Journal of Clinical and Experimental Hepatology最新文献

{"title":"Letter to the Editor on “Risk of Liver Fibrosis in Patients With Psoriasis on Long-term Methotrexate: Role of Cumulative Dose and Comorbidities in 483 Patients”","authors":"Enzo Emanuele, Piercarlo Minoretti","doi":"10.1016/j.jceh.2025.102632","DOIUrl":"10.1016/j.jceh.2025.102632","url":null,"abstract":"","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"15 6","pages":"Article 102632"},"PeriodicalIF":3.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679367","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)00117-3","DOIUrl":"10.1016/S0973-6883(25)00117-3","url":null,"abstract":"","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"15 4","pages":"Article 102617"},"PeriodicalIF":3.3,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501840","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":"Long-term Prognosis in Pediatric Living Donor Liver Transplant Recipients Who Have Survived the First Year of Surgery","authors":"Vipul Gautam ,&nbsp;Phani K. Nekarakanti ,&nbsp;Vikram Kumar ,&nbsp;Dibya J. Das ,&nbsp;Shahnawaz Bashir ,&nbsp;Shweta A. Singh ,&nbsp;Subhash Gupta","doi":"10.1016/j.jceh.2025.102631","DOIUrl":"10.1016/j.jceh.2025.102631","url":null,"abstract":"<div><h3>Background</h3><div>Understanding long-term pediatric living donor liver transplant (LDLT) outcomes is crucial for families when consenting to it. This study focussed on pre-liver transplant (LT) clinical parameters, surgical procedures, delayed complications, follow-up challenges, and long-term prognosis within a living-donor program in a developing country.</div></div><div><h3>Methods</h3><div>This single-center retrospective study was carried out using a prospectively maintained database spanning from September 2006 to January 2023. The study included all pediatric LT (pLT) recipients, aged 1 month to 17 years, who survived for more than a year following LT.</div></div><div><h3>Results</h3><div>During the study, 480 pLTs were performed, with 448 (93.3%) children surviving beyond one year. Of 448 pLT recipients, 358 with adequate follow-up data formed the study cohort for long-term outcomes, while 90 with poor medication adherence and/or insufficient follow-up were analyzed separately as a noncompliant group. The majority (232,65%) of patients supplemented physical outpatient visits with online follow-up consultations via email and other online platforms. Twenty-three percent necessitated intervention within three-months of the surgery; however, it had no impact on occurrence of late complications or overall survival (<em>P</em> = 0.398). The primary cause of noncompliance was socioeconomic factors, which contributed to an increased incidence of chronic rejection in this group (12/90, 13.3%). Out of 358 compliant children, 30 died and 23 survived following late radiological or surgical intervention, while the remaining 305 had an uneventful long-term course with a median follow-up of 62 (IQR:31–112) months. The life table showed survival probabilities of 95%, 93%, 91%, and 72.4% at 3, 5, 10, and 15 years, respectively. Pediatric end-stage liver disease (PELD) score and post-LT portal vein thrombosis (PVT) were independent prognostic factors for long-term survival.</div></div><div><h3>Conclusion</h3><div>Pediatric LDLT yields favorable long-term outcomes, especially in 1-year survivors. Online follow-ups are beneficial in developing countries. Pre-LT PELD score and post-LT PVT help assess risk and optimize care.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"15 6","pages":"Article 102631"},"PeriodicalIF":3.3,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631901","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":"Hyperammonemic Encephalopathy in Fibrolamellar Hepatocellular Carcinoma: A Case of Rapid Neurologic Recovery Following Cytoreductive Surgery","authors":"Robert Schoeneich,&nbsp;Udhayvir S. Grewal,&nbsp;Tanner J. Simonson,&nbsp;Bohae R. Lee,&nbsp;Rishi R. Patel,&nbsp;Andrew J. Vegel,&nbsp;Mark W. Karwal,&nbsp;Carlos H.F. Chan,&nbsp;Naomi H. Fei","doi":"10.1016/j.jceh.2025.102629","DOIUrl":"10.1016/j.jceh.2025.102629","url":null,"abstract":"","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"15 6","pages":"Article 102629"},"PeriodicalIF":3.3,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596897","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":"Congenital Intrahepatic Portosystemic Shunt Presenting With Recurrent Hepatic Encephalopathy in Adulthood","authors":"Arpit Shastri,&nbsp;Arka De,&nbsp;Muhammad U. Ashraf,&nbsp;Ganesh C.P.,&nbsp;Sreedhara B. Chaluvashetty,&nbsp;Harish Bhujade,&nbsp;Ajay Duseja","doi":"10.1016/j.jceh.2025.102630","DOIUrl":"10.1016/j.jceh.2025.102630","url":null,"abstract":"","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"15 6","pages":"Article 102630"},"PeriodicalIF":3.3,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596896","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":"Toward Real-time Detection of Drug-induced Liver Injury Using Large Language Models: A Feasibility Study From Clinical Notes","authors":"Thanathip Suenghataiphorn ,&nbsp;Pojsakorn Danpanichkul ,&nbsp;Narisara Tribuddharat ,&nbsp;Narathorn Kulthamrongsri","doi":"10.1016/j.jceh.2025.102627","DOIUrl":"10.1016/j.jceh.2025.102627","url":null,"abstract":"<div><h3>Background</h3><div>Drug-induced liver injury (DILI) is a significant clinical problem. Current detection methods are often delayed. Real-time analysis of electronic medical records (EMRs) using a large language model (LLM) could enable earlier surveillance.