Peptic Ulcer Disease

AI-assisted multi-OMICS analysis reveals new markers for the prediction of AD.

Alzheimer's Disease (AD) is the most prevalent neurodegenerative disorder, characterized by progressive cognitive decline. Early and accurate diagnosis is crucial for improving patient outcomes, yet current diagnostic methods remain invasive, costly, and limited in accessibility. This study leverages artificial intelligence (AI) and machine learning approaches to perform a multi-omics analysis, in...

Predicting the Compressive Properties of Carbon Foam Using Artificial Neural Networks.

This article focusses on predicting the compressive properties of polyurethane-derived carbon foam using an artificial neural network (ANN) approach. To train the model, strain, pore density (20, 40, and 60 ppi), and solvents (acetone, ethanol, and methanol) were used as inputs, while compressive stress was used as output. Categorical variables like acetone, ethanol, and methanol were converted to...

Machine learning to predict de novo protein-protein interactions.

Advances in machine learning for structural biology have dramatically enhanced our capacity to predict protein-protein interactions (PPIs). Here, we r...

Deep ensemble framework with Bayesian optimization for multi-lesion recognition in capsule endoscopy images.

In order to address the challenges posed by the large number of images acquired during wireless capsule endoscopy examinations and fatigue-induced lea...

Integrative machine learning and bioinformatics analysis unveil key genes for precise glioma classification and prognosis evaluation.

Gliomas exhibit significant heterogeneity and diverse molecular subtypes, and there are marked differences in treatment strategies and prognoses for g...

Recent advancement in size measurement during endoscopy.

Accurate lesion size measurement is essential in endoscopic practice as it influences treatment strategies, surveillance decisions, and clinical outco...

Association between fibrinogen levels and prognosis in critically bleeding patients: exploration of the optimal therapeutic threshold.

BACKGROUND: Severe bleeding is a leading cause of ICU admission and mortality. Fibrinogen plays a crucial role in prognosis, yet optimal thresholds an...

Unveiling the role of IL7R in metabolism-associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approaches.

Dysregulation of hepatic metabolism is a crucial factor in the development of fatty liver disease and significantly increases the risk of hepatocellul...

Diagnostic Methods and Biomarkers in Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD) refers to a chronic inflammatory condition involving the GI tract that includes Crohn's disease (CD) and ulcerative c...

Predicting Postoperative Blood Transfusion in Elderly Patients Undergoing Total Hip and Knee Arthroplasty Using Machine Learning Models.

PURPOSE: With the aging population, the demand for total hip arthroplasty (THA) and total knee arthroplasty (TKA) has risen significantly. Elderly pat...

Enhancing gastroenterology with multimodal learning: the role of large language model chatbots in digestive endoscopy.

INTRODUCTION: Advancements in artificial intelligence (AI) and large language models (LLMs) have the potential to revolutionize digestive endoscopy by...

Galar - a large multi-label video capsule endoscopy dataset.

Video capsule endoscopy (VCE) is an important technology with many advantages (non-invasive, representation of small bowel), but faces many limitation...

Future of image enhanced endoscopy of esophageal adenocarcinoma.

Barrett's esophagus is a premalignant precursor lesion of esophageal adenocarcinoma that affects approximately 1% of the population worldwide. Esophag...

Predicting the risk of postoperative gastrointestinal bleeding in patients with Type A aortic dissection based on an interpretable machine learning model.

BACKGROUND: Gastrointestinal bleeding (GIB) is a common complication following Type A aortic dissection (TAAD) surgery, significantly impacting progno...

Transforming Gastrointestinal Diagnosis with Molecular Endoscopy: Challenges and Opportunities.

Molecular endoscopy represents a transformative advance in the detection, diagnosis, and management of gastrointestinal diseases, addressing the criti...

The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients.

BACKGROUND: Acute upper gastrointestinal bleeding (AUGIB) is one of the most common critical diseases encountered in the intensive care unit (ICU), wi...

Exploring potential diagnostic markers and therapeutic targets for type 2 diabetes mellitus with major depressive disorder through bioinformatics and in vivo experiments.

Type 2 diabetes mellitus (T2DM) and Major depressive disorder (MDD) act as risk factors for each other, and the comorbidity of both significantly incr...

Direct evaluation of antiplatelet therapy in coronary artery disease by comprehensive image-based profiling of circulating platelets.

Coronary artery disease (CAD) is a leading cause of death globally. Antiplatelet therapy remains crucial in preventing and treating CAD-associated thr...

Triazolone-Functionalized Magnetic Nanoparticles for Hemoglobin Purification and Proteomics-Driven Biomarker Discovery in Diabetic Blood.

Hemoglobin, particularly glycated hemoglobin, serves as the gold standard for diagnosing diabetes in clinical settings. In this study, a triazolinone ...

Classification of image-enhanced endoscopy in colon tumors.

Colorectal cancer accounts for 10% of global cancer cases in each year, making accurate evaluation and resection crucial. Imaging-enhanced endoscopy h...