Journal of Clinical and Experimental Hepatology最新文献

{"title":"Fatal Herpes Simplex Hepatitis in a Live Liver Donor.","authors":"Sanjay Govil, Jayanth Reddy, Sandeep Satsangi, Raje Gowda, Karthik Raichurkar, Anindita Mukherjee, Nagesh Pn, Jayasree Shivadasan, Mukul Vij","doi":"10.1016/j.jceh.2025.103116","DOIUrl":"10.1016/j.jceh.2025.103116","url":null,"abstract":"<p><p>We present a case report of a living liver donor death from Herpes simplex hepatitis, highlighting the need for vigilance regarding rare but treatable causes of acute liver failure after donor hepatectomy. Early consideration of HSV hepatitis and prompt initiation of antiviral therapy may improve outcomes.</p>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"15 6","pages":"103116"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12357103/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144873436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Interaction of Human Factors and Resistance-associated Substitutions in Hepatitis C Elimination","authors":"Judah Kupferman,&nbsp;Paul Y. Kwo","doi":"10.1016/j.jceh.2025.103188","DOIUrl":"10.1016/j.jceh.2025.103188","url":null,"abstract":"","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"15 6","pages":"Article 103188"},"PeriodicalIF":3.2,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Practice Guidelines: How Much to Trust and Follow?","authors":"Anugrah Dhooria,&nbsp;Rakesh Aggarwal","doi":"10.1016/j.jceh.2025.103185","DOIUrl":"10.1016/j.jceh.2025.103185","url":null,"abstract":"<div><div>Clinical practice guidelines (CPGs) are aimed at guiding clinicians in making sound decisions and thus help optimize patient care. However, their development is a complex process, compromise with which can undermine the quality of the resultant CPG. The foremost risk lies in conflict of interest on part of those developing the CPG. In addition, formulation of a good-quality CPG requires balanced composition of the development panel, formulation of relevant clinical questions, use of rigorous systematic review methodology, well-defined processes for rating of evidence and grading of recommendations, complete transparency of processes, and full disclosure regarding funding and sponsorship.</div><div>This article reviews the steps in the formulation of a CPG, and various considerations that determine the quality of a CPG. It also discusses the common pitfalls in their development, and the issue of existence of multiple conflicting CPGs on the same topic, using guidelines from India on hepatocellular carcinoma published in this journal and elsewhere as an example.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"16 1","pages":"Article 103185"},"PeriodicalIF":3.2,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence for Predictive Diagnostics, Prognosis, and Decision Support in MASLD, Hepatocellular Carcinoma, and Digital Pathology","authors":"Nicholas Dunn ,&nbsp;Nipun Verma ,&nbsp;Winston Dunn","doi":"10.1016/j.jceh.2025.103184","DOIUrl":"10.1016/j.jceh.2025.103184","url":null,"abstract":"<div><div>Artificial intelligence (AI) has fundamentally transformed the landscape of hepatology by enhancing disease diagnosis, risk stratification, and decision support. In metabolic dysfunction–associated steatotic liver disease (MASLD), AI has been integrated into large-scale consortia such as NIMBLE, LITMUS, TARGET-NASH, and SteatoSITE to improve diagnostic accuracy and patient management. These consortia utilize AI to derive and validate non-invasive biomarkers in fibrosis staging. AI-based models also enhance the detection of hepatocyte ballooning and metabolic dysfunction–associated steatohepatitis, minimizing interobserver variability and improving clinical trial enrollment criteria. Additionally, AI applications differentiate MASLD from alcohol-associated liver disease using gut microbiome and metabolic profiling.</div><div>In hepatocellular carcinoma (HCC), AI has improved risk stratification, diagnosis, and prognostication. AI-driven models based on liver stiffness and clinical parameters can risk stratify patients for HCC development. Enhanced imaging techniques, radiomics, and histopathology powered by AI improve the accuracy of detecting indeterminate liver nodules and predicting microvascular invasion. AI also improves treatment response prediction for therapies such as transarterial chemoembolization (TACE) and immune checkpoint inhibitors and thereby individualizes therapeutic strategies and improves survival outcomes.</div><div>In digital pathology, AI has redefined fibrosis staging, donor liver steatosis assessment, and disease diagnosis. FibroNest™ and qFibrosis are two exceptional AI platforms that utilize imaging techniques for the purposes of both standardizing histological assessments, as well as increasing diagnostic precision. The field of MASLD, HCC, and digital pathology is advancing towards precision medicine.