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Pathophysiological responses from human gut microbiome

World Journal of Translational Medicine 2014年第3期3卷 133-140页

作者： anindya roy Chowdhury Utpal Bakshi Structural Biology and Bioinformatics Division CSIR-Indian Institute of Chemical Biology

The human gastrointestinal tract harbors a vast collection of symbiotic microorganisms-collectively termed as "gut microbiome". This microbiota has important effect in immune system and other host activities. Recent studies have suggested that alterations of the normal gut microbiota are associated with various human diseases and psychological disorders. The underlying cause, once proven, may provide novel insights into the importance of gut flora in human health. In this review, we give an attempt to describe how the alteration in the microbial community causes the development of certain widespread pathophysiological disorders; focusing on inflammatory bowel disease, colorectal cancer, obesity and autism. Proper knowledge about the hostmicrobiota interaction and linkage could be essential for the development of future personalized strategies of therapeutics.

关键词： Human gut microbiome Inflammatory bowel disease Colorectal cancer Obesity Autism

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Assessing Chemical Mixtures and Human Health: Use of Bayesian Belief Net Analysis

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Journal of Environmental Protection 2012年第6期3卷 462-468页

作者： anindya roy Neil J. Perkins Germaine M. Buck Louis Department of Mathematics and Statistics University of Maryland Baltimore County Baltimore USA Division of Epidemiology Statistics & Prevention Research Eunice Kennedy Shriver National Institute of Child Health & Human Development Rockville USA

Background: Despite humans being exposed to complex chemical mixtures, much of the available research continues to focus on a single compound or metabolite or a select subgroup of compounds inconsistent with the nature of human exposure. Uncertainty regarding how best to model chemical mixtures coupled with few analytic approaches remains a formidable challenge and served as the impetus for the study. Objectives: To identify the polychlorinated biphenyl (PCB) congener(s) within a chemical mixture that was most associated with an endometriosis diagnosis using novel graphical modeling techniques. Methods: Bayesian Belief Network (BBN) models were developed and empirically assessed in a cohort comprising 84 women aged 18 - 40 years who underwent a laparoscopy or laparotomy between 1999 and 2000;79 (94%) women had serum concentrations for 68 PCB congeners quantified. Adjusted odds ratios (AOR) for endometriosis were estimated for individual PCB congeners using BBN models. Results: PCB congeners #114 (AOR = 3.01;95% CI = 2.25, 3.77) and #136 (AOR = 1.79;95% CI = 1.03, 2.55) were associated with an endometriosis diagnosis. Combinations of mixtures inclusive of PCB #114 were all associated with higher odds of endometriosis, underscoring its potential relation with endometriosis. Conclusions: BBN models identified PCB congener 114 as the most influential congener for the odds of an endometriosis diagnosis in the context of a 68 congener chemical mixture. BBN models offer investigators the opportunity to assess which compounds within a mixture may drive a human health effect.

关键词： Bayesian Belief Network Endometriosis Environment Mixtures Polychlorinated Biphenyls

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