Classification of the mechanism of toxicity as applied to human cell line ECV304

• Published in: Computer methods in biomechanics and biomedical engineering Vol. 24; no. 9; pp. 933 - 944
• Main Authors: Djawad, Yasser Abd, Kiely, Janice, Luxton, Richard
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 11.08.2021; Taylor & Francis Ltd
• Subjects: Algorithms; Cell morphology; Classification; Cytology; Cytotoxicity; Cytotoxicity testing; Dimethyl sulfoxide; Feature extraction; Frequency ranges; Hydrogen peroxide; impedance; Lock in amplifiers; Machine learning; Morphology; Saponins; Toxicity testing; Toxins; machine learning; impedance; Cell morphology
• ISSN: 1025-5842; 1476-8259
• DOI: 10.1080/10255842.2020.1861255

The objective of this study was to identify the pattern of cytotoxicity testing of the human cell line ECV304 using three techniques of an ensemble learning algorithm (bagging, boosting and stacking). The study of cell morphology of ECV304 cell line was conducted using impedimetric measurement. Three types of toxins were applied to the ECV304 cell line namely 1 mM hydrogen peroxide (H 2 O 2 ), 5% dimethyl sulfoxide and 10 μg Saponin. The measurement was conducted using electrodes and lock-in amplifier to detect impedance changes during cytotoxicity testing within a frequency range 200 and 830 kHz. The results were analysed, processed and extracted using detrended fluctuation analysis to obtain characteristics and features of the cells when exposed to the each of the toxins. Three ensemble algorithms applied showed slightly different results on the performance for classifying the data set from the feature extraction that was performed. However, the results show that the cell reaction to the toxins could be classified.