</div><div>FibroNest™ and qFibrosis are two exceptional AI platforms that utilize imaging techniques for the purposes of both standardizing histological assessments, as well as increasing diagnostic precision.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"16 1","pages":"Article 103184"},"PeriodicalIF":3.2,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Foundations of Artificial Intelligence in Hepatology: What a Clinician Needs to Know","authors":"Nana Peng ,&nbsp;Sherlot J. Song ,&nbsp;Vicki Wing-Ki Hui ,&nbsp;Jimmy Che-To Lai ,&nbsp;Grace Lai-Hung Wong ,&nbsp;Vincent Wai-Sun Wong ,&nbsp;Terry Cheuk-Fung Yip","doi":"10.1016/j.jceh.2025.103183","DOIUrl":"10.1016/j.jceh.2025.103183","url":null,"abstract":"<div><div>This review focuses on foundational knowledge about artificial intelligence (AI) in hepatology, exploring how AI, including machine learning and deep learning, leverages large-scale clinical data to transform the diagnosis, risk assessment, prognostication, and management of liver diseases. Online resources are described to offer fundamental AI knowledge and essential technical skills and to facilitate clinician participation across the entire AI lifecycle, ensuring they contribute not only as end users but also in development and deployment. Unlike traditional statistical approaches that prioritize interpretable parameters and clinical insight, AI focuses on maximizing predictive accuracy by identifying complex, often non-linear patterns using high-dimensional data, albeit often at the cost of model interpretability. AI is demonstrating clinical utility in liver histopathology and radiological imaging, significantly improving detection accuracy for cirrhosis, clinically significant portal hypertension, and hepatocellular carcinoma. Beyond diagnostics, AI-driven prediction models are emerging to provide personalized risk stratification for the development of liver-related complications and treatment guidance, based on complex data including longitudinal laboratory results, comorbidities, and co-medication use to monitor disease progression and therapy response. The field is rapidly expanding into novel areas such as analyzing patient-reported outcomes, genomic data, and real-time liver function monitoring, offering deeper mechanistic insights alongside clinical tools. Despite the potential to revolutionize hepatology practice and research, successful integration into routine care faces challenges. These include seamless workflow integration with existing electronic health records, establishing clear liability frameworks, and guaranteeing protection of patient privacy. Addressing these hurdles requires collaborative efforts from clinicians, researchers, and regulators to develop best practices and governance. Understanding the transformative capabilities, current applications, emerging frontiers, and essential implementation considerations is crucial for clinicians navigating the evolving AI landscape and responsibly utilizing its power for improved patient outcomes.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"16 1","pages":"Article 103183"},"PeriodicalIF":3.2,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Prevailing Role of Diabetes Mellitus Among Cardiometabolic Risk Factors in Metabolic Dysfunction-associated Steatotic Liver Disease Prognostication.","authors":"Karen Cheuk-Ying Ho, Lung-Yi Mak","doi":"10.1016/j.jceh.2025.103119","DOIUrl":"10.1016/j.jceh.2025.103119","url":null,"abstract":"","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"15 5","pages":"103119"},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12356997/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144873435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of Magnetic Resonance Imaging in Evaluating Donor Eligibility for Living Donor Liver Transplantation: Present Status and Future Directions","authors":"Ruchi Rastogi ,&nbsp;Subash Gupta ,&nbsp;Sanjiv Saigal ,&nbsp;Mukesh Kumar ,&nbsp;Aditi Rastogi ,&nbsp;Bharat Aggarwal","doi":"10.1016/j.jceh.2025.103182","DOIUrl":"10.1016/j.jceh.2025.103182","url":null,"abstract":"<div><div>Contrast-enhanced computed tomography (CECT) evaluation of a potential living donor liver transplantation (LDLT) donor is an established component of donor eligibility tests. Usually noncontrast magnetic resonance imaging (MRI) is performed with the aim of assessing biliary anatomy and liver fat fraction. While a few donors are considered ineligible for LDLT after CECT, primarily due to moderate liver steatosis or inadequate liver remnant, other hepatic or extrahepatic abnormalities may also preclude donation. Knowledge regarding vascular anatomy is essential to provide a roadmap to the surgeon but is seldom a reason for donor rejection with the developments in surgical technique and expertise.