Equilibrative nucleotide transporter ENT3 (SLC29A3): A unique transporter for inherited disorders and cancers.

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.

Super-enhancer omics in stem cell.

The hallmarks of stem cells, such as proliferation, self-renewal, development, differentiation, and regeneration, are critical to maintain stem cell identity which is sustained by genetic and epigenetic factors. Super-enhancers (SEs), which consist of clusters of active enhancers, play a central role in maintaining stemness hallmarks by specifically transcriptional model. The SE-navigated transcriptional complex, including SEs, non-coding RNAs, master transcriptional factors, Mediators and other co-activators, forms phase-separated condensates, which offers a toggle for directing diverse stem cell fate. With the burgeoning technologies of multiple-omics applied to examine different aspects of SE, we firstly raise the concept of "super-enhancer omics", inextricably linking to Pan-omics. In the review, we discuss the spatiotemporal organization and concepts of SEs, and describe links between SE-navigated transcriptional complex and stem cell features, such as stem cell identity, self-renewal, pluripotency, differentiation and development. We also elucidate the mechanism of stemness and oncogenic SEs modulating cancer stem cells via genomic and epigenetic alterations hijack in cancer stem cell. Additionally, we discuss the potential of targeting components of the SE complex using small molecule compounds, genome editing, and antisense oligonucleotides to treat SE-associated organ dysfunction and diseases, including cancer. This review also provides insights into the future of stem cell research through the paradigm of SEs.

Development and validation of an automatic machine learning model to predict abnormal increase of transaminase in valproic acid-treated epilepsy.

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.