Antibody persistency and trend post-SARS-CoV-2 infection at eight months

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Abstract

An improved understanding of the immunity offered by the antibodies developed against SARS-CoV-2 is critical for the development of diagnostic tests and vaccines. Our study aimed at the longitudinal analysis of antibody presence, persistence and its trend over a period of eight months in a group of COVID-19 recovered patients who tested positive by real-time quantitative PCR for SARS-CoV-2 in the period between the 18th and 30th of March, 2020. The subjects were divided into two groups based on disease severity: mild and moderately-severe. The MAGLUMI 2019-nCoV lgM/lgG chemiluminescent analytical system (CLIA) assay was used to analyse the antibody titres. Robust IgG antibody persistency was demonstrated in 76.7 % of the subjects (23 out of 30) at eight months post-infection. The results of this study highlight an important point in terms of the association between humoral immune response and disease severity. Patients who might have experienced a relatively moderate-severe infection may develop a robust immunity that could persist for a longer duration.

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  1. SciScore for 10.1101/2020.11.21.20236117: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.


    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.