Search

Actual analytical methods make it possible to precisely determine a of chemical elements contained in water, including for the purpose of assessing the ecological state of the environment in connection with mining activities. The object of this research is the natural waters of the gold deposit “Novye Peski” located in the Pryazhinsky region (Republic of Karelia, Russia). Analytical data indicate that the geochemical background of the region is exceeded for most of the chemical elements. Research results indicate that the geochemical background of the region is exceeded for most of the considered chemical elements and ions, which can be divided into three groups: 1) the highest concentrations were determined in quarry water (Zn, As, Mo, U, Ni, Cu, Sr, Li, B, as well as Ca 2-); 2) the content is at the same level in all and other cations, HCO3samples (Al, Si, V, Ti, Ba); 3) the highest content in stream and tailing pond (Fe, Mn). We assume that the main factor influencing the behavior of elements of the first group is mining, and for the third is the presence of dissolved organic matter high content with the formation of metal-organic species in water.

This study investigates various sorbents for the effective sorption of dissolved organic substances, using tributyl phosphate (TBP) as a model compound. TBP is one of the most commonly used extractants in the nuclear industry. Four different carbon materials with high specific surface areas (2000-3000 m² g-1) were selected for evaluation. The sorption of TBP from nitric acid solutions was examined over a wide range of concentrations. The samples with the largest specific surface areas showed the highest sorption capacity for organic matter. To enable repeated use, a purification scheme was developed to restore the sorbents' original properties. The samples were subjected to various treatments, analyzed using X-ray photoelectron spectroscopy, and used in subsequent sorption experiments. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has prompted the medical systems of many countries to develop effective treatments to combat the high rate of infection and death caused by the disease. Within the array of proteins found in SARS-CoV-2, the 3 chymotrypsin-like protease (3CLpro) holds significance as it plays a crucial role in cleaving polyprotein peptides into distinct functional nonstructural proteins. Meanwhile, RNA-dependent RNA polymerase (RdRp) takes center stage as the key enzyme tasked with replicating the viral genomic RNA within host cells. These proteins, 3CLpro and RdRp, are deemed optimal subjects for QSAR modeling due to their pivotal functions in the viral lifecycle. In this study, SMILES-based QSAR classification models were developed for a dataset of 2377 compounds that were defined as either active or inactive against 3CLpro and RdRp. Pharmacophore (PH4) and QSAR modeling were used for the virtual screening on 60.2 million compounds including ZINC, ChEMBL, Molport, and MCULE databases to identify new potent inhibitors against 3CLpro and RdRp. Then, a filter was established based on typical molecular characteristics to identify drug-like molecules. The molecular docking was also performed to evaluate the binding affinity of 156 AND 51 potential inhibitors to 3CLpro and RdRp, respectively. Among the 15 hits identified based on molecular docking scores, M3, N2, and N4 were identified as promising inhibitors due to their good synthetic accessibility scores (3.07, 3.11, and 3.29 out of 10 for M3, N2, and N4 respectively). These compounds contain amine functional groups, which are known for their crucial role in the binding interactions between drugs and their targets. Consequently, these hits have been chosen for further biological assay studies to validate their activity. They may represent novel 3CLpro and RdRp inhibitors possessing drug-like properties suitable for COVID-19 therapy. [ABSTRACT FROM AUTHOR]

Models of toxicity to tadpoles have been developed as single parameters based on special descriptors which are sums of correlation weights, molecular features, and experimental conditions. This information is presented by quasi-SMILES. Fragments of local symmetry (FLS) are involved in the development of the model and the use of FLS correlation weights improves their predictive potential. In addition, the index of ideality correlation (IIC) and correlation intensity index (CII) are compared. These two potential predictive criteria were tested in models built through Monte Carlo optimization. The CII was more effective than IIC for the models considered here. [ABSTRACT FROM AUTHOR]

Typical in silico models for ecotoxicology focus on a few endpoints, but there is a need to increase the diversity of these models. This study proposes models using the NOEC for the harlequin fly (Chironomus riparius) and EC50 for swollen duckweed (Lemna gibba) for the first time. The data were derived from the EFSA OpenFoodTox database. The models were based on the correlation weights of molecular features used to calculate the 2D descriptor in CORAL software. The Monte Carlo method was used to calculate the correlation weights of the algorithms. The determination coefficients of the best models for the external validation set were 0.74 (NOAEC) and 0.85 (EC50). [ABSTRACT FROM AUTHOR]