Background Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China, in late 2019 and created a global pandemic that overwhelmed healthcare systems. COVID-19, as of July 3, 2021, yielded 182 million confirmed cases and 3.9 million deaths globally according to the World Health Organization. Several patients who were initially diagnosed with mild or moderate COVID-19 later deteriorated and were reclassified to severe disease type. Objective The aim is to create a predictive model for COVID-19 ventilatory support and mortality early on from baseline (at the time of diagnosis) and routinely collected data of each patient (CXR, CBC, demographics, and patient history). Methods Four common machine learning algorithms, three data balancing techniques, and feature selection are used to build and validate predictive models for COVID-19 mechanical requirement and mortality. Baseline CXR, CBC, demographic, and clinical data were retrospectively collected from April 2, 2020, till June 18, 2020, for 5739 patients with confirmed PCR COVID-19 at King Abdulaziz Medical City in Riyadh. However, of those patients, only 1508 and 1513 have met the inclusion criteria for ventilatory support and mortalilty endpoints, respectively. Results In an independent test set, ventilation requirement predictive model with top 20 features selected with reliefF algorithm from baseline radiological, laboratory, and clinical data using support vector machines and random undersampling technique attained an AUC of 0.87 and a balanced accuracy of 0.81. For mortality endpoint, the top model yielded an AUC of 0.83 and a balanced accuracy of 0.80 using all features with balanced random forest. This indicates that with only routinely collected data our models can predict the outcome with good performance. The predictive ability of combined data consistently outperformed each data set individually for intubation and mortality. For the ventilator support, chest X-ray severity annotations alone performed better than comorbidity, complete blood count, age, or gender with an AUC of 0.85 and balanced accuracy of 0.79. For mortality, comorbidity alone achieved an AUC of 0.80 and a balanced accuracy of 0.72, which is higher than models that use either chest radiograph, laboratory, or demographic features only. Conclusion The experimental results demonstrate the practicality of the proposed COVID-19 predictive tool for hospital resource planning and patients’ prioritization in the current COVID-19 pandemic crisis.

SARS-CoV-2, the virus that caused the widespread COVID-19 pandemic, is homologous to SARS-CoV. It would be ideal to develop antivirals effective against SARS-CoV-2. In this study, we chose one therapeutic target known as the main protease (Mpro) of SARS-CoV-2. A crystal structure (Id: 6LU7) from the protein data bank (PDB) was used to accomplish the screening and docking studies. A set of phytocompounds was used for the docking investigation. The nature of the interaction and the interacting residues indicated the molecular properties that are essential for significant affinity. Six compounds were selected, based on the docking as well as the MM-GBSA score. Pentagalloylglucose, Shephagenin, Isoacteoside, Isoquercitrin, Kappa-Carrageenan, and Dolabellin are the six compounds with the lowest binding energies (−12 to −8 kcal/mol) and show significant interactions with the target Mpro protein. The MMGBSA scores of these compounds are highly promising, and they should be investigated to determine their potential as Mpro inhibitors, beneficial for COVID-19 treatment. In this study, we highlight the crucial role of in silico technologies in the search for novel therapeutic components. Computational biology, combined with structural biology, makes drug discovery studies more rigorous and reliable, and it creates a scenario where researchers can use existing drug components to discover new roles as modulators or inhibitors for various therapeutic targets. This study demonstrated that computational analyses can yield promising findings in the search for potential drug components. This work demonstrated the significance of increasing in silico and wetlab research to generate improved structure-based medicines.

BACKGROUND: Newborn screening identifies individuals affected by a specific disorder within an apparently healthy population prior to the appearance of symptoms so that appropriate interventions can be initiated in time to minimize the harmful effects. Data on population based cut-off values, disease ranges for true positive cases, false positive rates, true positive rates, cut-off verification and comparisons with international cut-off ranges have not been done for Saudi Arabia. OBJECTIVE: Establish population-based cut-off values and analyte ratios for newborn screening assays and clinically validate the values. DESIGN: Population-based screening. SETTING: Tertiary care hospitals and laboratories. METHODS: After method verification, initial cut-off values were established by analyzing 400-500 dry blood spot (DBS) samples which were further evaluated after one year. About 74 000 patient results were reviewed to establish cut-off ranges from DBS samples received from five different hospitals during 2013-2020. Analysis was performed by tandem mass spectrometry (TMS) and a genetic screening processor. Confirmation of initial positive newborn screening results for different analytes were carried out using gas chromatography-mass spectrometry, high performance liquid chromatography and TMS. MAIN OUTCOME MEASURES: Cut-off values, ratios, positive predictive values, false positive rate, true positive rate and disease range. SAMPLE SIZE: 74 000 samples. RESULTS: Population based cut-off values were calculated at different percentiles. These values were compared with 156 true positive samples and 80 proficiency samples. The false positive rate was less than 0.04 for all the analytes, except for valine, leucine, isovalerylcarnitine (C5), biotinidase (BTD), 17-hydroxyprogesterone and thyroid stimulating hormone. The highest false positive rate was 0.14 for BTD which was due to pre-analytical errors. The analytical positive predictive values were greater than 80% throughout the eight years. CONCLUSION: We have established clinical disease ranges for most of the analytes tested in our lab and several ratios which gives excellent screening specificity and sensitivity for early detection. The samples were representative of the local populations. LIMITATIONS: Need for wider, population-based studies. CONFLICT OF INTEREST: None.

