1,721,088 research outputs found
Effect of CYP2B6, ABCB1, and CYP3A5 polymorphisms on efavirenz pharmacokinetics and treatment response: an AIDS Clinical Trials Group study
No full textIn AIDS Clinical Trials Group protocols 384, A5095, and A5097s, we characterized relationships between 22 polymorphisms in CYP2B6, ABCB1, and CYP3A5; plasma efavirenz exposure; and/or treatment responses. A stepwise logistic regression procedure selected polymorphisms associated with reduced drug clearance adjusted for body mass index and the composite CYP2B6 516/983 genotype. Relationships between selected polymorphisms and treatment responses were characterized by competing risk methodology. Association analyses involved 821 individuals (317 for pharmacokinetics and 643 for treatment response). Models that included CYP2B6 516/983 genotype best predicted pharmacokinetics. Slow-metabolizer genotypes were associated with increased central nervous system events among white participants and decreased virologic failure among black participants.Heather J. Ribaudo, Huan Liu, Matthias Schwab, Elke Schaeffeler, Michel Eichelbaum, Alison A. Motsinger-Reif, Marylyn D. Ritchie, Ulrich M. Zanger, Edward P. Acosta, Gene D. Morse, Roy M. Gulick, Gregory K. Robbins, David Clifford, and David W. Haa
Discussion of a Genome‐Wide Association Approach to Determine HIV‐1 Set Point in African Americans
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Reproductive Gene Expression in Male Sus scrofa: An examination of the differential gene expression of Divergent Testosterone selection and development of a Ribonucleic Acid extraction protocol from whole Porcine Spermatozoa
No full textThe ability to characterize and enhance market traits in livestock has facilitated a greater interest in determining the genetic tool kit available for manipulation. In swine, using new approaches in genomics, such as microarray analysis and biological pathway analysis, we can show genes up and down regulated in a variety of processes and conditions. To this end, we identify the genes, pathways, and disease biomarkers affected by divergent selection of testosterone in boars. Testicular samples were taken from boars at 0, 30, 120, 150, and 180 days of age for lines of high (HT) and low testosterone (LT). Evidence that many of the differences in gene expression were at the pubertal period of 150 days led to a subsequent microarray study of the 150 day HT and LT animals. Microarray studies were followed by validation with real-time RT-PCR of 11 genes and extensive GeneGo pathway analysis (Metacore) of differentially expressed genes. While increased testosterone has long been associated with increased growth rates, we now have supporting genomic evidence of the genes and pathways up-regulated and down-regulated in these lines. To this end, this study has identified several disease biomarkers that may require further investigation and biological pathways associated with growth and metabolism that allow the recommendation of selective breeding for high testosterone to increase lean growth traits.
The genetic blueprint contained in the spermatozoan transcriptome can also illuminate key issues in swine reproduction. By developing a procedure for effective RNA extraction of boar spermatozoa we are one step closer to elucidating the porcine sperm transcriptome and the genes implicated in growth and fertility. A viable protocol was developed to handle the complexities of large scale extraction of RNA from porcine semen utilizing an RNA carrier and Dnase treatment. This protocol was validated with PCR amplification of Sus scrofa prm1 in order to provide evidence of a successful RNA extraction from sperm
Embracing Integrative Multiomics Approaches
Get PDFAs “-omics” data technology advances and becomes more readily accessible to address complex biological questions, increasing amount of cross “-omics” dataset is inspiring the use and development of integrative bioinformatics analysis. In the current review, we discuss multiple options for integrating data across “-omes” for a range of study designs. We discuss established methods for such analysis and point the reader to in-depth discussions for the various topics. Additionally, we discuss challenges and new directions in the area
Optimization of Nonlinear Dose- and Concentration-Response Models Utilizing Evolutionary Computation
No full textAn essential part of toxicity and chemical screening is assessing the concentrated related effects of a test article. Most often this concentration-response is a nonlinear, necessitating sophisticated regression methodologies. The parameters derived from curve fitting are essential in determining a test article's potency (EC 50 ) and efficacy (E max ) and variations in model fit may lead to different conclusions about an article's performance and safety. Previous approaches have leveraged advanced statistical and mathematical techniques to implement nonlinear least squares (NLS) for obtaining the parameters defining such a curve. These approaches, while mathematically rigorous, suffer from initial value sensitivity, computational intensity, and rely on complex and intricate computational and numerical techniques. However if there is a known mathematical model that can reliably predict the data, then nonlinear regression may be equally viewed as parameter optimization. In this context, one may utilize proven techniques from machine learning, such as evolutionary algorithms, which are robust, powerful, and require far less computational framework to optimize the defining parameters. In the current study we present a new method that uses such techniques, Evolutionary Algorithm Dose Response Modeling (EADRM), and demonstrate its effectiveness compared to more conventional methods on both real and simulated data
The Effect of Retrospective Sampling on Estimates of Prediction Error for Multifactor Dimensionality Reduction
No full textThe power of quantitative grammatical evolution neural networks to detect gene-gene interactions
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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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