Peptic Ulcer Disease

Macrocyclic peptides as protein-protein interaction modulators: a review.

Protein-protein interactions (PPIs) govern essential cellular processes, such as signal transduction, enzymatic catalysis, and the formation of macromolecular complexes, but their broad and dynamic interfaces often lack deep binding pockets, making it difficult for them to target conventional small molecules. Macrocyclic peptides have emerged as a powerful alternative, bridging the gap between sma...

Methods for Mapping and Analyzing Context-Specific Protein-Protein Interaction Networks.

Protein-protein interactions (PPIs) form highly dynamic and context-specific networks that vary across cell types and perturbation states. Mapping and understanding these context-dependent interactomes is essential for unraveling cellular organization and function. This review examines computational and experimental approaches for mapping and analyzing context-specific PPI networks. Computational ...

Artificial Intelligence-assisted Periapical Radiographic Assessment: Lesion Detection, Endodontic Complication Analysis, and Review of Clinical Treatment Recommendations.

INTRODUCTION: Artificial intelligence (AI) systems are increasingly used in dental radiology to support endodontic diagnosis. However, their diagnosti...

High-precision classification of WCE-based gastrointestinal abnormality using a fusion deep learning approach.

Gastrointestinal abnormalities are widespread worldwide and pose a significant health challenge. However, their mortality rate can be significantly re...

MechMemDyn: Coupling Frustration Analysis with Membrane Dynamics to Target the TREM2-DAP12 Complex Interface.

The transmembrane complex formed by TREM2 (Triggering Receptor Expressed on Myeloid cells 2) and DAP12 (DNAX-activating protein of 12 kDa) constitutes...

[Bottlenecks and breakthroughs in gastric cancer diagnosis and treatment: Towards a new era of precision and intelligent integration].

Gastric cancer constitutes a significant global health burden, and its clinical management is undergoing a critical transition from a traditional expe...

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) ...

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...

Artificial Intelligence in Haemophilia Care: A Narrative Review of Current Evidence and Future Opportunities.

Artificial intelligence (AI) is increasingly explored in healthcare for its capacity to analyse complex data, support clinical decision-making and ena...

Integrating Next-Generation Sequencing into von Willebrand Disease Diagnostics: Insights from the PCM-EVW-ES Multicenter Project.

Von Willebrand disease (VWD) is the most common inherited bleeding disorder, caused by quantitative or qualitative defects in von Willebrand factor (V...

Machine learning for prognostic assessment in elderly patients with gastroduodenal perforation.

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 ...

In-hospital outcomes and cost-effectiveness of transcatheter aortic valve replacement among younger patients: a double/debiased machine learning approach using electronic health records in Germany.

BACKGROUND: The prevalence of severe symptomatic aortic stenosis is increasing with population aging. Although surgical aortic valve replacement (SAVR...

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...

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...

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...

Machine Learning in Predicting the Risk of Esophagogastric Variceal Bleeding Among Patients With Liver Cirrhosis: Systematic Review and Meta-Analysis.

BACKGROUND: Liver cirrhosis (LC) can lead to several complications. Esophageal variceal bleeding (EVB) and esophagogastric variceal bleeding (EGVB) ar...