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Full-Text Articles in Medicine and Health Sciences
Flame Retardants And Neurodevelopment: An Updated Review Of Epidemiological Literature, Ann M. Vuong, Kimberly Yolton, Kim M. Cecil, Joseph M. Braun, Bruce P. Lanphear, Aimin Chen
Flame Retardants And Neurodevelopment: An Updated Review Of Epidemiological Literature, Ann M. Vuong, Kimberly Yolton, Kim M. Cecil, Joseph M. Braun, Bruce P. Lanphear, Aimin Chen
Public Health Faculty Publications
Purpose of Review: Flame retardant (FR) compounds can adversely impact neurodevelopment. This updated literature review summarizes epidemiological studies of FRs and neurotoxicity published since 2015, covering historical (polybrominated biphenyls [PBBs], polychlorinated biphenyls [PCBs]), contemporary (polybrominated diphenyl ethers [PBDEs], hexabromocyclododecane [HBCD], and tetrabromobisphenol A [TBBPA]), and current-use organophosphate FRs (OPFRs) and brominated FRs (2-ethylhexyl 2,3,4,5-tetrabromobezoate [EH-TBB] TBB), bis(2-ethylhexyl) tetrabromophthalate [BEH-TEBP]), focusing on prenatal and postnatal periods of exposure. Recent Findings: Continuing studies on PCBs still reveal adverse associations with child cognition and behavior. Recent studies indicate PBDEs are neurotoxic, particularly for gestational exposures with decreased cognition and increased externalizing behaviors. Findings …
Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han
Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han
Public Health Faculty Publications
The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures …