Evaluation of protein C and S levels in patients with COVID-19 infection and their relation to disease severity.

Background: The COVID-19 pandemic has been associated with millions of deaths around the world. One of the important causes of death associated with COVID-19 was pulmonary thromboembolism. The risk for venous thromboembolism was markedly increased in patients with COVID-19 especially those admitted to the intensive care unit. The aims of our study were to measure the protein C and S levels in COVID-19-infected patients in comparison with the normal population and to assess the correlation of protein C and S levels in the plasma to the severity of infection. Methods: This was a case-control study measuring the protein C and S levels in patients infected with COVID-19 at the time of diagnosis compared to the normal population. The study included one hundred participants, sixty of them are patients with COVID-19, and forty of them are normal healthy adults. The patient group was subclassified into three subgroups according to disease severity: mild, moderate, and severe COVID-19 infections. Results: The activity of protein C in the patient group serum was significantly lower than that in the control group serum (79.35 ± 26.017 vs 97.43 ± 15.007; p < 0.001). Protein S is also significantly decreased in patients' serum when compared to the control group (70.233 ± 22.476 vs 91 ± 14.498; p < 0.001). There was a statistically significant decrease in the levels of protein C and S associated with the increase in disease severity (p < 0.05). However, protein S showed no statistically significant difference between the moderate and severe disease subgroups. Conclusion: The study concluded that the levels of protein C and S activities were both decreased in patients with COVID-19 when compared to the healthy population. It also concluded that the decrease in their levels is statistically significant in relation to the disease severity.

Developing a mortality risk prediction model using data of 3663 hospitalized COVID-19 patients: a retrospective cohort study in an Egyptian University Hospital.

PURPOSE: Since the declaration of COVID-19 as a pandemic, a wide between-country variation was observed regarding in-hospital mortality and its predictors. Given the scarcity of local research and the need to prioritize the provision of care, this study was conducted aiming to measure the incidence of in-hospital COVID-19 mortality and to develop a simple and clinically applicable model for its prediction. METHODS: COVID-19-confirmed patients admitted to the designated isolation areas of Ain-Shams University Hospitals (April 2020-February 2021) were included in this retrospective cohort study (n = 3663). Data were retrieved from patients' records. Kaplan-Meier survival and Cox proportional hazard regression were used. Binary logistic regression was used for creating mortality prediction models. RESULTS: Patients were 53.6% males, 4.6% current smokers, and their median age was 58 (IQR 41-68) years. Admission to intensive care units was 41.1% and mortality was 26.5% (972/3663, 95% CI 25.1-28.0%). Independent mortality predictors-with rapid mortality onset-were age ≥ 75 years, patients' admission in critical condition, and being symptomatic. Current smoking and presence of comorbidities particularly, obesity, malignancy, and chronic haematological disorders predicted mortality too. Some biomarkers were also recognized. Two prediction models exhibited the best performance: a basic model including age, presence/absence of comorbidities, and the severity level of the condition on admission (Area Under Receiver Operating Characteristic Curve (AUC) = 0.832, 95% CI 0.816-0.847) and another model with added International Normalized Ratio (INR) value (AUC = 0.842, 95% CI 0.812-0.873). CONCLUSION: Patients with the identified mortality risk factors are to be prioritized for preventive and rapid treatment measures. With the provided prediction models, clinicians can calculate mortality probability for their patients. Presenting multiple and very generic models can enable clinicians to choose the one containing the parameters available in their specific clinical setting, and also to test the applicability of such models in a non-COVID-19 respiratory infection.

Ivermectin role in COVID-19 treatment (IRICT): single-center, adaptive, randomized, double-blind, placebo-controlled, clinical trial.

BACKGROUND: To investigate the efficacy and safety of ivermectin compared to hydroxychloroquine and placebo in hospitalized moderate to severe COVID-19 patients. RESEARCH DESIGN AND METHODS: The study was an adaptive, randomized, double-blinded, controlled, single-center trial. The study was a series of 3-arm comparisons between two different investigational therapeutic agents (ivermectin and hydroxychloroquine) and a placebo. There was interim monitoring to allow early stopping for futility, efficacy, or safety. RESULTS: Ivermectin decreased survival time from 29 to 18.3 days (HR, 9.8, 95%CI, 3.7-26.2), while it did not shorten the recovery time (HR, 1.02, 95%CI, 0.69-1.5). Subgroup analysis showed an association between ivermectin-related mortality and baseline oxygen saturation level. Moreover, stratified groups showed higher risk among patients on high flow O2. Hydroxychloroquine delayed recovery from 10.1 to 12.5 days (HR, 0.62, 95%CI, 0.4-0.95) and non-significantly decreased survival time from 29 to 26.8 days (HR, 1.47, 95%CI, 0.73-2.9). However, 3 months mortality rates were increased with hydroxychloroquine (RR, 2.05, 95%CI, 1.33-3.16). Neither ivermectin nor hydroxychloroquine increased adverse events and demonstrated safety profile compared to placebo. CONCLUSIONS: The study recommends against using either ivermectin or hydroxychloroquine for treatment of COVID-19 in hospitalized patients with any degree of severity. Clinical trial registration: www.clinicaltrials.gov identifier is: NCT04746365.

