Gastroenterology

Self-supervised monocular depth estimation for colonoscopy using normal-guided cross-attention.

PURPOSE: Reliable 3D reconstruction of colonoscopic scenes can facilitate navigation and enhance lesion assessment by indicating the regions that have been inspected and revealing the geometric structure of polyps. However, accurate depth estimation in colonoscopy remains highly challenging due to specular highlights and low-texture areas. This study aims to improve monocular depth estimation in c...

VIGIL: Vision-Language Guided Multiple Instance Learning Framework for Ulcerative Colitis Histological Healing Prediction.

OBJECTIVE: Ulcerative colitis (UC) is characterized by chronic inflammation with alternating remission-relapse cycles, necessitating precise histological healing (HH) evaluation to improve clinical outcomes. To address the limitations of annotation-intensive deep learning methods and suboptimal multi-instance learning (MIL) in HH prediction, we propose VIGIL, the first vision-language-guided MIL f...

Smart-delivery systems for intestinal mucosal homeostasis: From barrier penetration to precision regulation.

As an important barrier tissue, the intestinal mucosa is responsible for regulating immunity, absorbing nutrients, and maintaining microbial homeostas...

aDISCO: A clearing method to enable 3D microscopy of large archival paraffin-embedded human tissue blocks.

Human surgery and autopsy specimens are routinely stored as formalin-fixed paraffin-embedded (FFPE) tissue blocks for decades, creating vast archives ...

Metabolic signatures and machine learning identify gut-liver-heart axis dysfunction as a potential link to major adverse cardiovascular events in coronary artery disease.

BACKGROUND: Traditional risk factors do not fully account for the residual cardiometabolic risk of major adverse cardiovascular events (MACE) in coron...

Building Machine Learning Models Based on Oxidative Stress Index Score to Predict Survival of Locally Advanced Rectal Cancer Receiving Different Neoadjuvant Therapy Patterns.

BACKGROUND: Liver enzyme biomarkers are known to contribute to the onset and progression of colorectal cancer. OBJECTIVE: To develop a novel oxidative...

LIMK1 and PKM as Integratively Identified Transcriptomic Signatures in Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD) involves complex immunometabolic dysregulation. Mitochondria-associated endoplasmic reticulum membranes (MAMs) link m...

Validation of tumour-negative lymph node size as a novel independent prognostic biomarker in oesophagogastric adenocarcinoma: post hoc analysis of the UK MRC OE05 and ST03 randomised trials.

BACKGROUND: The size of tumour-negative regional lymph nodes (LNnegs) may be associated with the host antitumour immune response in patients with oeso...

A multi-scale fusion model based on multi-phase contrast-enhanced CT for predicting pancreatic cancer resectability.

Purpose Develop a multi-scale fusion model (MSFM) based on multi-phase contrast-enhanced computed tomography (CECT) to predict pancreatic cancer (PC) ...

Stephania cepharantha Hayata (Baiyaozi) attenuates cirrhosis-associated intestinal barrier injury by restoring mitochondrial function and suppressing PI3K/AKT signaling.

ETHNOPHARMACOLOGICAL RELEVANCE: Baiyaozi, the dried tuberous root of Stephania cepharantha Hayata, has long been used in traditional Chinese medicine ...

Clinically interpretable prediction models of stroke functional outcomes: A national cohort study of adults in inpatient rehabilitation facilities in the US.

OBJECTIVE: To predict self-care and mobility function at discharge from inpatient rehabilitation for adults with stroke using only variables from the ...

Deep-learning triage of three-dimensional pathology datasets for comprehensive and efficient pathologist assessments.

Standard slide-based two-dimensional (2D) histopathology severely undersamples spatially heterogeneous tissue, with each thin 2D section representing ...

MAFLD/MASLD: Past, Present, and Future.

Metabolic dysfunction-associated steatotic liver disease (MASLD), also known as nonalcoholic fatty liver disease and metabolic dysfunction-associated ...

Machine Learning-Assisted Prediction of Varicocele Grade Using Multidimensional Spermatic Vein Reflux Time Analysis.

BACKGROUND: Varicocele (VC) grading has long relied on qualitative assessments of venous diameter and reflux signals, lacking standardization. OBJECTI...

From black-box prediction to transparent insight: the status quo and paradigm shift of explainable artificial intelligence in hepatocellular carcinoma research.

Hepatocellular carcinoma (HCC) is a malignancy with high global incidence and mortality, whose significant heterogeneity and poor prognosis pose sever...

Deciphering Anti-Cancer Drug Efficacy Through Nanomechanical Vibrations in Living Gastric Cancer Organoids.

Cancer poses a severe threat to human health. The limited sensitivity and efficiency of conventional drug screening methods create a pressing need for...

Cost-effectiveness and environmental impact of artificial intelligence-assisted colonoscopy with the resect-and-discard strategy: a microsimulation study.

Background Artificial intelligence (AI)-assisted colonoscopy reduces costs in colorectal cancer screening, but its incremental value relative to non-A...

Cross-Species Multitask Learning with Molecular and ADME Descriptors for Liver Microsomal Metabolic Stability.

Liver microsomal metabolic stability is a key determinant of in vivo exposure and an essential filter in lead optimization, yet cross-species predicti...

Precision Medicine in Transfusion-Dependent and Non-Transfusion-Dependent β-Thalassemia: Toward Personalized Diagnosis and Therapy.

β-thalassemia comprises a clinically heterogeneous group of disorders in which anemia severity, transfusion exposure, iron loading, and organ complica...

Histopathology-guided multimodal pathomics identifies a PLEKHA6-positive epithelial state associated with phosphoinositide metabolism in esophageal cancer.

Esophageal cancer is biologically heterogeneous, and conventional clinicopathological staging incompletely captures variation in patient outcomes. Thi...