Peptic Ulcer Disease

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 tool in computational biology, where graph data structures model entities like protein-protein interaction (PPI) networks and gene functional networks. These networks support crucial tasks in network medicine, including gene-disease association predicti...

Transformer-DAPT: AI-based dynamic assessment of ischemic and bleeding risks in patients on DAPT following PCI.

Dual antiplatelet therapy (DAPT) following percutaneous coronary intervention (PCI) is traditionally guided by rule-based scores providing static, single-time-point or fixed time-interval estimates with modest discrimination (C-index 0.63-0.73), limiting individualized DAPT duration decisions. We developed Transformer-DAPT, a transformer-based deep learning survival framework designed to estimate ...

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...

From Prediction to Action: Why Bleeding Risk in Cardiac Surgery Still Eludes Us in the Era of Artificial Intelligence.

Perioperative bleeding in cardiac surgery remains common, consequential, and difficult to predict despite decades of research and expanding data avail...

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-...

Evaluation of deep learning-based reconstruction models on non-TOF BGO PET/CT: impact of acquisition times and BSREM penalization factors on lesion detectability and SNR.

BACKGROUND: New long field-of-view (FOV) PET scanners using bismuth germanate (BGO) detectors without time-of-flight (TOF) capability are now availabl...

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, ...

Integrating epigenetic and metabolic indicators for non-invasive endometrial cancer triage: a machine learning approach.

OBJECTIVE: Abnormal uterine bleeding (AUB) is a primary symptom indicative of endometrial cancer (EC), yet its diagnosis still primarily relies on inv...

A vision transformer deep learning model for assessing pediatric ileocolic intussusception severity using ultrasound images.

Timely identification of children with ileocolic intussusception likely to fail air-enema reduction is critical to avoid delays and bowel perforation....

Identification of FOXK1 and SEMA7A as key genes associated with m6A-related programmed cell death in diabetic retinopathy.

BACKGROUND: Diabetic retinopathy (DR), a serious microvascular complication of diabetes, has a complex pathogenic mechanism that is intricately linked...

Bioinformatics analysis reveals the association of bile acid metabolism-related genes with sepsis.

BACKGROUND: Sepsis, is a life-threatening syndrome triggered by infection. Bile acid metabolism may be involved in the pathogenesis of sepsis, the und...

Evaluation of Large Language Models in the Clinical Management of Patients With Upper Gastrointestinal Bleeding: Insights From Real-World Patient Data.

OBJECTIVE: To evaluate large language models (LLMs) for pre-endoscopy (PE) risk stratification and prediction of endoscopic findings in upper gastroin...

Metal-organic frameworks (MOFs) for nonlinear optical properties: design principles, DFT insights, and future directions for photonic applications.

Metal-organic frameworks (MOFs) have emerged as a uniquely versatile platform for nonlinear optical (NLO) applications, combining the large hyperpolar...

Personalized antiplatelet therapy: are we ready for a precision medicine approach.

Dual antiplatelet therapy (DAPT), combining aspirin with a P2Y12 inhibitor, remains central to secondary prevention after acute coronary syndrome and ...

Bioinformatics analysis reveals the characteristics of immune microenvironment in major depressive disorder and vitiligo.

BACKGROUND: Major depressive disorder (MDD) and vitiligo often occur together, worsening patient outcomes. However, the shared pathogenic mechanisms r...

Mitochondrial dysfunction in peri-implantitis: bioinformatics and machine learning analysis with in vivo experiment.

This study aimed to identify mitochondria-related hub genes in peri-implantitis and to detect their expression in a Sprague-Dawley (SD) rat model. Hum...

TACTIC: A transfer learning framework to predict drug interactions in emerging pathogens.

Machine learning (ML) is necessary to efficiently identify potent drug combinations within a large candidate space to combat drug resistance. However,...

ALDH18A1 silencing inhibits lung adenocarcinoma progression through mitochondrial dysfunction and PPAR signaling pathway activation.

BACKGROUND: Lung adenocarcinoma (LUAD) remains a prevalent malignant tumor characterized by a dismal prognosis. This study aimed to reveal potential m...

Discovery of hub genes linking oxidative stress to type 2 diabetic sarcopenia using single-cell sequencing and machine learning.

Type 2 diabetes mellitus (T2DM) and sarcopenia demonstrate a significant comorbidity, particularly in the elderly, yet the molecular mechanisms linkin...