GERD

Development and validation of a novel blood-based biomarker for gastric cancer triage in chronic dyspepsia.

Global implementation of gastric cancer (GC) screening in chronic dyspepsia populations faces challenges due to the high number-needed-to-scope (NNS) for oesophagogastroduodenoscopy. Routine blood tests (RBT) have limited utility for GC screening but offer potential for risk stratification when repurposed through machine learning. This study develops and validates a machine-learning-integrated bio...

Novel biomarkers and multi-omics approaches for diagnosis and management of ulcerative colitis.

BACKGROUND: Ulcerative colitis (UC) remains challenging to diagnose, monitor, and treat due to heterogeneous disease presentation and lack of reliable non-invasive markers. Current diagnostic tools, including endoscopy and routine laboratory tests, are limited by invasiveness, cost, and low sensitivity. AIM: This review evaluates emerging biomarkers and multi-omics strategies in UC, highlighting t...

Automated echocardiographic detection of mitral valve prolapse and mitral regurgitation with video-based artificial intelligence algorithms.

AIMS: We aimed to develop and evaluate fully automated artificial intelligence (AI) system for detection of mitral valve prolapse (MVP) and mitral reg...

AI-assisted transformation of PD-L1 inhibitory peptides into small molecules using amino acid mapping descriptor and activity improvement through structure-based drug design.

We present a novel strategy for the conversion of macrocyclic peptides into small molecules to identify potent and membrane-permeable protein-protein ...

HopWD-DTA: a novel framework for drug-target affinity prediction fusing multi-hop neighborhoods and deep features.

CONTEXT: Accurate prediction of drug-target affinity (DTA) is crucial for accelerating drug discovery, but it remains a significant challenge. While d...

Using Machine Learning to Improve Screening for Oropharyngeal Dysphagia in Hospitalized Versus Primary Care Adult Patients With COVID-19 Disease: Retrospective Observational Study.

BACKGROUND: Oropharyngeal dysphagia (OD) commonly occurs in patients with COVID-19 disease, posing diagnostic challenges due to isolation protocols. O...

Explainable machine learning for predicting mechanical ventilation in Alzheimer's patients with pneumonia: a SHAP-guided XGBoost nomogram based on MIMIC-IV.

Alzheimer's disease (AD) patients are particularly vulnerable to pneumonia and subsequent respiratory failure due to neurodegeneration-induced dysphag...

Targeting protein protein interactions and their modulators to enable new therapeutic strategies for human diseases.

Protein-protein interactions (PPIs) are essential for cellular processes and play central roles in disease mechanisms, making them important therapeut...

Parsimonious machine learning model to predict 1-year mortality and procedural futility after transcatheter aortic valve replacement.

BACKGROUND: Current risk scores inadequately predict long-term mortality after transcatheter aortic valve replacement (TAVR), limiting their ability t...

Towards a neuro-symbolic approach for precision anti-reflux surgery.

Artificial intelligence and image processing framework for automated airway invasion detection and residue classification from swallowing endoscopy.

Fiberoptic endoscopic evaluation of swallowing (FEES) is a widely used instrumental method for dysphagia, but its interpretation depends on lighting a...

Integrative bioinformatics and machine learning identify shared molecular mechanisms and diagnostic biomarkers between Helicobacter pylori infection and atrial fibrillation.

BACKGROUND: Helicobacter pylori (H. pylori) infection and atrial fibrillation(AF) are major global health concerns. Emerging evidence has suggested a ...

Quantum-enhanced learning: Leveraging von Neumann entropy for enhanced graph neural network performance.

Graph Neural Networks (GNNs) have established themselves as powerful tools for learning from graph-structured data. However, their reliance on local m...

Volatomics-based biomarkers for non-invasive diagnosis and monitoring of inflammatory bowel disease.

BACKGROUND: Inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis, is a chronic disorder that markedly impairs quality of...

Artificial intelligence (AI) for early identification of radiotherapy related toxicities from the electronic health records of patients with head and neck cancer.

We evaluated artificial intelligence (AI) for detecting osteoradionecrosis, fibrosis, trismus, and dysphagia in 207 head and neck cancer patient elect...

Magnetically controlled capsule endoscopy: Innovations, standardized practice, and emerging clinical roles.

Capsule endoscopy has transformed small bowel evaluation but remains limited for gastric examination because of passive, peristalsis dependent movemen...

Intraoperative Transesophageal Echocardiography in Cardiac Surgery: Clinical Applications, Decision-Making, and Impact on Outcomes.

Intraoperative transesophageal echocardiography (TEE) has become a central component of modern cardiac surgery by providing real-time, high-resolution...

Development and validation of a machine learning model for predicting 6-month mortality in patients with infective endocarditis.

OBJECTIVE: This study aimed to develop and validate a machine learning (ML)-based model for predicting 6-month all-cause mortality in patients diagnos...

Integrated bioinformatics reveals tubulointerstitial immune microenvironment signatures and machine learning-driven prognostication of clinical treatment response in lupus nephritis.

Lupus nephritis (LN) represents the most severe renal manifestation of systemic lupus erythematosus (SLE), contributing to significant morbidity. Whil...

Construction of an interpretable multimodal image model for differentiating T1-stage nasopharyngeal carcinoma from benign hyperplasia.

BACKGROUND AND OBJECTIVE: Differentiating T1-stage nasopharyngeal carcinoma (NPC) from benign hyperplasia (BH) is challenging. This study aims to cons...