Liver Transplantation

Hepatology e-consult responses generated by artificial intelligence demonstrate accuracy but require human oversight.

BACKGROUND: Electronic consultations (e-consults) improve specialist access but burden providers. We developed LiVersa, a customized large language model (LLM) for liver diseases. We evaluated its performance in drafting hepatology e-consult responses and the equivalence between human and machine reviewers. METHODS: LiVersa-generated responses for hepatology e-consults answered at the University o...

Dissecting T-cell exhaustion heterogeneity and immune ecosystem dynamics in colorectal cancer through multi-omics machine learning.

BACKGROUND: Immunotherapy has shown limited efficacy in a substantial subset of CRC patients, yet the mechanisms underlying therapeutic resistance remain incompletely understood. T-cell exhaustion (TEX) in the tumor microenvironment has been identified as a pivotal driver of immune evasion and tumor progression. Dissecting its contribution to CRC is essential for the development of rational therap...

Multi-Omics Analysis Identifies Paxillin as a Biomarker of Doxorubicin Resistance via Cytoskeletal Remodeling and Immune Exhaustion.

Chemotherapy resistance remains a primary cause of treatment failure in breast cancer, yet the global proteomic landscape driving this phenotype has n...

Human serum albumin profiling by top-down analysis enables multi-class liver fibrosis staging: a cross-platform validation study.

Chronic liver disease (CLD) affects millions worldwide, yet accurately staging its progression without liver biopsy remains a major clinical challenge...

Immunologic clustering of donor-specific antibodies and clinical outcomes in kidney transplant recipients.

BACKGROUND: Donor-specific antibodies (DSA) against human leukocyte antigens (HLA) are associated with increased immunologic risk in kidney transplant...

Mechanism-Based Multitarget Modeling for Pathway-Level Prediction of PI3K/Akt Signaling Perturbation Induced by Liquid Crystal Monomers.

Liquid crystal monomers (LCMs) are emerging contaminants whose system-level toxicity mechanisms remain poorly understood. Here, we developed a pathway...

Machine learning model based on clinical and imaging features for predicting fungal infections in children with leukemia.

BACKGROUND: Due to immunosuppression, mucosal barrier injury, and prolonged neutropenia resulting from both the disease and chemotherapy, along with t...

High performance deep-learning model for the diagnosis of auto-immune hepatitis based on histological whole slide images.

Autoimmune Hepatitis (AIH) is a liver disease with a wide clinical spectrum, driven by an abnormal immune response against the liver parenchyma. Chall...

A donor-recipient ranking model to optimize long-term survival post liver transplant.

Liver transplant (LT) is a life-saving treatment for patients with cirrhosis and/or hepatocellular carcinoma (HCC), but organ shortages and suboptimal...

Transcriptomic profiling identifies immunotherapy-responsive phenotypes in microsatellite-stable metastatic colorectal cancer.

Conventional immune checkpoint inhibitors (ICIs) remain largely ineffective in microsatellite-stable metastatic colorectal cancer (MSS mCRC), where lo...

Dissecting PCD-driven molecular landscapes in AML: a multi-omic framework for prognostication and therapeutic targeting.

Acute myeloid leukemia (AML) remains a molecularly heterogeneous malignancy with poor prognosis, necessitating robust biomarkers for risk stratificati...

Multi-omics analysis identifies GINS1 as a prognostic biomarker in lung adenocarcinoma, linked to macrophage polarization and tumor cell survival.

This study aimed to identify essential genes driving lung adenocarcinoma (LUAD) progression by integrating CRISPR-Cas9 dependency data from the Cancer...

Machine learning-based identification of basement membrane-related signature to predict recurrence and immunotherapy benefit in bladder cancer.

The basement membrane (BM) plays a critical role in regulating bladder cancer (BC) progression. However, a BM-related signature for predicting BC recu...

A myeloid immunosuppressive phenotype defines primary refractoriness to atezolizumab Plus bevacizumab in hepatocellular caarcinoma.

BACKGROUND: Despite improved outcomes with atezolizumab plus bevacizumab (A+B) in hepatocellular carcinoma (HCC), primary refractoriness (PRef), chara...

RRBP1 promotes osimertinib resistance by activating fatty acid oxidation-dependent metabolic and immune reprogramming in EGFR-Mutant lung adenocarcinoma.

Osimertinib is a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that improves clinical outcomes in patients with E...

Infections in Burn Patients: Pathophysiology, Prevention, and Contemporary Therapeutic Strategies in the Era of Antimicrobial Resistance.

Burn injuries are associated with significant morbidity and mortality, largely driven by infectious complications. Disruption of the skin barrier, sys...

A decade of progress towards the elimination of hepatitis C: from the National Strategic Plan to Artificial Intelligence.

Geospatial variation and machine learning approaches to predict open defecation practice in Zambia.

BACKGROUND: Open defecation is the disposal of human feces in fields, bushes, forests, open waterways, beaches, and other open areas. It worsens the e...

Targeting colorectal cancer stem-like cells: Overcoming therapeutic resistance and reprogramming the immunosuppressive niche.

Colorectal cancer stem-like cells (CRC-SCs) represent a tumor subpopulation with enhanced tumor-initiating capacity and have been implicated in metast...

Prediction of ICU length of stay, hospital discharge outcomes and discharge location among ICU-admitted patients diagnosed with viral hepatitis using machine learning: a retrospective cohort study of the MIMIC-IV database.

BACKGROUND: Hepatitis, a disease characterised by inflammation of the liver, is a leading global health challenge that contributes to over 1.3 million...