Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors

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

Some uses of acyliminium ions in the synthesis of isoquinolones with potential biological activity

The preparation of a number of 2-substituted homophthalimides through the condensation of homophthalic anhydride with different arylalkyl arnines is reported. The prepared compounds were alkylated at the 4-position to generate 4-mono-, 4,4-disubstituted and 4-spirocyclic homphthalimides, the analogues of which were reported to have interesting biological activity. Regioselective reduction of the 4-substituted derivatives generated the corresponding carbinolamides. Treating the carbinolamides with mineral or Lewis acids generated N-acyliminiurn ions, which were trapped in situ by one of the following: ( 1) aromatic neucleophiles to generate analogues of the natural product berberine, (2) alkyl chain migration to generate tetrahydrophenanthridones and functionalised isoquinolones, (3) cyclopropane ring-opening to generate 4-alkylisoquinolones, (4) addition to double bond to generate cyclopentaisoquinolones and (5) benzyl or allyl elimination. The oxidation of 4-monosubstituted homophthalimides with triplet dioxygen in alkaline media was investigated, and it generated 4-hydroxyhomophthalimides and isobenzofurancarboxamides. Treating isobenzofurancarboxamides with POCI3 provided a concise route to analogues of the neuroactive naturally-occurring phthalideisoquinolines

Preparation of Polyester-Based Metal-Cross Linked Polymeric Composites as Novel Materials Resistant to Bacterial Adhesion and Biofilm Formation

Bacterial biofilms constitute an extremely resistant form of bacterial colonization with dire health and economical implications. Towards achieving polymeric composites capable of resisting bacterial adhesion and biofilm formation, we prepared five 2,6-pyridinedicarboxylate-based polyesters employing five different diol monomers. The resulting polyesters were complexed with copper (II) or silver (I). The new polymers were characterized by proton and carbon nuclear magnetic resonance spectroscopy, inherent viscosity, infrared spectroscopy, differential scanning calorimetry and thermogravimetric analysis. The corresponding metal complexes were characterized by differential scanning calorimery and infrared spectroscopy. The amounts of complexed copper and silver were determined by atomic absorption spectrophotometry. Finally, the resulting composites were tested for their antibacterial potential and were found to effectively resist bacterial attachment and growth

Elaborate Ligand-Based Modeling Coupled with Multiple Linear Regression and k Nearest Neighbor QSAR Analyses Unveiled New Nanomolar mTOR Inhibitors

The mammalian target of rapamycin (mTOR) has an important role in cell growth, proliferation, and survival. mTOR is frequently hyperactivated in cancer, and therefore, it is a clinically validated target for cancer therapy. In this study, we combined exhaustive pharmacophore modeling and quantitative structure–activity relationship (QSAR) analysis to explore the structural requirements for potent mTOR inhibitors employing 210 known mTOR ligands. Genetic function algorithm (GFA) coupled with k nearest neighbor (kNN) and multiple linear regression (MLR) analyses were employed to build self-consistent and predictive QSAR models based on optimal combinations of pharmacophores and physicochemical descriptors. Successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve (ROC) profiles. Optimal QSAR models and their associated pharmacophore hypotheses were validated by identification and experimental evaluation of several new promising mTOR inhibitory leads retrieved from the National Cancer Institute (NCI) structural database. The most potent hit illustrated an IC₅₀ value of 48 nM