Virtual Communities and Achieving Electronic Institutional Excellence in the Kingdom of Saudi Arabia- University of Hail as a Model

This study aims to examine the reality of virtual communities and institutional excellence in the Kingdom of Saudi Arabia, using the University of Hail as a case study. It aims also to investigate the extent to which mechanisms for virtual communities and institutional excellence are available at Hail University. the achievement of the goals of electronic institutional excellence, and the major obstacles that stand in the way of achieving the goals and electronic institutional excellence. This study employs a random sample and a descriptive analysis. Social survey method is descriptive and analytical studies. 245 students who got help were studied. To gain data, a sample was given a questionnaire. The study's spatial and human limitations were Hail University teachers and students. Finalizing the research will take 12 months. After analyzing the study's underlying assumptions, the first and third hypotheses were approved as the college's electronic information networks, academic communication, and information sources. Due to limited electronic collaboration, the second theory was partially accepted. Due to lack of experience, the report proposed building rehabilitation and training programs for "virtual communities." One researcher' biggest issues was not knowing how to use virtual communities to attain greatness. Main results The most important results were a high institutional level under the coronavirus pandemic (3.82), followed by an average of 3.81 for academic processes. The results highlight the prospects for effective application of the COVID-19 crisis responses by offering a secure electronic educational environment with expanded virtual capabilities. This highlights the University's role in handling the crisis, establishing institutional excellence, and addressing education.

Vitamin D deficiency and vitamin D receptor FokI polymorphism as risk factors for COVID-19.

BACKGROUND: Given the sparse data on vitamin D status in pediatric COVID-19, we investigated whether vitamin D deficiency could be a risk factor for susceptibility to COVID-19 in Egyptian children and adolescents. We also investigated whether vitamin D receptor (VDR) FokI polymorphism could be a genetic marker for COVID-19 susceptibility. METHODS: One hundred and eighty patients diagnosed to have COVID-19 and 200 matched control children and adolescents were recruited. Patients were laboratory confirmed as SARS-CoV-2 positive by real-time RT-PCR. All participants were genotyped for VDR Fok1 polymorphism by RT-PCR. Vitamin D status was defined as sufficient for serum 25(OH) D at least 30 ng/mL, insufficient at 21-29 ng/mL, deficient at <20 ng/mL. RESULTS: Ninety-four patients (52%) had low vitamin D levels with 74 (41%) being deficient and 20 (11%) had vitamin D insufficiency. Vitamin D deficiency was associated with 2.6-fold increased risk for COVID-19 (OR = 2.6; [95% CI 1.96-4.9]; P = 0.002. The FokI FF genotype was significantly more represented in patients compared to control group (OR = 4.05; [95% CI: 1.95-8.55]; P < 0.001). CONCLUSIONS: Vitamin D deficiency and VDR Fok I polymorphism may constitute independent risk factors for susceptibility to COVID-19 in Egyptian children and adolescents. IMPACT: Vitamin D deficiency could be a modifiable risk factor for COVID-19 in children and adolescents because of its immune-modulatory action. To our knowledge, ours is the first such study to investigate the VDR Fok I polymorphism in Caucasian children and adolescents with COVID-19. Vitamin D deficiency and the VDR Fok I polymorphism may constitute independent risk factors for susceptibility to COVID-19 in Egyptian children and adolescents. Clinical trials should be urgently conducted to test for causality and to evaluate the efficacy of vitamin D supplementation for prophylaxis and treatment of COVID-19 taking into account the VDR polymorphisms.

Rebalancing the research equation in Africa: principles and process.

BACKGROUND: Many examples of research excellence in Africa have been driven by partnerships led by the global North and have involved localised infrastructure improvements to support the best of international research practice. OBJECTIVE: In this article, we explore a possible mechanism by which local research networks, appropriately governed, could begin to support national African research programmes by allying research delivery to clinical service. SUMMARY: This article explores the concept that sustainable research effort needs a well-trained and mentored workforce, working to common standards, but which is practically supported by a much developed information technology (IT) infrastructure throughout the continent. CONCLUSIONS: The balance of investment and ownership of such a research programme needs to be shared between local and international funding, with the emphasis on developing global South-South collaborations and research strategies which address the environmental impact of medical research activity and mitigate the impact of climate change on African populations. Healthcare must be embedded in the post-COVID-19 approach to research development.

Possible Reasons Why Sub-Saharan Africa Experienced a Less Severe COVID-19 Pandemic in 2020.

Both scientific authorities and governments of nations worldwide were found lacking in their COVID-19 response and management, resulting in significant distrust by the general public in 2020. Scientific and medical bodies often failed to give the right counsel on the appropriate course of action on COVID-19, because proven steps were not known, while many governments around the world took ineffective, late or inappropriate COVID-19 control and containment strategies. If the 2020 COVID-19 incidence rates are to be believed, much of sub-Saharan Africa had a lower disease prevalence than expected. We put forward six factors peculiar to much of sub-Saharan Africa that may have accounted for the pandemic landscape there in 2020. We also discuss why the situation has become more serious in 2021.

A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences.

In this work, Deep Bidirectional Recurrent Neural Networks (BRNNs) models were implemented based on both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells in order to distinguish between genome sequence of SARS-CoV-2 and other Corona Virus strains such as SARS-CoV and MERS-CoV, Common Cold and other Acute Respiratory Infection (ARI) viruses. An investigation of the hyper-parameters including the optimizer type and the number of unit cells, was also performed to attain the best performance of the BRNN models. Results showed that the GRU BRNNs model was able to discriminate between SARS-CoV-2 and other classes of viruses with a higher overall classification accuracy of 96.8% as compared to that of the LSTM BRNNs model having a 95.8% overall classification accuracy. The best hyper-parameters producing the highest performance for both models was obtained when applying the SGD optimizer and an optimum number of unit cells of 80 in both models. This study proved that the proposed GRU BRNN model has a better classification ability for SARS-CoV-2 thus providing an efficient tool to help in containing the disease and achieving better clinical decisions with high precision.

Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach.

The sudden increase in patients with severe COVID-19 has obliged doctors to make admissions to intensive care units (ICUs) in health care practices where capacity is exceeded by the demand. To help with difficult triage decisions, we proposed an integration system Xtreme Gradient Boosting (XGBoost) classifier and Analytic Hierarchy Process (AHP) to assist health authorities in identifying patients' priorities to be admitted into ICUs according to the findings of the biological laboratory investigation for patients with COVID-19. The Xtreme Gradient Boosting (XGBoost) classifier was used to decide whether or not they should admit patients into ICUs, before applying them to an AHP for admissions' priority ranking for ICUs. The 38 commonly used clinical variables were considered and their contributions were determined by the Shapley's Additive explanations (SHAP) approach. In this research, five types of classifier algorithms were compared: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighborhood (KNN), Random Forest (RF), and Artificial Neural Network (ANN), to evaluate the XGBoost performance, while the AHP system compared its results with a committee formed from experienced clinicians. The proposed (XGBoost) classifier achieved a high prediction accuracy as it could discriminate between patients with COVID-19 who need ICU admission and those who do not with accuracy, sensitivity, and specificity rates of 97%, 96%, and 96% respectively, while the AHP system results were close to experienced clinicians' decisions for determining the priority of patients that need to be admitted to the ICU. Eventually, medical sectors can use the suggested framework to classify patients with COVID-19 who require ICU admission and prioritize them based on integrated AHP methodologies.