RESUMO
Liquid biopsy is a valuable emerging alternative to tissue biopsy with great potential in the noninvasive early diagnostics of cancer. Liquid biopsy based on single cell analysis can be a powerful approach to identify circulating tumor cells (CTCs) in the bloodstream and could provide new opportunities to be implemented in routine screening programs. Since CTCs are very rare, the accurate classification based on high-throughput and highly informative microscopy methods should minimize the false negative rates. Here, we show that holographic flow cytometry is a valuable instrument to obtain quantitative phase-contrast maps as input data for artificial intelligence (AI)-based classifiers. We tackle the problem of discriminating between A2780 ovarian cancer cells and THP1 monocyte cells based on the phase-contrast images obtained in flow cytometry mode. We compare conventional machine learning analysis and deep learning architectures in the non-ideal case of having a dataset with unbalanced populations for the AI training step. The results show the capacity of AI-aided holographic flow cytometry to discriminate between the two cell lines and highlight the important role played by the phase-contrast signature of the cells to guarantee accurate classification.
RESUMO
The article presents a simple expression of the power transferred from the electromagnetic field (EMF) to a biological nanomachine (NM) embedded in a background medium (BM). The expression is useful to analyse the interaction mechanism and test the hypothesis on its nature. Furthermore, it should represent a helpful tool to design remotely controlled NMs for bio-medical applications and the relative electromagnetic control apparatuses. Finally, to show its practical usefulness, we used it to discuss the hypothesis on the energy transfer mechanism proposed in the literature to explain intriguing experimental phenomena referring to the remotely controlled dehybridization of DNA molecules attached to gold nanocrystals.
