Jin Chen
Central South University
11 Papers
4 Citations
Jin Chen is an academic researcher from Central South University. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 4, co-authored 7 publications.
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Papers
Evaluation of variation in D-dimer levels among COVID-19 and bacterial pneumonia: a retrospective analysis.
Bilian Yu,Xin Li,Jin Chen,Ming-qi Ouyang,Hong Zhang,Xinge Zhao,Liang Tang,Qin Luo,Min Xu,Lizhen Yang,Guxiang Huang,Xianling Liu,Jianjun Tang +12 more
TL;DR: Elevated baseline D-dimer levels are associated with inflammation but not with VTE score in COVID-19 patients, suggesting that it is unreasonable to judge whether anticoagulation is needed only according to D- dimer levels.
Interaction between adipocytes and high-density lipoprotein:new insights into the mechanism of obesity-induced dyslipidemia and atherosclerosis.
TL;DR: The cross-talk between adipocytes and HDL related to cardiovascular disease is summarized, new insights of the potential mechanism underlying obesity and HDL dysfunction are focused on and adipose tissue is targeted for the treatment of HDL metabolism in obesity.
Comparison of calculated remnant lipoprotein cholesterol levels with levels directly measured by nuclear magnetic resonance
TL;DR: This study aimed to characterize RC at fasting and non-fasting states in more details and establish the performance of calculated RC and NMR-measured RC, and found that RC calculated from the standard lipid profile as TC minus LDL-C minus HDL-C is different from the N MR measured RC.
HDL-associated apoCIII plays an independent role in predicting postprandial hypertriglyceridemia.
Tianhua Zhang,Xiaoyu Tang,Ling Mao,Jin Chen,Jie Kuang,Xin Guo,Danyan Xu,Daoquan Peng,Bilian Yu +8 more
TL;DR: Enrichment of apoCIII in HDL particles potentially plays an independent role in postprandial hypertriglyceridemia.
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Prediction of Fatty Liver Disease in a Chinese Population Using Machine-Learning Algorithms
TL;DR: Wang et al. as discussed by the authors developed machine learning (ML) models for screening individuals at high risk of fatty liver disease (FLD) and provided a new perspective on early FLD diagnosis.