Artificial intelligence(AI)is revolutionizing medical imaging,particularly in chronic liver diseases assessment.AI technologies,including machine learning and deep learning,are increasingly integrated with multiparame...Artificial intelligence(AI)is revolutionizing medical imaging,particularly in chronic liver diseases assessment.AI technologies,including machine learning and deep learning,are increasingly integrated with multiparametric ultrasound(US)techniques to provide more accurate,objective,and non-invasive evaluations of liver fibrosis and steatosis.Analyzing large datasets from US images,AI enhances diagnostic precision,enabling better quantification of liver stiffness and fat content,which are essential for diagnosing and staging liver fibrosis and steatosis.Combining advanced US modalities,such as elastography and doppler imaging with AI,has demonstrated improved sensitivity in identifying different stages of liver disease and distinguishing various degrees of steatotic liver.These advancements also contribute to greater reproducibility and reduced operator dependency,addressing some of the limitations of traditional methods.The clinical implications of AI in liver disease are vast,ranging from early detection to predicting disease progression and evaluating treatment response.Despite these promising developments,challenges such as the need for large-scale datasets,algorithm transparency,and clinical validation remain.The aim of this review is to explore the current applications and future potential of AI in liver fibrosis and steatosis assessment using multiparametric US,highlighting the technological advances and clinical relevance of this emerging field.展开更多
Metabolic dysfunction-associated steatotic liver disease(MASLD)is an increasingly prevalent condition associated with hepatic complications and cardiovascular and renal events.Given its significant clinical impact,the...Metabolic dysfunction-associated steatotic liver disease(MASLD)is an increasingly prevalent condition associated with hepatic complications and cardiovascular and renal events.Given its significant clinical impact,the development of new strategies for early diagnosis and treatment is essential to improve patient outcomes.Over the past decade,the integration of artificial intelligence(AI)into gastroenterology has led to transformative advancements in medical practice.AI represents a major step towards personalized medicine,offering the potential to enhance diagnostic accuracy,refine prognostic assessments,and optimize treatment strategies.Its applications are rapidly expanding.This article explores the emerging role of AI in the management of MASLD,emphasizing its ability to improve clinical prediction,enhance the diagnostic performance of imaging modalities,and support histopathological confirmation.Additionally,it examines the development of AI-guided personalized treatments,where lifestyle modifications and close monitoring play a pivotal role in achieving therapeutic success.展开更多
BACKGROUND Liver failure,particularly acute-on-chronic liver failure,is associated with high mortality(50%-90%).The plasma exchange(PE)mode of the artificial liver support system has been shown to improve clinical out...BACKGROUND Liver failure,particularly acute-on-chronic liver failure,is associated with high mortality(50%-90%).The plasma exchange(PE)mode of the artificial liver support system has been shown to improve clinical outcomes,although its efficacy may vary depending on the regenerative capacity of the liver.Alpha-fetoprotein(AFP),an oncofetal glycoprotein,is reactivated during liver regeneration and may serve as a prognostic biomarker.Previous studies have reported significantly higher post-PE AFP levels in survivors than in non-survivors(286.5 ng/mL vs 82.3 ng/mL at day 7).However,the predictive value of baseline AFP stratification and serial AFP kinetics during PE therapy remains unestablished.This study investigated whether serial AFP measurements predict clinical outcomes in liver failure patients receiving PE.AIM To evaluate the predictive value of serial AFP measurements in liver failure patients receiving PE.METHODS This retrospective study included 194 liver failure patients with complete AFP data,excluding those with tumors,bleeding disorders,allergies,or unstable conditions.Patients were stratified by baseline AFP into low-AFP(<100 ng/mL,n=60),medium-AFP(100-200 ng/mL,n=70),and high-AFP(>200 ng/mL,n=64)groups.AFP was measured before PE and on days 1,10,20,and 25.RESULTS Stratification by baseline AFP revealed significant gradients.The high-AFP group required fewer PE sessions than the low-AFP group(2.8±1.0 vs 4.2±1.5)but exhibited greater post-PE AFP elevation(75.1±20.3 ng/mL vs 33.1±10.2 ng/mL;P<0.001).The high-AFP group demonstrated optimal values,including the lowest ammonia,bilirubin,alanine aminotransferase,aspartate aminotransferase,γ-glutamyl transferase,and the highest albumin and prothrombin activity(all post hoc P<0.05 vs low-AFP).The medium-AFP group showed intermediate values except for prothrombin activity(35.2%±8.6%),which was significantly lower than in both other groups(P<0.001).The high-AFP group had a reduced incidence of spontaneous bacterial peritonitis(9.4%vs 25.0%;P=0.003),superior three-month survival(90.6%vs 56.7%;P<0.001),and a higher post-treatment three-month receiver operating characteristic area under the curve(0.8851 vs 0.7051).CONCLUSION AFP dynamics correlate with regenerative capacity and clinical outcomes in liver failure.Serial AFP monitoring may enhance risk stratification and support personalized therapeutic strategies.展开更多
BACKGROUND In recent years,the utilization of artificial intelligence(AI)technology has gained prominence in the field of liver disease.AIM To analyzes AI research in the field of liver disease,summarizes the current ...BACKGROUND In recent years,the utilization of artificial intelligence(AI)technology has gained prominence in the field of liver disease.AIM To analyzes AI research in the field of liver disease,summarizes the current research status and identifies hot spots.METHODS We searched the Web of Science Core Collection database for all articles and reviews on hepatopathy and AI.The time spans from January 2007 to August 2023.We included 4051 studies for further collection of information,including authors,countries,institutions,publication years,keywords and references.VOS viewer,CiteSpace,R 4.3.1 and Scimago Graphica were used to visualize the results.RESULTS A total of 4051 articles were analyzed.China was the leading contributor,with 1568 publications,while the United States had the most international collaborations.The most productive institutions and journals were the Chinese Academy of Sciences and Frontiers in Oncology.Keywords co-occurrence analysis can be roughly summarized into four clusters:Risk prediction,diagnosis,treatment and prognosis of liver diseases."Machine learning","deep learning","convolutional neural network","CT",and"microvascular infiltration"have been popular research topics in recent years.CONCLUSION AI is widely applied in the risk assessment,diagnosis,treatment,and prognosis of liver diseases,with a shift from invasive to noninvasive treatment approaches.展开更多
BACKGROUND Kidney and liver transplantation are two sub-specialized medical disciplines,with transplant professionals spending decades in training.While artificial intelligencebased(AI-based)tools could potentially as...BACKGROUND Kidney and liver transplantation are two sub-specialized medical disciplines,with transplant professionals spending decades in training.While artificial intelligencebased(AI-based)tools could potentially assist in everyday clinical practice,comparative assessment of their effectiveness in clinical decision-making remains limited.AIM To compare the use of ChatGPT and GPT-4 as potential tools in AI-assisted clinical practice in these challenging disciplines.METHODS In total,400 different questions tested ChatGPT’s/GPT-4 knowledge and decision-making capacity in various renal and liver transplantation concepts.Specifically,294 multiple-choice questions were derived from open-access sources,63 questions were derived from published open-access case reports,and 43 from unpublished cases of patients treated at our department.The evaluation covered a plethora of topics,including clinical predictors,treatment options,and diagnostic criteria,among others.RESULTS ChatGPT correctly answered 50.3%of the 294 multiple-choice questions,while GPT-4 demonstrated a higher performance,answering 70.7%of questions(P<0.001).Regarding the 63 questions from published cases,ChatGPT achieved an agreement rate of 50.79%and partial agreement of 17.46%,while GPT-4 demonstrated an agreement rate of 80.95%and partial agreement of 9.52%(P=0.01).Regarding the 43 questions from unpublished cases,ChatGPT demonstrated an agreement rate of 53.49%and partial agreement of 23.26%,while GPT-4 demonstrated an agreement rate of 72.09%and partial agreement of 6.98%(P=0.004).When factoring by the nature of the task for all cases,notably,GPT-4 demonstrated outstanding performance,providing a differential diagnosis that included the final diagnosis in 90%of the cases(P=0.008),and successfully predicting the prognosis of the patient in 100%of related questions(P<0.001).CONCLUSION GPT-4 consistently provided more accurate and reliable clinical recommendations with higher percentages of full agreements both in renal and liver transplantation compared with ChatGPT.Our findings support the potential utility of AI models like ChatGPT and GPT-4 in AI-assisted clinical practice as sources of accurate,individualized medical information and facilitating decision-making.The progression and refinement of such AI-based tools could reshape the future of clinical practice,making their early adoption and adaptation by physicians a necessity.展开更多
