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"Qiang Yan"

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"Qiang Yan"

Original Articles
Fibrosis-4plus score: a novel machine learning-based tool for screening high-risk varices in compensated cirrhosis (CHESS2004): an international multicenter study
Bingtian Dong, Ruiling He, Shenghong Ju, Yuping Chen, Ivica Grgurevic, Jianzhong Ma, Ying Guo, Huizhen Fan, Qiang Yan, Chuan Liu, Huixiong Xu, Anita Madir, Kristian Podrug, Jia Wang, Linxue Qian, Zhengzi Geng, Shanghao Liu, Tao Ren, Guo Zhang, Kun Wang, Meiqin Su, Fei Chen, Sumei Ma, Liting Zhang, Zhaowei Tong, Yonghe Zhou, Xin Li, Fanbin He, Hui Huan, Wenjuan Wang, Yunxiao Liang, Juan Tang, Fang Ai, Tingyu Wang, Liyun Zheng, Zhongwei Zhao, Jiansong Ji, Wei Liu, Jiaojiao Xu, Bo Liu, Xuemei Wang, Yao Zhang, Qiong Yan, Hui Liu, Xiaomei Chen, Shuhua Zhang, Yihua Wang, Yang Liu, Li Yin, Yanni Liu, Yanqing Huang, Li Bian, Ping An, Xin Zhang, Shaoting Zhang, Jinhua Shao, Xiangman Zhang, Wei Rao, Chaoxue Zhang, Christoph Frank Dietrich, Won Kim, Xiaolong Qi
Clin Mol Hepatol 2025;31(3):881-898.
Published online February 5, 2025
DOI: https://doi.org/10.3350/cmh.2024.0898
Background/Aims
A large percentage of patients undergoing esophagogastroduodenoscopy (EGD) screening do not have esophageal varices (EV) or have only small EV. We evaluated a large, international, multicenter cohort to develop a novel score, termed FIB-4plus, by combining the fibrosis-4 (FIB-4) score, liver stiffness measurement (LSM), and spleen stiffness measurement (SSM) to identify high-risk EV (HRV) in compensated cirrhosis.
Methods
This international cohort study involved patients with compensated cirrhosis from 17 Chinese hospitals and one Croatian institution (NCT04546360). Two-dimensional shear wave elastography-derived LSM and SSM values, and components of the FIB-4 score (i.e., age, aspartate aminotransferase, alanine aminotransferase, and platelet count [PLT]) were combined using machine learning algorithms (logistic regression [LR] and extreme gradient boosting [XGBoost]) to develop the LR-FIB-4plus and XGBoost-FIB-4plus models, respectively. Shapley Additive exPlanations method was used to interpret the model predictions.
Results
We analyzed data from 502 patients with compensated cirrhosis who underwent EGD screening. The XGBoost-FIB-4plus score demonstrated superior predictive performance for HRV, with an area under the receiver operating characteristic curve (AUROC) of 0.927 (95% confidence interval [CI] 0.897–0.957) in the training cohort (n=268), and 0.919 (95% CI 0.843–0.995) and 0.902 (95% CI 0.820–0.984) in the first (n=118) and second (n=82) external validation cohorts, respectively. Additionally, the XGBoost-FIB-4plus score exhibited high AUROC values for predicting EV across all cohorts. The FIB-4plus score outperformed the individual parameters (LSM, SSM, PLT, and FIB-4).
Conclusions
The FIB-4plus score effectively predicted EV and HRV in patients with compensated cirrhosis, providing clinicians with a valuable tool for optimizing patient management and outcomes.

