OBJECTIVE: This study aims to explore the mediating role of comorbid depression and anxiety in the relationship between different dimensions of university students' personalities and SVA.

METHODS: The SPSS PROCESS was utilized to analyze data from 804 university students across seven universities in China.

RESULTS: The findings show that neuroticism, agreeableness, and extraversion in the personalities of Chinese university students are all significantly linked to SVA; neuroticism and agreeableness in the personalities of university students have a greater impact on SVA; both neuroticism and agreeableness can first induce depression and then lead to anxiety and SVA, whereas only agreeableness can first lead to anxiety and then result in depression and SVA.

METHODS: C0 were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC0-24 were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C0 and AUC0-24. An external dataset was used for predictive performance evaluation.

RESULTS: Before starting treatment, C0 can be predicted a priori using the Catboost-based C0-ML model combined with dosing regimen and nine covariates. External validation results showed a 42.5% improvement in prediction accuracy by using the ML model compared with the population pharmacokinetic model. The virtual trial showed that using the ML optimized dose; 80.3% of the virtual neonates achieved the pharmacodynamic target (C0 in the range of 10-20 mg/L), much higher than the international standard dose (37.7-61.5%). Once therapeutic drug monitoring (TDM) measurements (C0) in patients have been obtained, AUC0-24 can be further predicted using the Catboost-based AUC-ML model combined with C0 and nine covariates. External validation results showed that the AUC-ML model can achieve an prediction accuracy of 80.3%.