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10.1371/journal.pone.0254486

http://scihub22266oqcxt.onion/10.1371/journal.pone.0254486
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suck abstract from ncbi


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pmid34283860      PLoS+One 2021 ; 16 (7): e0254486
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  • The early detection of immunoglobulins via optical-based lateral flow immunoassay platform in COVID-19 pandemic #MMPMID34283860
  • Chen PY; Ko CH; Wang CJ; Chen CW; Chiu WH; Hong C; Cheng HM; Wang IJ
  • PLoS One 2021[]; 16 (7): e0254486 PMID34283860show ga
  • The coronavirus disease (COVID-19) is the global public health challenge currently persisting at a grand scale. A method that meets the rapid quantitative detection of antibodies to assess the body's immune response from natural COVID-19 illness or vaccines' effects is urgently needed. In the present study, an attempt was made to integrate a newly designed spectrometer to the COVID-19 test strip procedure; this augmentation provides the quantitative capacity to a lateral flow immunoassay (LFIA). Optical interpretation of results by quantitative alpha index, rather than visual qualification, can be done quickly, in 5-10 minutes. The developed product was compared with several other serological IgM/IgG antibody reagents on the market by recruiting 111 participants suspected of having COVID-19 infection from March to May 2020 in a hospital. Taking RT-PCR as the diagnostic gold standard, the quantitative spectral LIFA platform could correctly detect all 12 COVID-19 patients. Concerning RT-PCR negative patients, all three antibody testing methods found positive cases. The optical-based platform exhibited the ability of early detection of immunoglobulins of RT-PCR negative patients. There was an apparent trend that elevation of IgM levels in the acute phase of infection; then IgG levels rose later. It exhibited the risk of a false-negative diagnosis of RT-PCR in COVID-19 testing. The significant detection ability of this new optical-based platform demonstrated clinical potential.
  • |COVID-19/*immunology[MESH]
  • |Humans[MESH]
  • |Immunoassay/*methods[MESH]
  • |Immunoglobulin Isotypes/*analysis[MESH]
  • |Pandemics[MESH]


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