Protein phosphorylation is a post-translational modification that enables various cellular activities and plays essential roles in protein interactions. Phosphorylation is an important process for the replication of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). To shed more light on the effects of phosphorylation, we used an ensemble of neural networks to predict potential kinases that might phosphorylate SARS-CoV-2 nonstructural proteins (nsps) and molecular dynamics (MD) simulations to investigate the effects of phosphorylation on nsps structure, which could be a potential inhibitory target to attenuate viral replication. Eight target candidate sites were found as top-ranked phosphorylation sites of SARS-CoV-2. During the process of molecular dynamics (MD) simulation, the root-mean-square deviation (RMSD) analysis was used to measure conformational changes in each nsps. Root-mean-square fluctuation (RMSF) was employed to measure the fluctuation in each residue of 36 systems considered, allowing us to evaluate the most flexible regions. These analysis shows that there are significant structural deviations in the residues namely nsp1 THR 72, nsp2 THR 73, nsp3 SER 64, nsp4 SER 81, nsp4 SER 455, nsp5 SER284, nsp6 THR 238, and nsp16 SER 132. The identified list of residues suggests how phosphorylation affects SARS-CoV-2 nsps function and stability. This research also suggests that kinase inhibitors could be a possible component for evaluating drug binding studies, which are crucial in therapeutic discovery research.

B-lineage acute lymphocytic leukemia (B-ALL) is characterized by different genetic aberrations at a chromosomal and gene level which are very crucial for diagnosis, prognosis and risk assessment of the disease. However, there is still controversial arguments in regard to disease outcomes in specific genetic abnormalities, e.g., 9p-deletion. Moreover, in absence of cytogenetic abnormalities it is difficult to predict B-ALL progression. Here, we use the advantage of Next-generation sequencing (NGS) technology to study the mutation landscape of 12 patients with B-ALL using Comprehensive Cancer Panel (CCP) which covers the most common mutated cancer genes. Our results describe new mutations in CSF3R gene including S661N, S557G, and Q170X which might be associated with disease progression.

Dengue virus infection is a global health problem for which there have been challenges to obtaining a cure. Current vaccines and anti-viral drugs can only be narrowly applied in ongoing clinical trials. We employed computational methods based on structure-function relationships between human host kinases and viral nonstructural protein 3 (NS3) to understand viral replication inhibitors’ therapeutic effect. Phosphorylation at each of the two most evolutionarily conserved sites of NS3, serine 137 and threonine 189, compared to the unphosphorylated state were studied with molecular dynamics and docking simulations. The simulations suggested that phosphorylation at serine 137 caused a more remarkable structural change than phosphorylation at threonine 189, specifically located at amino acid residues 49–95. Docking studies supported the idea that phosphorylation at serine 137 increased the binding affinity between NS3 and nonstructural Protein 5 (NS5), whereas phosphorylation at threonine 189 decreased it. The interaction between NS3 and NS5 is essential for viral replication. Docking studies with the antiviral plant flavonoid Quercetin with NS3 indicated that Quercetin physically occluded the serine 137 phosphorylation site. Taken together, these findings suggested a specific site and mechanism by which Quercetin inhibits dengue and possible other flaviviruses.

Recent advances in peptide research revolutionized therapeutic discoveries for various infectious diseases. In view of the ongoing threat of the COVID-19 pandemic, there is an urgent need to develop potential therapeutic options. Intense and accomplishing research is being carried out to develop broad-spectrum vaccines and treatment options for corona viruses, due to the risk of recurrent infection by the existing strains or pandemic outbreaks by new mutant strains. Developing a novel medicine is costly and time consuming, which increases the value of repurposing existing therapies. Since, SARS-CoV-2 shares significant genomic homology with SARS-CoV, we have summarized various peptides identified against SARS-CoV using in silico and molecular studies and also the peptides effective against SARS-CoV-2. Dissecting the molecular mechanisms underlying viral infection could yield fundamental insights in the discovery of new antiviral agents, targeting viral proteins or host factors. We postulate that these peptides can serve as effective components for therapeutic options against SARS-CoV-2, supporting clinical scientists globally in selectively identifying and testing the therapeutic and prophylactic agents for COVID-19 treatment. In addition, we also summarized the latest updates on peptide therapeutics against SARS-CoV-2.

Background With the advances in genomics research, many countries still need more bioinformatics skills. This study aimed to assess the levels of awareness of bioinformatics and predictors of its use in genomics research among scientists in Saudi Arabia. Methods In a cross-sectional survey, 309 scientists of different biological and biomedical specialties were subjected to a previously validated e-questionnaire to collect data on (1) Knowledge about bioinformatics programming languages and tools, (2) Attitude toward acceptance of bioinformatics resources in genome-related research, and (3) The pattern of information-seeking to online bioinformatics resources. Logistic regression analysis was applied to identify the predictors of using bioinformatics in research. Significance was set at p<0.05. Results More than one-half (248, 56.4%) of all scientists reported a lack of bioinformatics knowledge. Most participants had a neutral attitude toward bioinformatics (295, 95.4%). The barriers facing acceptance of bioinformatics tools reported were; lack of training (210, 67.9%), insufficient support (180, 58.2%), and complexity of software (138, 44.6%). The limited experience was reported in; having one or more bioinformatics tools (98, 31.7%), using a supercomputer in their research inside (44, 14.2%) and outside Saudi Arabia (55, 17.8%), the need for developing a program to solve a biological problem (129, 41.7%), working in one or more fields of bioinformatics (93, 30.1%), using web applications (112, 36.2%), and using programming languages (102, 33.0%). Significant predictors of conducting genomics research were; younger scientists (p=0.039), Ph.D. education (p=0.003), more than five years of experience (p<0.05), previous training (p<0.001), and higher bioinformatics knowledge scores (p<0.001). Conclusion The study revealed a short knowledge, a neutral attitude, a lack of resources, and limited use of bioinformatics resources in genomics research. Education and training during each education level and during the job is recommended. Cloud-based resources may help scientists do research using publicly available Omics data. Further studies are necessary to evaluate collaboration among bioinformatics software developers and biologists.