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.
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