GERD

Integration of network toxicology, machine learning and single-cell sequencing identifies candidate molecular links between air pollutants and hepatocellular carcinoma.

BACKGROUND: Epidemiological studies link long-term air pollution to an increased risk of hepatocellular carcinoma (HCC), but the underlying toxicological targets remain poorly understood. We used an integrative computational framework to identify and prioritize candidate molecular mediators potentially linking pollutant-associated gene signatures with hepatocarcinogenesis. METHODS: We retrieved po...

Decoding dry eye disease based on bioinformatics and in vitro experimental: the role of immune responses and natural product intervention.

BACKGROUND: Dry eye disease (DED), a prevalent ocular condition, has seen rising incidence rates. Aberrant inflammation and immune dysregulation are key pathogenic factors in DED. However, the underlying mechanisms linking autoimmunity to DED therapy remain incompletely understood. Herbal medicine's potential in treating DED warrants further exploration due to rendering monotherapy insufficient fo...

Recent developments in the therapeutic management of acid-related gastrointestinal diseases.

INTRODUCTION: Acid-related gastrointestinal diseases, particularly gastroesophageal reflux disease (GERD) and peptic ulcer disease (PUD), remain major...

Artificial intelligence assessment of endoscopic severity and extent: A machine learning approach to continuous evaluation of endoscopic inflammation in ulcerative colitis.

BACKGROUND: The endoscopy subscore is a therapeutic endpoint in ulcerative colitis trials but is limited by reader variability and inability to fully ...

Multi-centre generalisability of deep learning-based dose prediction for head and neck radiotherapy using DAHANCA real-world data.

INTRODUCTION: Deep learning dose prediction shows promise for automated radiotherapy planning and quality assurance in head and neck cancer (HNC). Cli...

Preliminary Analysis of Biomarkers and Molecular Mechanisms in Graves' ophthalmopathy Patients Related to the Retinol Metabolic Pathway.

OBJECTIVE: This study aimed to identify potential biomarkers for Graves' ophthalmopathy (GO) through bioinformatics analyses and to validate their exp...

The VISION-AI Trial: protocol for a pragmatic randomized controlled non-inferiority trial comparing artificial intelligence-guided colonoscopy to pancolonic chromoendoscopy for neoplasia detection in adults with colorectal inflammatory bowel disease.

BACKGROUND: Current guidelines recommend pancolonic chromoendoscopy (pCE) over white light endoscopy (WLE) alone for colorectal neoplasia (CRN) detect...

SPPIPred: Stacking-based ensemble learning model for identification of protein-protein interaction.

Protein-protein interactions (PPIs) are essential for various biological functions and are crucial in drug discovery, signaling pathways, and network ...

Understanding HFpEF Through Imaging: Strengths, Limitations, and Current State of the Art.

Heart failure with preserved ejection fraction (HFpEF) is a clinical syndrome characterized by dyspnea caused by hemodynamic congestion, which develop...

Development and validation of a routine blood test-based model to predict in-hospital postoperative pulmonary infection in older patients with hip fracture.

BACKGROUND: Postoperative pulmonary infection (PPI) is a common and serious complication in older adults undergoing hip fracture surgery, leading to p...

Noble Metal-Metal Oxide Nanohybrids as a High-Performance LDI-MS Matrix for Machine Learning-Driven Metabolic Diagnosis of Esophageal Cancer.

Esophageal cancer represents a global health challenge with a notably high incidence and poor prognosis, necessitating the development of rapid, nonin...

Precision-Driven Management of Great Saphenous Varicose Veins: A Narrative Review.

OBJECTIVES: Great saphenous vein (GSV) incompetence is common, but numerous treatment options complicate patient-treatment matching. This narrative re...

A Comparative Study of the Psychometrics and Feasibility of Artificial Intelligence Algorithms Versus the Standard Screening Methods for Oropharyngeal Dysphagia in Older Adults: A Scoping Review.

Oropharyngeal dysphagia (OD) is highly prevalent (35.6%-47.4%) in hospitalized older patients but clinical screening is slow and labor-intensive, lead...

Real-time hallucination detection and intervention in medical LLMs via calibrated hidden-state probes.

OBJECTIVE: Most hallucination mitigation for large language models (LLMs) operates post-hoc, leaving safety-critical clinical deployment without real-...

Automated Detection of Clinically Significant Mitral Regurgitation from Single-View B-mode Echocardiography Using Deep Learning.

INTRODUCTION: Mitral regurgitation (MR) is one of the most prevalent valvular heart diseases, and its diagnosis traditionally relies on Doppler echoca...

VITAGRAPH: building a knowledge graph for biologically relevant learning tasks.

The complexity of human biology poses ongoing challenges, driving global interdisciplinary research. Artificial intelligence has become a powerful too...

Identification of ITGB2, ELN, and KLRK1 as stress granule-related biomarkers of pterygium through integrated transcriptomic analysis and machine learning.

BACKGROUND: Stress granules (SGs) play a crucial role in cellular stress responses, but their involvement remains unclear in pterygium. This study aim...

AI meets sleep surgery: assessing drug-induced sleep endoscopy interpretation with a large language model.

STUDY OBJECTIVES: To evaluate the performance and safety of a large language model in interpreting drug-induced sleep endoscopy (DISE) videos and prov...

The slope of enlightenment: Towards a more realistic understanding of the strengths and weaknesses of artificial intelligence for colorectal polyp assessment.

Artificial intelligence (AI) has rapidly evolved into a transformative adjunct to gastrointestinal (GI) endoscopy, particularly through deep-learning-...

Towards transparent and reliable AI in gastroenterology: pragmatic recommendations for evaluation and reporting.

Artificial intelligence (AI) is expanding in gastroenterology, particularly in endoscopy and imaging, where models support detection, classification, ...