Gastroenterology

M²B-Net: a lightweight multi-scale multi-attention boundary-aware network for liver tumor segmentation from CT images.

Liver tumor segmentation from CT images remains challenging due to large variations in lesion scale, blurred boundaries, low tissue contrast, and the high computational cost of existing deep learning models. This study aims to develop a lightweight yet accurate segmentation network suitable for clinical deployment. We propose a Multi-scale Multi-attention Boundary-aware Network (M²B-Net) based on ...

Shared molecular signatures linking gastroesophageal reflux disease and major depressive disorder revealed by integrated machine learning.

BACKGROUND: Gastroesophageal reflux disease (GERD) and major depressive disorder (MDD) frequently co-occur, yet their shared molecular mechanisms remain poorly understood. This study aimed to identify a robust gene signature linking GERD and MDD and to evaluate its clinical relevance. METHODS: Transcriptomic data from five GEO datasets were analyzed, including three GERD datasets (52 GERD samples ...

Metabolic SERS Profiling under Triple-Coupled Hotspots Aided by Machine Learning for Precise Identification of Gastrointestinal Malignancies.

Serum metabolic analysis based on label-free SERS fingerprints represents a highly promising technique for the detection of tumors. However, the insuf...

The gut microbiome in preterm infants: development, dysbiosis, and disease implications.

SUMMARYPreterm infants face a unique trajectory of gut microbiome assembly, shaped by the convergence of physiological immaturity, necessary clinical ...

Investigating Immune Microenvironment of Inflammatory Bowel Disease and the Mitochondrial Metabolism-Related Molecular Mechanisms: Combining a Multiomics Approach and Experimental Validation.

Inflammatory bowel disease (IBD) is a chronic and recurrent gastrointestinal disease, the pathogenesis of which has not been fully elucidated. Increas...

Machine learning in the diagnosis of Hirschsprung disease: a systematic review and meta-analysis.

PURPOSE: To evaluate current evidence on machine learning (ML) for the diagnosis of Hirschsprung disease (HSCR) and summarize its diagnostic performan...

A myeloid immunosuppressive phenotype defines primary refractoriness to atezolizumab Plus bevacizumab in hepatocellular caarcinoma.

BACKGROUND: Despite improved outcomes with atezolizumab plus bevacizumab (A+B) in hepatocellular carcinoma (HCC), primary refractoriness (PRef), chara...

Network-informed multi-trait genomic analysis decodes the shared genetic architecture and therapeutic landscape of pelvic floor disorders.

Pelvic floor disorders, including pelvic organ prolapse and urinary incontinence, represent a common health burden with substantial clinical comorbidi...

Development and validation of a radiomics-dosiomics model for predicting radiotherapy response in locally advanced, unresectable non-small cell lung cancer: a multi-center study.

Early assessment of treatment response is essential for optimizing cancer management, as it allows timely interventions during the course of therapy, ...

Machine learning-based identification of a metabolic cell death gene signature for assessing disease activity and immunological landscape in inflammatory bowel disease.

Inflammatory bowel disease (IBD) is characterized by a refractory, relapsing inflammatory state driven by a multifaceted and poorly understood interpl...

Machine learning-driven automated evaluation of surgical skills during laparoscopic distal gastrectomy based on blood pixel analysis.

BACKGROUND: In laparoscopic distal gastrectomy (LDG), intraoperative bleeding directly reflects surgical proficiency, including tissue handling and he...

Fusion prediction model for post-ERCP pancreatitis under NSAIDs prophylaxis.

BACKGROUND: Endoscopic retrograde cholangiopancreatography (ERCP) is a common procedure for treating biliary and pancreatic disorders; however, it is ...

Non-invasive imaging diagnostic techniques in metabolic dysfunction-associated steatotic liver disease: a roadmap for clinicians.

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed nonalcoholic fatty liver disease (NAFLD), is the most prevalent ch...

Integrated Multi-Omics Analysis and Experimental Validation Identify BCAT1 as a Critical Driver of Hepatocyte Pyroptosis in Acute-On-Chronic Liver Failure.

Acute-on-chronic liver failure (ACLF) is characterized by profound metabolic dysfunction and high mortality. Identifying amino acid metabolism-related...

A machine-learning-derived online screening tool for depressive symptoms in chronic digestive system diseases patients: a cross-sectional study with temporal validation from CHARLS.

Chronic digestive system diseases (CDSD) are common in older adults, and depressive symptoms are associated with poorer prognosis and quality of life....

Investigation of the molecular network underlying PET-MPs-induced inflammatory bowel disease via integrated machine learning and molecular docking approaches.

Polyethylene terephthalate microplastics (PET-MPs), as environmental contaminants, have raised significant concerns due to their potential toxicity, l...

Nationwide 30-Day Surgical Outcomes in Korea: An Interim Analysis of the Korean Quality Improvement Platform in Surgery.

BACKGROUND: National surgical registries such as the American College of Surgeons NSQIP and Japan's National Clinical Database have shown that structu...

A multidimensional risk prediction framework for malignant intestinal obstruction based on machine learning: computational model development and clinical validation.

Malignant intestinal obstruction (MIO) is a severe complication of advanced cancer. Traditional static assessment models struggle to capture its dynam...

Performance of GPT-based large language models in hepatocellular carcinoma stratification: liver function assessment, BCLC staging, and treatment recommendations.

Large language models (LLMs) like GPT have been proposed to support complex clinical decision-making. This study evaluated the performance of GPT-base...

CDSegNet: a multi-scale convolution and attention mechanism U-shaped network for Crohn's disease lesion segmentation.

Accurate segmentation of Crohn's disease (CD) lesions from computed tomography enterography (CTE) cross-sectional images is crucial for diagnosing CD ...