Citations

Citations to this article as recorded by  Crossref logo
  • The evolution of non-invasive strategies in cirrhosis management—from screening to precision monitoring: Editorial on “Fibrosis-4plus score: a novel machine learning-based tool for screening high-risk varices in compensated cirrhosis (CHESS2004): an inter
    Haiyu Wang, Jinjun Chen
    Clinical and Molecular Hepatology.2026; 32(1): 403.     CrossRef
  • Metabolic factor-based machine learning model for mortality prediction in acute hepatitis E: Development and validation from a dual-center cohort
    Haoshuang Fu, Shuying Song, Yuelin Xiao, Bingying Du, Gangde Zhao, Tianhui Zhou, Yanan Du
    Digestive and Liver Disease.2026; 58(5): 660.     CrossRef
  • Relative change rate of liver stiffness measurements predicts the risk of liver decompensation in compensated advanced chronic liver disease
    Yanqiu Li, Zihang Qiao, Jinze Li, Bingbing Zhu, Yu Lu, Ying Feng, Xianbo Wang
    Clinical and Experimental Medicine.2025;[Epub]     CrossRef
  • Artificial Intelligence Applications in the Diagnosis and Management of Cirrhosis and Portal Hypertension: A Narrative Review
    Amrit Khooblall, Satish E. Viswanath, Layth Khawaja, Sameer Gadani
    Techniques in Vascular and Interventional Radiology.2025; 28(4): 101078.     CrossRef
  • Liver stiffness measurement-based risk score for predicting liver decompensation risk: a single-center retrospective Chinese study
    Yanqiu Li, Zihang Qiao, Jinze Li, Yongqi Li, Ying Feng, Xianbo Wang
    Clinical and Experimental Medicine.2025;[Epub]     CrossRef
  • Metabolomics and metabolites in cancer diagnosis and treatment
    Minyi Cai, Haiyan Liu, Chen Shao, Tingting Li, Jun Jin, Yahui Liang, Jinhu Wang, Ji Cao, Bo Yang, Qiaojun He, Xuejing Shao, Meidan Ying
    Molecular Biomedicine.2025;[Epub]     CrossRef
  • 14,202 View
  • 309 Download
  • 6 Web of Science
  • Crossref

Cholestatic liver disease

JCAD deficiency attenuates activation of hepatic stellate cells and cholestatic fibrosis
Li Xie, Hui Chen, Li Zhang, Yue Ma, Yuan Zhou, Yong-Yu Yang, Chang Liu, Yu-Li Wang, Ya-Jun Yan, Jia Ding, Xiao Teng, Qiang Yang, Xiu-Ping Liu, Jian Wu
Clin Mol Hepatol 2024;30(2):206-224.
Published online January 8, 2024
DOI: https://doi.org/10.3350/cmh.2023.0506
Background/Aims
Cholestatic liver diseases including primary biliary cholangitis (PBC) are associated with active hepatic fibrogenesis, which ultimately progresses to cirrhosis. Activated hepatic stellate cells (HSCs) are the main fibrogenic effectors in response to cholangiocyte damage. JCAD regulates cell proliferation and malignant transformation in nonalcoholic steatoheaptitis-associated hepatocellular carcinoma (NASH-HCC). However, its participation in cholestatic fibrosis has not been explored yet.
Methods
Serial sections of liver tissue of PBC patients were stained with immunofluorescence. Hepatic fibrosis was induced by bile duct ligation (BDL) in wild-type (WT), global JCAD knockout mice (JCAD-KO) and HSC-specific JCAD knockout mice (HSC-JCAD-KO), and evaluated by histopathology and biochemical tests. In situ-activated HSCs isolated from BDL mice were used to determine effects of JCAD on HSC activation.
Results
In consistence with staining of liver sections from PBC patients, immunofluorescent staining revealed that JCAD expression was identified in smooth muscle α-actin (α-SMA)-positive fibroblast-like cells and was significantly up-regulated in WT mice with BDL. JCAD deficiency remarkably ameliorated BDL-induced hepatic injury and fibrosis, as documented by liver hydroxyproline content, when compared to WT mice with BDL. Histopathologically, collagen deposition was dramatically reduced in both JCAD-KO and HSC-JCAD-KO mice compared to WT mice, as visualized by Trichrome staining and semi-quantitative scores. Moreover, JCAD deprivation significantly attenuated in situ HSC activation and reduced expression of fibrotic genes after BDL.
Conclusions
JCAD deficiency effectively suppressed hepatic fibrosis induced by BDL in mice, and the underlying mechanisms are largely through suppressed Hippo-YAP signaling activity in HSCs.

