
    Abstract:

    Micro- and nanoelectromechanical systems, including cantilevers and other small scale structures, have been studied for sensor applications. Accurate sensing of gaseous or aqueous environments, Chemical vapors, and biomolecules have been demonstrated using a variety of these devices that undergo static deflections or shifts in resonant frequency upon analyte binding. In particular, biological detection of viruses, antigens, DNA, and other proteins is of great interest. While the majority of currently used detection schemes are reliant on biomarkers, such as fluorescent labels, time, effort, and Chemical activity could be saved by developing an ultrasensitive method of label-free mass detection. Micro- and nanoscale sensors have been effectively applied as label-free detectors. In the following, we review the technologies and recent developments in the field of micro- and nanoelectromechanical sensors with particular emphasis on their application as biological sensors and recent work towards integrating these sensors in microfluidic systems.