Cancerous cell viability affected by synergism between electric pulses and a low dose of silver nanoparticle: An adaptive neuro-fuzzy inference system

Abstract

In the current study, applying simultaneously electroporation and silver nanoparticles (SNPs) are considered. Moreover, one restriction normally assigned to such nanoparticles is their side effects on the vital organs of the body. To mitigate such deleterious effects, it is better to use lower dosages of them. However, this can result in a decline in the technique's effectiveness. To compensate for the lower dose of SNPs, one can use secondary method like electroporation to deliver SNPs directly into the cells and reinforces the effect of electric pulses due to the high electrical conductivity of SNPs while having a minimal cytotoxicity effect on normal cells that are not treated with electroporation. In the present study, synergism effects of both procedures (SNPs and electroporation) experimentally and theoretically are considered to investigate the property of each technique in increasing the performance with respect to both procedures' limitations. To investigate more, adaptive neuro-fuzzy inference system (ANFIS) is used to predict the percent cell viability of cancerous cells affected by both procedures by considering amplitude and duration as inputs affecting on change of cell viability as an output. The results obtained from both experimental and simulation procedures showed that the maximum synergism between nanoparticles and electric pulses was recorded at 700 ​V/cm strength and 100 μs duration. Also, Results indicated high correlation between observed and predicted data (r2 ​= ​0.88). Moreover, the calculated root mean square error for the results of the ANFIS model was equal to 1.1. This implies that the model has practical value and can estimate the percent cell viability of cancerous cells influenced by both procedures with varying electric field amplitude and duration. This method can be proposed for other biophysical or drug delivery applications to save time and resources by utilizing the previous experimental data rather than performing more experiments.