Inflammatory Bowel Disease

Effects of biologic and targeted synthetic disease modifying antirheumatic drugs (b/tsDMARDs) on patient-reported outcome domains in rheumatoid arthritis: a systematic review and network meta-analyses.

INTRODUCTION: Evidence from head-to-head comparisons of biologic/targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) on patient-reported outcomes (PROs) in patients with rheumatoid arthritis (RA) is lacking. This systematic review and network meta-analysis (NMA) aims to evaluate the relative effects of b/tsDMARDs on the International Consortium for Health Outcomes Measurement (IC...

Identification of small-molecule TNF-α inhibitor candidates using machine learning-guided screening and multiscale molecular modelling.

Lumbar disc herniation (LDH) is a major cause of chronic low back pain, in which tumor necrosis factor-alpha (TNF-α) plays a central role in inflammation and pain signaling. While biologic TNF-α inhibitors have shown therapeutic benefit, their systemic administration, high cost, and limited penetration into the avascular disc environment restrict their clinical utility. In this study, we present a...

Predicting High-Risk Colorectal Polyps in African Americans Using Pre-colonoscopy Clinical Features: Machine Learning Model Development and Temporal Validation.

PURPOSE: Risk stratification for advanced colorectal polyps typically relies on colonoscopy and/or pathology findings. However, there is growing inter...

Gastrointestinal endoscopic image classification using a hybrid modified inception network and a customized vision transformer with tree growth feature selection.

The Gastrointestinal (GI) tract plays a vital role in digestion by breaking down food into essential nutrients. Disorders such as bleeding lesions, ul...

Artificial intelligence in pediatric endoscopy for hereditary polyposis syndromes: promises and challenges.

PURPOSE OF REVIEW: Children with hereditary polyposis syndromes require long-term endoscopic surveillance to reduce risks of gastrointestinal complica...

Integrating neurobiological markers to prospectively predict adolescent non-suicidal self-injury and suicide attempts: a machine learning approach.

BACKGROUND: Suicide attempts (SA) and non-suicidal self-injury (NSSI) are closely related phenomena, both common in adolescent clinical samples. This ...

Optimising biologic therapy in psoriasis: dose adjustment, treatment duration, and tapering-an expert perspective.

INTRODUCTION: Biologic therapies have transformed the management of moderate-to-severe psoriasis; however, long-term treatment requires optimization s...

Development and validation of the SYSU-score for MRI-based transmural healing assessment in Crohn's disease: a dual-center study.

OBJECTIVES: Standardizing magnetic resonance enterography (MRE)-defined transmural healing (TH) remains challenging in Crohn's disease (CD) despite it...

IBS-CNN: a novel computer vision-based kinship classification model for high-density SNP microarray.

This study addresses multiple challenges in applying forensic genealogy to Chinese populations by exploring novel kinship classification strategies ba...

FD-MSP: feature decoupling network with multi-scale prototypes for domain-adaptive polyp segmentation.

Polyp segmentation in colonoscopy images plays a critical role in the early detection and treatment of colorectal cancer. Although deep learning-based...

Automated extraction of colonoscopy quality indicators from free-text reports using a prompt-based large language model pipeline.

BACKGROUND AND AIMS: Artificial intelligence has increasingly enabled large-scale analysis of clinical documentation, offering new opportunities to im...

Single-cell transcriptomic analysis reveals the T lymphocyte microenvironment and identifies HOPX as a hub gene in membranous nephropathy.

BACKGROUND: Membranous nephropathy (MN) is an autoimmune disease characterized by immune complex deposition and progressive renal function impairment....

Radiomics-based unsupervised clustering and deep learning nomogram for response prediction after neoadjuvant chemotherapy in locally advanced laryngeal cancer.

OBJECTIVES: To develop a nomogram model for individualized prediction of neoadjuvant chemotherapy (NAC) response in locally advanced laryngeal cancer ...

[Construction and validation of a prognostic prediction model for children-onset initial steroid-resistant nephrotic syndrome].

Objective: To analyze the risk factors for poor prognosis in children with steroid-resistant nephrotic syndrome (SRNS) and to construct and validate a...

Fecal and colonic microbiota differ in composition, function, diagnostic utility and correlation with inflammatory cytokines in ulcerative colitis.

Fecal samples have been frequently used to investigate ulcerative colitis (UC)-related gut microbiota study due to their easy availability. However, w...

Investigating Immune Microenvironment of Inflammatory Bowel Disease and the Mitochondrial Metabolism-Related Molecular Mechanisms: Combining a Multiomics Approach and Experimental Validation.

Inflammatory bowel disease (IBD) is a chronic and recurrent gastrointestinal disease, the pathogenesis of which has not been fully elucidated. Increas...

Heterogeneous developmental trajectories and predictors of self-harm in early adolescence: A machine learning study.

BACKGROUND: Although prior studies have examined developmental trajectories of adolescent self-harm in terms of frequency and severity, a fundamental ...

Machine learning-based identification of a metabolic cell death gene signature for assessing disease activity and immunological landscape in inflammatory bowel disease.

Inflammatory bowel disease (IBD) is characterized by a refractory, relapsing inflammatory state driven by a multifaceted and poorly understood interpl...

Investigation of the molecular network underlying PET-MPs-induced inflammatory bowel disease via integrated machine learning and molecular docking approaches.

Polyethylene terephthalate microplastics (PET-MPs), as environmental contaminants, have raised significant concerns due to their potential toxicity, l...

Machine learning prediction of childhood nephrotic syndrome outcomes.

BACKGROUND: Children with steroid-resistant, frequently relapsing, and steroid-dependent nephrotic syndrome experience high disease and treatment-rela...