</div><div>Noncontrast MRI can be utilized to comprehensively screen eligible LDLT donors, even before CECT evaluation, as it provides a detailed hepatic and extrahepatic abdominal evaluation along with volumetric estimation without any extra expenditure. This practice not only helps to avoid undue exposure to CT radiation and iodinated contrast in unsuitable donors but also provides guidance for pretransplant modifications in terms of weight reduction in marginal donors with borderline high-fat content by taking advantage of the robust MRI-based liver fat estimation.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"16 1","pages":"Article 103182"},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Issue Highlights","authors":"","doi":"10.1016/S0973-6883(25)00665-6","DOIUrl":"10.1016/S0973-6883(25)00665-6","url":null,"abstract":"","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"15 5","pages":"Article 103165"},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Letter to the Editor Regarding “Plasma Exchange With Corticosteroids as a Rescue Therapy for Severe Prolonged Cholestasis in Acute Viral Hepatitis”","authors":"Parth Aphale,&nbsp;Himanshu Shekhar,&nbsp;Shashank Dokania","doi":"10.1016/j.jceh.2025.103181","DOIUrl":"10.1016/j.jceh.2025.103181","url":null,"abstract":"","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"16 1","pages":"Article 103181"},"PeriodicalIF":3.2,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early Prediction of Acute Kidney Injury Following Liver Transplantation: Development and Validation of a Clinical Risk Model","authors":"Yuzhi Wei ,&nbsp;Ziheng Qi ,&nbsp;Wenyan Wu ,&nbsp;Chunyu Feng ,&nbsp;Bo Yang ,&nbsp;Haolin Yin ,&nbsp;Caiyun Zhang ,&nbsp;Xiaoyan Gao ,&nbsp;Haotian Wu ,&nbsp;Shichao Sun ,&nbsp;Wenfang Zhang ,&nbsp;Huan Zhang","doi":"10.1016/j.jceh.2025.103179","DOIUrl":"10.1016/j.jceh.2025.103179","url":null,"abstract":"<div><h3>Background</h3><div>The first 48 h following liver transplantation (LT) represent a critical therapeutic window. Early identification of patients who are at high risk of developing acute kidney injury (AKI) can optimize treatment strategies and improve patient outcomes. This study aimed to develop and validate a clinical risk prediction model for AKI within 48 h following LT by utilizing preoperative and intraoperative parameters.</div></div><div><h3>Methods</h3><div>A total of 453 adult LT recipients treated at the Beijing Tsinghua Changgung Hospital between January 2018 and October 2022 were enrolled. Patients were randomly assigned to a development cohort and a validation cohort at a 6:4 ratio. AKI was diagnosed using the 2012 Kidney Disease Improving Global Outcomes (KDIGO) criteria. Univariate and multivariate logistic regression analyses identified clinical factors associated with early AKI. A predictive model was constructed and internally validated. Additionally, stages 2 and 3 AKI, as defined by the KDIGO criteria, were classified as severe AKI. Independent risk factors for severe AKI within 48 h following LT were similarly identified using logistic regression analyses.</div></div><div><h3>Results</h3><div>At 48 h following LT, 125 (46%) patients developed AKI. Univariate analysis identified 17 potential predictive factors for AKI, including preoperative hepatic encephalopathy (HE), a history of alcohol-associated cirrhosis, body mass index ≥28 kg/m², and a prognostic nutritional index &gt; 43 (<em>P</em> &lt; 0.1). A backward stepwise regression model was utilized to develop a clinical risk prediction model incorporating the following variables: HE, alcohol-associated cirrhosis, preoperative albumin–bilirubin score ≥ −1.78, operation time ≥560 min, and intraoperative fresh frozen plasma transfusion volume (per 1000 mL). The model achieved an area under the curve (AUC) of 0.760 (<em>P</em> &lt; 0.05) in the development cohort and 0.759 (<em>P</em> &lt; 0.05) in the validation cohort. The calibration curve indicated excellent agreement between predicted and observed probabilities of early AKI (<em>P</em> &gt; 0.05). Multivariate logistic regression analysis identified the preoperative model of end-stage liver disease score ≥14, operation time ≥560 min, intraoperative blood loss ≥1000 mL, intraoperative urine output &lt;1000 mL, and elevated lactic acid level as independent risk factors for severe AKI.</div></div><div><h3>Conclusion</h3><div>The proposed predictive model could promote the identification of high-risk LT recipients immediately following surgery, enabling clinicians to intervene early to mitigate the risk of developing AKI within 48 h postoperatively. This approach has the potential to improve patient prognosis by supporting timely and targeted management strategies.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"16 1","pages":"Article 103179"},"PeriodicalIF":3.2,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}