Effect of Corticosteroids therapy on mortality in the COVID-19 patients- A systematic review and Meta-analysis (preprint)

Background: and Purpose: Corticosteroid therapy is still controversial to use for treatment of coronavirus disease-2019 (COVID-19). The results of multiple randomized clinical trials (RCTs) and observational studies are very diverse and contradictory, which arising difficulty in the clinical decision-making. The objective of this study is to investigate the effect of corticosteroids on mortality by systematic review and meta-analysis. External Approach: A systematic search was performed on different databases namely Medline/PubMed, Cochrane and Google scholar on 10 February 2022, according to PRISMA guidelines. The 28-days mortality was considered as outcome of study. A pooled estimate was calculated with random effects and fixed effects models. Cochran’s Q test and the I2 statistic were conducted for statistical heterogeneity. Key Results: 38 studies were included, having sample size of 87,781 patients. Amongst them, 16437 patients received corticosteroid therapy (intervention group) while 71344 patients were standard (noncorticosteroids) therapy (control group). 12.68% (2084) mortality observed in the intervention group while 5.93% (4227) mortality observed in the control group. The overall pooled estimate showed a significantly (OR2.305;95%CI: 2.1810 to 2.4370) increased mortality in intervention group. A pooled fold change estimation showed significantly increased in the mortality in methylprednisolone (OR 1.206;95%CI: 1.0770 to 1.3500) and dexamethasone (OR 1.388;95% CI:1.1870 to 1.6220) therapy. Conclusion and Implication: In conclusion, corticosteroid therapy produced a negative prognosis as depicted by increased mortality among COVID-19 patients. The possible reasons might be delay in virus clearance and secondary infection due to initiation of the corticosteroids at high dose in the early stage of infection.

The trustworthiness and impact of trial preprints for COVID-19 decision-making: A methodological study (preprint)

Purpose: To assess the trustworthiness and impact of preprint trial reports during the COVID-19 pandemic. Data sources: WHO COVID-19 database and the L-OVE COVID-19 platform by the Epistemonikos Foundation (up to August 3rd, 2021) Design: We compare the characteristics of COVID-19 trials with and without preprints, estimate time to publication of COVID-19 preprint reports, describe discrepancies in key methods and results between preprint and published trial reports, report the number of retracted preprints and publications, and assess whether including versus excluding preprint reports affects meta-analytic estimates and the certainty of evidence. For the effects of eight therapies on mortality and mechanical ventilation, we performed meta-analyses including preprints and excluding preprints at 1 month, 3 months, and 6 months after the first trial addressing the therapy became available either as a preprint or publication (120 meta-analyses in total). Results: We included 356 trials, 101 of which are only available as preprints, 181 as journal publications, and 74 as preprints first and subsequently published in journals. Half of all preprints remain unpublished at six months and a third at one year. There were few important differences in key methods and results between trial preprints and their subsequent published reports. We identified four retracted trials, three of which were published in peer-reviewed journals. With two exceptions (2/60; 3.3%), point estimates were consistent between meta-analyses including versus excluding preprints as to whether they indicated benefit, no appreciable effect, or harm. There were nine comparisons (9/60; 15%) for which the rating of the certainty of evidence differed when preprints were included versus excluded, for four of these comparisons the certainty of evidence including preprints was higher and for five of these comparisons the certainty of evidence including preprints was lower. Limitations: The generalizability of our results is limited to COVID-19. Preprints that are subsequently published in journals may be the most rigorous and may not represent all trial preprints. Conclusion: We found no compelling evidence that preprints provide less trustworthy results than published papers. We show that preprints remain the only source of findings of many trials for several months, for a length of time that is unacceptable in a health emergency. We show that including preprints may affect the results of meta-analyses and the certainty of evidence. We encourage evidence users to consider data from preprints in contexts in which decisions are being made rapidly and evidence is being produced faster than can be peer-reviewed.