Citations

Citations to this article as recorded by  Crossref logo
  • Biliary YB-1/GLI2 axis facilitates ductular reaction and promotes HSC activation via SPP1/integrin αvβ1 signaling during liver fibrogenesis
    Yuecheng Guo, Qingqing Zhang, Binghang Li, Weiming Dai, Bo Shen, Zhenyang Shen, Junjun Wang, Qichao Ge, Hanjing Zhangdi, Guangwen Chen, Qidi Zhang, Xiaobo Cai, Hui Dong, Guangjian Fan, Lungen Lu, Fei Li
    Hepatology.2026; 83(6): 1365.     CrossRef
  • Features and functional mechanisms of super-enhancers in cardiovascular disorders, cancer, autoimmune diseases and neurodegenerative disorders
    Zi-Rong Li, Yong-Yan Wang, Chao Zhang, Jin-Sha Shi, Xiao Yu, Ni-Tong Ying, Xiao-Ke Xu, Juan-Juan Li, Tao Guo
    Cellular Signalling.2026; 138: 112252.     CrossRef
  • Huanggan decoction ameliorates cholestatic hepatic fibrosis in rats via TGF-β1/Smad3 signaling pathway
    Yaya Lei, Xueli Ma, Xiaohui Jin, Yanping He, Jianhong Yang, Yuna Zhao, Jing Chen, Ting Gao, Sharon DeMorrow
    PLOS One.2026; 21(3): e0344168.     CrossRef
  • Underestimated and Overlooked Factors in PBC Progression: Bacterial and Fungal Infections
    Yaxin Zhu, Sumeng Li, Shiqi Li, Yichen Wang, Yanqin Du, Xin Zheng, Jun Wu
    International Journal of Molecular Sciences.2026; 27(6): 2766.     CrossRef
  • Activated hepatic stellate cells maintain hepatic bile acid homeostasis through paracrine FGF10/FGFR2 signaling
    Santie Li, Gaozan Tong, Mei Xue, Leyi Shen, Kunxuan Zhu, Jianjun Feng, Junfu Fan, Junjie Lu, Xiaojing Yi, Luhai Wang, Jiaqi Liang, Weitao Cong, Xiaokun Li
    Journal of Hepatology.2026; 85(2): 314.     CrossRef
  • Integrating network pharmacology and experimental validation to uncover the therapeutic mechanisms of Chaigui decoction in Schistosoma japonicum-induced liver fibrosis
    Kaiyuan Deng, Xinyao Du, Qiao Liu, Zhi Lan, Fengning Wang, Yulin Cao, Song Xu, Xiaoli Deng, Xiang Wu, Guangjie Li, Yujiao Yang, Xin Wang, Fengyu Yang, Qingyuan Gu, Qingyang Yao, Liangzheng Zou, Wanning Wang, Mijia Yuan, Teng Zhong, Pei Huang, Yonghua Zhou
    Acta Tropica.2026; 280: 108160.     CrossRef
  • From network prediction to in vivo validation: luteolin attenuates cholestatic liver injury in association with PI3K/Akt/GSK-3β signalling modulation
    Yuxin Wu, Shiji Gong, Xin Wu, Wei Wang, Dongming Zhang, Jianxin Jia
    Naunyn-Schmiedeberg's Archives of Pharmacology.2026;[Epub]     CrossRef
  • Matrix stiffness may drive multi-cellular crosstalk via YAP signaling in biliary atresia liver fibrosis: a mechanistic review
    Jiwen Cheng
    Frontiers in Cell and Developmental Biology.2026;[Epub]     CrossRef
  • Transient receptor potential channel 6 knockout ameliorates hepatic fibrosis by inhibiting the activation and proliferation of hepatic stellate cells
    Xixi Zeng, Yanhong Liao, Weiyi Cheng
    Journal of Gastroenterology and Hepatology.2025; 40(1): 294.     CrossRef
  • Hepatic Stellate Cell TM4SF1 Accelerates Hepatic Fibrosis Progression via Interacting With the Tyrosine Kinase c-Src
    Shenglu Liu, Peng Tan, Jiatong Chen, Zhiwei Huang, Bingyu Ren, Zhonghao Jiang, Boyuan Gu, Wenhao Yu, Lei Sun, Yingjun Chen, Jian Ruan, Wenguang Fu
    Cellular and Molecular Gastroenterology and Hepatology.2025; 19(10): 101559.     CrossRef
  • JCAD deficiency delayed liver regenerative repair through the Hippo–YAP signalling pathway
    Li Zhang, Yong‐Yu Yang, Li Xie, Yuan Zhou, Zhenxing Zhong, Jia Ding, Zhong‐Hua Wang, Yu‐Li Wang, Xiu‐Ping Liu, Fa‐Xing Yu, Jian Wu
    Clinical and Translational Medicine.2024;[Epub]     CrossRef
  • JCAD, a new potential therapeutic target in cholestatic liver disease
    Byoung Kuk Jang
    Clinical and Molecular Hepatology.2024; 30(2): 166.     CrossRef
  • Correspondence on Letter regarding “Both liver parenchymal and non-parenchymal cells express JCAD proteins under various circumstances”
    Byoung Kuk Jang
    Clinical and Molecular Hepatology.2024; 30(2): 297.     CrossRef
  • Both liver parenchymal and non-parenchymal cells express JCAD protein under various circumstances
    Li Xie, Li Zhang, Hui Chen, Yong-Yu Yang, Jian Wu
    Clinical and Molecular Hepatology.2024; 30(2): 279.     CrossRef
  • 13,354 View
  • 310 Download
  • 12 Web of Science
  • Crossref