A meta-meta-analysis of co-infection, secondary infections, and antimicrobial resistance in COVID-19 patients

The newly discovered coronavirus SARS-CoV-2 has sparked a worldwide pandemic of COVID-19, which has caused havoc on medical infrastructures, economies, and cultures around the world. Determining the whole scenario is essential since SARS-CoV-2 variants and sub-variants keep appearing after vaccinations and booster doses. The objective of this secondary meta-analysis is to analysis co-infection, secondary infections, and antimicrobial resistance (AMR) in COVID-19 patients. This study used five significant databases to conduct a systematic review and an overlap meta-analysis to evaluate the pooled estimates of co-infections and secondary infections. The summary of the meta-analysis showed an overall co-infection effect of 26.19% (95% confidence intervals CI: 21.39–31.01, I2 =98.78, n = 14 meta-analysis) among patients with COVID-19. A coinfection effect of 11.13% (95% CI: 9.7–12.56, I2 =99.14, n = 11 meta-analysis) for bacteria; 9.69% (95% CI: 1.21–7.90, I2 =98.33) for fungal and 3.48% (95% CI: 2.15–4.81, I2 =95.84) for viruses. A secondary infection effect of 19.03% (95% CI: 9.53–28.54, I2 =85.65) was pooled from 2 meta-analyses (Ave: 82 primary studies). This is the first study that compiles the results of all the previous three years meta-analyses into a single source and offers strong proof of co-infections and secondary infections in COVID-19 patients. Early detection of co-infection and AMR is crucial for COVID-19 patients in order to effective treatment

Antiviral Activity, Pharmacoinformatics, Molecular Docking, and Dynamics Studies of Against Nipah Virus by Targeting Envelope Glycoprotein: Emerging Strategies for Developing Antiviral Treatment

The Nipah virus (NiV) belongs to the Henipavirus genus is a serious public health concern causing numerous outbreaks with higher fatality rate. Unfortunately, there is no effective medication available for NiV. To investigate possible inhibitors of NiV infection, we used in silico techniques to discover treatment candidates in this work. As there are not any approved treatments for NiV infection, the NiV-enveloped attachment glycoprotein was set as target for our study, which is responsible for binding to and entering host cells. Our in silico drug design approach included molecular docking, post-docking molecular mechanism generalised born surface area (MM-GBSA), absorption, distribution, metabolism, excretion/toxicity (ADME/T), and molecular dynamics (MD) simulations. We retrieved 418 phytochemicals associated with the neem plant ( Azadirachta indica ) from the IMPPAT database, and molecular docking was used to ascertain the compounds’ binding strength. The top 3 phytochemicals with binding affinities of −7.118, –7.074, and −6.894 kcal/mol for CIDs 5280343, 9064, and 5280863, respectively, were selected for additional study based on molecular docking. The post-docking MM-GBSA of those 3 compounds was −47.56, –47.3, and −43.15 kcal/mol, respectively. As evidence of their efficacy and safety, all the chosen drugs had favorable toxicological and pharmacokinetic (Pk) qualities. We also performed MD simulations to confirm the stability of the ligand-protein complex structures and determine whether the selected compounds are stable at the protein binding site. All 3 phytochemicals, Quercetin (CID: 5280343), Cianidanol (CID: 9064), and Kaempferol (CID: 5280863), appeared to have outstanding binding stability to the target protein than control ribavirin, according to the molecular docking, MM-GBSA, and MD simulation outcomes. Overall, this work offers a viable approach to developing novel medications for treating NiV infection