Application of machine learning techniques in real-world research to predict the risk of liver metastasis in rectal cancer.

Background: The liver is the most common site of distant metastasis in rectal cancer, and liver metastasis dramatically affects the treatment strategy of patients. This study aimed to develop and validate a clinical prediction model based on machine learning algorithms to predict the risk of liver metastasis in patients with rectal cancer.

Methods: We integrated two rectal cancer cohorts from Surveillance, Epidemiology, and End Results (SEER) and Chinese multicenter hospitals from 2010-2017.

Isolation and identification of bovine nasopharyngeal mucosal epithelial cells and establishment of cell models of acute infection by foot-and-mouth disease virus.

Foot-and-mouth disease (FMD) commonly occurs via the respiratory tract, and bovine nasopharyngeal mucosal epithelial cells are the primary infection cells in cattle. The aim of the present study was to isolate and culture epithelial cells from the bovine nasopharyngeal mucosa in vitro using a mechanical separation method. The cells were expanded, established in continuous cell culture, and used for immunofluorescence cytochemistry and establishment of infection models.