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Yingying Mao on Muck Rack

Yingying Mao

As seen in: Nature, BMJ Open, Frontiers, MDPI, Arthritis Research & Therapy, Nutrition Journal, EClinicalMedicine, Diabetes Care Magazine
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Articles

Independent and combined associations of diabetes and thyroid disorders with risks of incident dementia

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By Jing Guo, jiayu Li, Bin Gao, Ke Liu, Xiaohui Sun, Yingying Mao
| Frontiers
Abstract Background: Diabetes and thyroid disorders are modifiable risk factors for cognitive impairment. However, no studies have been conducted to comprehensively examine associations of different types of the two diseases, as well as their combinations, with dementia risks. Methods: Based on the data from UK Biobank study, patients with different types of diabetes and thyroid disorders were identified with the hospital inpatient record.
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AAV9-mediated GAA gene therapy following enzyme replacement therapy discontinuation in children with infantile-onset Pompe disease

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By Xinting Liu, Yingying Mao, Linyan Hu
| EClinicalMedicine Verified
ArticlesVolume 98104081Open access Affiliations & Notes aSenior Department of Pediatrics, Chinese PLA General Hospital, Beijing, 100700, China bDepartment of Pediatrics, First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China cBeijing Genecradle Therapeutics Inc., Beijing, China dDepartment of Rehabilitation Medicine, First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China eDepartment of Rheumatology and Immunology, First Medical Center, Chinese PLA...
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UK BioCoin: swift trait-specific summary statistics regression for UK Biobank

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By Guoan Qi, Jiacheng Ying, Yu Qian, Lin-Lin Gu, Qi-Xin Zhang, Lide Han, Yingying Mao, Hou-Feng Zheng, Hangjin Jiang, Guo-Bo Chen, Jingcheng He
| Nature Verified
Abstract Summary statistics derived from large-scale biobanks facilitate the sharing of genetic discoveries but are typically limited by fixed covariate adjustments, preventing the exploration of trait-specific adjustments. Here we present UK BioCoin (UKC), a computational device designed to enable efficient, user-specified covariate adjustment without requiring access to individual-level data.
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