Gastroenterology

Identification and validation of NETs-related biomarkers in hepatocellular carcinoma through bioinformatics analysis and machine learning algorithms.

BACKGROUND: Emerging evidence highlights the significant role of Neutrophil Extracellular Traps (NETs) in hepatocellular carcinoma (HCC), but the underlying molecular mechanisms involving NETs formation remain incompletely understood. This study aims to identify key biomarkers related to NETs in HCC through bioinformatic analysis. METHODS: We obtained RNA sequencing data of hepatocellular carcinom...

Interpretable machine learning reveals phosphorus-to-albumin ratio as a novel predictor of mortality and acute kidney injury in critically ill pancreatitis patients: a multi-center retrospective analysis.

Acute kidney injury (AKI), a common and severe complication of acute pancreatitis (AP), is amenable to early intervention. Phosphorus-to-albumin ratio (PAR) is a novel composite biomarker unexplored in AP largely. Data of ICU patients with AP were extracted from MIMIC-IV database and eICU-CRD, respectively. PAR's link to prognosis and AKI in AP were analyzed via Kaplan-Meier curves, Cox regression...

Polyethylene Terephthalate Microplastics Exposure Enhances the Risk of Ulcerative Colitis: Insights From Multi-Omics Integration, Machine Learning, and Molecular Docking Reveal Intestinal Toxicity Mechanisms: A Commentary.

Decoding Protein-Membrane Binding Interfaces from Surface-Fingerprint-Based Geometric Deep Learning and Molecular Dynamics Simulations.

Predicting protein-membrane interactions is a formidable challenge due to the subtle physicochemical features that distinguish membrane-binding region...

Development of a Machine-Learning Model for Predicting Postoperative Complication Occurrence After Radical Gastrectomy Using Electronic Medical Records.

This study aims to develop a machine-learning model using electronic medical records to predict postoperative complications after radical gastrectomy ...

Construction of immune related genes predictive models of elderly rectal cancer patients based on machine learning.

Single cell RNA sequencing analysis and molecular biology experiments identify NAFLD-related fibroblasts and highlights seven hub genes in NAFLD.

BACKGROUND: The Nonalcoholic fatty liver disease (NAFLD) is a major chronic liver condition, with its pathology remaining elusive. Consequently, a tho...

Real-world Study of Three-gas Breath Testing Nationwide and the Association With Symptoms.

GOALS AND BACKGROUND: Hydrogen (H 2 ) breath testing (BT) is used to diagnose small intestinal bacterial overgrowth (SIBO), and methane (CH 4 ) to dia...

Artificial intelligence for early detection of pancreatic cancer in prediagnostic and diagnostic computed tomography examinations: A multicenter retrospective case-control study.

PURPOSE: The purpose of this study was to develop and validate a computer-aided detection (CAD) tool for the detection of pancreatic cancer (PC) on di...

Machine Learning-based Prediction of Mortality Among Malnourished Patients Hospitalized With Inflammatory Bowel Disease.

BACKGROUND: Malnourished patients hospitalized with inflammatory bowel disease (IBD) have a high risk of morbidity and mortality. Risk stratification ...

Knowledge-Augmented Patient Network Embedding-Based Dynamic Model Selection for Predictive Analysis of Pediatric Drug-Induced Liver Injury.

OBJECTIVE: To address the challenges of developing machine learning frameworks for Electronic Health Records (EHRs)-based predictive tasks, such as th...

GI-ScreenNet v2: A Modular Framework for Gastrointestinal Disease Detection Based on an Integrated Transfer Learning.

BACKGROUND: Gastrointestinal diseases pose a significant global health challenge, and their early screening relies on wireless capsule endoscopy (WCE)...

Genetic Information Enhances a Novel Non-Invasive Steatosis Index and Outperforms Existing Steatosis Indices.

BACKGROUND AND AIMS: Early detection of individuals with metabolic dysfunction-associated steatotic liver disease (MASLD) is important as intervention...

AI-driven ANN and RSM-CCD integrated optimization of cinnarizine-domperidone bilayer tablet: In-vitro evaluation and in-silico PBPK modeling using GastroPlus®.

BACKGROUND: Response surface methodology coupled with the design of experiments identifies the optimal response surface function related to selected i...

Stereo Vision-Based Control of Continuum Robots and Hyper-Redundant Robots for Endoscopy.

BACKGROUND: Traditional control of continuum robots (CRs) and hyper-redundant robots (HRRs) typically relies on large, costly consoles to capture phys...

Endoscopic Diagnosis of Eosinophilic Esophagitis Using a Multi-Task U-Net: A Pilot Study.

PURPOSE: Endoscopically identifying eosinophilic esophagitis (EoE) is difficult due to its rare incidence and subtle morphology. We aimed to develop a...

Impact of Artificial Intelligence for Detection of Precancerous Colonic Lesions in a Fecal Immunochemical Blood Test-Based Organized Screening Program in Italy: A Randomized Control Trial.

BACKGROUND: The fecal immunochemical test (FIT) is widely implemented as a first-line tool in organized colorectal cancer (CRC) screening programs, in...

Exploring key genes in NAFLD linked to glutamine metabolism: A comprehensive analysis combining multi-omics, machine learning and SHAP.

BACKGROUND AND OBJECTIVES: Non-alcoholic fatty liver disease (NAFLD) is a prevalent liver condition glob-ally, with an escalating incidence and a stro...

Beam angle optimization for radiotherapy using LLMs via reinforcement-learning inspired iterative refinement.

BACKGROUND: Radiotherapy treatment planning (TP) aims to maximize radiation dose delivered to tumors while minimizing exposure to surrounding healthy ...

A segmentation method with a large vision model for magnetic resonance imaging-guided adaptive radiotherapy.

BACKGROUND: Segmentation is the most effort-consuming step for magnetic resonance imaging guided adaptive radiotherapy (MRIgART). Although the segment...