Investigating the Molecular Interactions of Quinoline Derivatives for Antibacterial Activity Against Bacillus subtilis: Computational Biology and In Vitro Study Interpretations

Bacterial infections are evolving and one of the chief problems is emergence and prevalence of antibacterial resistance. Moreover, certain strains of Bacillus subtilis have become resistant to several antibiotics. To counteract this menace, the present work aimed to comprehend the antibacterial activity of synthesized two quinoline derivatives against Bacillus subtilis. Toxicity predictions via Protox II, SwissADME and T.E.S.T (Toxicity Estimation Software Tool) revealed that these derivatives were non-toxic and had little to no adverse effects. Molecular docking studies carried out in Schrodinger with two quinoline derivatives (referred Q1 and Q2) docked against selected target proteins (PDB IDs: 2VAM and1FSE) of B. subtilis demonstrated ideal binding energies (2VAM-Q1: - 4.63 kcal/mol and 2VAM-Q2: - 4.46 kcal/mol, and 1FSE-Q1: - 3.51 kcal/mol, 1FSE-Q2: - 6.34 kcal/mol). These complexes were simulated at 100 ns and the outcomes revealed their stability with slight conformational changes. Anti-microbial assay via disc diffusion method revealed zones of inhibition showing that B. subtilis was inhibited by both Q1 and Q2, with Q2 performing slightly better than Q1, pointing towards its effectiveness against this organism and necessitating further study on other bacteria in prospective studies. Thus, this study demonstrates that our novel quinoline derivatives exhibit antibacterial properties against Bacillus subtilis and can act as potent anti-bacterials.

A content analysis of social media on outbreak of infodemic amidst COVID-19 pandemic

BACKGROUND

With so much content on social media platforms about COVID-19, determining which information is reliable can be a daunting task. Hence, this study is aimed to analyze various posts with regard to COVID-19 on various social media platforms for their reliability and also examined various factors that influence information reliability.

MATERIALS AND METHODS

A cross-sectional study was conducted, with 934 samples related to coronavirus pandemic published on Twitter, Instagram, and Facebook using systematic random sampling. We adopted the criteria given by Paul Bradshaw and modified to assess the characteristics of the samples. Training and calibration of the investigators were carried out for 3 consecutive days before beginning the study. The data were analyzed using the Chi-square test and multinomial logistic regression to estimate the odds ratios.

RESULTS

Out of 934 samples studied, only 570 (61%) were found to be reliable of which 243 (42.6%) were from Twitter, 117 (20.6%) from Instagram, and 210 (36.8%) from Facebook. We found that the reliability of the information on social media platforms is significantly influenced by network (odds: 1.32; 95% confidence interval [CI]: 1.16-1.52; P = 0.036), content (odds: 1.83; 95% CI: 1.69-1.92; P = 0.009), contextual update (odds: 1.41; 95% CI: 1.24-1.53) and age of the account (odds: 1.92; 95% CI: 1.64-2.09; P = 0.002).

CONCLUSION

Our study shows that the reliability of the social media posts significantly depends on the network, contextual update, and age of the account. Hence, cross verifying the information from a reliable source is the need of the hour to prevent panic and mental distress.