Hepatitis

Neoantigen Targeting as a Novel Approach for Therapy-Resistant Tumors.

Neoantigens have emerged as central targets in the development of individualized cancer vaccines, as they are recognized by the immune system as foreign and can elicit potent anti-tumor responses. Advances in computational algorithms and machine learning have improved the accuracy of neoantigen prediction, and clinical trials have reported promising results of vaccines incorporating validated neoa...

Nanoparticle-based immunotherapeutic strategies to overcome cancer drug resistance: From biological barriers to artificial intelligence-driven design.

Although cancer immunotherapy has revolutionized oncology, its clinical efficacy remains substantially limited by both primary and acquired resistance. These resistance mechanisms are largely driven by complex biological barriers within the tumor microenvironment (TME) and insufficient tumor immunogenicity. Nanotechnology offers a promising strategy to overcome these barriers by enabling precise s...

Univariate- and machine learning-based plasma metabolite signature differentiates PSC-IBD from IBD and is predicted to be driven by gut microbial changes.

INTRODUCTION: Inflammatory bowel disease (IBD) is a group of chronic inflammatory conditions of the gastrointestinal tract comprising two major phenot...

DoTT-ML: Condition-aware detection of transcriptional readthrough from RNA-seq with optional ML-based prioritization.

Disruption of transcription termination (DoTT) occurs when RNA polymerase II reads past a gene's normal 3' end, generating downstream "readthrough" RN...

AI-driven body composition atlas reveals its association with NSCLC immunotherapy outcome and molecular background: a multicenter study.

Although previous studies have linked body composition to immunotherapy efficacy, comprehensive multidimensional analyses with biological explanations...

An improved dataset for predicting mammal infecting viruses from genetic sequence information.

There have been several attempts to develop machine learning (ML) models to identify human infecting viruses from their genomic sequences, with varyin...

Lessons learned from bat and rodent reservoir hosts of zoonotic viruses.

Zoonotic virus emergence is a continued threat to global health, though more than two-thirds originate in wildlife. Within natural reservoirs, bats an...

Computational Discovery of MERS-CoV Main Protease Inhibitors Through Screening and Molecular Dynamics Simulations.

Targeting the main protease (Mpro) of coronaviruses has emerged as a promising therapeutic strategy for combating viral infections. Despite the global...

SGA-DT: An adaptive fusion framework for missing data imputation and interpretable healthcare classification.

Despite advances in machine learning and medical data processing, handling missing values remains a critical and complex challenge in healthcare analy...

Evaluation of large language models in supporting autoimmune liver disease diagnosis and clinical decision-making: advantages of reasoning-based models.

INTRODUCTION: Large-language models (LLMs) have demonstrated increasing potential in healthcare, including applications in clinical information retrie...

Quantitative regression of qFibrosis with resmetirom: Exploratory histologic endpoints from the MAESTRO-NASH phase III clinical trial.

BACKGROUND & AIMS: Resmetirom, a thyroid hormone beta (THR-β) agonist, has been shown to improve metabolic dysfunction-associated steatohepatitis (MAS...

Clinical prediction of the mortality for acute kidney injury in decompensated cirrhosis.

Acute kidney injury (AKI) is a severe complication in patients with decompensated cirrhosis, associated with increased mortality. This study aimed to ...

Predicting the Risk of Decompensation in Patients with Cirrhosis - a Validated Machine Learning Approach.

INTRODUCTION: There is an unmet need for simple tools to predict development of hepatic decompensation among patients with compensated cirrhosis. We a...

Optimising pandemic response through vaccination strategies using neural networks.

Epidemic risk assessment poses inherent challenges, with traditional approaches often failing to balance health outcomes and economic constraints. Thi...

Severe COVID-19 in the Republic of Korea: Epidemiology, Risk Factors, Therapeutics, and Prognostic Models From Nationwide Data.

Severe coronavirus disease 2019 (COVID-19) has posed ongoing clinical and public health challenges worldwide, with Korea providing a unique perspectiv...

Artificial Intelligence in hepatology: A position paper by the Italian Association for the Study of the Liver.

Artificial Intelligence (AI) is transforming medicine, providing unprecedented opportunities to enhance diagnosis, prognosis, and treatment across all...

Predictive value of Hypotension Prediction Index in liver transplantation: a prospective study.

BACKGROUND: Intraoperative hypotension (IOH) is associated with increased postoperative morbidity and mortality. This prospective study evaluated the ...

Stage-dependent gut microbiome and functional signatures across the liver disease spectrum: an integrative multicohort study.

BACKGROUND: The gut-liver axis plays a critical role in liver disease progression; however, how gut microbial ecology and function vary across disease...

GCN combined with snake convolution for enhanced topological perception in thrombotic hepatic portal vein segmentation.

The hemodynamic status of the portal vein plays a crucial role in the identification, treatment, and prognostic prediction of complications associated...

Endothelial cell senescence shapes T cell activity in late-stage of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a leading cause of death with few effective therapies. While clinical staging distinguishes mild to ve...