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Yun Yin on Muck Rack

Yun Yin

As seen in: Nature, Wiley Online Library, Frontiers in Immunology, Taylor & Francis Online, ACS Publications, Cell Press, Cell Reports Medicine
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Articles

Pentose phosphate pathway-derived NADPH facilitates physiological hypertrophy and alleviates ischemia–reperfusion injury in the heart

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By Dan Wu, Tao Chen, Xin Dong, Haoqi Li, Zijie Cheng, Huimin Li, Yufan Chao, Fangling Li, Yun Yin, Fujun Guo, Xinran Xu, Yuxuan Zhang, Kun Ning, Xiaoqing Fu, Yuemin Bian, Fenfen Ma, Julia Ritterhoff, Wang Wang, Qingxun Hu
| Nature Verified
Abstract Exercise promotes physiological cardiomyocyte growth and protects against ischaemia–reperfusion (IR) injury in the heart. The molecular mechanism by which exercise benefits cardiac metabolism and function remains largely unknown. Here, using a genetically encoded fluorescent sensor, we show that exercise increases cytosolic, but not mitochondrial, NADPH levels in cardiomyocytes. This effect is mediated by activation of the pentose phosphate pathway (PPP).
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Prediction of maternal and infant outcomes from longitudinal electronic health records with a Mother-Child AI agent

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By Sian Liu, Wenxin Zheng, Jin Mo Kang, Tianyi Xu, Siming Chen, Junlong Li, Hang Siang Wong, Meihao Wang, Changxi Hu, Cheng Tang, Shengwei Jin, Zixing Zou, Ieng Chong, Yuxing Lu, Hui XU, Charlotte Zhang, Erhu Feng, Jinyu Gu, Zhuo Sun, Haibo Chen, YUAN ZHANG, Xian Zhu, Huanhuan Huang, Xiuyuan Xu, Xue Li Verified, Hongbo Qi, Xinyu Lu, Sicheng Pan, Ning Sun, Yun Yin, Michelle Williams, Eric K. Oermann, John Rasko, Jin Billy Li, Kai Wang, Kang Zhang, Hao Wu, Yubin Xia, Fanxin Zeng, Xiaokai Bai, Jingman Shi, Zhao Zhenhui, Ngaman Cheng
| Nature Verified
Abstract Current predictive models for pregnancy and infant outcomes often focus on limited endpoints and rely on costly tests or imaging. Here we developed the Mother-Child AI Agent (MoChiAgent), an LLM-based clinical assistant that orchestrates multiple tools to integrate sequential electronic health record (EHR) data, including routine laboratory tests, for forecasting maternal and infant diseases.
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Advancing cancer detection and treatment using longitudinal routine clinical data

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By Kai Wang, Hui XU, Cheng Tang, Xian Shen, Meihao Wang, Li N Liu, Changxi Hu, Wei Wu, Zixing Zou, Bingzhou Li, Sian Liu, Jin Ho Kang, JungHo Kong, Ting Li, Xiaoying Huang, Gang Chen, Wenyang Lu, Charlotte Zhang, Yiwen Sun, Weihong Lin, Caiwen Ou, Manson Fok, Taiwa Hou, Winston Wang, Kanmin Xue, Yun Yin, Hao Yue Zhu, Jonathan S. Gootenberg, Omar O. Abudayyeh, Michael Karin, Alexandre Loupy, John Rasko, Trey Ideker, Huiyan Luo, Eric K. Oermann, Kang Zhang, Ian Ziyar
| Cell Press Verified
Results To build a predictive model capable of learning cancer-associated signals from routine clinical data, we developed a unified, multimodal framework, Oncoformer, and validated it on a large-scale, multi-cohort dataset (Figure 1). The core of our study is a large-scale discovery cohort, COMPASS-Main, which includes longitudinal data from 2,810,742 individuals.
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