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 June 14 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.

Microgravity affects plant growth and content. A three-dimensional clinostat was used at 4 rotations/min to rotate the seeds of Triticum aestivum cultivar (Ammon) in three dimensions for 7 days, following which the antioxidant activities of ethanolic extracts were evaluated using both nitric oxide-and hydrogen peroxide-scavenging activities. The antidiabetic activities of ethanolic extracts were evaluated by measuring the concentration of plasma glucose, insulin, C peptide, and glycated hemoglobin (HbA1c); determining the number of β cells in the pancreatic islets; and performing the glucose tolerance test. Furthermore, the effects of the ethanolic extracts on the lipid profile and liver function were estimated. After rats were sacrificed, their pancreases were isolated and used for histopathological processing. The results indicated that the antioxidant potential and antioxidant metabolite content were significantly increased under microgravity conditions in comparison to those under normal gravity conditions. Rats treated with an extract of wheatgrass (T. aestivum) germinated over a period of 6-10 days under microgravity (WGM) showed a significant reduction in the levels of serum glucose, HbA1C, urea, creatinine, aspartate aminotransferase and alanine aminotransferase, and insulin resistance compared to rats treated with an extract of wheatgrass germinated under gravity. Additionally, the total cholesterol and low-density lipoprotein cholesterol levels were significantly decreased. In contrast, highdensity lipoprotein cholesterol, C-peptide, and insulin levels rose significantly after treatment with T. aestivum germinated under microgravity. WGM is a promising potential diabetic treatment without side effects with a low manufacturing cost.

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.

1999). X-ray repair cross-complementing group 1 (XRCC1) is a key protein in BER and closely associated with BER pathway coordination due to interacting with most components of the BER short-patch pathway (Caldecott et al., 1996; Marintchev et al., 2000). There are many single

Paracetamol and nonsteroidal anti-inflammatory drugs are widely used in the management of respiratory viral infections. This study aimed to determine the effects of the most commonly used analgesics (paracetamol, ibuprofen, and diclofenac) on the mRNA expression of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) entry and arachidonic-acid-metabolizing genes in mouse lungs. A total of twenty eight Balb/c mice were divided into four groups and treated separately with vehicle, paracetamol, ibuprofen, and diclofenac in clinically equivalent doses for 14 days. Then, the expressions of SARS-CoV-2 entry, ACE2, TMPRSS2, and Ctsl genes, in addition to the arachidonic-acid-metabolizing cyp450, cox, and alox genes, were analyzed using real-time PCR. Paracetamol increased the expressions of TMPRSS2 and Ctsl genes by 8.5 and 5.6 folds, respectively, while ibuprofen and diclofenac significantly decreased the expression of the ACE2 gene by more than 2.5 folds. In addition, all tested drugs downregulated (p < 0.05) cox2 gene expression, and paracetamol reduced the mRNA levels of cyp4a12 and 2j5. These molecular alterations in diclofenac and ibuprofen were associated with pathohistological alterations, where both analgesics induced the infiltration of inflammatory cells and airway wall thickening. It is concluded that analgesics such as paracetamol, ibuprofen, and diclofenac alter the expression of SARS-CoV-2 entry and arachidonic-acid-metabolizing genes in mouse lungs.