Machine learning-based prediction of intraoperative hypoxemia for pediatric patients.

Background: Reducing the duration of intraoperative hypoxemia in pediatric patients by means of rapid detection and early intervention is considered crucial by clinicians. We aimed to develop and validate a machine learning model that can predict intraoperative hypoxemia events 1 min ahead in children undergoing general anesthesia.

Methods: This retrospective study used prospectively collected intraoperative vital signs and parameters from the anesthesia ventilator machine extracted every 2 s in pediatric patients undergoing surgery under general anesthesia between January 2019 and October 2020 in a tertiary academic hospital.

Evaluation and optimization of a conventional SPCE for FMD post-vaccination monitoring.

Background: Foot-and-mouth disease (FMD) can be controlled by either stamping out or vaccination, a choice which depends on both the economic importance of the livestock sector as well as the disease status. In FMD-free countries with vaccination, such as Korea, vaccination programs should guarantee prevention against transmission of FMD. Monitoring of vaccination programs is also essential for ensuring sufficient coverage that will limit the transmission of FMDV.