Objective: Through the treatment of liver failure using artificial liver plasma exchange (PE), this study aims to explore the predictive value and clinical significance of alpha-fetoprotein (AFP) levels in the prognos...Objective: Through the treatment of liver failure using artificial liver plasma exchange (PE), this study aims to explore the predictive value and clinical significance of alpha-fetoprotein (AFP) levels in the prognosis of liver failure patients. Methods: A retrospective analysis was conducted on the clinical data of 96 liver failure patients, all of whom underwent artificial liver plasma exchange therapy in addition to standard medical treatment. Based on AFP test values, patients were divided into three groups: low AFP group (AFP < 100 ng/mL, n = 32), medium AFP group (100 ≤ AFP < 200 ng/mL, n = 32), and high AFP group (AFP ≥ 200 ng/mL, n = 32). Serum AFP levels were measured before artificial liver therapy (on the second day of hospitalization), on days 1, 10, and 20 after treatment, and at the final evaluation (before discharge or prior to death) to observe changes. Results: Among the 96 patients, 4 (4.2%) had acute liver failure (ALF), 7 (7.3%) had subacute liver failure (SALF), 57 (59.4%) had acute-on-chronic liver failure (ACLF), and 28 (29.2%) had chronic liver failure (CLF), with an overall survival rate of 82.3% (79/96). Patients in the AFP < 100 ng/mL group had a lower survival rate compared to the other two groups, and survival rates increased with higher AFP levels (P < 0.05). Conclusion: Serum AFP levels are closely related to the efficacy of artificial liver plasma exchange therapy for liver failure, and dynamic monitoring of AFP changes can help assess disease progression.展开更多
BACKGROUND We have innovatively amalgamated membrane blood purification and centrifugal blood cell separation technologies to address the limitations of current artificial liver support(ALS)models,and develop a versat...BACKGROUND We have innovatively amalgamated membrane blood purification and centrifugal blood cell separation technologies to address the limitations of current artificial liver support(ALS)models,and develop a versatile plasma purification system(VPPS)through centrifugal plasma separation.AIM To investigate the influence of VPPS on long-term rehospitalization and mortality rates among patients with acute-on-chronic liver failure(ACLF).METHODS This real-world,prospective study recruited inpatients diagnosed with ACLF from the Second Xiangya Hospital of Central South University between October 2021 and March 2024.Patients were categorized into the VPPS and non-VPPS groups based on the distinct ALS models administered to them.Self-administered questionnaires,clinical records,and self-reported data served as the primary methods for data collection.The laboratory results were evaluated at six distinct time points.All patients were subjected to follow-up assessments for>12 months.Kaplan-Meier survival analyses and Cox proportional hazards models were used to evaluate the risks of hospitalization and mortality during the follow-up period.RESULTS A cohort of 502 patients diagnosed with ACLF was recruited,with 260 assigned to the VPPS group.On comparing baseline characteristics,the VPPS group exhibited a significantly shorter length of stay,higher incidence of spontaneous peritonitis and pulmonary aspergillosis compared to the non-VPPS group(P<0.05).Agehazard ratio(HR=1.142,95%CI:1.01-1.23,P=0.018),peritonitis(HR=2.825,95%CI:1.07-6.382,P=0.026),albumin(HR=0.67,95%CI:0.46-0.942,P=0.023),total bilirubin(HR=1.26,95%CI:1.01-3.25,P=0.021),international normalized ratio(HR=1.97,95%CI:1.21-2.908,P=0.014),and VPPS/non-VPPS(HR=3.24,95%CI:2.152-4.76,P<0.001)were identified as significant independent predictors of mortality in both univariate and multivariate analyses throughout the follow-up period.Kaplan-Meier survival analyses demonstrated significantly higher rehospitalization and mortality rates in the non-VPPS group compared to the VPPS group during follow-up of≥2 years(log-rank test,P<0.001).CONCLUSION These findings suggest that VPPS is safe and has a positive influence on prognostic outcomes in patients with ACLF.展开更多
Metabolic-associated steatotic liver disease(MASLD),formerly referred to as non-alcoholic fatty liver disease,represents an escalating worldwide medical burden defined by hepatic steatosis,inflammation,fibrosis,and po...Metabolic-associated steatotic liver disease(MASLD),formerly referred to as non-alcoholic fatty liver disease,represents an escalating worldwide medical burden defined by hepatic steatosis,inflammation,fibrosis,and potential progression to cirrhosis or hepatocellular carcinoma.Artificial intelligence(AI)has emerged as a transformative tool in MASLD management,enhancing diagnostic accuracy,risk stratification,and treatment optimization.This review explores the integration of AI in MASLD diagnosis,including AI-based histopathological assessment,non-invasive screening models,imaging diagnostics,and gut microbiota-based app-roaches.Additionally,AI-driven treatment strategies facilitate personalized management,assess therapeutic response,and contribute to drug discovery.Despite its advantages,challenges such as data integration,model interpretability,and cost-effectiveness remain obstacles to widespread adoption.Future advan-cements in explainable AI,multi-modal data fusion,and cost-efficient implement-ations will be crucial for maximizing AI’s impact on MASLD care.AI-driven innovations hold great promise for improving early detection,guiding person-alized treatment,and ultimately enhancing patient outcomes in MASLD.展开更多
Artificial intelligence(AI)-augmented contrast-enhanced ultrasonography(CEUS)is emerging as a powerful tool in liver imaging,particularly in enhancing the accuracy of Liver Imaging Reporting and Data System(known as L...Artificial intelligence(AI)-augmented contrast-enhanced ultrasonography(CEUS)is emerging as a powerful tool in liver imaging,particularly in enhancing the accuracy of Liver Imaging Reporting and Data System(known as LI-RADS)classi-fication.This review synthesized published data on the integration of machine learning and deep learning techniques into CEUS,revealing that AI algorithms can improve the detection and quantification of contrast enhancement patterns.Such improvements led to more consistent LI-RADS categorization,reduced interoperator variability,and enabled real-time analysis that streamlined work-flow.The enhanced sensitivity of AI tools facilitated better differentiation between benign and malignant lesions,ultimately optimizing patient management.These advances suggest that AI-augmented CEUS could transform liver imaging by providing rapid,reliable,and objective assessments.However,the review also highlighted the need for further large-scale,multicenter studies to fully validate these findings and ensure the safe integration of AI into routine clinical practice.INTRODUCTION International hepatology society guidelines have established contrast-enhanced computed tomography(CT)and contrast-enhanced magnetic resonance imaging(MRI)as the imaging modalities of choice for diagnosing hepatocellular carcinoma(HCC)lesions larger than 1 cm.MRI remains the gold standard for detecting small HCC nodules in cirrhotic livers due to its superior soft-tissue contrast and functional imaging capabilities.However,early or atypical presentations remain challenging for differential diagnosis,staging,and treatment planning.In these scenarios contrast-enhanced ultrasonography(CEUS)is a valuable second-line tool,offering real-time,radiation-free evaluation and repeatability for follow-up.A recent meta-analysis of head-to-head studies reported comparable diagnostic performance between CEUS and CT/MRI with pooled sensitivities and specificities of 0.67/0.88 for CEUS vs 0.60/0.98 for CT/MRI in non-HCC malignancies,and similar specificities for HCC diagnosis(0.70 for CEUS vs 0.59 for CT;0.81 for CEUS vs 0.79 for MRI)[1].Given the limitations of individual imaging modalities,hybrid techniques and multimodal approaches are gaining traction for improving lesion detection,especially in cases where standard methods fall short.Artificial intelligence(AI)has emerged as a powerful tool in medical imaging,enhancing diagnostic accuracy and reliability across platforms.In CEUS liver imaging dynamic enhancement patterns often challenge consistent interpretation across observers.AI holds particular promise for standardizing assessments.The growing complexity of liver tumor evaluation has also driven interest in approaches that integrate serum bio-markers with advanced imaging.However,no single strategy currently meets all the diagnostic and prognostic re-quirements.Recent studies highlighted the potential of AI to bridge this gap by enabling precise image interpretation and facilitating the integration of heterogeneous clinical and imaging data[2].Altogether the convergence of CEUS with AI and radiomics offers a dynamic,quantitative,and potentially reproducible paradigm for liver lesion assessment,comple-menting traditional imaging methods.This review aimed to provide an overview of current advances in AI-driven CEUS for liver lesion assessment with a particular focus on automated Liver Imaging Reporting and Data System(LI-RADS)classification,radiomics-based models,and future clinical integration.While another recent systematic review[3]provided a comprehensive analysis of AI applications in CEUS,our approach offers a targeted perspective,emphasizing LI-RADS-centered scoring,automated lesion characterization,and clinical utility,particularly in the context of HCC diagnosis and management.In the methodological process of this narrative mini-review,the literature selection was primarily based on targeted PubMed searches.ChatGPT-4o(OpenAI)[4]was employed to assist in refining query parameters and identifying relevant,up-to-date peer-reviewed sources on CEUS-based AI applications.展开更多
