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Development of an automated chemiluminescence assay system for quantitative measurement of multiple anti-SARS-CoV-2 antibodies (preprint)
medrxiv; 2020.
Preprint
in English
| medRxiv | ID: ppzbmed-10.1101.2020.11.04.20225805
ABSTRACT
ObjectiveSerological tests for COVID-19 have been instrumental in studying the epidemiology of the disease. However, the performance of the currently available tests is plagued by the problem of variability. We have developed a high-throughput serological test capable of simultaneously detecting total immunoglobulins (Ig) and immunoglobulin G (IgG) against two of the most immunologically relevant SARS-CoV-2 antigens, nucleocapsid protein (NP) and spike protein (SP) and report its performance in detecting COVID-19 in clinical samples. MethodsWe designed and prepared reagents for measuring NP-IgG, NP-Total Ig, SP-IgG, and SP-Total Ig (using N-terminally truncated NP ({Delta}N-NP) or receptor-binding domain (RBD) antigen) on the advanced chemiluminescence enzyme immunoassay system TOSOH AIA-CL. After determining the basal thresholds based on 17 sera obtained from confirmed COVID-19 patients and 600 negative sera. Subsequently, the clinical validity of the assay was evaluated using independent 202 positive samples and 1,000 negative samples from healthy donors. ResultsAll of the four test parameters showed 100% specificity individually (1,000/1,000; 95%CI, 99.63-100). The sensitivity of the assay increased proportionally to the elapsed time from symptoms onset, and all the tests achieved 100% sensitivity (153/153; 95%CI, 97.63-100) after 13 days from symptoms onset. NP-Total Ig was the earliest to attain maximal sensitivity among the other antibodies tested. ConclusionOur newly developed serological testing exhibited 100% sensitivity and specificity after 13 days from symptoms onset. Hence, it could be used as a reliable method for accurate detection of COVID-19 patients and to evaluate seroprevalence and possibly for surrogate assessment of herd immunity.
Full text:
Available
Collection:
Preprints
Database:
medRxiv
Main subject:
COVID-19
Language:
English
Year:
2020
Document Type:
Preprint
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