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SERS-CNN approach for non-invasive and non-destructive monitoring of stem cell growth on a universal substrate through an analysis of the cultivation medium
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SYSNO ASEP 0567074 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title SERS-CNN approach for non-invasive and non-destructive monitoring of stem cell growth on a universal substrate through an analysis of the cultivation medium Author(s) Skvortsova, A. (CZ)
Trelin, A. (CZ)
Sedlář, Antonín (FGU-C) ORCID
Erzina, M. (CZ)
Trávníčková, Martina (FGU-C) RID, ORCID, SAI
Svobodová, Lucie (FGU-C) RID
Kolská, Z. (CZ)
Siegel, J. (CZ)
Bačáková, Lucie (FGU-C) RID, ORCID
Švorčík, V. (CZ)
Lyutakov, O. (CZ)Number of authors 11 Article number 132812 Source Title Sensors and Actuators B - Chemical. - : Elsevier
Roč. 375, 15 January (2023)Number of pages 9 s. Language eng - English Country CH - Switzerland Keywords SERS ; artificial intelligence ; stem cells ; non-invasive detection OECD category Biomaterials (as related to medical implants, devices, sensors) R&D Projects GA21-06065S GA ČR - Czech Science Foundation (CSF) Method of publishing Limited access Institutional support FGU-C - RVO:67985823 UT WOS 000904973600005 EID SCOPUS 85140356459 DOI 10.1016/j.snb.2022.132812 Annotation The development of advanced methods of SERS-CNN data analysis seems to provide a perfect analytical system that is capable of solving the sophisticated task of determining the species and the behavior of microorganisms. Unlike the widely-used analytical approach, machine learning allows precise analysis even of very complex spectra of biological samples, and can provide precise decisions for a specific biochemical or microbiological task. In this article, we show for the first time the utilization of the SERS-CNN approach for remote observation of mesenchymal stem cell behavior. Our approach is based on SERS measurements of the biochemical changes taking place in the surrounding culture media due to stem cell proliferation and their biochemical activity. The cells were cultivated on various substrates supporting random or oriented cell growth, and also on „surface-toxic“ substrates. SERS-CNN analysis reveals the ability to perform „remote“ non-invasive estimation (i.e. using the surrounding medium analysis) of the degree of cell survival and the proliferation rate, using Raman measurements and advanced spectra data processing. It should be noted that the proposed approach makes it possible to analyze cell behavior without disrupting cell growth, and it can also be performed by untrained staff with the use of widely-available equipment. Workplace Institute of Physiology Contact Lucie Trajhanová, lucie.trajhanova@fgu.cas.cz, Tel.: 241 062 400 Year of Publishing 2024 Electronic address https://doi.org/10.1016/j.snb.2022.132812
Number of the records: 1