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Concurrent study of stability and cytotoxicity of a novel nanoemulsion system - an artificial neural networks approach

机译:新型纳米乳液系统稳定性和细胞毒性的同时研究 - 一种人工神经网络方法

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ABSTRACT Problems commonly associated with using nanoemulsions are their cytotoxic effects and low stability profiles. Here, for the first time, concentrations of ingredients of a nanoemulsion system were investigated to obtain the most stable nanoemulsion system with the least cytotoxic effect on MCF7 cell line. Artificial neural networks (ANNs) were used to model the experimentally obtained data. Surfactant concentration was found to be the dominant factor in determining the stability - surfactant concentration above a critical point made the preparation unstable, while it appeared not to be influencing the cytotoxicity. Concentration of oil showed a direct relationship to the cytotoxicity with a minimum value required to provide an acceptable safety profile for the preparation. Co-surfactant appeared not to be considerably effective on neither stability nor cytotoxicity. To obtain the optimum preparation with maximum stability and minimum cytotoxicity, surfactant and oil values need to be kept at their maximum and minimum possible, respectively.
机译:使用纳米乳液通常与纳米乳液相关的抽象问题是它们的细胞毒性效应和低稳定性曲线。这里,首次研究纳米乳液系统成分的浓度,得到最稳定的纳米乳液系统,具有对MCF7细胞系最小的细胞毒性作用。人工神经网络(ANNS)用于建模实验获得的数据。发现表面活性剂浓度是确定临界点高于临界点的稳定性表面活性剂浓度的显性因素,使其成为不稳定的制剂,而似乎不影响细胞毒性。油浓度显示与细胞毒性的直接关系,具有提供可接受的安全型材的最小值所需的最小值。共表面活性剂似乎不具有相当有效的稳定性和细胞毒性。为了获得最大稳定性和最小细胞毒性的最佳制剂,需要分别保持最大和最小的表面活性剂和油值。

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