Ye Hua Shen on Muck Rack

Ye Hua Shen

Athens
As seen in: Color Research & Application, Nature, Wiley Online Library, BMJ Open, Frontiers, ACS Publications, JAMA (The Journal of the American Medical Association), JAMA Network, JAMA: Journal of the American Medical Association, Nutrition Journal and
Ernest Corn Professor at University of Georgia Department Head, Epidemiology and Biostatistics Biostatistician/Epidemiologist
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Articles

SGLT2 inhibition with empagliflozin attenuates retinal oxidative stress and damage in diabetic mice

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By Yutong Xia, Jiru Zhu, Xin Yu, Zhitong Chen, Jiayun Ge, Liyue Zhang, Yanqing Li, Ye Hua Shen, XiuYi Li, Jianping Tong, Qianjie Yang
| Nature Verified
Abstract To investigate the effects of empagliflozin (EMPA), an SGLT2 inhibitor, on retinal damage in diabetic mice, exploring its potential as a therapeutic strategy for diabetic retinopathy (DR). DR was induced by a high-fat diet and streptozotocin injections, followed by EMPA treatment.
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Effects of the National Health Service Corps expansion on dental care use in underserved areas

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By Ye Hua Shen, Lisa Simon, Hao Yu
| The Journal of the American Dental Association Verified
Key Words Dental workforce dental care use dental Health Professional Shortage Areas Abbreviation Key ACA (Patient Protection and Affordable Care Act) AHRF (Area Health Resource File) ARRA (American Recovery and Reinvestment Act) BRFSS (Behavioral Risk Factor Surveillance System) HPSA (Health Professional Shortage Area) HRSA (Health Resources and Services Administration) NHSC (National Health Service Corps) SMART (Selected Metropolitan and Micropolitan Area Risk Trends) Get full text access...
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Machine learning-based predictive models and subtypes patterns in peripheral blood of schizophrenia based on a machine learning computational framework

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By Zhijun Li, Qing Sun, Haoyu Li, Jing Ni, Jing L. Wang, Xiaolei Xu, Ye Hua Shen, Siyu Sun, Naiyu Guan
| Nature Verified
Abstract Schizophrenia (SCZ) is a complex psychiatric disorder, and its pathogenic mechanisms are not yet fully understood. The identification of reliable blood biomarkers and molecular subtypes for early diagnosis and effective therapy remains a significant challenge. To address this issue, we utilized a combination of bioinformatics and machine learning (ML) to identify potential biomarkers for SCZ.
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