Real time scalable data acquisition of COVID-19 in six continents through PySpark - a big data tool (preprint)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was declared as a global emergency in January 2020 due to its pandemic outbreak. To examine this Coronavirus disease 2019 (COVID-19) effects various data are being generated through different platforms. This study was focused on the clinical data of COVID-19 which relied on python programming. Here, we proposed a machine learning approach to provide a insights into the COVID-19 information. PySpark is a machine learning approach which also known as Apache spark an accurate tool for the searching of results with minimum time intervals as compare to Hadoop and other tools. World Health Organization (WHO) started gathering corona patients’ data from last week of the February 2020. On March 11, 2020, the WHO declared COVID-19 a global pandemic. The cases became more evident and common after mid-March. This paper used the live owid (our world in data) dataset and will analyse and find out the following details on the live COVID-19 dataset. (1) The daily Corona virus scenario on various continents using PySpark in microseconds of Processor time. (2) After the various antibodies have been implemented, how they impact new cases on a regular basis utilizing various graphs. (3) Tabular representation of COVID-19 new cases in all the continents.

Evolving Phenotypes of non-hospitalized Patients that Indicate Long Covid (preprint)

Many of the symptoms characterized as the post-acute sequelae of SARS-CoV-2 infection (PASC) could have multiple causes or similarly seen in non-COVID patients. Accurate identification of phenotypes will be important to guide future research and the healthcare system to focus its efforts and resources on adequately controlled age- and gender-specific sequelae of COVID-19 infection. In this retrospective electronic health records (EHR) cohort study, we applied a computational framework for knowledge discovery from clinical data, MLHO, to identify phenotypes that positively associate with a past positive PCR test for COVID-19. We evaluated the post-test phenotypes in two temporal windows at 3-6 and 6-9 months after the test and by age and gender. We utilized longitudinal diagnosis records stored in EHRs from Mass General Brigham (MGB) 57 thousand patients who tested positive or negative for COVID-19 and were not hospitalized. Statistical analyses were performed on data from March 2020 to March 2021. PCR test results and subsequent diagnosis records that were recorded for the first time two months or later after the PCR test. We identified 28 phenotypes among different age/gender cohorts or time windows that positively associated with past SARS-CoV-2 infection. All identified phenotypes were newly recorded in patients' medical records two months or longer after a COVID-19 PCR test in non-hospitalized patients regardless of the test result. Among these phenotypes, a new diagnosis record for anosmia and dysgeusia (OR 2.17, 95% CI [1.42 - 3.25]), alopecia (OR 3.54, 95% CI [2.92 - 4.3]), chest pain (OR 1.35, 95% CI [1.16 - 1.56]), or chronic fatigue syndrome (OR 1.81-2.28, 95% CI [1.38 - 3.68]) are the most significant indicators of a past COVID-19 infection, especially among women younger than 65. Among men, edema (OR 1.83, 95% CI [1.23 - 2.66]) and disease of the nail (OR 3.54, 95% CI [1.63 - 7.29]) in patients 65 and older or proteinuria (OR 2.66, 95% CI [1.61 - 4.34]) in patients under 65 are associated with a positive COVID-19 PCR test in the past few months. Our approach avoids a flood of false positive discoveries while offering a more probabilistic flexible criterion than the standard linear phenome-wide association study (PheWAS). These findings suggest that some of the previously identified post sequelae of COVID-19 may not be accurate and that most of the PASC are observed in patients under 65 years of age.

Identifying communities at risk for COVID-19-related burden across 500 U.S. Cities and within New York City (preprint)

Background While it is well-known that older individuals with certain comorbidities are at highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at highest risk with fine-grained spatial and temporal resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health. Methods We develop a robust COVID-19 Community Risk Score (C-19 Risk Score) that summarizes the complex disease co-occurrences for individual census tracts with unsupervised learning, selected on their basis for association with risk for COVID complications, such as death. We mapped the C-19 Risk Score onto neighborhoods in New York City and associated the score with C-19 related death. We further predict the C-19 Risk Score using satellite imagery data to map the built environment in C-19 Risk. Results The C-19 Risk Score describes 85% of variation in co-occurrence of 15 diseases that are risk factors for COVID complications among 26K census tract neighborhoods (median population size of tracts: 4,091). The C-19 Risk Score is associated with a 40% greater risk for COVID-19 related death across NYC (April and September 2020) for a 1SD change in the score (Risk Ratio for 1SD change in C19 Risk Score: 1.4, p < .001). Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the C-19 Risk Score in the United States in held-out census tracts (R 2 of 0.87). Conclusions The C-19 Risk Score localizes COVID-19 risk at the census tract level and predicts COVID-19 related morbidity and mortality.