INTRODUCTIONFulminant hepatic failure(FHF)is a severe disease with devastating consequences;the incidence is high in China.Before the availability of liver transplantation,the mortality rate was more than 80%[1,2].The...INTRODUCTIONFulminant hepatic failure(FHF)is a severe disease with devastating consequences;the incidence is high in China.Before the availability of liver transplantation,the mortality rate was more than 80%[1,2].The advent of liver transplantation revolutionized the outcome of FHF[3,4].However,many patients were unwilling to accept liver transplantation until very late,hence most of them died because of donor shortage and urgency of the disease[5-7],To overcome he problems,we performed orthotopic liver transplantation(OLT)in combination with artificial liver support(ALS) in the treatment of FHF in the past 2 years with satisfactory results.Our experience was reported below.展开更多
AIM： To characterize and evaluate the therapeutic efficacy of bioartificial liver （BAL） as compared to that of continuous hemodiafiltration （CHDF） with plasma exchange （PE）, which is the current standard therap...AIM： To characterize and evaluate the therapeutic efficacy of bioartificial liver （BAL） as compared to that of continuous hemodiafiltration （CHDF） with plasma exchange （PE）, which is the current standard therapy for fulminant hepatic failure （FHF） in Japan. METHODS： Pigs with hepatic devascularization were divided into three groups： （1） a non-treatment group （NT; n = 4）; （2） a BAL treatment group （BAL; n = 4）, （3） a PE ＋ CHDF treatment group using 1.5 L of normal porcine plasma with CHDF （PE ＋ CHDF, n -- 4）. Our BAL system consisted of a hollow fiber module with 0.2 i^m pores and 1 × 10^10 of microcarrier-attached hepatocytes inoculated into the extra-fiber space. Each treatment was initiated 4 h after hepatic devascularization. RESULTS： The pigs in the BAL and the PE ＋ CHDF groups survived longer than those in the NT group. The elimination capacity of blood ammonia by both BAL and PE ＋ CHDF was significantly higher than that in NT. Aromatic amino acids （AAA） were selectively eliminated by BAL, whereas both AAA and branched chain amino acids, which are beneficial for life, were eliminated by PE ＋ CHDF. Electrolytes maintenance and acid-base balance were better in the CPE ＋ CHDF group than that in the BAL group. CONCLUSION： Our results suggest that PE ＋ CHDF eliminate all factors regardless of benefits, whereas BAL selectively metabolizes toxic factors such as AAA. However since PE ＋ CHDF maintain electrolytes and acid-base balance, a combination therapy of BAL plus CPE ＋ CHDF might be more effective for FHF.展开更多
Alcohol-related liver disease(ARLD)remains a major public health concern,often diagnosed at advanced stages with limited treatment options.Early identification of high-risk individuals is crucial for timely interventi...Alcohol-related liver disease(ARLD)remains a major public health concern,often diagnosed at advanced stages with limited treatment options.Early identification of high-risk individuals is crucial for timely intervention and improved patient outcomes.Artificial intelligence(AI)has emerged as a powerful tool for predicting ARLD,leveraging multi-omics data,machine learning algorithms,and non-invasive biomarkers.This review explores the current advancements in AIdriven ARLD prediction,highlighting key methodologies such as multi-omics data integration,gut microbiome-based modeling,and predictive analytics using machine learning techniques.AI models incorporating transcriptomics,proteomics,and clinical data have demonstrated high diagnostic accuracy,with some achieving an area under the curve exceeding 0.90.Furthermore,non-invasive biomarkers,including liver stiffness measurements and circulating proteomic panels,have been successfully integrated into AI frameworks for early detection and risk stratification.Despite these advancements,challenges such as data heterogeneity,model generalizability,and ethical considerations remain.Future directions include the development of advanced biomarker discovery,wearable and point-of-care AI-integrated technologies,and precision medicine approaches tailored to individual risk profiles.AI-driven models hold significant potential in transforming ARLD prediction and management,ultimately contributing to early diagnosis and improved clinical outcomes.展开更多
The management of liver transplant recipients and their outcome prediction is complex due to nonlinear interaction of multiple pre,peri and postoperative factors.Artificial intelligence(AI)has a potentially significan...The management of liver transplant recipients and their outcome prediction is complex due to nonlinear interaction of multiple pre,peri and postoperative factors.Artificial intelligence(AI)has a potentially significant role in understanding and decision making at all stages of liver transplantation procedure.The role starts right from diagnosis of liver cirrhosis,followed by best course of action and prognostication.By analyzing numerous data points,AI can assist in the complex decision-making process of determining transplant candidacy.AI algorithms can analyze vast datasets of donor and recipient characteristics to improve the accuracy of matching,leading to better graft survival rates.This will help in optimizing the allocation of scarce organs,ensuring that they go to the most suitable recipients.AI can be used to predict the pre-operative risk factors and risk of post-operative complications such as graft rejection or post-transplant infections,allowing timely and tailored treatment.AI-powered imaging analysis can assist surgeons in preoperative planning and provide real-time guidance during surgery,increasing precision and improved safety.Therefore,AI has the potential to enhance long term patient and graft survival.The major challenges on use of AI are data availability,data quality,ethical considerations and clinical integration.In essence,AI holds great promise for revolutionizing liver transplantation albeit with some challenges.展开更多
BACKGROUND Metabolic dysfunction-associated steatotic liver disease(MASLD)is a leading cause of chronic liver disease globally.Current diagnostic methods,such as liver biopsies,are invasive and have limitations,highli...BACKGROUND Metabolic dysfunction-associated steatotic liver disease(MASLD)is a leading cause of chronic liver disease globally.Current diagnostic methods,such as liver biopsies,are invasive and have limitations,highlighting the need for non-invasive alternatives.AIM To investigate extracellular vesicles(EVs)as potential biomarkers for diagnosing and staging steatosis in patients with MASLD using machine learning(ML)and explainable artificial intelligence(XAI).METHODS In this single-center observational study,798 patients with metabolic dysfunction were enrolled.Of these,194 met the eligibility criteria,and 76 successfully completed all study procedures.Transient elastography was used for steatosis and fibrosis staging,and circulating plasma EV characteristics were analyzed through nanoparticle tracking.Twenty ML models were developed:Six to differentiate non-steatosis(S0)from steatosis(S1-S3);and fourteen to identify severe steatosis(S3).Models utilized EV features(size and concentration),clinical(advanced fibrosis and presence of type 2 diabetes mellitus),and anthropomorphic(sex,age,height,weight,body mass index)data.Their performance was assessed using receiver operating characteristic(ROC)-area under the curve(AUC),specificity,and sensitivity,while correlation and XAI analysis were also conducted.RESULTS The CatBoost C1a model achieved an ROC-AUC of 0.71/0.86(train/test)on average across ten random five-fold cross-validations,using EV features alone to distinguish S0 from S1-S3.The CatBoost C2h-21 model achieved an ROC-AUC of 0.81/1.00(train/test)on average across ten random three-fold cross-validations,using engineered features including EVs,clinical features like diabetes and advanced fibrosis,and anthropomorphic data like body mass index and weight for identifying severe steatosis(S3).Key predictors included EV mean size and concentration.Correlation,XAI,and SHapley Additive exPlanations analysis revealed non-linear feature relationships with steatosis stages.CONCLUSION The EV-based ML models demonstrated that the mean size and concentration of circulating plasma EVs constituted key predictors for distinguishing the absence of significant steatosis(S0)in patients with metabolic dysfunction,while the combination of EV,clinical,and anthropomorphic features improved the diagnostic accuracy for the identification of severe steatosis.The algorithmic approach using ML and XAI captured non-linear patterns between disease features and provided interpretable MASLD staging insights.However,further large multicenter studies,comparisons,and validation with histopathology and advanced imaging methods are needed.展开更多
