Predictive Capacities of a Machine Learning Decision Tree Model Created to Analyse Feasibility of an Open or Robotic Kidney Transplant.

Background: Machine learning has emerged as a potent tool in healthcare. A decision tree model was built to improve the decision-making process when determining the optimal choice between an open or robotic surgical approach for kidney transplant.

Methods: 822 patients (OKT) and 169 (RKT) underwent kidney transplantation at our centre during the study period.

The cards they're dealt: types of gambling activity, online gambling, and risk of problem gambling in European adolescents.

Objective: This study aims to identify risk factors associated with gambling engagement and the likelihood of problem behavior, distinguishing by type of gambling activity and examining the impact of online gambling.

Methods: Data about 85,420 students aged 16 from 33 countries participating in the 2019 European School Survey Project on Alcohol and Other Drugs (ESPAD) were analyzed through a three-stage sequential probit model, specifically focusing on four types of activity: lotteries, slot machines, cards, and betting. Furthermore, predicted probabilities were calculated for subsamples of students engaging in different types of gambling activities to explore their influence on the likelihood of problem gambling behavior, conditioned on online gambling involvement.

Design and Evaluation of Azaspirocycles as RNA binders.

This study presents efficient synthetic pathways for preparing novel azaspirocycles. These methodologies involve functionalizing key bicyclic hydrazines with a substituent on one of their bridgehead carbon atoms. The desired spirocyclic cores were successfully obtained through double reductive amination reactions, intramolecular cyclizations, and cleavages of the N-N bond.