Gastroenterology

X-LAT-Net: An Interpretable Lightweight Axial Transformer Network for Pancreatic CT Segmentation.

Accurate segmentation of the pancreas in CT images is notoriously difficult due to its deep-seated anatomical position, complex morphological variations, and low contrast against surrounding soft tissues. Although deep learning has significantly improved segmentation performance, state-of-the-art models often suffer from excessive parameter counts and high computational complexity, hindering their...

Perspectives from machine learning and multi-omics to decoding the effects of VDAC2 malignant subsets on tumor evolution.

VDAC2's known role in cancer and immune regulation via enhancing the CD8+ T cell-mediated killing, and it is worth systematically digging out the role of VDAC2 in pan-cancer based on this research. Bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomic analyses were utilized to explore the role of VDAC2 from multiple perspectives in pan-cancers. RT-PCR, cell co-culture, CCK-8 ...

The Gut-Liver Axis in Metabolic Dysfunction-Associated Steatotic Liver Disease: From Mechanistic Insights to Precision Therapeutics.

Metabolic dysfunction-associated steatotic liver disease (MASLD) has become the most prevalent chronic liver condition globally, shifting the diagnost...

[Machine learning and radiomics-based model for predicting response to lenvatinib combined with TACE in patients with unresectable hepatocellular carcinoma].

Objective: To evaluate the predictive value of machine learning combined with radiomics for treatment response to lenvatinib combined with transarteri...

Artificial Intelligence prognostication of liver disease using imaging.

Accurate prognostic tools in patients with chronic liver disease (CLD) have the potential to improve clinical outcomes and reduce health care costs. I...

Whole Metagenomic Profiling Identifies a Gut Microbial Signature for Chronic Pancreatitis via Machine Learning.

BACKGROUND: Pancreatitis significantly alters the microbial composition of the oral and intestinal compartments, causing dysbiosis that may contribute...

Keypoint detection network for needle localization on intra-procedural MRI in MRI-guided liver interventions.

PURPOSE: Segmentation neural networks have demonstrated promising results for interventional needle localization on MRI. However, these networks requi...

Integration of protein and metabolomic blood biomarkers enhances classification of radiation exposure in X-irradiated humanized mice.

PURPOSE: The general population is at risk of exposure to ionizing radiation due to nuclear warfare, terrorism, or radiological accidents. Such exposu...

Machine learning prediction of common bile duct stones using synthetic data to guide emergency ERCP decisions.

Endoscopic retrograde cholangiopancreatography (ERCP) remains the standard for common bile duct (CBD) stone removal. However, current guidelines often...

Deep learning-based detection of bowel sound events in continuous recordings.

Machine learning identifies a 10-gene signature predicting hepatocellular carcinoma recurrence and immune-metabolic reprogramming.

BACKGROUND: Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality, with frequent recurrence after curative treatment. Conve...

Dual-Peptide Nanoplatform: Mesoporous Silica Nanoparticles Functionalized With a Cell-Penetrating Peptide and Loaded With Rationally Designed Antimicrobial Peptides for Tuberculosis Therapy.

Tuberculosis remains a major infectious disease worldwide and demands new therapeutic approaches capable of targeting intracellular Mycobacterium tube...

Identifying active compounds and revealing integrated mechanism of phytomedicines via AI-driven chemical-biological information fusion: a case study of Weifuchun.

ETHNOPHARMACOLOGICAL RELEVANCE: Chinese phytomedicines exert holistic effects, yet their multidimensional mechanisms of action (MOAs) remain challengi...

Integrating network toxicology, machine learning, and experimental evidence reveals candidate targets and pathways in PCDD/F-related colon cancer.

Previous studies have suggested that exposure to carcinogenic polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) pollutants...

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...

Domain specific multimodal large language model for automated endoscopy reporting with multicenter prospective validation.

Accurate endoscopy reports are crucial for the diagnosis and management of patients with upper gastrointestinal (UGI) diseases, yet errors and omissio...

Network toxicology and machine learning revealed the association between non-alcoholic fatty liver disease induced by polystyrene nanoplastics.

This study systematically evaluated the hepatotoxic potential of common microplastic monomers using ADMETlab 3.0, identifying styrene (PubChem ID: 750...

Cell Painting for cytotoxicity and mode-of-action analysis in primary human hepatocytes.

Scalable, human-relevant approaches for detecting compound modes of action are needed to improve chemical safety evaluation. Here, we apply image-base...

Hypergraph Representations of Single-Cell RNA Sequencing Data for Improved Cell Clustering.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) data analysis is often performed using network projections that produce co-expression networks. The...

Artificial Intelligence for Colorectal Surgeons - Part I: Foundations, Clinical and Educational Applications.

BACKGROUND: Artificial intelligence (AI) is rapidly transforming colorectal surgery through applications spanning screening to postoperative care. Thi...