The recent outbreak of coronavirus disease-19 (COVID-19) continues to drastically affect healthcare throughout the world. To date, no approved treatment regimen or vaccine is available to effectively attenuate or prevent the infection. Therefore, collective and multidisciplinary efforts are needed to identify new therapeutics or to explore effectiveness of existing drugs and drug-like small molecules against SARS-CoV-2 for lead identification and repurposing prospects. This study addresses the identification of small molecules that specifically bind to any of the three essential proteins (RdRp, 3CL-protease and helicase) of SARS-CoV-2. By applying computational approaches we screened a library of 4574 compounds also containing FDA-approved drugs against these viral proteins. Shortlisted hits from initial screening were subjected to iterative docking with the respective proteins. Ranking score on the basis of binding energy, clustering score, shape complementarity and functional significance of the binding pocket was applied to identify the binding compounds. Finally, to minimize chances of false positives, we performed docking of the identified molecules with 100 irrelevant proteins of diverse classes thereby ruling out the non-specific binding. Three FDA-approved drugs showed binding to 3CL-protease either at the catalytic pocket or at an allosteric site related to functionally important dimer formation. A drug-like molecule showed binding to RdRp in its catalytic pocket blocking the key catalytic residues. Two other druglike molecules showed specific interactions with helicase at a key domain involved in catalysis. This study provides lead drugs or drug-like molecules for further in vitro and clinical investigation for drug repurposing and new drug development prospects.

Cotton (Gossypium hirsutum) is an economically important crop and is widely cultivated around the globe. However, the major problem of cotton is its high vulnerability to biotic and abiotic stresses. It has been around three decades since the cotton plant was genetically engineered with genes encoding insecticidal proteins (mainly Cry proteins) with an aim to protect it against insect attack. Several studies have been reported on the impact of these genes on cotton production and fiber quality. However, the metabolites responsible for conferring resistance in genetically modified cotton need to be explored. The current work aims to unveil the key metabolites responsible for insect resistance in Bt cotton and also compare the conventional multivariate analysis methods with deep learning approaches to perform clustering analysis. We aim to unveil the marker compounds which are responsible for inducing insect resistance in cotton plants. For this purpose, we employed 1H-NMR spectroscopy to perform metabolite profiling of Bt and non-Bt cotton varieties, and a total of 42 different metabolites were identified in cotton plants. In cluster analysis, deep learning approaches (linear discriminant analysis (LDA) and neural networks) showed better separation among cotton varieties compared to conventional methods (principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLSDA)). The key metabolites responsible for inter-class separation were terpinolene, α-ketoglutaric acid, aspartic acid, stigmasterol, fructose, maltose, arabinose, xylulose, cinnamic acid, malic acid, valine, nonanoic acid, citrulline, and shikimic acid. The metabolites which regulated differently with the level of significance p < 0.001 amongst different cotton varieties belonged to the tricarboxylic acid cycle (TCA), Shikimic acid, and phenylpropanoid pathways. Our analyses underscore a biosignature of metabolites that might involve in inducing insect resistance in Bt cotton. Moreover, novel evidence from our study could be used in the metabolic engineering of these biological pathways to improve the resilience of Bt cotton against insect/pest attacks. Lastly, our findings are also in complete support of employing deep machine learning algorithms as a useful tool in metabolomics studies.

OBJECTIVE: To investigate the dental practitioner's knowledge, attitude and practice towards dental implants. METHODOLOGY: This study was carried out from May'2019 - Oct 2019. 752 dental practitioners who were currently practicing were included in the study. A well-structured questionnaire was used for data collection. SPSS-25 was used for statistical analysis. Spearman correlation was used to find the effect of gender, knowledge and attitude. The P <0.05 was considered statistically significant. RESULTS: In this study 80.9% dental practitioners were aware of the appropriate implant material while, 57.9% had knowledge about the types. 30.3% knew about the implant surface modifications whereas 46.1% dentists were aware of possible implant placement approaches. 49.3% believed that the distance between dental implants to be 3mm and between a dental implant and natural tooth to be 1.5 mm. 76% dentists claimed that dental implants have biomechanical complications. Whereas 67.8% knew about the Branemark's theory of osseointegration. Regarding the attitude of dental practitioners, 28.9% had received implant hands on trainings while 9% felt competent to place an implant. CONCLUSION: This study describes that dental practitioners had an appropriate knowledge of each aspect of implantology. Moreover despite the fact majority felt that they are not competent enough to practice it. Thus, it is important that the curriculum, teaching standards, the materials and methods regarding dental implants need to be reviewed and more exposure of hands-on workshops is to be provided not only for the graduates but also the undergraduates during their clinical learning. KEYWORDS: Dental Implants, Dentist's opinion, Knowledge HOW TO CITE: Ahmed N, Abbasi MS, Mariam Q, William H, Iftikhar H, Badar H, Irfan AB. Analysis of dental practitioners perception towards dental implants. J Pak Dent Assoc 2021;30(1):45-49