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首页> 外文期刊>Microbial Pathogenesis >Application of artificial neural networks to describe the combined effect of pH, time, NaCl and ethanol concentrations on the biofilm formation of Staphylococcus aureus
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Application of artificial neural networks to describe the combined effect of pH, time, NaCl and ethanol concentrations on the biofilm formation of Staphylococcus aureus

机译：人工神经网络在描述pH，时间，NaCl和乙醇浓度对金黄色葡萄球菌生物膜形成的综合作用

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摘要

Biofilms are organized communities, adherent to the surface and resistant to adverse environmental and antimicrobial agents. So, its control is very important. Staphylococcus aureus is an opportunistic pathogen with the biofilm-forming ability that causes numerous problems in the medicine and food industry. Therefore, this study aimed to investigate the effect of pH, ethanol and NaCl concentrations after 24 and 48 h incubation times at 37 degrees C, also modeling the results with artificial neural network (ANN). For this purpose, after both incubation times, the effect of each parameter was studied, separately and also in combination at the levels in which the highest biofilm was formed. All results were modeled using multiple ANN and compared in terms of R-value and MSE. The highest biofilm formation ability was in neutral pH. Adding the ethanol and NaCl stimulated biofilm formation, but the inhibitory effect was observed at high concentrations of ethanol and NaCl and very acidic or highly alkaline pH levels. The more incubation time also led to an increase in biofilm formation. Eventually, the Feed-Forward, Back-Propagation Neural Network model with the Levenberg-Marquardt training algorithm and 4-12-1 topology was chosen (R-value = 0.995 and validation MSE = 0.011467). This ANN had high modeling ability because there was a high correlation between experimental data and modeling data. Therefore, it was concluded that pH, ethanol, NaCl, and time are effective parameters in the biofilm formation and there is a nonlinear relationship between these factors that the ANN is capable of modeling them.
机译：生物膜是组织的社区，粘附在表面上并抵抗不利的环境和抗微生物剂。因此，它的控制非常重要。金黄色葡萄球菌是一种机会主义病原体，具有生物膜形成能力，导致药物和食品行业的许多问题。因此，该研究旨在研究在37℃下24和48小时孵育时间后pH，乙醇和NaCl浓度的影响，也用人工神经网络（ANN）建模结果。为此目的，在孵育时间进行后，在形成最高生物膜的水平下，分别研究每个参数的效果。所有结果均使用多个ANN进行建模，并在R值和MSE方面进行比较。最高的生物膜形成能力是中性pH值。加入乙醇和NaCl刺激的生物膜形成，但在高浓度的乙醇和NaCl和非常酸性或高碱性pH水平下观察到抑制作用。孵育时间越多也导致生物膜形成的增加。最终，选择了前馈，回到传播神经网络模型与Levenberg-Marquardt训练算法和4-12-1拓扑（R值= 0.995和验证MSE = 0.011467）。该ANN具有高的建模能力，因为实验数据与建模数据之间存在高相关性。因此，得出结论是，pH，乙醇，NaCl和时间是生物膜形成中有效参数，并且这些因素之间存在非线性关系，即ANN能够建模它们。

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