The incidence and prevalence of metabolic dysfunction-associated steatotic liver disease(MASLD)have continued to increase in recent years,making it one of the most common chronic liver diseases worldwide.MASLD is high...The incidence and prevalence of metabolic dysfunction-associated steatotic liver disease(MASLD)have continued to increase in recent years,making it one of the most common chronic liver diseases worldwide.MASLD is highly comorbid with obesity,type 2 diabetes,cardiovascular disease,and chronic kidney disease,posing a serious threat to public health and creating a significant medical and socioeconomic burden.Despite advances in research,current clinical practice still faces considerable challenges in early screening,risk stratification,prognostic prediction,and long-term therapeutic monitoring.Recent advances in artificial intelligence(AI)have provided transformative opportunities to address these challenges.AI has demonstrated unique advantages in imaging interpretation,multiomics integration,electronic health record analysis,and remote health management,significantly improving the accuracy and efficiency of the noninvasive diagnosis,individualized risk stratification,precision therapy,and dynamic disease monitoring of MASLD.In this mini-review,the latest advances in AI applications for MASLD diagnosis and management are systematically summarized,and a forward-looking perspective on the role of AI in enabling the next generation of smart health care systems for MASLD is offered,with the aim of providing theoretical and practical guidance for the clinical management of this disease.展开更多
AIM: To evaluate the efficacy and safety of the TECA-I bioartificial liver support system (BALSS) in treating canines with acute liver failure (ALF). METHODS: Ten canines with ALF induced by 80% liver resection receiv...AIM: To evaluate the efficacy and safety of the TECA-I bioartificial liver support system (BALSS) in treating canines with acute liver failure (ALF). METHODS: Ten canines with ALF induced by 80% liver resection received BALSS treatment (BALSS group). Blood was perfused through a hollow fiber tube containing 1X10(10) porcine hepatocytes.Four canines with ALF were treated with BALSS without porcine hepatocytes (control group), and five canines with ALF received drug treatment (drug group). Each treatment lasted 6 hours. RESULTS: BALSS treatment yielded beneficial effects for partial liver resection induced ALF canines with survival and decreased plasma ammonia, ALT, AST and BIL. There was an obvious decrease in PT level and increase in PA level, and there were no changes in the count of lymphocytes, immunoglobulins (IgA, IgG and IgM) and complement (C3 and C4) levels after BALSS treatment. In contrast, for the canines with ALF in non-hepatocyte BALSS group (control group) and drug group, there were no significant changes in ammonia, ALT, AST, BIL, PT and PA levels. ALF canines in BALSS group, control group and drug group lived respectively an average time of 108.0h +/- 12.0h, 24.0h +/- 6.0h and 20.4h +/- 6.4h,and three canines with ALF survived in BALSS group. CONCLUSION: TECA-I BALSS is efficacious and safe for ALF canines induced by partial liver resection.展开更多
AIM: To assess the efficacy and safety of TECA type hybrid artificial liver support system (TECA-HALSS) in providing liver function of detoxification, metabolism and physiology by treating the patients with acute live...AIM: To assess the efficacy and safety of TECA type hybrid artificial liver support system (TECA-HALSS) in providing liver function of detoxification, metabolism and physiology by treating the patients with acute liver failure (ALF). METHODS: The porcine liver cells (1-2) x 10(10) were separated from the Chinese small swine and cultured in the bioreactor of TECA-BALSS at 37.0 degrees C and circulated through the outer space of the hollow fiber tubes in BALSS. The six liver failure patients with various degree of hepatic coma were treated by TECA-HALSS and with conventional medicines. The venous plasma of the patients was separated by a plasma separator and treated by charcoal adsorbent or plasma exchange. The plasma circulated through the inner space of the hollow fiber tubes of BALSS and mixed with the patients' blood cells and flew back to their blood circulation. Some small molecular weight substances were exchanged between the plasma and porcine liver cells. Each treatment lasted 6.0-7.0 h. Physiological and biochemical parameters were measured before,during and after the treatment. RESULTS: The average of porcine liver cells was (1.0-3.0) x 10(10) obtained from each swine liver using our modified enzymatic digestion method. The survival rate of the cells was 85%-93% by trypan blue stain and AO/PI fluorescent stain. After cultured in TECA-BALSS bioreactor for 6 h, the survival rate of cells still remained 70%-85%. At the end of TECA-HALSS treatment, the levels of plasma NH(3), ALT, TB and DB were significantly decreased. The patients who were in the state of drowsiness or coma before the treatment improved their appetite significantly and regained consciousness, some patients resumed light physical work on a short period after the treatment.One to two days after the treatment, the ratio of PTA increased warkedly. During the treatment, the heart rates, blood pressure, respiration condition and serum electrolytes (K(+), Na(+) and Cl(-)) were stable without thrombosis and bleeding in all the six patients. CONCLUSION: TECA-HALSS treatment could be a rapid, safe and efficacious method to provide temporary liver support for patients with ALF.展开更多
BACKGROUND： Plasma exchange （PE）-centered artificial liver support system reduced the high mortality rate of hepa titis B virus （HBV）-related acute-on-chronic liver failure （ACLF）. But the data were diverse in ...BACKGROUND： Plasma exchange （PE）-centered artificial liver support system reduced the high mortality rate of hepa titis B virus （HBV）-related acute-on-chronic liver failure （ACLF）. But the data were diverse in different medical centers. The present prospective nationwide study was to evaluate the effects of PE on patients with HBV-ACLF at different stages.展开更多
BACKGROUND: Orthotopic liver transplantation (OLT) is the most effective therapy for liver failure. However, OLT is severely limited by the shortage of liver donors. Bioartificial liver (BAL) shows great potential as ...BACKGROUND: Orthotopic liver transplantation (OLT) is the most effective therapy for liver failure. However, OLT is severely limited by the shortage of liver donors. Bioartificial liver (BAL) shows great potential as an alternative therapy for liver failure In recent years, progress has been made in BAL regarding genetically engineered cell lines, immortalized human hepatocytes, methods for preserving the phenotype of primary human hepatocytes, and other functional hepatocytes derived from stem cells. DATA SOURCES: A systematic search of PubMed and ISI Web of Science was performed to identify relevant studies in English language literature using the Key words such as liver failure bioartificial liver, hepatocyte, stem cells, differentiation, and immortalization. More than 200 articles related to the cell sources of hepatocyte in BAL were systematically reviewed. RESULTS: Methods for preserving the phenotype of primary human hepatocytes have been successfully developed. Many genetically engineered cell lines and immortalized human hepatocytes have also been established. Among these cell lines the incorporation of BAL with GS-HepG2 cells or alginate encapsulated HepG2 cells could prolong the survival time and improve pathophysiological parameters in an animal model of liver failure. The cBAL111 cells were evaluated using the AMC-BAL bioreactor, which could eliminate ammonia and lidocaine, and produce albumin. Importantly, BAL loading with HepLi-4 cells could significantly improve the blood biochemical parameters, and prolong the survival time in pigs with liver failure. Other functional hepatocytes differentiated from stem cells, such as human liver progenitor cells, have been successfully achieved. CONCLUSIONS: Aside from genetically modified liver cell lines and immortalized human hepatocytes, other functionalhepatocytes derived from stem cells show great potential as cell sources for BAL. BAL with safe and effective liver cells may be achieved for clinical liver failure in the near future.展开更多
文摘Artificial intelligence(AI)is revolutionizing medical imaging,particularly in chronic liver diseases assessment.AI technologies,including machine learning and deep learning,are increasingly integrated with multiparametric ultrasound(US)techniques to provide more accurate,objective,and non-invasive evaluations of liver fibrosis and steatosis.Analyzing large datasets from US images,AI enhances diagnostic precision,enabling better quantification of liver stiffness and fat content,which are essential for diagnosing and staging liver fibrosis and steatosis.Combining advanced US modalities,such as elastography and doppler imaging with AI,has demonstrated improved sensitivity in identifying different stages of liver disease and distinguishing various degrees of steatotic liver.These advancements also contribute to greater reproducibility and reduced operator dependency,addressing some of the limitations of traditional methods.The clinical implications of AI in liver disease are vast,ranging from early detection to predicting disease progression and evaluating treatment response.Despite these promising developments,challenges such as the need for large-scale datasets,algorithm transparency,and clinical validation remain.The aim of this review is to explore the current applications and future potential of AI in liver fibrosis and steatosis assessment using multiparametric US,highlighting the technological advances and clinical relevance of this emerging field.
