Gastroenterology

An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer.

BACKGROUND: Programmed death ligand 1 (PD-L1) expression status, closely related to immunotherapy outcomes, is a reliable biomarker for screening patients who may benefit from immunotherapy. Here, we developed and validated an interpretable machine learning (ML) model based on contrast-enhanced computed tomography (CECT) radiomics for preoperatively predicting PD-L1 expression status in patients w...

A Novel Mouse Model of Type 2 Diabetes Using a Medium-Fat Diet, Fructose, and Streptozotocin to Study the Complications of Human Disease.

The study of type 2 diabetes mellitus (T2DM) pathophysiology relies mainly on the use of animal models, the most common of which involves the consumption of high-fat diets comprising 60% calories from fat. Although these models reproduce the onset and most complications associated with T2DM, they do not accurately mimic human dietary patterns, as they lack the addition of carbohydrates such as fru...

AI-driven synthetic data generation for accelerating hepatology research: A study of the United Network for Organ Sharing (UNOS) database.

BACKGROUND AND AIMS: Clinical hepatology research often faces limited data availability, underrepresentation of minority groups, and complex data-shar...

Applying machine learning to predict bowel preparation adequacy in elderly patients for colonoscopy: development and validation of a web-based prediction tool.

BACKGROUND: Adequate bowel preparation is crucial for effective colonoscopy, especially in elderly patients who face a high risk of inadequate prepara...

Therapeutic potential of Da Cheng Qi Decoction and its ingredients in regulating ferroptosis via the NOX2-GPX4 signaling pathway to alleviate and predict severe acute pancreatitis.

OBJECTIVE: This study aimed to elucidate the protective effects of Da Cheng Qi Decoction (DCQD) on severe acute pancreatitis (SAP) by targeting ferrop...

Development and validation of an interpretable machine learning model for predicting the risk of hepatocellular carcinoma in patients with chronic hepatitis B: a case-control study.

BACKGROUND: The aim of this study was to develop and internally validate an interpretable machine learning (ML) model for predicting the risk of hepat...

Multimodal deep learning for predicting PD-L1 biomarker and clinical immunotherapy outcomes of esophageal cancer.

Although the immune checkpoint inhibitors (ICIs) have demonstrated remarkable anti-tumor efficacy in solid tumors, the proportion of ESCC patients who...

Post-Bariatric Hypoglycemia After Gastric Bypass: Clinical Characteristics, Risk Factors, and Future Directions-A Response to Grover et al.

BACKGROUND: Post-bariatric hypoglycemia (PBH) after Roux-en-Y gastric bypass (RYGB) is a complex complication, often characterized by potentially seve...

Innovative construction and application of bile duct organoids: Unraveling the complexity of bile duct diseases and potential therapeutic strategies.

The biliary system is crucial for liver function, regulating bile production, secretion, and transport. Dysfunctions within this system can lead to va...

Enhancing pancreatic cancer diagnostics: Ensemble-based model for automated urine biomarker classification.

This research addresses the critical challenge of early detection in pancreatic ductal adenocarcinoma (PDAC) by exploring urinary biomarkers and integ...

Predicting Liver-Related In Vitro Endpoints with Machine Learning to Support Early Detection of Drug-Induced Liver Injury.

Drug-induced liver injury (DILI) is a major cause of drug development failures and postmarket drug withdrawals, posing significant challenges to publi...

Trans-ancestral rare variant association study with machine learning-based phenotyping for metabolic dysfunction-associated steatotic liver disease.

BACKGROUND: Genome-wide association studies (GWAS) have identified common variants associated with metabolic dysfunction-associated steatotic liver di...

Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer.

Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-eosin-stained whole s...

Prognostic model for log odds of negative lymph node in locally advanced rectal cancer via interpretable machine learning.

No studies have examined the prognostic value of the log odds of negative lymph nodes/T stage (LONT) in locally advanced rectal cancer (LARC) treated ...

Development of a MVI associated HCC prognostic model through single cell transcriptomic analysis and 101 machine learning algorithms.

Hepatocellular carcinoma (HCC) is an exceedingly aggressive form of cancer that often carries a poor prognosis, especially when it is complicated by t...

A Preoperative CT-based Multiparameter Deep Learning and Radiomic Model with Extracellular Volume Parameter Images Can Predict the Tumor Budding Grade in Rectal Cancer Patients.

RATIONALE AND OBJECTIVES: To investigate a computed tomography (CT)-based multiparameter deep learning-radiomic model (DLRM) for predicting the preope...

Development of a natural language processing algorithm to extract social determinants of health from clinician notes.

Disparities in access to the organ transplant waitlist are well-documented, but research into modifiable factors has been limited due to a lack of acc...

Advanced NLP-driven predictive modeling for tailored treatment strategies in gastrointestinal cancer.

Gastrointestinal cancer represents a significant health burden, necessitating innovative approaches for personalized treatment. This study aims to dev...

Machine Learning Models With Prognostic Implications for Predicting Gastrointestinal Bleeding After Coronary Artery Bypass Grafting and Guiding Personalized Medicine: Multicenter Cohort Study.

BACKGROUND: Gastrointestinal bleeding is a serious adverse event of coronary artery bypass grafting and lacks tailored risk assessment tools for perso...

An Ogilvie's syndrome: a rare case of large bowel pseudo-obstruction.

INTRODUCTION: Ogilvie's Syndrome (OS) is a rare but serious functional disorder characterized by dilatation of the colon, typically affecting the cecu...