The novel coronavirus disease-2019 (COVID-19), caused by SARS-CoV-2, is a global health pandemic beginning in early December 2019 in Wuhan, Hubei province, China. The effective drug target among coronaviruses is the SARS-CoV-2 main protease (Mpro), because of its crucial role in processing viral polyproteins translated from the viral RNA. In this study, the bioactivity of the selected thiol drug named Bucillamine (BUC) was evaluated as a potential drug for COVID-19 treatment by using computational modeling strategies. First, the molecular electrostatic potential density (ESP) calculation was performed to estimate the chemically active atoms of BUC. Additionally, BUC was docked to the Mpro (PDB: 6LU7) to evaluate the protein–ligand binding affinities. Besides, the estimated ESP results by density functional theory (DFT) were used to illustrate the molecular docking findings. Moreover, the frontier orbitals analysis was calculated to determine the charge transfer between the Mpro and BUC. Then, the stability of protein–ligand complex was subjected to the molecular dynamic simulations. Finally, an in silico study was performed to predict drug-likeness and absorption, distribution, metabolism, excretion and toxicity profiles (ADMET) of BUC. These results propose that BUC can be a potential drug candidate against the COVID-19 disease progression. Communicated by Ramaswamy H. Sarma

Multiple methods consisting of geographic information system (GIS) technique, enrichment factor (EF), potential ecological risk index (PEI) and multivariate statistical methods was developed to identify anthropogenic heavy metal sources in marine sediments of Hong Kong. The distributions of heavy metals in sediments have been analyzed, and their pollution degrees, corresponding potential ecological risks and source identifications have been studied using geo-accumulation index, potential ecological risk index and integrated multivariate statistical methods, respectively. Three different types of anthropogenic inputs could be identified via multivariate analysis. Acoording to the findings, the first principal component might originate from the industrial discharges and shipping activities. The second principal component were identified from the natural sources. The third component mainly from the municipal discharges and industrial wastewater. These results provide baseline information for both the coastal environment management and the worldwide heavy metal distribution and assessment.