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Chih Yin Wu
Researcher at Taipei Medical University
Publications - 4
Citations - 57
Chih Yin Wu is an academic researcher from Taipei Medical University. The author has contributed to research in topics: Internal medicine & Medicine. The author has an hindex of 1, co-authored 1 publications receiving 21 citations.
Papers
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Journal ArticleDOI
The association between plasma selenium and chronic kidney disease related to lead, cadmium and arsenic exposure in a Taiwanese population.
Chih Yin Wu,Chung Shun Wong,Chi Jung Chung,Mei Yi Wu,Ya Li Huang,Pui Lam Ao,Yuh Feng Lin,Ying Chin Lin,Horng Sheng Shiue,Chien Tien Su,Hsi Hsien Chen,Hsi Hsien Chen,Yu Mei Hsueh +12 more
TL;DR: This study is the first to suggest that selenium modifies the eGFR and OR in CKD induced by environmental toxicants.
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Sex and Age Differences Modulate Association of Vitamin D with Serum Triglyceride Levels
TL;DR: Young individuals, women, and middle-aged men with hypertriglyceridemia are at higher risk of vitamin D deficiency, and Serum 25(OH)D levels were inversely associated with TG levels.
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Chronic Kidney Disease: Interaction of Adiponectin Gene Polymorphisms and Diabetes
Hsi Hsien Chen,Ya-Li Huang,Mei-Chieh Chen,Chih Yin Wu,Ying Lin,Horng-Sheng Shiue,Sheng-Lun Hsu,Yu Mei Hsueh +7 more
TL;DR: The ADIPOQ polymorphisms were determined using the Agena Bioscience Mass ARRAY System in this paper , and the results suggest that the polymorphisms may significantly modify the association between diabetes and CKD, but not the relationship between total urinary arsenic and blood cadmium and chronic kidney disease.
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Using Anti-Malondialdehyde-Modified Peptide Adduct Autoantibodies in Serum of Taiwanese Women to Diagnose Primary Sjogren's Syndrome.
Yuarn Jang Lee,Ying Chin Lin,Chen Chung Liao,Yu Sheng Chang,Yu Hui Huang,I-Jung Tsai,Jin-Hua Chen,Sheng Hong Lin,Yu-Fang Lin,Ting-Wan Hsieh,Yi Su Chen,Chih Yin Wu,Chi-Ching Chang,Ching-Yu Lin +13 more
TL;DR: In this article , the authors used serum levels of anti-MDA-modified peptide adducts autoantibodies to evaluate predictive performance by machine learning algorithms in primary Sjögren's syndrome (pSS) and assess the association between pSS and healthy controls.