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Sci Rep ; 12(1): 1614, 2022 01 31.
Artículo en Inglés | MEDLINE | ID: covidwho-1661979

RESUMEN

As the SARS-CoV-2 pandemic persists, methods that can quickly and reliably confirm infection and immune status is extremely urgently and critically needed. In this contribution we show that combining laser induced breakdown spectroscopy (LIBS) with machine learning can distinguish plasma of donors who previously tested positive for SARS-CoV-2 by RT-PCR from those who did not, with up to 95% accuracy. The samples were also analyzed by LIBS-ICP-MS in tandem mode, implicating a depletion of Zn and Ba in samples of SARS-CoV-2 positive subjects that inversely correlate with CN lines in the LIBS spectra.


Asunto(s)
COVID-19/sangre , COVID-19/diagnóstico , Inmunidad , Rayos Láser , Pandemias , SARS-CoV-2/inmunología , Espectrofotometría Atómica/métodos , Bario/análisis , COVID-19/epidemiología , COVID-19/virología , Exactitud de los Datos , Análisis Discriminante , Reacciones Falso Negativas , Reacciones Falso Positivas , Humanos , Aprendizaje Automático , Reacción en Cadena de la Polimerasa de Transcriptasa Inversa/métodos , SARS-CoV-2/genética , Sensibilidad y Especificidad , Zinc/análisis
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