文摘Metabolic dysfunction-associated steatotic liver disease(MASLD)is an increasingly prevalent condition associated with hepatic complications and cardiovascular and renal events.Given its significant clinical impact,the development of new strategies for early diagnosis and treatment is essential to improve patient outcomes.Over the past decade,the integration of artificial intelligence(AI)into gastroenterology has led to transformative advancements in medical practice.AI represents a major step towards personalized medicine,offering the potential to enhance diagnostic accuracy,refine prognostic assessments,and optimize treatment strategies.Its applications are rapidly expanding.This article explores the emerging role of AI in the management of MASLD,emphasizing its ability to improve clinical prediction,enhance the diagnostic performance of imaging modalities,and support histopathological confirmation.Additionally,it examines the development of AI-guided personalized treatments,where lifestyle modifications and close monitoring play a pivotal role in achieving therapeutic success.
基金Supported by National Natural Science Foundation of China,No.82160106.
文摘BACKGROUND Liver failure,particularly acute-on-chronic liver failure,is associated with high mortality(50%-90%).The plasma exchange(PE)mode of the artificial liver support system has been shown to improve clinical outcomes,although its efficacy may vary depending on the regenerative capacity of the liver.Alpha-fetoprotein(AFP),an oncofetal glycoprotein,is reactivated during liver regeneration and may serve as a prognostic biomarker.Previous studies have reported significantly higher post-PE AFP levels in survivors than in non-survivors(286.5 ng/mL vs 82.3 ng/mL at day 7).However,the predictive value of baseline AFP stratification and serial AFP kinetics during PE therapy remains unestablished.This study investigated whether serial AFP measurements predict clinical outcomes in liver failure patients receiving PE.AIM To evaluate the predictive value of serial AFP measurements in liver failure patients receiving PE.METHODS This retrospective study included 194 liver failure patients with complete AFP data,excluding those with tumors,bleeding disorders,allergies,or unstable conditions.Patients were stratified by baseline AFP into low-AFP(<100 ng/mL,n=60),medium-AFP(100-200 ng/mL,n=70),and high-AFP(>200 ng/mL,n=64)groups.AFP was measured before PE and on days 1,10,20,and 25.RESULTS Stratification by baseline AFP revealed significant gradients.The high-AFP group required fewer PE sessions than the low-AFP group(2.8±1.0 vs 4.2±1.5)but exhibited greater post-PE AFP elevation(75.1±20.3 ng/mL vs 33.1±10.2 ng/mL;P<0.001).The high-AFP group demonstrated optimal values,including the lowest ammonia,bilirubin,alanine aminotransferase,aspartate aminotransferase,γ-glutamyl transferase,and the highest albumin and prothrombin activity(all post hoc P<0.05 vs low-AFP).The medium-AFP group showed intermediate values except for prothrombin activity(35.2%±8.6%),which was significantly lower than in both other groups(P<0.001).The high-AFP group had a reduced incidence of spontaneous bacterial peritonitis(9.4%vs 25.0%;P=0.003),superior three-month survival(90.6%vs 56.7%;P<0.001),and a higher post-treatment three-month receiver operating characteristic area under the curve(0.8851 vs 0.7051).CONCLUSION AFP dynamics correlate with regenerative capacity and clinical outcomes in liver failure.Serial AFP monitoring may enhance risk stratification and support personalized therapeutic strategies.
基金Supported by Natural Science Foundation of Sichuan Province,China,No.2022NSFSC1378.
文摘BACKGROUND In recent years,the utilization of artificial intelligence(AI)technology has gained prominence in the field of liver disease.AIM To analyzes AI research in the field of liver disease,summarizes the current research status and identifies hot spots.METHODS We searched the Web of Science Core Collection database for all articles and reviews on hepatopathy and AI.The time spans from January 2007 to August 2023.We included 4051 studies for further collection of information,including authors,countries,institutions,publication years,keywords and references.VOS viewer,CiteSpace,R 4.3.1 and Scimago Graphica were used to visualize the results.RESULTS A total of 4051 articles were analyzed.China was the leading contributor,with 1568 publications,while the United States had the most international collaborations.The most productive institutions and journals were the Chinese Academy of Sciences and Frontiers in Oncology.Keywords co-occurrence analysis can be roughly summarized into four clusters:Risk prediction,diagnosis,treatment and prognosis of liver diseases."Machine learning","deep learning","convolutional neural network","CT",and"microvascular infiltration"have been popular research topics in recent years.CONCLUSION AI is widely applied in the risk assessment,diagnosis,treatment,and prognosis of liver diseases,with a shift from invasive to noninvasive treatment approaches.
文摘BACKGROUND Kidney and liver transplantation are two sub-specialized medical disciplines,with transplant professionals spending decades in training.While artificial intelligencebased(AI-based)tools could potentially assist in everyday clinical practice,comparative assessment of their effectiveness in clinical decision-making remains limited.AIM To compare the use of ChatGPT and GPT-4 as potential tools in AI-assisted clinical practice in these challenging disciplines.METHODS In total,400 different questions tested ChatGPT’s/GPT-4 knowledge and decision-making capacity in various renal and liver transplantation concepts.Specifically,294 multiple-choice questions were derived from open-access sources,63 questions were derived from published open-access case reports,and 43 from unpublished cases of patients treated at our department.The evaluation covered a plethora of topics,including clinical predictors,treatment options,and diagnostic criteria,among others.RESULTS ChatGPT correctly answered 50.3%of the 294 multiple-choice questions,while GPT-4 demonstrated a higher performance,answering 70.7%of questions(P<0.001).Regarding the 63 questions from published cases,ChatGPT achieved an agreement rate of 50.79%and partial agreement of 17.46%,while GPT-4 demonstrated an agreement rate of 80.95%and partial agreement of 9.52%(P=0.01).Regarding the 43 questions from unpublished cases,ChatGPT demonstrated an agreement rate of 53.49%and partial agreement of 23.26%,while GPT-4 demonstrated an agreement rate of 72.09%and partial agreement of 6.98%(P=0.004).When factoring by the nature of the task for all cases,notably,GPT-4 demonstrated outstanding performance,providing a differential diagnosis that included the final diagnosis in 90%of the cases(P=0.008),and successfully predicting the prognosis of the patient in 100%of related questions(P<0.001).CONCLUSION GPT-4 consistently provided more accurate and reliable clinical recommendations with higher percentages of full agreements both in renal and liver transplantation compared with ChatGPT.Our findings support the potential utility of AI models like ChatGPT and GPT-4 in AI-assisted clinical practice as sources of accurate,individualized medical information and facilitating decision-making.The progression and refinement of such AI-based tools could reshape the future of clinical practice,making their early adoption and adaptation by physicians a necessity.
