Article
Alternative General Fitting Methods for Real-Time Cell-Count Experimental Data Processing
Author/s | Serrano, Juan Alfonso
Pérez García, Pablo Huertas Sánchez, Gloria Yúfera García, Alberto |
Department | Universidad de Sevilla. Departamento de Tecnología Electrónica Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo |
Publication Date | 2020 |
Deposit Date | 2021-03-25 |
Published in |
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Abstract | This paper reports two general methods for
extraction of cell-electrode electrical model parameters in
cell culture (CC) assays. The presented approaches can
be applied to CC assays based on electrical cell-substrate
impedance ... This paper reports two general methods for extraction of cell-electrode electrical model parameters in cell culture (CC) assays. The presented approaches can be applied to CC assays based on electrical cell-substrate impedance spectroscopy (ECIS) technique for real-time supervision, providing the cell number per square centimeter, i.e., the cell density, asmain result.Both of the proposedmethods - minimization of system equations and data predictive model - search, during the experiment, the optimum values of the electrical model parameters employed for the electrodecellmodel. The results of this searchenablea fast and efficient calculation of the involved cell-electrode model parameters and supply real-time information on the cell number. For the sake of experimental validation, we applied the proposed methods to practical CCs in cell growth assays with a cell line of AA8 Chinese hamster ovarian fibroblasts and the Oscillation Based Test technique for bioimpedance measurements. These methods can be easily extrapolated to any general cell lines and/or other bioimpedance test methodologies. |
Funding agencies | Ministerio de Ciencia, Innovación y Universidades (MICINN). España |
Project ID. | RTI2018-093512-B-C21 |
Citation | Serrano, J.A., Pérez García, P., Huertas Sánchez, G. y Yúfera García, A. (2020). Alternative General Fitting Methods for Real-Time Cell-Count Experimental Data Processing. IEEE Sensors Journal, 20 (24), 15177-15184. |
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