Cell growth is very important for the development and maintenance of organisms, and it is essential to study factors that affect cell growth, such as gas concentrations that affect cell metabolism, especially oxygen and carbon dioxide. In this study, the dynamic activity of cells according to oxygen concentration was analyzed using a dynamic full-field optical coherence tomography imaging system in a gas-controlled chamber for label-free analysis of living cells. This study aims to understand the relationship between gas supply levels and intracellular activity through non-invasive observation. This discovery could have important implications for biomedicine and biotechnology.
We introduce cell dynamic activity analysis method-based combination of dynamic full-field optical coherence tomography (DFFOCT) and machine learning (ML) models. DFFOCT can monitor intracellular migration label-free by capturing scatters movement inside of cells. Since ML builds classification criteria through learning a lot of data, based on the intracellular scatter migration observed through DFFOCT, it is possible to judge abnormal signs of cells regardless of changes in the external experimental environment. We compared the suggested analysis method and staining analysis method for the change of state of HeLa cells (including cell data) and verified the validity.
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