文摘Objective: Through the treatment of liver failure using artificial liver plasma exchange (PE), this study aims to explore the predictive value and clinical significance of alpha-fetoprotein (AFP) levels in the prognosis of liver failure patients. Methods: A retrospective analysis was conducted on the clinical data of 96 liver failure patients, all of whom underwent artificial liver plasma exchange therapy in addition to standard medical treatment. Based on AFP test values, patients were divided into three groups: low AFP group (AFP < 100 ng/mL, n = 32), medium AFP group (100 ≤ AFP < 200 ng/mL, n = 32), and high AFP group (AFP ≥ 200 ng/mL, n = 32). Serum AFP levels were measured before artificial liver therapy (on the second day of hospitalization), on days 1, 10, and 20 after treatment, and at the final evaluation (before discharge or prior to death) to observe changes. Results: Among the 96 patients, 4 (4.2%) had acute liver failure (ALF), 7 (7.3%) had subacute liver failure (SALF), 57 (59.4%) had acute-on-chronic liver failure (ACLF), and 28 (29.2%) had chronic liver failure (CLF), with an overall survival rate of 82.3% (79/96). Patients in the AFP < 100 ng/mL group had a lower survival rate compared to the other two groups, and survival rates increased with higher AFP levels (P < 0.05). Conclusion: Serum AFP levels are closely related to the efficacy of artificial liver plasma exchange therapy for liver failure, and dynamic monitoring of AFP changes can help assess disease progression.
基金Supported by Natural Science Foundation of Hunan Province,China,No.2022JJ30842 and No.2024JJ6560Clinical Medical Research Center for Viral Hepatitis of Hunan Province,No.2023SK4009Beijing iGandan Foundation,No.RGGJJ-2021-017 and No.iGandanF-1082022-RGG023.
文摘BACKGROUND We have innovatively amalgamated membrane blood purification and centrifugal blood cell separation technologies to address the limitations of current artificial liver support(ALS)models,and develop a versatile plasma purification system(VPPS)through centrifugal plasma separation.AIM To investigate the influence of VPPS on long-term rehospitalization and mortality rates among patients with acute-on-chronic liver failure(ACLF).METHODS This real-world,prospective study recruited inpatients diagnosed with ACLF from the Second Xiangya Hospital of Central South University between October 2021 and March 2024.Patients were categorized into the VPPS and non-VPPS groups based on the distinct ALS models administered to them.Self-administered questionnaires,clinical records,and self-reported data served as the primary methods for data collection.The laboratory results were evaluated at six distinct time points.All patients were subjected to follow-up assessments for>12 months.Kaplan-Meier survival analyses and Cox proportional hazards models were used to evaluate the risks of hospitalization and mortality during the follow-up period.RESULTS A cohort of 502 patients diagnosed with ACLF was recruited,with 260 assigned to the VPPS group.On comparing baseline characteristics,the VPPS group exhibited a significantly shorter length of stay,higher incidence of spontaneous peritonitis and pulmonary aspergillosis compared to the non-VPPS group(P<0.05).Agehazard ratio(HR=1.142,95%CI:1.01-1.23,P=0.018),peritonitis(HR=2.825,95%CI:1.07-6.382,P=0.026),albumin(HR=0.67,95%CI:0.46-0.942,P=0.023),total bilirubin(HR=1.26,95%CI:1.01-3.25,P=0.021),international normalized ratio(HR=1.97,95%CI:1.21-2.908,P=0.014),and VPPS/non-VPPS(HR=3.24,95%CI:2.152-4.76,P<0.001)were identified as significant independent predictors of mortality in both univariate and multivariate analyses throughout the follow-up period.Kaplan-Meier survival analyses demonstrated significantly higher rehospitalization and mortality rates in the non-VPPS group compared to the VPPS group during follow-up of≥2 years(log-rank test,P<0.001).CONCLUSION These findings suggest that VPPS is safe and has a positive influence on prognostic outcomes in patients with ACLF.
文摘Metabolic-associated steatotic liver disease(MASLD),formerly referred to as non-alcoholic fatty liver disease,represents an escalating worldwide medical burden defined by hepatic steatosis,inflammation,fibrosis,and potential progression to cirrhosis or hepatocellular carcinoma.Artificial intelligence(AI)has emerged as a transformative tool in MASLD management,enhancing diagnostic accuracy,risk stratification,and treatment optimization.This review explores the integration of AI in MASLD diagnosis,including AI-based histopathological assessment,non-invasive screening models,imaging diagnostics,and gut microbiota-based app-roaches.Additionally,AI-driven treatment strategies facilitate personalized management,assess therapeutic response,and contribute to drug discovery.Despite its advantages,challenges such as data integration,model interpretability,and cost-effectiveness remain obstacles to widespread adoption.Future advan-cements in explainable AI,multi-modal data fusion,and cost-efficient implement-ations will be crucial for maximizing AI’s impact on MASLD care.AI-driven innovations hold great promise for improving early detection,guiding person-alized treatment,and ultimately enhancing patient outcomes in MASLD.
文摘Artificial intelligence(AI)-augmented contrast-enhanced ultrasonography(CEUS)is emerging as a powerful tool in liver imaging,particularly in enhancing the accuracy of Liver Imaging Reporting and Data System(known as LI-RADS)classi-fication.This review synthesized published data on the integration of machine learning and deep learning techniques into CEUS,revealing that AI algorithms can improve the detection and quantification of contrast enhancement patterns.Such improvements led to more consistent LI-RADS categorization,reduced interoperator variability,and enabled real-time analysis that streamlined work-flow.The enhanced sensitivity of AI tools facilitated better differentiation between benign and malignant lesions,ultimately optimizing patient management.These advances suggest that AI-augmented CEUS could transform liver imaging by providing rapid,reliable,and objective assessments.However,the review also highlighted the need for further large-scale,multicenter studies to fully validate these findings and ensure the safe integration of AI into routine clinical practice.INTRODUCTION International hepatology society guidelines have established contrast-enhanced computed tomography(CT)and contrast-enhanced magnetic resonance imaging(MRI)as the imaging modalities of choice for diagnosing hepatocellular carcinoma(HCC)lesions larger than 1 cm.MRI remains the gold standard for detecting small HCC nodules in cirrhotic livers due to its superior soft-tissue contrast and functional imaging capabilities.However,early or atypical presentations remain challenging for differential diagnosis,staging,and treatment planning.In these scenarios contrast-enhanced ultrasonography(CEUS)is a valuable second-line tool,offering real-time,radiation-free evaluation and repeatability for follow-up.A recent meta-analysis of head-to-head studies reported comparable diagnostic performance between CEUS and CT/MRI with pooled sensitivities and specificities of 0.67/0.88 for CEUS vs 0.60/0.98 for CT/MRI in non-HCC malignancies,and similar specificities for HCC diagnosis(0.70 for CEUS vs 0.59 for CT;0.81 for CEUS vs 0.79 for MRI)[1].Given the limitations of individual imaging modalities,hybrid techniques and multimodal approaches are gaining traction for improving lesion detection,especially in cases where standard methods fall short.Artificial intelligence(AI)has emerged as a powerful tool in medical imaging,enhancing diagnostic accuracy and reliability across platforms.In CEUS liver imaging dynamic enhancement patterns often challenge consistent interpretation across observers.AI holds particular promise for standardizing assessments.The growing complexity of liver tumor evaluation has also driven interest in approaches that integrate serum bio-markers with advanced imaging.However,no single strategy currently meets all the diagnostic and prognostic re-quirements.Recent studies highlighted the potential of AI to bridge this gap by enabling precise image interpretation and facilitating the integration of heterogeneous clinical and imaging data[2].Altogether the convergence of CEUS with AI and radiomics offers a dynamic,quantitative,and potentially reproducible paradigm for liver lesion assessment,comple-menting traditional imaging methods.This review aimed to provide an overview of current advances in AI-driven CEUS for liver lesion assessment with a particular focus on automated Liver Imaging Reporting and Data System(LI-RADS)classification,radiomics-based models,and future clinical integration.While another recent systematic review[3]provided a comprehensive analysis of AI applications in CEUS,our approach offers a targeted perspective,emphasizing LI-RADS-centered scoring,automated lesion characterization,and clinical utility,particularly in the context of HCC diagnosis and management.In the methodological process of this narrative mini-review,the literature selection was primarily based on targeted PubMed searches.ChatGPT-4o(OpenAI)[4]was employed to assist in refining query parameters and identifying relevant,up-to-date peer-reviewed sources on CEUS-based AI applications.
