Gastroenterology

Latest AI and machine learning research in gastroenterology for healthcare professionals.

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Transforming Esogastric Cancer Surgery Integrating SpiderMass Mass Spectrometry with Clinical and Microbiome Data for Margin Delineation and Prognosis

Esophageal-gastric cancers (EC) represent a significant global health concern, with esophageal cancer ranking seventh in terms of incidence and mortality worldwide. Gastric cancer is especially concerning, with an estimated one million new cases and 800,000 deaths annually. Late diagnoses often lead to poor outcomes, requiring critical interventions such as radical surgical resection with clear ma...

The Regional Landscape of the Human Colon Culturome in Health and Cystic Fibrosis

Cystic fibrosis (CF) alters gut physiology, yet its impact on microbial communities across colonic regions (ascending, transverse, descending colon) and microhabitats (lumen, mucosa) remains incompletely understood. Here, we applied culturomics to characterize gut microbiota in 32 individuals (22 nonCF, 10 CF). Persons with CF (pwCF) exhibited significantly higher viable bacterial loads than nonCF...

Enhancing Medical Image Segmentation through Negative Sample Integration: A Study on Kvasir-SEG and Augmented Datasets

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, with early and accurate detection being critical for improving ...

Gut Microbiota Modulates and Predicts Disease Severity in Experimental Pemphigoid Disease

Pemphigoid diseases (PD) are autoimmune blistering diseases with reported alterations in skin and gut microbiota, though their causal contribution to ...

Integration of In Vitro and In Silico Approaches Enables Prediction of Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a major cause of drug attrition and poses a significant threat to patient safety. However, current preclinical pre...

Single-cell RNA sequencing and large-scale bulk combination with machine learning reveal gastric cancer-related macrophage heterogeneity

The tumor microenvironment (TME) significantly impacts cancer progression and overall patient survival. However, the complexity of tumor cell-TME inte...

A large language model for predicting pancreatic ductal adenocarcinoma patients from blood-derived exosomal transcriptomics data

Traditional machine learning approaches for text or sequence classification rely on converting textual data into numerical representations. In this st...

Integrative transcriptomic analysis identifies miR-642a-5p as a regulator of POFUT1 expression in colon cancer

Colorectal cancer (CRC) is one of the most common and deadly cancers worldwide, underscoring the urgent need for novel biomarkers and therapeutic targ...

Viral protease-Initiated Pyroptosis Activator mRNA therapy as a Universal Antiviral Strategy

Although therapeutic drugs targeting gasdermin (GSDM)-mediated pyroptosis have made remarkable progress in treating various diseases, their potential ...

The Spatial Atlas of Human Anatomy (SAHA): A Multimodal Subcellular-Resolution Reference Across Human Organs

The Spatial Atlas of Human Anatomy (SAHA) represents the first multimodal, subcellular- resolution reference of healthy adult human tissues across mul...

A generalizable cross-continent prediction of esophageal squamous cell carcinoma using the oral microbiome

Esophageal squamous cell carcinoma (ESCC) is a disease with limited tools for early screening and a poor prognosis. Symptoms typically appear late, an...

Batch-Harmonized Machine Learning Framework for Cross-Cohort RNA Biomarker Discovery in Pancreatic Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) lacks reliable prognostic biomarkers. RNA-based signatures suffer from poor reproducibility due to batch effec...

Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage

Cells must adopt flexible regulatory strategies to make decisions regarding their fate, including differentiation, apoptosis, or survival in the face ...

Inhibitory cell type heterogeneity in a spatially structured mean-field model of V1

Inhibitory interneurons in the cortex are classified into cell types differing in their morphology, electrophysiology, and connectivity. Although it i...

Revealing the Paper Mill Iceberg: AI-Based Screening of Cancer Research Publications

To train and validate a machine learning model to distinguish paper mill publications from genuine cancer research articles, and to screen the cancer ...

Unraveling miRNA-Driven DNA Damage Response Networks in Pancreatic Adenocarcinoma: A Multi-Omics and Machine Learning Approach

Due to the late detection, aggressive nature, and paucity of treatment options, pancreatic adenocarcinoma (PAAD) remains one of the most lethal cancer...

Colon-Specific Epigenetic Clocks from Minimal Features Reveal Disease-Driven Aging

Epigenetic clocks estimate chronological and biological age from DNA methylation patterns, but conventional models typically train on hundreds of thou...

TRIO-AI: Hybrid temporal graph, ODE, and VAE modeling for high-resolution cellular trajectory inference in liver injury

Resolving dynamic cellular transitions at single-cell resolution is essential for understanding complex biological processes in development, disease, ...

MetaPaCS: A Novel Meta-Learning Framework for Pancreatic Cancer Subtype Identification

As the third leading cause of cancer related deaths in the United States, pancreatic cancer (PaC) is a highly heterogenous malignancy that can be divi...

Phenotypic Screening Coupled with AI-Driven Target Deconvolution Identifies α-Terthienyl as a Dual DPP-IV/HSD17β13 Modulator with Efficacy in a Mouse Model of MASLD

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a highly prevalent condition characterized by fat build-up in the liver and ranges...

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