Background SARS-CoV-2, an emerging strain of coronavirus, has affected millions of people from all the continents of world and received worldwide attention. This emerging health crisis calls for the urgent development of specific therapeutics against COVID-19 to potentially reduce the burden of this emerging pandemic. Purpose This study aims to evaluate the anti-viral efficacy of natural bioactive entities against COVID-19 via molecular docking and molecular dynamics simulation. Methods A library of 27 caffeic-acid derivatives was screened against 5 proteins of SARS-CoV-2 by using Molegro Virtual Docker 7 to obtain the binding energies and interactions between compounds and SARS-CoV-2 proteins. ADME properties and toxicity profiles were investigated via www.swissadme.ch web tools and Toxtree respectively. Molecular dynamics simulation was performed to determine the stability of the lead-protein interactions. Results Our obtained results has uncovered khainaoside C, 6-O-Caffeoylarbutin, khainaoside B, khainaoside C and vitexfolin A as potent modulators of COVID-19 possessing more binding energies than nelfinavir against COVID-19 M pro , Nsp15, SARS-CoV-2 spike S2 subunit, spike open state and closed state structure respectively. While Calceolarioside B was identified as pan inhibitor, showing strong molecular interactions with all proteins except SARS-CoV-2 spike glycoprotein closed state. The results are supported by 20 ns molecular dynamics simulations of the best complexes. Conclusion This study will hopefully pave a way for development of phytonutrients-based antiviral therapeutic for treatment or prevention of COVID-19 and further studies are recommended to evaluate the antiviral effects of these phytochemicals against SARS-CoV-2 in in vitro and in vivo models.

A BCR-sequential extraction procedure for the determination of extractable heavy metals was applied to sediments of various rivers and lakes. There are many rivers basins in Turkey. Sakarya River Basin is one of the most important basins, which consists of three parts: Upper, Middle and Lower Sakarya River Basins. In this study, the Lower Sakarya River was selected as the study area for sediments. The samples were collected monthly from 10 pre-determined stations through the river for 10 months time and analysed for the distribution of Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn elements. The determination of extractable heavy metals in sediments was carried out by using Flame Atomic Absorption Spectrometer. The validation of the results was performed by the analysis of a BCR 701 standard reference material.

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In this study soft computing techniques including, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were investigated for the prediction of Cr(VI) transport efficiency by novel Polymer Inclusion Membranes (PIMs). Transport experiments carried out by varying parameters such as time, film thickness, carrier type, carier rate, plasticizer type, and plasticizer rate. The predictive performance of ANN and ANFIS model was evaluated by using statistical performance criteria such as Root Mean Standard Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R 2). Moreover, Sensitivity Analysis (SA) was carried out to investigate the effect of each input on PIMs Cr(VI) removal efficiency. The proposed ANN model presented reliable and valid results, followed by ANFIS model results. RMSE and MAE values were 0.00556, 0.00163 for ANN and 0.00924, 0.00493 for ANFIS model in the prediction of Cr(VI) removal efficiency on testing data sets. The R 2 values were 0.973 and 0.867 on testing data sets by ANN and ANFIS, respectively. Results show that the ANN-based prediction model performed better than ANFIS. SA demonstrated that time; film thickness; carrier type and plasticizer type are major operating parameters having 33.61%, 26.85%, 21.07% and 8.917% contribution, respectively.