Prognostic prediction models for postoperative patients with stage I to III colorectal cancer based on machine learning.

Background: Colorectal cancer (CRC) is characterized by high heterogeneity, aggressiveness, and high morbidity and mortality rates. With machine learning (ML) algorithms, patient, tumor, and treatment features can be used to develop and validate models for predicting survival. In addition, important variables can be screened and different applications can be provided that could serve as vital references when making clinical decisions and potentially improving patient outcomes in clinical settings.

Commensal Enterococcus faecalis W5 ameliorates hyperuricemia and maintains the epithelial barrier in a hyperuricemia mouse model.

Objective: The intestine is responsible for approximately one-third of uric acid (UA) excretion. The effect of commensal Enterococcus faecalis (E. faecalis), one of the most colonized bacteria in the gut, on UA excretion in the intestine remains to be investigated.