</div></div><div><h3>Objective</h3><div>To evaluate the technical feasibility of an LLM-powered system for real-time DILI identification assessment by extracting medication information from unstructured clinical notes.</div></div><div><h3>Methods</h3><div>We developed a system using a large language model (LLM) to extract medication lists from clinical text. Prompts were iteratively refined for optimal performance. We integrated DILI risk data from DILIrank and LiverTox, utilizing LLM and algorithmic matching to link extracted medications to database entries. We utilized the RxNORM database and manual mistyped medication, as well as the NHANES database for a structured medication list, to verify accurate results.</div></div><div><h3>Results</h3><div>Using 30 entries each from NHANES, RxNORM, and real-world cases, the LLM-based medication extraction achieved a precision of 0.96, recall of 0.97, and an F1-score of 0.97%. For NHANES data, no errors were found. Applying to real-world cases and mistyped dataset, the LLM-based extraction fared acceptably, with F1-scores of 0.94 and 0.97, respectively. The majority of error are due to trade name and combined medication names.</div></div><div><h3>Conclusion</h3><div>This study demonstrates the potential of LLMs for accurate medication extraction from clinical notes, a crucial step towards real-time DILI risk assessment. However, the system requires further development and clinical validation before implementation. Future work will focus on matching methods, clinical validation, EMR integration, and development of an agentic AI to triage future DILI risk.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"15 6","pages":"Article 102627"},"PeriodicalIF":3.3,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144587793","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":"Ethics, Bias, and Governance in Artificial Intelligence for Hepatology: Toward Building a Safe and Fair Future","authors":"Chanda K. Ho ,&nbsp;Sumeet K. Asrani","doi":"10.1016/j.jceh.2025.102628","DOIUrl":"10.1016/j.jceh.2025.102628","url":null,"abstract":"<div><div>Artificial intelligence (AI) is fundamentally changing how modern medicine is practiced with the intent of advancing and accelerating patient care and improving both patient experience and outcomes. AI, however, has been confronted by several challenges, including but not limited to ethics, regulation, and public trust. This paper explores an approach to AI governance in healthcare, specifically in hepatology. As AI continues to grow, it will be crucial for healthcare providers and our community as hepatologists to understand the implications and impact of this growth and what this means for our practice and patients. We draw from existing AI frameworks, principles of medical ethics, as well as quality healthcare principles to propose our framework for AI in hepatology. Our proposed framework includes patient-centered care, non-maleficence and safety, equity, transparency, accountability, and security and privacy. For each of these topics, we discuss examples relevant to hepatology. We also propose an action plan for hepatologists on how each of these principles can be upheld in our day-to-day practice. While many hepatology specific AI applications are currently being tested in research studies, they have not yet made it to “prime time.” As a result, the hepatology community has time to consider governance structures to put in place in preparation for the inherent challenges that come with AI implementation and integration into clinical care to ensure that care is responsible, ethical, safe, and secure. With careful and conscientious planning, the inclusion of relevant stakeholders, and laying the groundwork for governance, AI can improve quality of health care in hepatology with efficiency, improved safety, and equity.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"15 6","pages":"Article 102628"},"PeriodicalIF":3.3,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631902","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 Integration of Multi-omics With Artificial Intelligence in Hepatology: A Comprehensive Review of Personalized Medicine, Biomarker Identification, and Drug Discovery","authors":"Devina Ramesh ,&nbsp;Praveen Manickavel ,&nbsp;Soumita Ghosh ,&nbsp;Mamatha Bhat","doi":"10.1016/j.jceh.2025.102611","DOIUrl":"10.1016/j.jceh.2025.102611","url":null,"abstract":"<div><div>The evolution of high-throughput technologies has expanded the role of multi-omics in hepatology, moving away from traditional hypothesis-driven research toward integrative, data-driven models. However, high cost and resource intensity have limited widespread adoption. The multi-omics datasets for liver diseases are still relatively small. Progress has been made in integrating a few omics types, particularly in combining genomics with transcriptomics, proteomics, or metabolomics for liver disease research. However, fully integrated multi-omics studies remain limited, with most research focusing on two or three omics layers rather than comprehensive multi-modal integration. Emerging approaches such as federated learning can be leveraged to securely integrate multi-omics data, advance AI-driven biomarker discovery, and enhance precision medicine strategies across institutions.