COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections
| Autor: | L. Bianchi, S. Monteleone, A. Caronni, Alice Gualerzi, J. Navarro, Silvia Picciolini, Marzia Bedoni, Paolo Banfi, Enza Messina, F. Marenco, D. Bertazioli, Cristiano Carlomagno, Chiara Arienti, Agata Lax |
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| Přispěvatelé: | Carlomagno, C, Bertazioli, D, Gualerzi, A, Picciolini, S, Banfi, P, Lax, A, Messina, E, Navarro, J, Bianchi, L, Caronni, A, Marenco, F, Monteleone, S, Arienti, C, Bedoni, M |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Male 2019-20 coronavirus outbreak Saliva Coronavirus disease 2019 (COVID-19) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Science Normal Distribution Reproducibility of Result Computational biology Comorbidity Antibodies Viral Spectrum Analysis Raman Predictive markers 01 natural sciences Sensitivity and Specificity Article 03 medical and health sciences Deep Learning Fingerprint Biochemical composition Medicine Humans Aged Aged 80 and over Multidisciplinary business.industry 010401 analytical chemistry Healthy subjects Reproducibility of Results COVID-19 Computational Biology Diagnostic markers Middle Aged 0104 chemical sciences Computational biology and bioinformatics Data processing 030104 developmental biology Female business Human |
| Zdroj: | Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021) Scientific Reports |
| ISSN: | 2045-2322 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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