DOI:
10.1038/s41598-022-06393-3
IAA authors:
Muñoz, Olga;Gomez-Martin, Juan Carlos;Aceituno-Castro, Jesus
Authors:
Gomez-Gonzalez, Emilio;Barriga-Rivera, Alejandro;Fernandez-Muñoz, Beatriz;Navas-Garcia, Jose Manuel;Fernandez-Lizaranzu, Isabel;Munoz-Gonzalez, Francisco Javier;Parrilla-Giraldez, Ruben;Requena-Lancharro, Desiree;Gil-Gamboa, Pedro;Rosell-Valle, Cristina;Gomez-Gonzalez, Carmen;Mayorga-Buiza, Maria Jose;Martin-Lopez, Maria;Muñoz, Olga;Gomez-Martin, Juan Carlos;Relimpio-Lopez, Maria Isabel;Aceituno-Castro, Jesus;Perales-Esteve, Manuel A.;Puppo-Moreno, Antonio;Garcia-Cozar, Francisco Jose;Olvera-Collantes, Lucia;Gomez-Diaz, Raquel;de los Santos-Trigo, Silvia;Huguet-Carrasco, Monserrat;Rey, Manuel;Gomez, Emilia;Sanchez-Pernaute, Rosario;Padillo-Ruiz, Javier;Marquez-Rivas, Javier
Abstract:
Effective testing is essential to control the coronavirus disease 2019 (COVID-19) transmission. Here we report a-proof-of-concept study on hyperspectral image analysis in the visible and near-infrared range for primary screening at the point-of-care of SARS-CoV-2. We apply spectral feature descriptors, partial least square-discriminant analysis, and artificial intelligence to extract information from optical diffuse reflectance measurements from 5 µL fluid samples at pixel, droplet, and patient levels. We discern preparations of engineered lentiviral particles pseudotyped with the spike protein of the SARS-CoV-2 from those with the G protein of the vesicular stomatitis virus in saline solution and artificial saliva. We report a quantitative analysis of 72 samples of nasopharyngeal exudate in a range of SARS-CoV-2 viral loads, and a descriptive study of another 32 fresh human saliva samples. Sensitivity for classification of exudates was 100% with peak specificity of 87.5% for discernment from PCR-negative but symptomatic cases. Proposed technology is reagent-free, fast, and scalable, and could substantially reduce the number of molecular tests currently required for COVID-19 mass screening strategies even in resource-limited settings.
URL:
https://ui.adsabs.harvard.edu/#abs/2022NatSR..12.2356G/abstract