Diabetic nephropathy (DN) is a severe complication of diabetes mellitus, posing significant challenges in terms of early prevention, clinical diagnosis, and treatment. Consequently, it has emerged as a major contributor to end-stage renal disease. The glomerular filtration barrier, composed of podocytes, endothelial cells, and the glomerular basement membrane, plays a vital role in maintaining renal function. Disruptions in podocyte function, including hypertrophy, shedding, reduced density, and apoptosis, can impair the integrity of the glomerular filtration barrier, resulting in elevated proteinuria, abnormal glomerular filtration rate, and increased creatinine levels. Hence, recent research has increasingly focused on the role of podocyte injury in DN, with a growing emphasis on exploring therapeutic interventions targeting podocyte injury. Studies have revealed that factors such as lipotoxicity, hemodynamic abnormalities, oxidative stress, mitochondrial dysfunction, and impaired autophagy can contribute to podocyte injury. This review aims to summarize the underlying mechanisms of podocyte injury in DN and provide an overview of the current research status regarding experimental drugs targeting podocyte injury in DN. The findings presented herein may offer potential therapeutic targets and strategies for the management of DN associated with podocyte injury.

Purpose: This study aimed to establish an optimal model to predict vancomycin trough concentrations by using machine learning. Patients and Methods: We enrolled 407 pediatric patients (age < 18 years) who received vancomycin intravenously and underwent therapeutic drug monitoring from June 2013 to April 2020 at Xinhua Hospital affiliated to Shanghai Jiaotong University School of Medicine. The median (interquartile range) age and weight of the patients were 2 (0.63– 5) years and 12 (7.8– 19) kg. Vancomycin trough concentrations were considered as the target variable, and eight different algorithms were used for predictive performance comparison. The whole dataset (407 cases) was divided into training group and testing group at the ratio of 80%: 20%, which were 325 and 82 cases, respectively. Results: Ultimately, five algorithms (XGBoost, GBRT, Bagging, ExtraTree and decision tree) with high R ² (0.657, 0.514, 0.468, 0.425 and 0.450, respectively) were selected and further ensembled to establish the final model and achieve an optimal result. For missing data, through filling the missing values and model ensemble, we obtained R ²=0.614, MAE=3.32, MSE=24.39, RMSE=4.94 and a prediction accuracy of 51.22% (predicted trough concentration within ± 30% of the actual trough concentration). In comparison with the pharmacokinetic models (R ²=0.3), the machine learning model works better in model fitting and has better prediction accuracy. Conclusion: Therefore, the ensemble model is useful for the vancomycin concentration prediction, especially in the population of children with great individual variation. As machine learning methods evolve, the clinical value of the ensemble model will be demonstrated in the clinical practice.

Despite therapeutic vancomycin is regularly monitored, its dose requirements vary considerably between individuals. Various innovative vancomycin dosing strategies have been developed for dose opti...