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Karas BY, Sitnikova VE, Nosenko TN, Dedkov VG, Arsentieva NA, Gavrilenko NV, Moiseev IS, Totolian AA, Kajava AV, Uspenskaya MV. ATR-FTIR spectrum analysis of plasma samples for rapid identification of recovered COVID-19 individuals. J Biophotonics 2023:e202200166. [PMID: 36869427 DOI: 10.1002/jbio.202200166] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 01/08/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
The development of fast, cheap and reliable methods to determine seroconversion against infectious agents is of great practical importance. In the context of the COVID-19 pandemic, an important issue is to study the rate of formation of the immune layer in the population of different regions, as well as the study of the formation of post-vaccination immunity in individuals after vaccination. Currently, the main method for this kind of research is enzyme immunoassay (ELISA, enzyme-linked immunosorbent assay). This technique is sufficiently sensitive and specific, but it requires significant time and material costs. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in blood plasma to detect seroconversion against SARS-CoV-2. The study included samples of 60 patients. Clear spectral differences in plasma samples from recovered COVID-19 patients and conditionally healthy donors were identified using multivariate and statistical analysis. The results showed that ATR-FTIR spectroscopy, combined with principal components analysis (PCA) and linear discriminant analysis (LDA) or artificial neural network (ANN), made it possible to efficiently identify specimens from recovered COVID-19 patients. We built classification models based on PCA associated with LDA and ANN. Our analysis led to 87% accuracy for PCA-LDA model and 91% accuracy for ANN, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective tool for detecting seroconversion against SARS-CoV-2. This approach could be used as an alternative to ELISA.
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Affiliation(s)
- Boris Y Karas
- Institute BioEngineering, ITMO University, St. Petersburg, Russia
| | - Vera E Sitnikova
- Institute BioEngineering, ITMO University, St. Petersburg, Russia
| | | | - Vladimir G Dedkov
- Saint-Petersburg Pasteur Institute, Federal Service on Consumers' Rights Protection and Human Well-Being Surveillance, St. Petersburg, Russia
- Martsinovsky Institute of Medical Parasitology, Tropical and Vector Borne Diseases, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Natalia A Arsentieva
- Saint-Petersburg Pasteur Institute, Federal Service on Consumers' Rights Protection and Human Well-Being Surveillance, St. Petersburg, Russia
| | - Natalia V Gavrilenko
- Raisa Gorbacheva memorial Research Institute for Pediatric Oncology, Hematology and Transplantation, Pavlov First Saint Petersburg State Medical University, St. Petersburg, Russia
| | - Ivan S Moiseev
- Raisa Gorbacheva memorial Research Institute for Pediatric Oncology, Hematology and Transplantation, Pavlov First Saint Petersburg State Medical University, St. Petersburg, Russia
| | - Areg A Totolian
- Saint-Petersburg Pasteur Institute, Federal Service on Consumers' Rights Protection and Human Well-Being Surveillance, St. Petersburg, Russia
| | - Andrey V Kajava
- Centre de Recherche en Biologie cellulaire de Montpellier, Université Montpellier, Montpellier, France
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