基金the grant of key Clinical Programme of China Ministry Public Health,No.97040230
文摘INTRODUCTIONFulminant hepatic failure(FHF)is a severe disease with devastating consequences;the incidence is high in China.Before the availability of liver transplantation,the mortality rate was more than 80%[1,2].The advent of liver transplantation revolutionized the outcome of FHF[3,4].However,many patients were unwilling to accept liver transplantation until very late,hence most of them died because of donor shortage and urgency of the disease[5-7],To overcome he problems,we performed orthotopic liver transplantation(OLT)in combination with artificial liver support(ALS) in the treatment of FHF in the past 2 years with satisfactory results.Our experience was reported below.
基金Supported by National High Technology Research and Development Program of China 863 Programs No.2006AA02A141 and No.2012AA020505the Medical Research Fund of Guangdong Province No.2009164
文摘AIM: To evaluate a hybrid bioartificial liver support system (HBALSS) in cynomolgus monkeys with acute liver failure.
文摘AIM： To characterize and evaluate the therapeutic efficacy of bioartificial liver （BAL） as compared to that of continuous hemodiafiltration （CHDF） with plasma exchange （PE）, which is the current standard therapy for fulminant hepatic failure （FHF） in Japan. METHODS： Pigs with hepatic devascularization were divided into three groups： （1） a non-treatment group （NT; n = 4）; （2） a BAL treatment group （BAL; n = 4）, （3） a PE ＋ CHDF treatment group using 1.5 L of normal porcine plasma with CHDF （PE ＋ CHDF, n -- 4）. Our BAL system consisted of a hollow fiber module with 0.2 i^m pores and 1 × 10^10 of microcarrier-attached hepatocytes inoculated into the extra-fiber space. Each treatment was initiated 4 h after hepatic devascularization. RESULTS： The pigs in the BAL and the PE ＋ CHDF groups survived longer than those in the NT group. The elimination capacity of blood ammonia by both BAL and PE ＋ CHDF was significantly higher than that in NT. Aromatic amino acids （AAA） were selectively eliminated by BAL, whereas both AAA and branched chain amino acids, which are beneficial for life, were eliminated by PE ＋ CHDF. Electrolytes maintenance and acid-base balance were better in the CPE ＋ CHDF group than that in the BAL group. CONCLUSION： Our results suggest that PE ＋ CHDF eliminate all factors regardless of benefits, whereas BAL selectively metabolizes toxic factors such as AAA. However since PE ＋ CHDF maintain electrolytes and acid-base balance, a combination therapy of BAL plus CPE ＋ CHDF might be more effective for FHF.
文摘Alcohol-related liver disease(ARLD)remains a major public health concern,often diagnosed at advanced stages with limited treatment options.Early identification of high-risk individuals is crucial for timely intervention and improved patient outcomes.Artificial intelligence(AI)has emerged as a powerful tool for predicting ARLD,leveraging multi-omics data,machine learning algorithms,and non-invasive biomarkers.This review explores the current advancements in AIdriven ARLD prediction,highlighting key methodologies such as multi-omics data integration,gut microbiome-based modeling,and predictive analytics using machine learning techniques.AI models incorporating transcriptomics,proteomics,and clinical data have demonstrated high diagnostic accuracy,with some achieving an area under the curve exceeding 0.90.Furthermore,non-invasive biomarkers,including liver stiffness measurements and circulating proteomic panels,have been successfully integrated into AI frameworks for early detection and risk stratification.Despite these advancements,challenges such as data heterogeneity,model generalizability,and ethical considerations remain.Future directions include the development of advanced biomarker discovery,wearable and point-of-care AI-integrated technologies,and precision medicine approaches tailored to individual risk profiles.AI-driven models hold significant potential in transforming ARLD prediction and management,ultimately contributing to early diagnosis and improved clinical outcomes.
文摘The management of liver transplant recipients and their outcome prediction is complex due to nonlinear interaction of multiple pre,peri and postoperative factors.Artificial intelligence(AI)has a potentially significant role in understanding and decision making at all stages of liver transplantation procedure.The role starts right from diagnosis of liver cirrhosis,followed by best course of action and prognostication.By analyzing numerous data points,AI can assist in the complex decision-making process of determining transplant candidacy.AI algorithms can analyze vast datasets of donor and recipient characteristics to improve the accuracy of matching,leading to better graft survival rates.This will help in optimizing the allocation of scarce organs,ensuring that they go to the most suitable recipients.AI can be used to predict the pre-operative risk factors and risk of post-operative complications such as graft rejection or post-transplant infections,allowing timely and tailored treatment.AI-powered imaging analysis can assist surgeons in preoperative planning and provide real-time guidance during surgery,increasing precision and improved safety.Therefore,AI has the potential to enhance long term patient and graft survival.The major challenges on use of AI are data availability,data quality,ethical considerations and clinical integration.In essence,AI holds great promise for revolutionizing liver transplantation albeit with some challenges.
文摘BACKGROUND Metabolic dysfunction-associated steatotic liver disease(MASLD)is a leading cause of chronic liver disease globally.Current diagnostic methods,such as liver biopsies,are invasive and have limitations,highlighting the need for non-invasive alternatives.AIM To investigate extracellular vesicles(EVs)as potential biomarkers for diagnosing and staging steatosis in patients with MASLD using machine learning(ML)and explainable artificial intelligence(XAI).METHODS In this single-center observational study,798 patients with metabolic dysfunction were enrolled.Of these,194 met the eligibility criteria,and 76 successfully completed all study procedures.Transient elastography was used for steatosis and fibrosis staging,and circulating plasma EV characteristics were analyzed through nanoparticle tracking.Twenty ML models were developed:Six to differentiate non-steatosis(S0)from steatosis(S1-S3);and fourteen to identify severe steatosis(S3).Models utilized EV features(size and concentration),clinical(advanced fibrosis and presence of type 2 diabetes mellitus),and anthropomorphic(sex,age,height,weight,body mass index)data.Their performance was assessed using receiver operating characteristic(ROC)-area under the curve(AUC),specificity,and sensitivity,while correlation and XAI analysis were also conducted.RESULTS The CatBoost C1a model achieved an ROC-AUC of 0.71/0.86(train/test)on average across ten random five-fold cross-validations,using EV features alone to distinguish S0 from S1-S3.The CatBoost C2h-21 model achieved an ROC-AUC of 0.81/1.00(train/test)on average across ten random three-fold cross-validations,using engineered features including EVs,clinical features like diabetes and advanced fibrosis,and anthropomorphic data like body mass index and weight for identifying severe steatosis(S3).Key predictors included EV mean size and concentration.Correlation,XAI,and SHapley Additive exPlanations analysis revealed non-linear feature relationships with steatosis stages.CONCLUSION The EV-based ML models demonstrated that the mean size and concentration of circulating plasma EVs constituted key predictors for distinguishing the absence of significant steatosis(S0)in patients with metabolic dysfunction,while the combination of EV,clinical,and anthropomorphic features improved the diagnostic accuracy for the identification of severe steatosis.The algorithmic approach using ML and XAI captured non-linear patterns between disease features and provided interpretable MASLD staging insights.However,further large multicenter studies,comparisons,and validation with histopathology and advanced imaging methods are needed.
