Predictive effects (study in silico) of cadmium/fungicide cocktail on biomarkers of snail oxidative stress: Cantareus aspersus (Müller, 1774) using the forecasting grey model GM (1, N)
Paper Details
Predictive effects (study in silico) of cadmium/fungicide cocktail on biomarkers of snail oxidative stress: Cantareus aspersus (Müller, 1774) using the forecasting grey model GM (1, N)
Abstract
In this study, we were interested in the physiological and biochemical predicted effects of a copper-based fungicide (Vacomil-PLUS) (0.5, 1 and 2g/l), cadmium (200, 400, 800µg/l) and their mixtures (CdCl2 + Copper fungicide, 1/1, V/V) on bio-indicator organisms of pollution: the snail Cantareus aspersus. Our work consists in predicting the long-term effect of these xenobiotics using the forecasting grey model (1, N) as a predicting model. To our knowledge, this is the first study evaluating the predictive effects of toxicants on the snail Cantareus aspersus, using a computer prediction model. Our predictive results obtained from the modeling of predictive values, using the Grey model show that the presence of cadmium and/or fungicide causes growth inhibition of the treated animals, thus reducing the weight of the digestive gland. In addition, more disturbances that are significant also noted in the biochemical composition of the hepatopancreas of Cantareus aspersus (total proteins) after treatment with the cadmium/fungicide mixture. Monitoring of oxidative stress biomarkers shows disturbances due to contamination by these pollutants. We revealed an induction of MDA as well as a depletion of the GSH level, testifying to the occurrence of lipid peroxidation. Finally, cadmium, copper-based fungicide and their mixture significantly inhibits AChE activity.
Bradford MM. 1976. A rapid and sensitive method for quantitation of microgram quantities of protein utilizing the principal of protein-dye binding. Analytical Biochemistry 72, 248-254.
Che-Chiang H, Chia-Yon C. 2003. Applications of improved grey prediction model for power demand forecasting. Energy Conversion and Management 44, 2241-2249. https://doi.org/10.1016/S0196-8904020
Chen JHY, Wu SH, Lin PM, Chang HF. 2022. Exploring Role Behavior in Restaurant by Grey Model and Grey Structural Model. Axioms 11, 333. https://doi.org/10.3390/axioms11070333
De Vaufleury A, Coeurdassier M, Pandard P, Scheifler R, Lovy C, Crini N, Badot PM. 2006. How terrestrial snails can be used in risk assessment of soils. Environmental Toxicology and Chemistry 25, 797-806. https://hal.archives-ouvertes.fr/hal-003743
Ding S. 2018. A novel self-adapting intelligent grey model for forecasting China’s natural-gas demand. Energy 162, 393-407. https://doi.org/10.1016/ j.energy.2018.08.040
Draper HH, Hadley M. 1990. Malondialdehyde determinationas index of lipid peroxidation. Methods in Enzymology 186, 421-431. https://doi.org/10.1016 /0076-6879(90)86135-i
Ellman GL, Courtney KD, Andres V, Featherstone RM. 1961. A new and rapid colorimetric determination of acetylcholinesterase activity. Biochemistry and Pharmacology 7, 88-95. https://doi.org/10.1016/0006-2952(61)90145-9
Hui F, Li Y Shi. 2013. An optimal multi-variable grey model for logistics demand forcast. International journal of innovative computing, information & control 9, 2907-2918. http://www.ijicic.org/ijicic-12-03088.pdf
Jiang P, Hu YC. 2022. Constructing interval models using neural networks with non-additive combinations of grey prediction models in tourism demand, Grey Systems: Theory and Application. Vol. ahead-of-print, No. ahead-of-print.
Jingzheng R. 2017. GM (1,N) method for the prediction of anaerobic digestion system and sensitivity analysis of influential factors. Bioresource Technology 247, 1258-1261.
Liu S, Dang Y, Fang Z, Xie N. 2010. Grey System Theory and Its Application. Science Press, Beijing China. P. 20-22.
Vijayan VK, Bindu KR. 2017. Parameswaran, “A comprehensive study of text classification algorithms. International Conference on Advances in Computing, Communications and Informatics (ICACCI) 1109-1113
Wang WX, Rainbow PS. 2005. Influence of metal exposure history on trace metal uptake and accumulation by marine invertebrates. Ecotoxicology and Environmental Safety 61,145-159.
Wang ZX, Li Q. 2019. “Modelling the nonlinear relationship between CO2 emissions and economic growth using a PSO algorithm-based grey Verhulst model”. Journal of Cleaner Production 207, 214–224.
Weckberker G, Cory G. 1988. Ribonucleotide reductase activity and growth of glutathione depleted mouse leukemial 1210 cells in vitro. Cacer letters 40, 257-264. https://doi.org/10.1016/0304-3835(88)95
Witten IH, Frank E. 2000. Data Mining: Pratical machine learning tools and techniques with java implementation. Morgan Kaufmann Publishers, San Franscisco, California 31, 76-77.
Wu W, Ma X, Zeng B, Wang Y, Cai W. 2019. Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model, Renewable Energy 140, 70-87.
Khadidja Farfar, Amira Youbi, Mohamed El Taher Kimour, Mohamed El Hadi Khebbeb, Mohamed Reda Djebar, Zihad Bouslama, 2022. Predictive effects (study in silico) of cadmium/fungicide cocktail on biomarkers of snail oxidative stress: Cantareus aspersus (Müller, 1774) using the forecasting grey model GM (1, N). Int. J. Biosci., 21(6), 197-205.
Copyright © 2022 by the Authors. This article is an open access article and distributed under the terms and conditions of the Creative Commons Attribution 4.0 (CC BY 4.0) license.