Peptic Ulcer Disease

MuSL: Multimodal deep learning for generalizable prediction of synthetic lethality from sequence, transcriptomic, and network data.

Synthetic lethality (SL) offers a promising paradigm for identifying selective anticancer targets. How ever, many computational SL prediction methods rely on handcrafted expression summaries or single data modalities, limiting their ability to capture fine-grained genegene dependency patterns directly from raw expression profiles and to generalize to unseen genes. We propose MuSL, a multimodal dee...

KRT6A derived from mesenchymal stem cells as a potential biomarker and therapeutic target for alopecia areata: insights from multi-omics analysis and experimental evidence.

BACKGROUND: Mesenchymal stem cells (MSCs) secretome have shown promise in the treatment of alopecia areata (AA). However, the key therapeutic genes remain unclear. This study aimed to identify potential critical therapeutic molecules using multi-omics approaches. METHODS: MSCs extracellular vesicles related genes was retrieved from ExoCarta database, and which integrated with transcriptomic and pr...

Traumatic Inflammatory-Coagulation Coactivation Occurs Early After Injury and Impacts Mortality.

OBJECTIVES: Acute traumatic coagulopathy is known to occur early following severe injury. However, the role and impact of both pro- and anti-inflammat...

Colorless image processing technique combining chromaticity transfer and image feature extraction in visual graphic design.

This study presents a verifiable framework for high-fidelity colorless image processing by integrating depth-guided chrominance transfer with multi-le...

Evaluating deep coal rock gas fracturing sweet spot intervals using PSO-ELM algorithm and petrophysical logging data.

The Daning Jixian block is situated on the eastern periphery of the Ordos Basin. it faces multiple challenges including strong reservoir heterogeneity...

A machine learning-based risk prediction framework for atypical hyperplasia and endometrial cancer in postmenopausal women.

BACKGROUND: This study aimed to develop and internally validate a machine learning-based model for predicting endometrial malignancy, defined as atypi...

Endoscopy-based Deep Learning Algorithms versus Endoscopists in Early Esophageal Squamous Cell Carcinoma Detection: A Systematic Review and Meta-Analysis.

INTRODUCTION: To compare the diagnostic performance of endoscopy-based deep learning (DL) algorithms with endoscopists of different experience levels ...

Elucidating the pathogenic mechanism underlying plasticizer-induced chronic kidney disease via integrated network toxicology and machine learning.

Plasticizers are ubiquitous plastic additives that have been linked to multiple adverse health outcomes and elevated risks of chronic kidney disease (...

Integration of single-cell RNA-sequencing and machine learning identifies GRN and FCER1G as potential peroxisomal targets in influenza pathogenesis.

BACKGROUND: Influenza, an acute respiratory infection caused by influenza viruses, imposes a significant burden on the healthcare system. Peroxisomes ...

Advances in Nano-Enabled Biosensing Technology for Gastric Cancer Diagnosis: Mechanistic Insights and Translational Perspectives.

Gastric cancer (GC) remains a critical burden on healthcare, affecting millions of people annually. Helicobacter pylori infection is a critical contri...

Healthcare Providers' Perspectives on the Role of Artificial Intelligence in the Care of Patients with Inflammatory Bowel Disease: An International Survey.

BACKGROUND: Artificial intelligence (AI) is transforming inflammatory bowel disease (IBD) care, but concerns over data quality, privacy, and provider ...

Identification and Validation of Glycosylation-Related Biomarkers in the Hippocampus for Alzheimer's Disease Diagnosis and Drug Repurposing.

As a crucial type of post-translational modification, glycosylation plays a fundamental role in maintaining cellular homeostasis and is closely associ...

AI-based automated bleeding monitoring in conventional and robot-assisted laparoscopic surgery: a systematic review.

Artificial intelligence has emerged as a promising approach for improving the detection and management of intraoperative bleeding during conventional ...

Advancing stroke prevention in atrial fibrillation: a systematic review of machine learning-based risk prediction models.

BACKGROUND: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and confers a four to fivefold increase in ischemic stroke risk, ...

Virtual crystal violet chromoendoscopy: a digital alternative to dye-based staining.

BACKGROUND AND AIMS: Routine use of crystal violet (CV) chromoendoscopy for pit pattern-based colorectal lesion characterization is limited by safety ...

Transcriptome and experimental verification identified candidate biomarkers related to mitochondrial metabolism in sepsis-associated encephalopathy.

BACKGROUND: Mitochondrial metabolism (MM) abnormalities have been implicated in multiple diseases, but its contribution to sepsis-associated encephalo...

Identification and analysis of oxidative stress-related genes associated with the occurrence and development of diabetic retinopathy.

Diabetic retinopathy (DR) develops via complex interactions of multiple risk factors and molecular pathways. However, the association between oxidativ...

Machine learning based hepatic safety score predicts decompensation in hepatocellular carcinoma systemic therapy.

Hepatocellular carcinoma (HCC) frequently coexists with portal hypertension, significantly increasing the risk of hepatic decompensation (HD) and vari...

Integrative transcriptomic analysis identifies immune-associated candidate genes and altered immune cell infiltration in pulmonary arterial hypertension.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a progressive vascular disease characterized by immune dysregulation and pulmonary vascular remod...

Development and validation of a novel YOLOv5-based artificial intelligence model for gastric mucosal lesion detection.

BACKGROUND AND AIMS: Artificial intelligence (AI) has been widely used in endoscopic diagnosis; however, an AI model capable of comprehensively diagno...