基金Supported by Shanghai Pujiang Program,No.24PJD071National Natural Science Foundation of China,No.82100605Star Program of Shanghai Jiao Tong University,No.YG2021QN54.
文摘The incidence and prevalence of metabolic dysfunction-associated steatotic liver disease(MASLD)have continued to increase in recent years,making it one of the most common chronic liver diseases worldwide.MASLD is highly comorbid with obesity,type 2 diabetes,cardiovascular disease,and chronic kidney disease,posing a serious threat to public health and creating a significant medical and socioeconomic burden.Despite advances in research,current clinical practice still faces considerable challenges in early screening,risk stratification,prognostic prediction,and long-term therapeutic monitoring.Recent advances in artificial intelligence(AI)have provided transformative opportunities to address these challenges.AI has demonstrated unique advantages in imaging interpretation,multiomics integration,electronic health record analysis,and remote health management,significantly improving the accuracy and efficiency of the noninvasive diagnosis,individualized risk stratification,precision therapy,and dynamic disease monitoring of MASLD.In this mini-review,the latest advances in AI applications for MASLD diagnosis and management are systematically summarized,and a forward-looking perspective on the role of AI in enabling the next generation of smart health care systems for MASLD is offered,with the aim of providing theoretical and practical guidance for the clinical management of this disease.
文摘AIM: To evaluate the efficacy and safety of the TECA-I bioartificial liver support system (BALSS) in treating canines with acute liver failure (ALF). METHODS: Ten canines with ALF induced by 80% liver resection received BALSS treatment (BALSS group). Blood was perfused through a hollow fiber tube containing 1X10(10) porcine hepatocytes.Four canines with ALF were treated with BALSS without porcine hepatocytes (control group), and five canines with ALF received drug treatment (drug group). Each treatment lasted 6 hours. RESULTS: BALSS treatment yielded beneficial effects for partial liver resection induced ALF canines with survival and decreased plasma ammonia, ALT, AST and BIL. There was an obvious decrease in PT level and increase in PA level, and there were no changes in the count of lymphocytes, immunoglobulins (IgA, IgG and IgM) and complement (C3 and C4) levels after BALSS treatment. In contrast, for the canines with ALF in non-hepatocyte BALSS group (control group) and drug group, there were no significant changes in ammonia, ALT, AST, BIL, PT and PA levels. ALF canines in BALSS group, control group and drug group lived respectively an average time of 108.0h +/- 12.0h, 24.0h +/- 6.0h and 20.4h +/- 6.4h,and three canines with ALF survived in BALSS group. CONCLUSION: TECA-I BALSS is efficacious and safe for ALF canines induced by partial liver resection.
基金Supported by the Research Initiation Fund for Returned Students from Overseas,Ministry of Education,No.94001
文摘AIM: To assess the efficacy and safety of TECA type hybrid artificial liver support system (TECA-HALSS) in providing liver function of detoxification, metabolism and physiology by treating the patients with acute liver failure (ALF). METHODS: The porcine liver cells (1-2) x 10(10) were separated from the Chinese small swine and cultured in the bioreactor of TECA-BALSS at 37.0 degrees C and circulated through the outer space of the hollow fiber tubes in BALSS. The six liver failure patients with various degree of hepatic coma were treated by TECA-HALSS and with conventional medicines. The venous plasma of the patients was separated by a plasma separator and treated by charcoal adsorbent or plasma exchange. The plasma circulated through the inner space of the hollow fiber tubes of BALSS and mixed with the patients' blood cells and flew back to their blood circulation. Some small molecular weight substances were exchanged between the plasma and porcine liver cells. Each treatment lasted 6.0-7.0 h. Physiological and biochemical parameters were measured before,during and after the treatment. RESULTS: The average of porcine liver cells was (1.0-3.0) x 10(10) obtained from each swine liver using our modified enzymatic digestion method. The survival rate of the cells was 85%-93% by trypan blue stain and AO/PI fluorescent stain. After cultured in TECA-BALSS bioreactor for 6 h, the survival rate of cells still remained 70%-85%. At the end of TECA-HALSS treatment, the levels of plasma NH(3), ALT, TB and DB were significantly decreased. The patients who were in the state of drowsiness or coma before the treatment improved their appetite significantly and regained consciousness, some patients resumed light physical work on a short period after the treatment.One to two days after the treatment, the ratio of PTA increased warkedly. During the treatment, the heart rates, blood pressure, respiration condition and serum electrolytes (K(+), Na(+) and Cl(-)) were stable without thrombosis and bleeding in all the six patients. CONCLUSION: TECA-HALSS treatment could be a rapid, safe and efficacious method to provide temporary liver support for patients with ALF.
基金supported by grants from the National Science and Technology Major Project(2012ZX10002004)Scientific Research Fund of Zhejiang Provincial Education Department(Y201328037)the opening foundation of the State Key Laboratory for Diagnosis and Treatment of Infectious Diseases and Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases,First Affiliated Hospital,Zhejiang University School of Medicine(2015KF04)
文摘BACKGROUND： Plasma exchange （PE）-centered artificial liver support system reduced the high mortality rate of hepa titis B virus （HBV）-related acute-on-chronic liver failure （ACLF）. But the data were diverse in different medical centers. The present prospective nationwide study was to evaluate the effects of PE on patients with HBV-ACLF at different stages.
基金supported by grants from the Chinese High-Tech Research & Development (863) Program (2011AA020104)Science Fund for Creative Research Groups of the National Natural Science Foundation of China (81121002)+1 种基金the Fundamental Research Funds for the Central Universitiesthe Technology Group Project for Infectious Disease Control of Zhejiang Province (2009R50041)
文摘BACKGROUND: Orthotopic liver transplantation (OLT) is the most effective therapy for liver failure. However, OLT is severely limited by the shortage of liver donors. Bioartificial liver (BAL) shows great potential as an alternative therapy for liver failure In recent years, progress has been made in BAL regarding genetically engineered cell lines, immortalized human hepatocytes, methods for preserving the phenotype of primary human hepatocytes, and other functional hepatocytes derived from stem cells. DATA SOURCES: A systematic search of PubMed and ISI Web of Science was performed to identify relevant studies in English language literature using the Key words such as liver failure bioartificial liver, hepatocyte, stem cells, differentiation, and immortalization. More than 200 articles related to the cell sources of hepatocyte in BAL were systematically reviewed. RESULTS: Methods for preserving the phenotype of primary human hepatocytes have been successfully developed. Many genetically engineered cell lines and immortalized human hepatocytes have also been established. Among these cell lines the incorporation of BAL with GS-HepG2 cells or alginate encapsulated HepG2 cells could prolong the survival time and improve pathophysiological parameters in an animal model of liver failure. The cBAL111 cells were evaluated using the AMC-BAL bioreactor, which could eliminate ammonia and lidocaine, and produce albumin. Importantly, BAL loading with HepLi-4 cells could significantly improve the blood biochemical parameters, and prolong the survival time in pigs with liver failure. Other functional hepatocytes differentiated from stem cells, such as human liver progenitor cells, have been successfully achieved. CONCLUSIONS: Aside from genetically modified liver cell lines and immortalized human hepatocytes, other functionalhepatocytes derived from stem cells show great potential as cell sources for BAL. BAL with safe and effective liver cells may be achieved for clinical liver failure in the near future.
