| Autor: |
Al Haj Ishak Al Ali R; Institute of Chemistry, Materials and Natural Resources of Poitiers, UMR CNRS 7285, University of Poitiers, 86000 Poitiers, France., Mondamert L; Institute of Chemistry, Materials and Natural Resources of Poitiers, UMR CNRS 7285, University of Poitiers, 86000 Poitiers, France., Berjeaud JM; Ecology and Biology of Interactions, UMR CNRS 7267, University of Poitiers, 86000 Poitiers, France., Jandry J; Faculty of Agronomy and Veterinary Sciences, Lebanese University, Dekwaneh, Lebanon., Crépin A; Ecology and Biology of Interactions, UMR CNRS 7267, University of Poitiers, 86000 Poitiers, France., Labanowski J; Institute of Chemistry, Materials and Natural Resources of Poitiers, UMR CNRS 7285, University of Poitiers, 86000 Poitiers, France. |
| Abstrakt: |
The release of a wide variety of persistent chemical contaminants into wastewater has become a growing concern due to their potential health and environmental risks. While the toxic effects of these pollutants on aquatic organisms have been extensively studied, their impact on microbial pathogens and their virulence mechanisms remains largely unexplored. This research paper focuses on the identification and prioritization of chemical pollutants that increase bacterial pathogenicity, which is a public health concern. In order to predict how chemical compounds, such as pesticides and pharmaceuticals, would affect the virulence mechanisms of three bacterial strains ( Escherichia coli K12, Pseudomonas aeruginosa H103, and Salmonella enterica serovar. Typhimurium), this study has developed quantitative structure-activity relationship (QSAR) models. The use of analysis of variance (ANOVA) functions assists in developing QSAR models based on the chemical structure of the compounds, to predict their effect on the growth and swarming behavior of the bacterial strains. The results showed an uncertainty in the created model, and that increases in virulence factors, including growth and motility of bacteria, after exposure to the studied compounds are possible to be predicted. These results could be more accurate if the interactions between groups of functions are included. For that, to make an accurate and universal model, it is essential to incorporate a larger number of compounds of similar and different structures. |
