Liver Transplantation

Identification of clinical phenotypes and disease trajectories in SLE using AI through a natural language processing framework.

OBJECTIVES: Electronic Health Records (EHRs) contain a wealth of unstructured patient data that can be leveraged using Artificial Intelligence (AI). This study aimed to develop a Natural Language Processing (NLP)pipeline to identify clinical phenotypes and disease trajectories in patients with Systemic Lupus Erythematosus (SLE) from EHRs. METHODS: EHR data from SLE patients were included. A standa...

Advancements and challenges in CAR-NK cell therapy for cancer treatment.

Chimeric antigen receptor natural killer (CAR-NK) cell therapy is an emerging cancer treatment offering advantages over CAR-T therapy, including reduced risks of cytokine release syndrome and graft-versus-host disease. This review highlights how genetic engineering enhances natural killer cells' innate tumor-killing abilities through CAR expression. Preclinical and clinical studies show promising ...

Association of cardiovascular-kidney-metabolic syndrome stages with MASLD prevalence and liver fibrosis severity: evidence from traditional and machine learning approaches.

INTRODUCTION: Cardiovascular-Kidney-Metabolic (CKM) syndrome reflects the convergence of cardiovascular, renal, and metabolic disorders. Metabolic dys...

Interpretable prognostic modeling of glioblastoma using cross-cohort transcriptome integration and machine learning approaches.

Glioblastoma (GBM) is an aggressive brain tumor with highly variable patient outcomes due to pronounced molecular heterogeneity. Prognosis remains dis...

Hydrogel-enabled drug delivery for ulcerative colitis.

Ulcerative colitis (UC) represents a chronic, relapsing inflammatory condition primarily affecting the colonic and rectal mucosa, presenting substanti...

The dynamics of hepatitis B virus transmission using a stochastic differential model and feed forward neural network approach.

Hepatitis B transmission is influenced by environmental variability, immune response and vaccination, making its spread inherently stochastic, while t...

Enhancing Inpatient Mortality Prognostication in Cirrhosis Patients Through Machine Learning Techniques.

Machine Learning in Cirrhosis: Moving From Static Admission Risk to Dynamic Decision Support.

Noninvasive Staging of Hepatic Fibrosis in Patients with Autoimmune Liver Disease Using Deep Learning.

RATIONALE AND OBJECTIVES: Accurate staging of hepatic fibrosis is essential for guiding immunosuppressive and antifibrotic therapies. However, percuta...

Multi-modal AI for opportunistic screening, staging and progression risk stratification of steatotic liver disease.

The global rise in steatotic liver disease poses a significant public health challenge. While non-contrast computed tomography scans hold promise for ...

Metabolomics-guided machine learning reveals diagnostic and mechanistic biomarkers in CHB with MASLD.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) often coexists with chronic hepatitis B (CHB), yet early diagnosis remain...

Exploring the toxicological mechanisms of Benzo[a]anthracene (BaA) exposure in lung adenocarcinoma (LUAD) via network toxicology, machine learning, and multi-dimensional bioinformatics analysis.

BACKGROUND: Lung adenocarcinoma (LUAD) is a major lung cancer subtype influenced by environmental factors. Benzo[a]anthracene (BaA), a common Group 2B...

Detection of esophageal varices and prediction of hepatic decompensation in unresectable hepatocellular carcinoma using AI.

BACKGROUND & AIMS: In hepatocellular carcinoma (HCC) with cirrhosis, portal hypertension worsens outcomes. Esophagogastroduodenoscopy (EGD), the curre...

Development and validation of a prognosis model for low-grade gliomas based on metabolic gene risk scoring and immune microenvironment interaction.

PURPOSE: Low-grade gliomas(LGGs) show significant clinical and molecular heterogeneity, complicating progression prediction with conventional indicato...

A novel integrative machine learning-based prognostic model reveals lactylation regulation in hepatocellular carcinoma progression.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignant liver tumor with poor clinical outcomes. Accumulated evidence has demon...

Development of a hybrid deep learning-based framework for liver fibrosis classification using ultrasound images.

BACKGROUND AND AIMS: Liver fibrosis is a progressive accumulation of extracellular matrix proteins with distortion of hepatic architecture and can pro...

Development and validation of a machine learning stratified prediction model for early warning of anti-tuberculosis drug-induced liver injury risk based on real-world data: a retrospective cohort study.

OBJECTIVE: This study aimed to develop and validate stratified machine learning models for early prediction of anti-tuberculosis drug-induced liver in...

Updates on Kidney Transplantation From the World Transplant Congress 2025.

The World Transplant Congress 2025 showcased pivotal advances shaping the future of transplantation. Global efforts to expand access to transplantatio...

Exfoliated kidney cells from urine for non-invasive kidney transplant monitoring: A potential opportunity?

Kidney transplantation is usually the optimal treatment option for patients living with kidney failure given its associations with improved survival, ...

Risk prediction in IgA nephropathy: from conventional models to machine learning, deep learning, and precision nephrology.

IgA nephropathy (IgAN) is the most prevalent primary glomerular disease worldwide and a leading cause of end-stage kidney disease (ESKD). Its clinical...