Efficacy of Sofosbuvir plus Ledipasvir in Egyptian patients with COVID-19 compared to standard treatment: a randomized controlled trial

Discovery of Bacterial Key Genes from 16S rRNA-Seq Profiles That Are Associated with the Complications of SARS-CoV-2 Infections and Provide Therapeutic Indications

SARS-CoV-2 infections, commonly referred to as COVID-19, remain a critical risk to both human life and global economies. Particularly, COVID-19 patients with weak immunity may suffer from different complications due to the bacterial co-infections/super-infections/secondary infections. Therefore, different variants of alternative antibacterial therapeutic agents are required to inhibit those infection-causing drug-resistant pathogenic bacteria. This study attempted to explore these bacterial pathogens and their inhibitors by using integrated statistical and bioinformatics approaches. By analyzing bacterial 16S rRNA sequence profiles, at first, we detected five bacterial genera and taxa (Bacteroides, Parabacteroides, Prevotella Clostridium, Atopobium, and Peptostreptococcus) based on differentially abundant bacteria between SARS-CoV-2 infection and control samples that are significantly enriched in 23 metabolic pathways. A total of 183 bacterial genes were found in the enriched pathways. Then, the top-ranked 10 bacterial genes (accB, ftsB, glyQ, hldD, lpxC, lptD, mlaA, ppsA, ppc, and tamB) were selected as the pathogenic bacterial key genes (bKGs) by their protein-protein interaction (PPI) network analysis. Then, we detected bKG-guided top-ranked eight drug molecules (Bemcentinib, Ledipasvir, Velpatasvir, Tirilazad, Acetyldigitoxin, Entreatinib, Digitoxin, and Elbasvir) by molecular docking. Finally, the binding stability of the top-ranked three drug molecules (Bemcentinib, Ledipasvir, and Velpatasvir) against three receptors (hldD, mlaA, and lptD) was investigated by computing their binding free energies with molecular dynamic (MD) simulation-based MM-PBSA techniques, respectively, and was found to be stable. Therefore, the findings of this study could be useful resources for developing a proper treatment plan against bacterial co-/super-/secondary-infection in SARS-CoV-2 infections.

An Update on SARS-CoV-2 Clinical Trial Results-What We Can Learn for the Next Pandemic

The coronavirus disease 2019 (COVID-19) pandemic has claimed over 7 million lives worldwide, providing a stark reminder of the importance of pandemic preparedness. Due to the lack of approved antiviral drugs effective against coronaviruses at the start of the pandemic, the world largely relied on repurposed efforts. Here, we summarise results from randomised controlled trials to date, as well as selected in vitro data of directly acting antivirals, host-targeting antivirals, and immunomodulatory drugs. Overall, repurposing efforts evaluating directly acting antivirals targeting other viral families were largely unsuccessful, whereas several immunomodulatory drugs led to clinical improvement in hospitalised patients with severe disease. In addition, accelerated drug discovery efforts during the pandemic progressed to multiple novel directly acting antivirals with clinical efficacy, including small molecule inhibitors and monoclonal antibodies. We argue that large-scale investment is required to prepare for future pandemics; both to develop an arsenal of broad-spectrum antivirals beyond coronaviruses and build worldwide clinical trial networks that can be rapidly utilised.

Comparison of Preprint Postings of Randomized Clinical Trials on COVID-19 and Corresponding Published Journal Articles: A Systematic Review

IMPORTANCE

Randomized clinical trials (RCTs) on COVID-19 are increasingly being posted as preprints before publication in a scientific, peer-reviewed journal.

OBJECTIVE

To assess time to journal publication for COVID-19 RCT preprints and to compare differences between pairs of preprints and corresponding journal articles.

EVIDENCE REVIEW

This systematic review used a meta-epidemiologic approach to conduct a literature search using the World Health Organization COVID-19 database and Embase to identify preprints published between January 1 and December 31, 2021. This review included RCTs with human participants and research questions regarding the treatment or prevention of COVID-19. For each preprint, a literature search was done to locate the corresponding journal article. Two independent reviewers read the full text, extracted data, and assessed risk of bias using the Cochrane Risk of Bias 2 tool. Time to publication was analyzed using a Cox proportional hazards regression model. Differences between preprint and journal article pairs in terms of outcomes, analyses, results, or conclusions were described. Statistical analysis was performed on October 17, 2022.

FINDINGS

This study included 152 preprints. As of October 1, 2022, 119 of 152 preprints (78.3%) had been published in journals. The median time to publication was 186 days (range, 17-407 days). In a multivariable model, larger sample size and low risk of bias were associated with journal publication. With a sample size of less than 200 as the reference, sample sizes of 201 to 1000 and greater than 1000 had hazard ratios (HRs) of 1.23 (95% CI, 0.80-1.91) and 2.19 (95% CI, 1.36-3.53) for publication, respectively. With high risk of bias as the reference, medium-risk articles with some concerns for bias had an HR of 1.77 (95% CI, 1.02-3.09); those with a low risk of bias had an HR of 3.01 (95% CI, 1.71-5.30). Of the 119 published preprints, there were differences in terms of outcomes, analyses, results, or conclusions in 65 studies (54.6%). The main conclusion in the preprint contradicted the conclusion in the journal article for 2 studies (1.7%).

CONCLUSIONS AND RELEVANCE

These findings suggest that there is a substantial time lag from preprint posting to journal publication. Preprints with smaller sample sizes and high risk of bias were less likely to be published. Finally, although differences in terms of outcomes, analyses, results, or conclusions were observed for preprint and journal article pairs in most studies, the main conclusion remained consistent for the majority of studies.

Development and validation of a predictive scoring system for in-hospital mortality in COVID-19 Egyptian patients: a retrospective study

SARS-CoV-2 virus has rapidly spread worldwide since December 2019, causing COVID-19 disease. In-hospital mortality is a common indicator for evaluating treatment outcomes. Therefore, the developing and validating a simple score system from observational data could assist in modulating the management procedures. A retrospective cohort study included all data records of patients with positive PCR for SARS-CoV-2. The factors that associated with mortality were analyzed, then allocation of potential predictors of mortality was executed using different logistic regression modeling, subsequently scoring system was developed from the most weighted predictors. The mortality rate of patients with COVID-19 pneumonia was 28.5% and 28.74%, respectively. The most significant factors that affected in-hospital mortality were old age (> 60 years), delay in hospital admission (> 4 days), high neutrophil/lymphocyte ratio "NLR" (> 3); higher computed tomography severity score; and CT-SS (> 20), in addition to using remdesivir and tocilizumab in the treatment protocol (P < 0.001 for all). The validity of the newly performed score was significant; the AUC was 85%, P < 0.001, and its prognostic utility was good; the AUC was 75%, P < 0.001. The prognostic utility of newly developed score system (EGY.Score) was excellent and could be used to adjust the treatment strategy of highly at-risk patients with COVID-19 pneumonia.