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COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections

The pandemic of COVID-19 is continuously spreading, becoming a worldwide emergency. Early and fast identification of subjects with a current or past infection must be achieved to slow down the epidemiological widening. Here we report a Raman-based approach for the analysis of saliva, able to signifi...

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Autores principales: Carlomagno, C., Bertazioli, D., Gualerzi, A., Picciolini, S., Banfi, P. I., Lax, A., Messina, E., Navarro, J., Bianchi, L., Caronni, A., Marenco, F., Monteleone, S., Arienti, C., Bedoni, M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925543/
https://www.ncbi.nlm.nih.gov/pubmed/33654146
http://dx.doi.org/10.1038/s41598-021-84565-3
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author Carlomagno, C.
Bertazioli, D.
Gualerzi, A.
Picciolini, S.
Banfi, P. I.
Lax, A.
Messina, E.
Navarro, J.
Bianchi, L.
Caronni, A.
Marenco, F.
Monteleone, S.
Arienti, C.
Bedoni, M.
author_facet Carlomagno, C.
Bertazioli, D.
Gualerzi, A.
Picciolini, S.
Banfi, P. I.
Lax, A.
Messina, E.
Navarro, J.
Bianchi, L.
Caronni, A.
Marenco, F.
Monteleone, S.
Arienti, C.
Bedoni, M.
author_sort Carlomagno, C.
collection PubMed
description The pandemic of COVID-19 is continuously spreading, becoming a worldwide emergency. Early and fast identification of subjects with a current or past infection must be achieved to slow down the epidemiological widening. Here we report a Raman-based approach for the analysis of saliva, able to significantly discriminate the signal of patients with a current infection by COVID-19 from healthy subjects and/or subjects with a past infection. Our results demonstrated the differences in saliva biochemical composition of the three experimental groups, with modifications grouped in specific attributable spectral regions. The Raman-based classification model was able to discriminate the signal collected from COVID-19 patients with accuracy, precision, sensitivity and specificity of more than 95%. In order to translate this discrimination from the signal-level to the patient-level, we developed a Deep Learning model obtaining accuracy in the range 89–92%. These findings have implications for the creation of a potential Raman-based diagnostic tool, using saliva as minimal invasive and highly informative biofluid, demonstrating the efficacy of the classification model.
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spelling pubmed-79255432021-03-04 COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections Carlomagno, C. Bertazioli, D. Gualerzi, A. Picciolini, S. Banfi, P. I. Lax, A. Messina, E. Navarro, J. Bianchi, L. Caronni, A. Marenco, F. Monteleone, S. Arienti, C. Bedoni, M. Sci Rep Article The pandemic of COVID-19 is continuously spreading, becoming a worldwide emergency. Early and fast identification of subjects with a current or past infection must be achieved to slow down the epidemiological widening. Here we report a Raman-based approach for the analysis of saliva, able to significantly discriminate the signal of patients with a current infection by COVID-19 from healthy subjects and/or subjects with a past infection. Our results demonstrated the differences in saliva biochemical composition of the three experimental groups, with modifications grouped in specific attributable spectral regions. The Raman-based classification model was able to discriminate the signal collected from COVID-19 patients with accuracy, precision, sensitivity and specificity of more than 95%. In order to translate this discrimination from the signal-level to the patient-level, we developed a Deep Learning model obtaining accuracy in the range 89–92%. These findings have implications for the creation of a potential Raman-based diagnostic tool, using saliva as minimal invasive and highly informative biofluid, demonstrating the efficacy of the classification model. Nature Publishing Group UK 2021-03-02 /pmc/articles/PMC7925543/ /pubmed/33654146 http://dx.doi.org/10.1038/s41598-021-84565-3 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Carlomagno, C.
Bertazioli, D.
Gualerzi, A.
Picciolini, S.
Banfi, P. I.
Lax, A.
Messina, E.
Navarro, J.
Bianchi, L.
Caronni, A.
Marenco, F.
Monteleone, S.
Arienti, C.
Bedoni, M.
COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections
title COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections
title_full COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections
title_fullStr COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections
title_full_unstemmed COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections
title_short COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections
title_sort covid-19 salivary raman fingerprint: innovative approach for the detection of current and past sars-cov-2 infections
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925543/
https://www.ncbi.nlm.nih.gov/pubmed/33654146
http://dx.doi.org/10.1038/s41598-021-84565-3
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