Gastroenterology

study of sodium butyrate on soyasaponin challenged intestinal epithelial cells of turbot ( L.) refer to inflammation, apoptosis and antioxidant enzymes.

The study is aimed to investigate the protective effect and potential mechanisms of sodium butyrate (NaBT) on soyasaponins (SA) induced intestinal epithelial cells (IECs) injury . The primary IECs of turbot were developed and treated with 0.4, 1 and 4 mM NaBT in the presence of 0.4 mg/mL SA for 6 h to explore the protective effects of NaBT. The results showed that the addition of NaBT significantl...

Machine learning models compared to existing criteria for noninvasive prediction of endoscopic retrograde cholangiopancreatography-confirmed choledocholithiasis.

BACKGROUND AND AIMS: Noninvasive predictors of choledocholithiasis have generally exhibited marginal performance characteristics. We aimed to identify noninvasive independent predictors of endoscopic retrograde cholangiopancreatography (ERCP)-confirmed choledocholithiasis and accordingly developed predictive machine learning models (MLMs).

A deep-learning-based prediction model for the biodistribution of Y microspheres in liver radioembolization.

BACKGROUND: Radioembolization with Y microspheres is a treatment approach for liver cancer. Currently, employed dosimetric calculations exhibit low a...

Simple surgical method for clamping the rectum in robot-assisted laparoscopic rectal surgery for rectal cancer, a simple clamping technique: A video vignette.

Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study.

BACKGROUND: To reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting ...

Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning.

Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches...

Correlation of serum microRNA-122 level with the levels of Alanine aminotransferase and HBV-DNA in Chronic HBV-infected patients.

The microRNA-122 (miR-122) is a liver-specific microRNA that can be used as a potential molecular marker for predicting liver injury. There is a posi...

Early prediction of severe acute pancreatitis using machine learning.

BACKGROUND: Acute pancreatitis (AP) is one of the most common causes of gastrointestinal-related hospitalizations in the United States. Severe AP (SAP...

A Wavelet-Based Learning Model Enhances Molecular Prognosis in Pancreatic Adenocarcinoma.

Genome-wide omics technology boosts deep interrogation into the clinical prognosis and inherent mechanism of pancreatic oncology. Classic LASSO method...

Contribution of the Japan Gastroenterological Endoscopy Society to promote computer-aided diagnosis/detection system development using artificial intelligence technology.

Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma.

PURPOSE: Diagnosis of lymph node metastasis (LNM) is critical for patients with pancreatic ductal adenocarcinoma (PDAC). We aimed to build deep learni...

Development and evaluation of a double-check support system using artificial intelligence in endoscopic screening for gastric cancer.

BACKGROUND: This study aimed to prevent missing gastric cancer and point out low-quality images by developing a double-check support system (DCSS) for...

The use of deep learning on endoscopic images to assess the response of rectal cancer after chemoradiation.

BACKGROUND: Accurate response evaluation is necessary to select complete responders (CRs) for a watch-and-wait approach. Deep learning may aid in this...

Short-term outcomes of robot-assisted minimally invasive esophagectomy with extended lymphadenectomy for esophageal cancer compared with video-assisted minimally invasive esophagectomy: A single-center retrospective study.

BACKGROUND: The safety and feasibility of robot-assisted minimally invasive esophagectomy (RAMIE) remain unclear. The aim of this study was to compare...

CT classification model of pancreatic serous cystic neoplasms and mucinous cystic neoplasms based on a deep neural network.

BACKGROUND: At present, numerous challenges exist in the diagnosis of pancreatic SCNs and MCNs. After the emergence of artificial intelligence (AI), m...

Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts.

PURPOSE: Current diagnostic and treatment modalities for pancreatic cysts (PCs) are invasive and are associated with patient morbidity. The purpose of...

Systemic inflammatory response in robot-assisted and laparoscopic surgery for colon cancer (SIRIRALS): study protocol of a randomized controlled trial.

BACKGROUND: Robot-assisted surgery is being increasingly adopted in treating colorectal cancer, and the transition from laparoscopic surgery to robot-...

Predicting microvascular invasion in hepatocellular carcinoma: a deep learning model validated across hospitals.

BACKGROUND: The accuracy of estimating microvascular invasion (MVI) preoperatively in hepatocellular carcinoma (HCC) by clinical observers is low. Mos...

Automatic Prediction of Ischemia-Reperfusion Injury of Small Intestine Using Convolutional Neural Networks: A Pilot Study.

Acute intestinal ischemia is a life-threatening condition. The current gold standard, with evaluation based on visual and tactile sensation, has low s...

StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors.

Fast and accurate identification of inhibitors with potency against HCV NS5B polymerase is currently a challenging task. As conventional experimental ...