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.

Sepsis, a life-threatening condition caused by the body’s dysregulated response to infection, presents a significant challenge in clinical management. Timely and accurate diagnosis is paramount for initiating appropriate interventions and improving patient outcomes. In recent years, there has been growing interest in identifying biomarkers that can aid in the early detection and prognostication of sepsis. MicroRNAs (miRNAs) have emerged as potential biomarkers for sepsis due to their involvement in the regulation of gene expression and their stability in various biological fluids, including blood. MiRNAs are small non-coding RNA molecules that play crucial roles in post-transcriptional gene regulation by binding to target messenger RNAs (mRNAs), leading to mRNA degradation or translational repression. The diagnostic and prognostic potential of miRNAs in sepsis stems from their ability to serve as sensitive and specific biomarkers reflective of the underlying pathophysiological processes. Compared to traditional biomarkers such as C-reactive protein (CRP) and procalcitonin (PCT), miRNAs offer several advantages, including their early and sustained elevation during sepsis, as well as their stability in stored samples, making them attractive candidates for clinical use. However, despite their promise, the clinical translation of miRNAs as sepsis biomarkers faces several challenges. These include the need for standardized sample collection and processing methods, the identification of optimal miRNA panels or signatures for differentiating sepsis from other inflammatory conditions, and the validation of findings across diverse patient populations and clinical settings. In conclusion, miRNAs hold great promise as diagnostic and prognostic biomarkers for sepsis, offering insights into the underlying molecular mechanisms and potential therapeutic targets. However, further research is needed to overcome existing challenges and realize the full clinical utility of miRNAs in improving sepsis outcomes.

Background: Acinetobacter baumannii is regarded as a significant cause of death in hospitals. The WHO recently added carbapenem-resistant Acinetobacter baumannii (CRAB) to its global pathogen priority list. There is a dearth of information on CRAB from our region. Methods: Fifty CRAB isolates were collected from four main hospitals in Bahrain for this study. Bacterial identification and antibiotic susceptibility tests were carried out using the BD Phoenix^TM and VITEK-2 compact, respectively. Using conventional PCR, these isolates were further screened for carbapenem resistance markers (bla_OXA-51, bla_OXA-23, blaO_XA-24, bla_OXA-40, bla_IMP, bla_NDM, bla_VIM, and bla_KPC). Results: All of the isolates were resistant to imipenem (100%), meropenem (98%), and cephalosporins (96–98%), followed by other commonly used antibiotics. All these isolates were least resistant to gentamicin (64%). The detection of resistance determinants showed that the majority harbored bla_OXA-51 (100%) and bla_IMP (94%), followed by bla_OXA-23 (82%), bla_OXA-24 (46%), bla_OXA-40 (14%), bla_NDM (6%), bla_VIM (2%), and bla_KPC (2%). Conclusion: The study isolates showed a high level of antibiotic resistance. Class D carbapenemases were more prevalent in our CRAB isolate collection. The resistance genes were found in various combinations. This study emphasizes the importance of strengthening surveillance and stringent infection control measures in clinical settings to prevent the emergence and further spread of such isolates.

Coronavirus (COVID-19) was introduced into society in late 2019 and has now reached over 88 million cases and 1.9 million deaths. The Middle East has a death toll of ~80,000 and over 35000 of these are in Iran, which has over 1.2 million confirmed cases. We expect that Iranian cases caused outbreaks in the neighbouring countries and that variant mapping and phylogenetic analysis can be used to prove this. We also aim to analyse the variants of severe acute respiratory syndrome coronavirus-2 (SARS -CoV-2) to characterise the common genome variants and provide useful data in the global effort to prevent further spread of COVID-19. The approach uses bioinformatics approaches including multiple sequence alignment, variant calling and annotation and phylogenetic analysis to identify the genomic variants found in the region. The approach uses 122 samples from the 13 countries of the Middle East sourced from the Global Initiative on Sharing All Influenza Data (GISAID). We identified 2200 distinct genome variants including 129 downstream gene variants, 298 frame shift variants, 789 missense variants, 1 start lost, 13 start gained, 1 stop lost, 249 synonymous variants and 720 upstream gene variants. The most common, high impact variants were 10818delTinsG, 2772delCinsC, 14159delCinsC and 2789delAinsA. These high impact variant ultimately results in 36 number of mutations on spike glycoprotein. Variant alignment and phylogenetic tree generation indicates that samples from Iran likely introduced COVID-19 to the rest of the Middle East. The phylogenetic and variant analysis provides unique insight into mutation types in genomes. Initial introduction of COVID-19 was most likely due to Iranian transmission. Some countries show evidence of novel mutations and unique strains. Increased time in small populations is likely to contribute to more unique genomes. This study provides more in depth analysis of the variants affecting in the region than any other study.

OBJECTIVE: To determine the molecular characterization of extended-spectrum beta-lactamases (ESBL) isolates from a tertiary center in Saudi Arabia using multiplex polymerase chain reaction (PCR) technique and assess their antibiotic susceptibility pattern. METHODS: Prospective study conducted at the Saudi Aramco Dhahran Health Center, Dhahran, Saudi Arabia between April-December 2006. Extended-spectrum beta-lactamases phenotype of isolates identified by automated methods was confirmed using E-test. Multiplex PCR for the detection of blaTEM, blaSHV and blaCTX-M was performed. Susceptibility to a panel of antibiotics was determined. RESULTS: One hundred isolates (Escherichia coli [E.coli] n=84; Klebsiella pneumoniae [K. pneumoniae] n=16) were studied and 71% harbored the blaCTX-M gene. For E.coli isolates 43 (51%) harbored CTX-M+TEM combination and 21 (25%) had CTX-M alone. In contrast, only one K. pneumoniae isolate (6.2%) harbored the CTX-M+TEM combination and 3 (18.8%) isolates had CTX-M only. One E.coli and 7 K. pneumoniae isolates were blaSHV positive. The blaCTX-M gene was found predominantly in urinary isolates (n=63/71; 88.7%). The presence of blaCTX-M was significantly higher in isolates from outpatients compared to inpatient (p<0.05). Sensitivity to imipenem was 100% and 78% to nitrofurantoin. Resistance to amoxicillin-sulbactam was significantly higher in blaCTX-M positive isolates (p<0.05). CONCLUSION: The findings indicate a high-level of blaCTX-M positive ESBL isolates circulating in our setting with the dissemination of these in the community. The trend of multidrug resistance profile associated with carriage of blaCTX-M gene is cause for concern.