Hydroxychloroquine for treatment of nonsevere COVID-19 patients: Systematic review and meta-analysis of controlled clinical trials.

Being a pandemic and having a high global case fatality rate directed us to assess the evidence strength of hydroxychloroquine efficacy in treating coronavirus disease-2019 (COVID-19) arising from clinical trials and to update the practice with the most reliable clinical evidence. A comprehensive search was started in June up to 18 July, 2020 in many databases, including PubMed, Embase, and others. Of 432 studies found, only six studies fulfilled the inclusion criteria, which includes: clinical trials, age more than 12 years with nonsevere COVID-19, polymerase chain reaction-confirmed COVID-19, hydroxychloroquine is the intervention beyond the usual care. Data extraction and bias risk assessment were done by two independent authors. Both fixed-effect and random-effect models were utilized for pooling data using risk difference as a summary measure. The primary outcomes are clinical and radiological COVID-19 progression, severe acute respiratory syndrome coronavirus-2 clearance in the pharyngeal swab, and mortality. The secondary outcomes are the adverse effects of hydroxychloroquine. Among 609 COVID-19 confirmed patients obtained from pooling six studies, 294 patients received hydroxychloroquine and 315 patients served as a control. Hydroxychloroquine significantly prevents early radiological progression relative to control with risk difference and 95% confidence interval of -0.2 (-0.36 to -0.03). On the other hand, hydroxychloroquine did not prevent clinical COVID-19 progression, reduce 5-day mortality, or enhance viral clearance on days 5, 6, and 7. Moreover, many adverse effects were reported with hydroxychloroquine therapy. Failure of hydroxychloroquine to show viral clearance or clinical benefits with additional adverse effects outweigh its protective effect from radiological progression in nonsevere COVID-19 patients. Benefit-risk balance should determine the hydroxychloroquine use in COVID-19.

Comparative study on quantum descriptors, Molecular docking and dynamic simulation of antiviral drugs with Covid-19

Covid-19 is a beta-coronavirus that was first identified during the Wuhan COVID-19 epidemic in 2019. This study is focused on the quantum descriptors of the proposed antiviral drugs, molecular docking, and dynamics simulation with the main protease of coronavirus. Such drugs are Baloxavir, Chloroquine, Avigan, Plaquenil, oseltamivir, Remdesivir, Arbidol, and Sofosbuvir were used for comparison. Density functional theory (DFT) may help find the relevancy of quantum chemical descriptors to explain the potential antiviral activity, Some quantum descriptors such as ΔE;the energy gap, η;global hardness, S;global softness, I: ionization potential, A: electron affinity, χ: absolute electronegativity, ω;ΔE Back-donation;the back donation were calculated based on EHOMO;energy of the highest occupied molecular orbital, and ELUMO;energy of the lowest unoccupied molecular orbital. Fukui indices (f+, f-);for local nucleophilic and electrophilic attacks are investigated for the investigated antiviral drugs. The reported genomic sequence of Covid-19 main protease in complex with an inhibitor N3 (DOI: 10.2210/pdb6LU7/pdb) was used as a precursor for docking with the selected drugs after removing the attached inhibitors N3 and water. Molecular docking was performed using Autodock 4.2, with the Lamarckian Genetic Algorithm, and was analyzed by Autodock 1.5.6 and Pymol version 1.7.4.5 Edu, However, further research is necessary to investigate their potential medicinal use.

Efficacy and safety of remdesivir in hospitalized Covid-19 patients: Systematic review and meta-analysis including network meta-analysis.

Remdesivir is an antiviral agent that has shown broad-spectrum activity, including against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Clinical trials investigating the role of remdesivir in coronavirus disease 2019 (Covid-19) reported conflicting results. This study aimed to systematically review the best available evidence and synthesize the results. Several electronic databases were searched for candidate studies up to 12 October 2020. Studies eligible for meta-analysis were selected based on the inclusion criteria. Primary outcomes are the recovery and mortality rates, while secondary outcomes are the safety profile of remdesivir. The main effective measures are the rate ratio (RR) and rate difference (RD). Four clinical trials and one observational study were included. Remdesivir treatment for 10 days increased the recovery rate on day 14 by 50% among severe Covid-19 patients (RR = 1.5, 95%CI = 1.33-1.7), while on day 28 it was increased by 14% among moderate and severe Covid-19 patients (RR = 1.14, 95%CI = 1.06-1.22). Additionally, remdesivir decreased the mortality rate on day 14 by 36% among all patients (RR = 0.64, 95%CI = 0.45-0.92) but not on day 28 (RR = 1.05, 95%CI = 0.56-1.97). Nonmechanically ventilated Covid-19 patients showed better response to remdesivir in the recovery (RR = 0.3, 95%CI = 0.13-0.7) and mortality (RR = 2.33, 95%CI = 1.24-4.4) rates on day 14. Remdesivir reduced serious adverse effects by absolute 6% and no significant Grade 3 or 4 adverse effects were reported. At this early stage of the pandemic, there is evidence that remdesivir can be safely administered for hospitalized Covid-19 patients. It improves the recovery rate in both moderate and severe patients but, the optimal effect is achieved for those who are severely affected but not mechanically ventilated.