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.

Carlomagno, C., Bertazioli, D., Gualerzi, A., Picciolini, S., Banfi, P., Lax, A., et al. (2021). COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections. SCIENTIFIC REPORTS, 11(1) [10.1038/s41598-021-84565-3].

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

Picciolini S.;Lax A.;Messina Enza.;
2021

Abstract

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.
Articolo in rivista - Articolo scientifico
Scientifica
Aged; Aged, 80 and over; Antibodies, Viral; COVID-19; Comorbidity; Computational Biology; Deep Learning; Female; Humans; Male; Middle Aged; Normal Distribution; Reproducibility of Results; Saliva; Sensitivity and Specificity; Spectrum Analysis, Raman;
English
Carlomagno, C., Bertazioli, D., Gualerzi, A., Picciolini, S., Banfi, P., Lax, A., et al. (2021). COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections. SCIENTIFIC REPORTS, 11(1) [10.1038/s41598-021-84565-3].
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
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/332859
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