Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from 14 June to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer-BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization.

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.

Type 2 diabetes mellitus (T2DM) is a multifactorial disease associated with many genetic polymorphisms; among them is the FokI polymorphism in the vitamin D receptor (VDR) gene. In this case-control study, samples from 82 T2DM patients and 82 healthy controls were examined to investigate the association of the FokI polymorphism and lipid profile with T2DM in the Jordanian population. DNA was extracted from blood and genotyped for the FokI polymorphism by polymerase chain reaction (PCR) and DNA sequencing. Lipid profile and fasting blood sugar were also measured. There were significant differences in high-density lipoprotein (HDL) cholesterol and triglyceride levels between T2DM and control samples. Frequencies of the FokI polymorphism (CC, CT and TT) were determined in T2DM and control samples and were not significantly different. Furthermore, there was no significant association between the FokI polymorphism and T2DM or lipid profile. A feed-forward neural network (FNN) was used as a computational platform to predict the persons with diabetes based on the FokI polymorphism, lipid profile, gender and age. The accuracy of prediction reached 88% when all parameters were included, 81% when the FokI polymorphism was excluded, and 72% when lipids were only included. This is the first study investigating the association of the VDR gene FokI polymorphism with T2DM in the Jordanian population, and it showed negative association. Diabetes was predicted with high accuracy based on medical data using an FNN. This highlights the great value of incorporating neural network tools into large medical databases and the ability to predict patient susceptibility to diabetes.

Glioblastoma multiforme continues to be one of the most aggressive brain cancers, posing a serious health challenge, as it offers a median survival of only 15–23 months and a 5-year survival rate of less than 6%. Current treatments often prove inadequate, underscoring the urgency for new therapeutic strategies. This study investigated the potential of silencing the PFKFB4 and HMOX1 genes in U87-MG glioblastoma cells using small interfering RNAs (siRNAs), both alone and alongside the chemotherapeutic agents temozolomide (TMZ) and doxorubicin (DOX). Through MTT assays, qPCR, and wound healing techniques, we assessed cell viability, gene expression, and cell migration. Notably, siPFKFB4 enhanced DOX’s cytotoxic effect, reducing its IC₅₀ by six-fold, while having a milder impact with TMZ. When both siRNAs were combined with DOX, the IC₅₀ decreased by two-fold without harming normal cells. Although siHMOX1 reduced cell migration, it only modestly affected cell proliferation with either DOX or TMZ. The gene expression analysis demonstrated that the siPFKFB4/DOX treatment led to an upregulation of pro-apoptotic genes such as DPYSL4, while simultaneously downregulating anti-apoptotic genes, including BCL-2 and PARP2. In contrast, the siHMOX1 combination influenced the expression of 14 genes, significantly enhancing the levels of CYLD, FAS, and CASP3, which are key promoters of apoptosis. In migration assays, siPFKFB4/DOX and siHMOX1/DOX reduced cell migration by 65 and 75%, respectively. These findings suggest that siPFKFB4 combined with DOX offers a promising pathway for GBM therapy, advocating further exploration into effective central nervous system drug delivery methods.

CD36 (cluster of differentiation 36) is a membrane protein involved in lipid metabolism and has been linked to pathological conditions associated with metabolic disorders, such as diabetes and dyslipidemia. A case-control study was conducted and included 177 patients with type-2 diabetes mellitus (T2DM) and 173 control subjects to study the involvement of CD36 gene rs1761667 (G>A) and rs1527483 (C>T) polymorphisms in the pathogenesis of T2DM and dyslipidemia among Jordanian population. Lipid profile, blood sugar, gender and age were measured and recorded. Also, genotyping analysis for both polymorphisms was performed. Following statistical analysis, 10 different neural networks and machine learning (ML) tools were used to predict subjects with diabetes or dyslipidemia. Towards further understanding of the role of CD36 protein and gene in T2DM and dyslipidemia, a protein-protein interaction network and meta-analysis were carried out. For both polymorphisms, the genotypic frequencies were not significantly different between the two groups (p > 0.05). On the other hand, some ML tools like multilayer perceptron gave high prediction accuracy (≥ 0.75) and Cohen’s kappa (κ) (≥ 0.5). Interestingly, in K-star tool, the accuracy and Cohen’s κ values were enhanced by including the genotyping results as inputs (0.73 and 0.46, respectively, compared to 0.67 and 0.34 without including them). This study confirmed, for the first time, that there is no association between CD36 polymorphisms and T2DM or dyslipidemia among Jordanian population. Prediction of T2DM and dyslipidemia, using these extensive ML tools and based on such input data, is a promising approach for developing diagnostic and prognostic prediction models for a wide spectrum of diseases, especially based on large medical databases.