Using machine learning to identify risk factors for pancreatic cancer: a retrospective cohort study of real-world data.

Objectives: This study aimed to identify the risk factors for pancreatic cancer through machine learning.

Methods: We investigated the relationships between different risk factors and pancreatic cancer using a real-world retrospective cohort study conducted at West China Hospital of Sichuan University. Multivariable logistic regression, with pancreatic cancer as the outcome, was used to identify covariates associated with pancreatic cancer.

In vitro Assessment of the Effects of Silybin on CYP2B6-mediated Metabolism.

Silybin is a flavonol compound with a variety of physiological properties, such as hepatoprotective, anti-fibrogenic, and hypocholesterolemic effects. Although the and effects of silybin are frequently reported, studies on herb-drug interactions have yet to be performed. With the discovery of multiple important substrates of CYP2B6 recently, there is a growing body of evidence indicating that CYP2B6 plays a much larger role in human drug metabolism than previously thought.