RESUMEN
Valproic acid (VPA) is a primary medication for epilepsy, yet its hepatotoxicity consistently raises concerns among individuals. This study aims to establish an automated machine learning (autoML) model for forecasting the risk of abnormal increase of transaminase levels while undergoing VPA therapy for 1995 epilepsy patients. The study employed the two-tailed T test, Chi-square test, and binary logistic regression analysis, selecting six clinical parameters, including age, stature, leukocyte count, Total Bilirubin, oral dosage of VPA, and VPA concentration. These variables were used to build a risk prediction model using "H2O" autoML platform, achieving the best performance (AUC training = 0.855, AUC test = 0.789) in the training and testing data set. The model also exhibited robust accuracy (AUC valid = 0.742) in an external validation set, underscoring its credibility in anticipating VPA-induced transaminase abnormalities. The significance of the six variables was elucidated through importance ranking, partial dependence, and the TreeSHAP algorithm. This novel model offers enhanced versatility and explicability, rendering it suitable for clinicians seeking to refine parameter adjustments and address imbalanced data sets, thereby bolstering classification precision. To summarize, the personalized prediction model for VPA-treated epilepsy, established with an autoML model, displayed commendable predictive capability, furnishing clinicians with valuable insights for fostering pharmacovigilance.
RESUMEN
As a crucial gene associated with diseases, the SLC29A3 gene encodes the equilibrative nucleoside transporter 3 (ENT3). ENT3 plays an essential regulatory role in transporting intracellular hydrophilic nucleosides, nucleotides, hydrophilic anticancer and antiviral nucleoside drugs, energy metabolism, subcellular localization, protein stability, and signal transduction. The mutation and inactivation of SLC29A3 are intimately linked to the occurrence, development, and prognosis of various human tumors. Moreover, many hereditary human diseases, such as H syndrome, pigmentary hypertrichosis and non-autoimmune insulin-dependent diabetes mellitus (PHID) syndrome, Faisalabad histiocytosis (FHC), are related to SLC29A3 mutations. This review explores the mechanisms of SLC29A3 mutations and expression alterations in inherited disorders and cancers. Additionally, we compile studies on the inhibition of ENT3, which may serve as an effective strategy to potentiate the anticancer activity of chemotherapy. Thus, the synopsis of genetics, permeant function and drug therapy of ENT3 provides a new theoretical and empirical foundation for the diagnosis, prognosis of evaluation and treatment of various related diseases.