Causal link between gut microbiota and four types of pancreatitis: a genetic association and bidirectional Mendelian randomization study.

Background: A number of recent observational studies have indicated a correlation between the constitution of gut microbiota and the incidence of pancreatitis. Notwithstanding, observational studies are unreliable for inferring causality because of their susceptibility to confounding, bias, and reverse causality, the causal relationship between specific gut microbiota and pancreatitis is still unclear. Therefore, our study aimed to investigate the causal relationship between gut microbiota and four types of pancreatitis.

Convolutional neural network-based segmentation network applied to image recognition of angiodysplasias lesion under capsule endoscopy.

Background: Small intestinal vascular malformations (angiodysplasias) are common causes of small intestinal bleeding. While capsule endoscopy has become the primary diagnostic method for angiodysplasia, manual reading of the entire gastrointestinal tract is time-consuming and requires a heavy workload, which affects the accuracy of diagnosis.

Aim: To evaluate whether artificial intelligence can assist the diagnosis and increase the detection rate of angiodysplasias in the small intestine, achieve automatic disease detection, and shorten the capsule endoscopy (CE) reading time.

Saccharomyces cerevisiae may serve as a probiotic in colorectal cancer by promoting cancer cell apoptosis.

Objectives: Shotgun metagenomic sequencing of human fecal samples has shown that Saccharomyces cerevisiae (S. cerevisiae) is significantly suppressed in colorectal cancer (CRC) and probably plays an important role in CRC progression. However, these results need to be validated.

[Applied study on support vector machine (SVM) regression method in quantitative analysis with near-infrared spectroscopy].

This paper introduced the application of support vector machines(SVM) regression method based on statistics studytheory to the quantitative analysis with near-infrared (NIR) spectroscopy. Sixty-six wheat samples were used as experimental materials, and thirty-three of them were used as calibration samples. The protein contents and NIR spectra of the calibration samples were used to build SVM regression models by four different kernel functions.