Molecular docking studies, in silico admet screening of selected novel azetidine substituted naphthalene’s targeting protease enzyme against SARS cov-19

The emergence and dissemination of SARS COVID-19 has resulted in a high death rate, necessitating a large-scale search for viable anti-SARS COVID-19 therapeutics. The binding mechanisms of 25 azetidines bearing naphthalene derivatives as Anti-SARS COVID-19 inhibitors, targeting protease enzyme via molecular docking, ADME and Toxicity Prediction (TOPKAT) investigations were investigated in this work, and they were compared to the FDA-approved medicine remdesivir. Compounds 22, 18, 17, 14 had the highest Lib Dock score among the 25 derivatives, with the X-ray crystallographic structure of M pro (PDB ID: 6LU7) revealing important interactions with residues Glu166, Gln192, Ala191, Thr190, Ser144, Cys145. These findings imply that these azetidine derivatives may be useful in the development of more effective anti-SARS COVID-19 agents. © 2021 The authors.

Location-aware hazardous litter management for smart emergency governance in urban eco-cyber-physical systems

Smart city management is facing a new challenge from littered face masks during COVID-19 pandemic. Addressing the issues of detection and collection of this hazardous waste that is littered in public spaces and outside the controlled environments, usually associated with biomedical waste, is urgent for the safety of the communities around the world. Manual management of this waste is beyond the capabilities of governments worldwide as the geospatial scale of littering is very high and also because this contaminated litter is a health and safety issue for the waste collectors. In this paper, an autonomous biomedical waste management framework that uses edge surveillance and location intelligence for detection of the littered face masks and predictive modelling for emergency response to this problem is proposed. In this research a novel dataset of littered face masks in various conditions and environments is collected. Then, a new deep neural network architecture for rapid detection of discarded face masks on the video surveillance edge nodes is proposed. Furthermore, a location intelligence model for prediction of the areas with higher probability of hazardous litter in the smart city is presented. Experimental results show that the accuracy of the proposed model for detection of littered face masks in various environments is 96%, while the speed of processing is ten times faster than comparable models. The proposed framework can help authorities to plan for timely emergency response to scattering of hazardous material in residential environments.