Publishing of COVID-19 Preprints in Peer-reviewed Journals, Preprinting Trends, Public Discussion and Quality Issues (preprint)

IntroductionCOVID-19-related (vs. non-related) articles appear to be more expeditiously processed and published in peer-reviewed journals. We aimed to evaluate: (i) whether COVID-19-related preprints were favoured for publication, (ii) preprinting trends and public discussion of the preprints and (iii) relationship between the publication topic (COVID-19-related or not) and quality issues. MethodsManuscripts deposited at bioRxiv and medRxiv between January 1 and October 21 were assessed for the probability of publishing in peer-reviewed journals, and those published were evaluated for submission-to-acceptance time. The extent of public discussion was assessed based on Altmetric and Disqus data. The Retraction Watch database and PubMed were used to explore the retraction of COVID-19 and non-COVID-19 articles and preprints. ResultsWith adjustment for the preprinting server and number of deposited versions, COVID-19-related preprints were more likely to be published within 120 days since the deposition of the first version (OR=1.99, 95%CI 1.76-2.25) as well as over the entire observed period (OR=1.49, 95%CI 1.36-1.62). Submission-to-acceptance was by 41.69 days (95%CI 46.56-36.80) shorter for COVID-19 articles. Public discussion of preprints was modest and COVID-19 articles were overrepresented in the pool of retracted articles in 2020. ConclusionCurrent data suggest a preference for publication of COVID-19-related preprints over the observed period.

In-Silico analysis reveals lower transcription efficiency of C241T variant of SARS-CoV-2 with host replication factors MADP1 and hnRNP-1 (preprint)

Novel severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has claimed more than 1.5 million lives worldwide and counting. As per the GISAID database, the genomics of SARS-CoV2 is extensively studied with more than 500 genome submissions per day. Out of several hotspot mutations within the SARS-CoV-2 genome, researchers have focused a lot on missense variants but the least work is done on the UTRs. One of the most frequent UTR variants in the SARS-CoV-2 genome is the C241T with a global frequency of more than 0.9. In the present study, the effect of the C241T mutation has been studied with respect to change in RNA structure and its interaction with the host replication factors MADP1 Zinc finger CCHC-type and RNA-binding motif 1 (hnRNP1). The results obtained from molecular docking and molecular dynamics simulation indicated weaker interaction of C241T mutant stem loops with host transcription factor MADP1 indicating reduced replication efficiency. The results are also correlated with increased recovery rates and decreased death rates of global SARS-CoV-2 cases.

Identification of Potential Binders of the SARS-Cov-2 Spike Protein via Molecular Docking, Dynamics Simulation and Binding Free Energy Calculation (preprint)

The pandemic outbreak of COVID-19 virus (SARS-CoV-2) has become critical global health issue. The biophysical and structural evidence shows that SARS-CoV-2 spike protein possesses higher binding affinity towards angiotensin-converting enzyme 2 (ACE2) and hemagglutinin-acetylesterase (HE) glycoprotein receptor. Hence, it was selected as a target to generate the potential candidates for the inhibition of HE glycoprotein. The present study focuses on extensive computational approaches which contains molecular docking, ADMET prediction followed by molecular dynamics simulations and free energy calculations. Furthermore, virtual screening of NPACT compounds identified 3,4,5-Trihydroxy-1,8-bis[(2R,3R)-3,5,7-trihydroxy-3,4-dihydro-2H-chromen-2-yl]benzo[7]annulen-6-one, Silymarin, Withanolide D, Spirosolane and Oridonin were interact with high affinity. The ADMET prediction revealed pharmacokinetics and drug-likeness properties of top-ranked compounds. Molecular dynamics simulations and binding free energy calculations affirmed that these five NPACT compounds were robust HE inhibitor.