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1.
Sci Rep ; 12(1): 2356, 2022 02 18.
Article in English | MEDLINE | ID: covidwho-1706307

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.


Subject(s)
Exudates and Transudates/virology , Mass Screening/methods , SARS-CoV-2/isolation & purification , Saliva/virology , Spectroscopy, Near-Infrared , Humans , Point-of-Care Testing , Proof of Concept Study
2.
Sci Rep ; 11(1): 16201, 2021 08 10.
Article in English | MEDLINE | ID: covidwho-1351977

ABSTRACT

Optical spectroscopic techniques have been commonly used to detect the presence of biofilm-forming pathogens (bacteria and fungi) in the agro-food industry. Recently, near-infrared (NIR) spectroscopy revealed that it is also possible to detect the presence of viruses in animal and vegetal tissues. Here we report a platform based on visible and NIR (VNIR) hyperspectral imaging for non-contact, reagent free detection and quantification of laboratory-engineered viral particles in fluid samples (liquid droplets and dry residue) using both partial least square-discriminant analysis and artificial feed-forward neural networks. The detection was successfully achieved in preparations of phosphate buffered solution and artificial saliva, with an equivalent pixel volume of 4 nL and lowest concentration of 800 TU·[Formula: see text]L-1. This method constitutes an innovative approach that could be potentially used at point of care for rapid mass screening of viral infectious diseases and monitoring of the SARS-CoV-2 pandemic.


Subject(s)
Image Processing, Computer-Assisted/methods , Lentivirus Infections/diagnosis , Molecular Diagnostic Techniques/methods , Spectroscopy, Near-Infrared/methods , HEK293 Cells , Humans , Image Processing, Computer-Assisted/standards , Lentivirus/isolation & purification , Lentivirus/pathogenicity , Lentivirus Infections/virology , Molecular Diagnostic Techniques/standards , Point-of-Care Systems , Saliva/virology , Sensitivity and Specificity , Spectroscopy, Near-Infrared/standards
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