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"15 6","pages":"Article 102611"},"PeriodicalIF":3.3,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572580","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":"Comparison of the GALAD, GAAP, and ASAP Scores for Hepatocellular Carcinoma Detection in Patients With Chronic Liver Diseases","authors":"Kessarin Thanapirom ,&nbsp;Sirinporn Suksawatamnuay ,&nbsp;Panarat Thaimai ,&nbsp;Nipaporn Siripon ,&nbsp;Nopavut Geratikornsupuk ,&nbsp;Sombat Treeprasertsuk ,&nbsp;Piyawat Komolmit","doi":"10.1016/j.jceh.2025.102607","DOIUrl":"10.1016/j.jceh.2025.102607","url":null,"abstract":"<div><h3>Background</h3><div>Developing biomarker panels for early hepatocellular carcinoma (HCC) detection is crucial to overcome the limitations of current imaging-based surveillance strategies. The GALAD, GAAP, and ASAP scores are well-established algorithms for estimating the risk of HCC based on gender, age, alpha-fetoprotein (AFP), protein induced by vitamin K absence or antagonist-II, and AFP-L3. This study aimed to evaluate the diagnostic performance of these biomarkers and models in detecting HCC in patients with chronic liver diseases (CLDs).</div></div><div><h3>Methods</h3><div>The study enrolled 529 patients, comprising 193 with HCC, 223 with chronic hepatitis, and 113 with cirrhosis. HCC was diagnosed based on the standard imaging criteria. The diagnostic performance of the GALAD, GAAP, and ASAP models, along with individual biomarkers, was assessed using the area under the receiver operating characteristic curve (AUC) to identify HCC in patients with various etiologies of CLDs.</div></div><div><h3>Results</h3><div>The GALAD, GAAP, and ASAP models showed better AUCs (0.876–0.889) in detecting any stage of HCC in patients with CLD than individual biomarkers (0.741–0.842). These models also exhibited improved accuracy for early HCC detection (0.825–0.889) compared with individual biomarkers (0.654–0.710). The GAAP score achieved the best accuracy in detecting early HCC in patients with CLD. Furthermore, the GAAP and ASAP models performed best in identifying all-stage HCC in patients with viral hepatitis, while GAAP and GALAD scores were most effective in those with nonviral etiologies. The optimal cutoff values for detecting HCC were GALAD &gt;0.13, GAAP &gt; −0.64, and ASAP &gt; −0.71, all with sensitivities and specificities above 80%.</div></div><div><h3>Conclusions</h3><div>The GAAP model demonstrated excellent discriminatory ability between HCC and CLD in both viral and nonviral subgroups and outperformed other models in detecting early-stage HCC.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"15 6","pages":"Article 102607"},"PeriodicalIF":3.3,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535432","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":"Novel Insights into Noninvasive Assessment of Liver Fibrosis in Chronic Hepatitis C Patients","authors":"Guanlan Liu ,&nbsp;Li Liu ,&nbsp;Xing Yang ,&nbsp;Qihao Wang ,&nbsp;Mingqin Qian","doi":"10.1016/j.jceh.2025.102610","DOIUrl":"10.1016/j.jceh.2025.102610","url":null,"abstract":"<div><div>Chronic hepatitis C (CHC) represents a significant global public health challenge, with patient prognosis intricately linked to the severity of liver fibrosis. This issue is particularly critical for individuals with advanced fibrosis or cirrhosis, as they face an elevated risk of decompensation, hepatocellular carcinoma (HCC), and liver-related mortality. Consequently, the early detection and diagnosis of liver fibrosis are imperative. Although liver biopsy (LB) is considered the gold standard for fibrosis assessment, its widespread use is constrained by its invasive nature, complexity, and low patient acceptance. In recent years, various noninvasive tests (NITs) have been developed and increasingly adopted, including abdominal ultrasound, elastography, and serologic tests. This article reviews recent advancements in noninvasive diagnostic methods for assessing liver fibrosis in CHC patients, evaluates the accuracy and reliability of these methods, and proposes individualized testing protocols tailored to patients with diverse characteristics. The objective is to assist clinicians in timely intervention and to enhance patient prognosis.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"15 6","pages":"Article 102610"},"PeriodicalIF":3.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523798","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}