Long-term research on various types of RNAs has led to further understanding of diverse mechanisms, which eventually resulted in the rapid development of RNA-based therapeutics as powerful tools in clinical disease treatment. Some of the developing RNA drugs obey the antisense mechanisms including antisense oligonucleotides, small interfering RNAs, microRNAs, small activating RNAs, and ribozymes. These types of RNAs could be utilized to inhibit/activate gene expression or change splicing to provide functional proteins. In the meantime, some others based on different mechanisms like modified messenger RNAs could replace the dysfunctional endogenous genes to manage some genetic diseases, and aptamers with special three-dimensional structures could bind to specific targets in a high-affinity manner. In addition, the recent most popular CRISPR-Cas technology, consisting of a crucial single guide RNA, could edit DNA directly to generate therapeutic effects. The desired results from recent clinical trials indicated the great potential of RNA-based drugs in the treatment of various diseases, but further studies on improving delivery materials and RNA modifications are required for the novel RNA-based drugs to translate to the clinic. This review focused on the advances and clinical studies of current RNA-based therapeutics, analyzed their challenges and prospects.

Zwitterions consist of equal molar cationic and anionic moieties and thus exhibit overall electroneutrality. Zwitterionic materials include phosphorylcholine, sulfobetaine, carboxybetaine, zwitterionic amino acids/peptides, and other mix-charged zwitterions that could form dense and stable hydration shells through the strong ion–dipole interaction among water molecules and zwitterions. As a result of their remarkable hydration capability and low interfacial energy, zwitterionic materials have become ideal choices for designing therapeutic vectors to prevent undesired biosorption especially nonspecific biomacromolecules during circulation, which was termed antifouling capability. And along with their great biocompatibility, low cytotoxicity, negligible immunogenicity, systematic stability and long circulation time, zwitterionic materials have been widely utilized for the delivery of drugs and therapeutic genes. In this review, we first summarized the possible antifouling mechanism of zwitterions briefly, and separately introduced the features and advantages of each type of zwitterionic materials. Then we highlighted their applications in stimuli-responsive “intelligent” drug delivery systems as well as tumor-targeting carriers and stressed the multifunctional role they played in therapeutic gene delivery.

Introduction: Adverse drug events (ADEs) pose a significant challenge in current clinical practice. Machine learning (ML) has been increasingly used to predict specific ADEs using electronic health record (EHR) data. This systematic review provides a comprehensive overview of the application of ML in predicting specific ADEs based on EHR data.Methods: A systematic search of PubMed, Web of Science, Embase, and IEEE Xplore was conducted to identify relevant articles published from the inception to 20 May 2024. Studies that developed ML models for predicting specific ADEs or ADEs associated with particular drugs were included using EHR data.Results: A total of 59 studies met the inclusion criteria, covering 15 drugs and 15 ADEs. In total, 38 machine learning algorithms were reported, with random forest (RF) being the most frequently used, followed by support vector machine (SVM), eXtreme gradient boosting (XGBoost), decision tree (DT), and light gradient boosting machine (LightGBM). The performance of the ML models was generally strong, with an average area under the curve (AUC) of 76.68% ± 10.73, accuracy of 76.00% ± 11.26, precision of 60.13% ± 24.81, sensitivity of 62.35% ± 20.19, specificity of 75.13% ± 16.60, and an F1 score of 52.60% ± 21.10. The combined sensitivity, specificity, diagnostic odds ratio (DOR), and AUC from the summary receiver operating characteristic (SROC) curve using a random effects model were 0.65 (95% CI: 0.65–0.66), 0.89 (95% CI: 0.89–0.90), 12.11 (95% CI: 8.17–17.95), and 0.8069, respectively. The risk factors associated with different drugs and ADEs varied.Discussion: Future research should focus on improving standardization, conducting multicenter studies that incorporate diverse data types, and evaluating the impact of artificial intelligence predictive models in real-world clinical settings.Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024565842, identifier CRD42024565842.

Proton pump inhibitors (PPIs) were widely used. Observational studies suggested increasing risk of kidney injury in patients with PPIs treatment. We gathered six PPI regimens and adverse reports of acute kidney injury (AKI) and chronic kidney disease (CKD) based on US FDA Adverse Event Reporting System (FAERS) database from 2004 to 2019. We employed reporting odds ratio (ROR) to detect signals. Finally, we identified 3187 PPIs-associated AKI cases and 3457 PPIs-associated CKD cases. We detected significant signals between PPIs and AKI as well as CKD. The signal strength was stronger for CKD (ROR = 8.80, 95% CI 8.49–9.13) than AKI (ROR = 3.95, 95% CI 3.81–4.10), while dexlansoprazole performed stronger association for CKD (ROR = 34.94, 95% CI 30.89–39.53) and AKI (ROR = 8.18, 95% CI 7.04–9.51) than the other five PPIs. The median time from PPIs use to event occurrence was 23 days for AKI and 177 days for CKD. PPIs-associated AKI resulted larger proportion of death, life-threatening, hospitalization and disability events than PPIs-associated CKD. By mining the FAERS big data, we provided more information between PPIs use and the AKI and CKD events. PPIs rational use should be repeatedly stressed.