On-chip dielectrophoretic device for cancer cell manipulation: A numerical and artificial neural network study

Abstract

Breast cancer, as one of the most frequent types of cancer in women, imposes large financial and human losses annually. MCF-7, a well-known cell line isolated from the breast tissue of cancer patients, is usually used in breast cancer research. Microfluidics is a newly established technique that provides many benefits, such as sample volume reduction, high-resolution operations, and multiple parallel analyses for various cell studies. This numerical study presents a novel microfluidic chip for the separation of MCF-7 cells from other blood cells, considering the effect of dielectrophoretic force. An artificial neural network, a novel tool for pattern recognition and data prediction, is implemented in this research. To prevent hyperthermia in cells, the temperature should not exceed 35 °C. In the first part, the effect of flow rate and applied voltage on the separation time, focusing efficiency, and maximum temperature of the field is investigated. The results denote that the separation time is affected by both the input parameters inversely, whereas the two remaining parameters increase with the input voltage and decrease with the sheath flow rate. A maximum focusing efficiency of 81% is achieved with a purity of 100% for a flow rate of 0.2μL/min and a voltage of 3.1V. In the second part, an artificial neural network model is established to predict the maximum temperature inside the separation microchannel with a relative error of less than 3% for a wide range of input parameters. Therefore, the suggested label-free lab-on-a-chip device separates the target cells with high-throughput and low voltages.