Gastroenterology

Brain network correlates of fatigue, depression, and anxiety in patients with Crohn's Disease in different disease states.

BACKGROUND: Symptoms of fatigue, depression or anxiety are frequent in Crohn's Disease (CD) and may relate to disturbed brain-gut interactions. While more prevalent in active disease, these symptoms are also experienced by many individuals with CD during remission. Little is known about neural networks underlying such extraintestinal symptoms in CD and their relationship with the current disease s...

Epigenetic editing approaches maturity: AI-driven precision design, delivery innovation, and the road to clinical translation.

Epigenetic editing achieves durable gene silencing through targeted modification of chromatin and DNA methylation states without altering the genomic sequence-modifications that remain fundamentally reversible compared with genome editing. Long constrained by transient efficacy, insufficient precision, and delivery bottlenecks, the field reached a critical inflection point in 2024-2025, measurable...

Colorectal Cancer Screening in Hereditary and Familial High-Risk Populations: Best Practices and Future Directions.

Colorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality worldwide yet is largely preventable through effective scree...

Automatic recognition of anatomical structures and surgical phases in robot-assisted minimally invasive esophagectomy (RAMIE) using deep learning: a retrospective cohort study.

BACKGROUND: Curative treatment of resectable esophageal cancer comprises neoadjuvant chemoradiotherapy and esophagectomy. Robot-assisted minimally inv...

Evaluating human-AI interaction in optical diagnosis of early colorectal carcinoma through endoscopist responses to simulated artificial intelligence: a multinational online study.

Background Suboptimal human - artificial intelligence (AI) interaction is a potential roadblock for implementation of AI in clinical practice. We aime...

Machine Learning-Based Privacy Preserving via CT/MRI and Organ Metadata Prediction.

Medical imaging plays a significant role in diagnosis and treatment planning, with significant efforts focused on training machine learning (ML) algor...

Using artificial intelligence to identify characteristics associated with clinical and economic outcomes in MASH (FOCUS-MASH).

BACKGROUND: Heterogeneity in the disease characteristics of metabolic dysfunction-associated steatohepatitis (MASH) complicates efforts to identify in...

A systematic review on colon cancer classification by convolutional neural networks: Architecture, accuracy, and research directions.

INTRODUCTION: Recent studies indicated the advancement of Convolutional Neural Networks (CNNs) have facilitated the colon cancer diagnosis process via...

Dysregulated glucocorticoid-responsive immune genes in peripheral blood mononuclear cells as a shared molecular signature of autism spectrum disorder and irritable bowel syndrome.

BACKGROUND: Autism spectrum disorder (ASD) is frequently accompanied by gastrointestinal (GI) disturbances resembling irritable bowel syndrome (IBS). ...

Physically intelligent capsule robots with embodied memory and logic in the gastrointestinal tract.

Miniaturized medical robots offer a promising solution for minimally invasive measurements and interventions in the gastrointestinal (GI) tract. Clini...

Comment on "A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer".

Perioperative corticosteroids and pancreatic surgery outcomes: a systematic review and meta-analysis combining human expertise and AI support (ChatGPT).

Pancreatic surgery is associated with high postoperative morbidity. The role of corticosteroids (CCS) in pancreatic surgery remains uncertain. Systema...

Virus infections and cancers: from mechanisms to therapeutics.

Viral infections are a major contributor to global cancer incidence and mortality. However, integrative reviews that connect viral classification, car...

Neuropsychological and metabolic interconnectivity in obesity, anorexia and bulimia nervosa - an integrative literature review.

A dysfunctional bi-directional signalling of plural neural networks expresses distinct metabolic disruption with mental health consequences in obesity...

Validation of a pan-ELastography Machine-learning (ELM) score to predict clinically significant portal hypertension in compensated advanced chronic liver disease.

BACKGROUND: Clinically significant portal hypertension (CSPH) drives decompensation and mortality in advanced chronic liver disease (ACLD). Although n...

Context-Aware Multilevel Classification of Semantic Relations in Drug-Adverse Drug Reaction (ADR) Networks-Predicting Drug-Induced Liver Injury (DILI) as a Case Study.

Adverse drug reactions (ADRs) are a major concern for public health and patient safety and a burden in drug discovery, with drug-induced liver injury ...

Leveraging a vendor-neutral deep learning reconstruction algorithm to reduce scan time and enhance image quality in T2-weighted liver MRI.

This study evaluated whether a vendor-neutral deep learning reconstruction (DLR) can improve image quality in accelerated T2-weighted imaging (T2WI) o...

Integrated transcriptomic and experimental validation reveal that N‑acetylcysteine ameliorates acute liver failure through multi‑target synergistic regulation of pyroptosis and related signaling networks.

Pyroptosis is a major contributor to the pathophysiology of acute liver failure (ALF), a condition with high mortality. Although N‑acetylcysteine (NAC...

Large language models enable prognostic stratification of cancer patients using real-world clinical notes.

In medical documentation, vast amounts of unstructured text are generated that are still underutilized in current prognostic models. We investigate th...

Large language models as versatile predictive engines for notifiable infectious diseases.

Accurate forecasting of infectious disease cases and deaths is crucial for public health decision-making. Traditional statistical and machine learning...