COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections.
Sci Rep
; 11(1): 4943, 2021 03 02.
Article
in English
| MEDLINE | ID: covidwho-1114729
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Saliva
/
Spectrum Analysis, Raman
/
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Randomized controlled trials
Limits:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
English
Journal:
Sci Rep
Year:
2021
Document Type:
Article
Affiliation country:
S41598-021-84565-3
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