This study aimed at fabricating gold (Au), iron (Fe) and selenium (Se) nanoparticles (NPs) using various natural plant extracts from the Fertile Crescent area and evaluating their potential application as antioxidant and biocompatible agents to be used in the pharmaceutical field, especially in drug delivery. The Au-NPs were synthesized using Ephedra alata and Pistacia lentiscus extracts, whereas the Fe-NPs and Se-NPs were synthesized using peel, fruit and seed extracts of Punica granatum. The phytofabricated NPs were characterized by the UV-visible spectroscopy, scanning electron microscope, Fourier transform infrared spectroscopy, X-ray diffraction (XRD) and energy-dispersive X-ray (EDS) spectroscopy. Scanning electron microscope technique showed that the synthesized NPs surface was spherical, and the particle size analysis confirmed a particle size of 50 nm. The crystalline nature of the NPs was confirmed by the XRD analysis. All synthesized NPs were found to be biocompatible in the fibroblast and human erythroleukemic cell lines. Se-NPs showed a dose-dependent antitumor activity as evidenced from the experimental results with breast cancer (MCF-7) cells. A dose-dependent, free-radical scavenging effect of the Au-NPs and Se-NPs was observed in the DPPH (2,2-Diphenyl-1-picrylhydrazyl) assay, with the highest effect recorded for Au-NPs.

Background: Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with over 75% of deaths occurring in low- and middle-income countries, where conventional risk models often demonstrate poor calibration and limited generalizability. Objective: This study aimed to develop an interpretable, pharmacotherapy-informed machine learning model for cardiovascular risk prediction using national electronic health record (EHR) data from Jordan. Methods: A retrospective cohort study was conducted using approximately 600,000 individuals from the national Hakeem EHR system (2018–2022). Demographic, clinical, blood pressure, laboratory, and medication data were integrated to construct three datasets reflecting varying levels of feature completeness. Multiple machine learning models were benchmarked, followed by optimization, hybrid modeling, and probability calibration. Model interpretability was assessed using SHAP analysis. Results: The national cohort demonstrated a high cardiometabolic burden, with prevalence of hypertension (50.2%), hyperlipidemia (54.9%), and diabetes (47.9%). Antihypertensive and lipid-lowering therapies were more frequently used among CVD patients (56.9% and 49.6%, respectively). Treatment patterns were dominated by amlodipine (19.9%) and atorvastatin (74.4%). The final calibrated seed-bagged gradient boosting model achieved robust performance (ROC-AUC 0.844; PR-AUC 0.813) with consistent generalization across datasets. Key predictors included antihyperlipidemic therapy, systolic blood pressure variability, age, and sex. Conclusions: This study presents JoRisk, a calibrated and interpretable machine learning framework that integrates pharmacotherapy and clinical data for short-term cardiovascular risk prediction. The model demonstrates strong performance using routinely available EHR variables and offers a scalable decision-support tool for risk stratification in resource-constrained healthcare systems.