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The S100 calcium binding protein A11 promotes liver fibrogenesis by targeting TGF-βsignaling 被引量:3
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作者 Tingting Zhu Linqiang zhang +12 位作者 Chengbin Li Xiaoqiong Tan Jing Liu Huiqin Li Qijing Fan Zhiguo zhang mingfeng zhan Lin Fu Jinbo Luo Jiawei Geng Yingjie Wu Xiaoju Zou Bin Liang 《Journal of Genetics and Genomics》 SCIE CAS CSCD 2022年第4期338-349,共12页
Liver fibrosis is a key transformation stage and also a reversible pathological process in various types of chronic liver diseases.However,the pathogenesis of liver fibrosis still remains elusive.Here,we report that t... Liver fibrosis is a key transformation stage and also a reversible pathological process in various types of chronic liver diseases.However,the pathogenesis of liver fibrosis still remains elusive.Here,we report that the calcium binding protein A11(S100A11)is consistently upregulated in the integrated data from GSE liver fibrosis and tree shrew liver proteomics.S100A11 is also experimentally activated in liver fibrosis in mouse,rat,tree shrew,and human with liver fibrosis.While overexpression of S100A11 in vivo and in vitro exacerbates liver fibrosis,the inhibition of S100A11 improves liver fibrosis.Mechanistically,S100A11 activates hepatic stellate cells(HSCs)and the fibrogenesis process via the regulation of the deacetylation of Smad3 in the TGF-βsignaling pathway.S100A11 physically interacts with SIRT6,a deacetylase of Smad2/3,which may competitively inhibit the interaction between SIRT6 and Smad2/3.The subsequent release and activation of Smad2/3 promote the activation of HSCs and fibrogenesis.Additionally,a significant elevation of S100A11 in serum is observed in clinical patients.Our study uncovers S100A11 as a novel profibrogenic factor in liver fibrosis,which may represent both a potential biomarker and a promising therapy target for treating liver fibrosis and fibrosis-related liver diseases. 展开更多
关键词 Chronic liver diseases Liver fibrosis S100A11 TGF-Β SIRT6 SMAD3
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面板数据分位数处理效应的估计
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作者 蔡宗武 方颖 +1 位作者 林明 詹铭峰 《中国科学:数学》 北大核心 2026年第1期33-54,共22页
本文提出一种适用于面板数据的分位数处理效应模型,以刻画处理的分布效应.本文还提出识别性条件并估计被处理个体的反事实分位数,证明所提出的分位数处理效应估计量的渐近性质.同时,本文考虑高维的情形,讨论如何通过LASSO(least absolut... 本文提出一种适用于面板数据的分位数处理效应模型,以刻画处理的分布效应.本文还提出识别性条件并估计被处理个体的反事实分位数,证明所提出的分位数处理效应估计量的渐近性质.同时,本文考虑高维的情形,讨论如何通过LASSO(least absolute shrinkage and selection operator)方法选择对照个体和协变量.本文通过数值模拟来说明所提出的模型和方法的有限样本性质.最后,本文的方法被用于估计引入沪深300指数期货交易对中国股市的对数收益率和波动率的分位数处理效应. 展开更多
关键词 LASSO方法 面板数据 非参数估计 分位数回归 处理效应
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