Gastroenterology

Evaluation of Machine Learning and Statistical Models for Predicting Long Term Gastrostomy Tube Dependency in Patients Undergoing Major Oral Cavity Cancer Surgery With Free Flap Reconstruction.

BACKGROUND: Swallowing dysfunction and long-term gastrostomy tube dependence are common morbidities after oral cavity cancer resection with free flap reconstruction, highlighting the need for preoperative risk prediction. METHODS: We performed a multicenter retrospective pooled cohort study of patients with non-metastatic oral cavity cancer undergoing free flap reconstruction. Firth's penalized lo...

Evaluation of deep learning-based reconstruction models on non-TOF BGO PET/CT: impact of acquisition times and BSREM penalization factors on lesion detectability and SNR.

BACKGROUND: New long field-of-view (FOV) PET scanners using bismuth germanate (BGO) detectors without time-of-flight (TOF) capability are now available. These systems incorporate deep learning-based TOF (DLb-TOF) models to compensate for the absence of TOF. There is a lack of studies systematically investigating the optimal balance between signal to noise ratio and lesion detectability across a br...

Machine learning and multi-omic empowered risk stratification and therapeutic framework targeting nucleotide metabolism and mast cell for stomach adenocarcinoma patients.

BACKGROUND: Mast cell and nucleotide metabolism(NM) encodes the Stomach adenocarcinoma (STAD) progression and tumor immune microenvironment(TME) heter...

Towards transparent and reliable AI in gastroenterology: pragmatic recommendations for evaluation and reporting.

Artificial intelligence (AI) is expanding in gastroenterology, particularly in endoscopy and imaging, where models support detection, classification, ...

Assessing collagenase-a and type-II mediated degradation of human type-I collagen by photoacoustic spectroscopy toward clinical translation in cancer progression.

The current study reports the design and development of a compact photoacoustic (PA) sensor for label‑free detection of collagen degradation, a key ha...

Development and validation of a machine learning model for identifying liver fibrosis in MAFLD: a retrospective model development study.

BACKGROUND: Metabolic dysfunction-associated fatty liver disease (MAFLD), as the most prevalent chronic liver disease globally, is closely related to ...

RRM2B is associated with low-level viremia during entecavir therapy in pediatric chronic hepatitis B and promotes profibrotic signaling in vitro.

BACKGROUND: Low-level viremia (LLV) during nucleos(t)ide analogue therapy for chronic hepatitis B (CHB) is clinically relevant, but its biological bas...

Periodic surveillance of intraductal papillary mucinous neoplasms: gaps in evidence and future directions.

Intraductal papillary mucinous neoplasm (IPMN) is a well-established precursor to pancreatic cancer; however, its generally indolent biological behavi...

KG-Microbe - Building Modular and Scalable Knowledge Graphs for Microbiome and Microbial Sciences.

BACKGROUND: The integration of many disparate forms of data is essential for understanding the microbial world and its interaction with the environmen...

Adaptive graph learning of microbial phylogeny enables accurate and interpretable microbiome-based host phenotype prediction.

The human microbiome is inherently structured by phylogeny, yet most predictive models treat microbial taxa as independent features, thereby underusin...

Biomarkers for minimally invasive multigrade liver cancer diagnosis using integrative heterogeneous models and evolutionary algorithms.

Liver cancer, a leading global cause of death, requires early and accurate diagnosis for better treatment outcomes and reduced mortality rates. This r...

Identification of therapeutic targets and postoperative recurrence prediction model construction for hepatocellular carcinoma based on systematic mendelian randomization.

BACKGROUND AND STUDY AIMS: Hepatocellular carcinoma (HCC) has a high risk of postoperative recurrence. This study aimed to develop a transcriptome-bas...

CT-based Deep Learning Model for Automatic Segmentation and Early Predicting of Pyogenic Liver Abscess Caused by Extended-Spectrum β-lactamase-Producing Enterobacteriaceae: A Multicenter Retrospective Study (CLASS2401).

RATIONALE AND OBJECTIVE: Pyogenic liver abscess (PLA) caused by extended-spectrum β-lactamase-producing Enterobacteriaceae (ESBL-PLA) presents major a...

Prediction of microvascular invasion in hepatocellular carcinoma using contrast-enhanced ultrasound and deep learning.

Microvascular invasion (MVI) is a key prognostic factor in hepatocellular carcinoma but is currently only detectable after surgery. Here, we develop M...

SLIC-Former: a superpixel-guided transformer framework for automatic liver segmentation in CT images.

Accurate and efficient liver segmentation from computed tomography (CT) images remains a critical challenging task due to the organ's irregular shape,...

A vision transformer deep learning model for assessing pediatric ileocolic intussusception severity using ultrasound images.

Timely identification of children with ileocolic intussusception likely to fail air-enema reduction is critical to avoid delays and bowel perforation....

Uncovering the mechanism of DCLK3 in colorectal cancer progression through WGCNA and machine learning.

BACKGROUND: Colorectal cancer (CRC) is a prevalent malignant tumor of the digestive tract with high morbidity and mortality rates. Although doublecort...

Evaluation of Large Language Models in the Clinical Management of Patients With Upper Gastrointestinal Bleeding: Insights From Real-World Patient Data.

OBJECTIVE: To evaluate large language models (LLMs) for pre-endoscopy (PE) risk stratification and prediction of endoscopic findings in upper gastroin...

Human gut flagellome profiling using FlaPro reveals TLR5-related phenotype-specific alterations in IBD.

Flagellin, the structural protein of bacterial flagella, activates the innate immune receptor Toll-like receptor 5 (TLR5). However, the ability of dif...

Trait-based machine learning modeling of soluble carbohydrate content in Trachyspermum ammi exposed to organic and chemical fertilization and salicylic acid.

Accurate assessment of biochemical traits in medicinal plants is essential for supporting environmentally responsible agriculture, improving crop qual...