Monitoring of SARS-CoV-2 antibodies using dried blood spot for at-home collection

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Abstract

The utilization of vaccines to fight the spread of SARS-CoV-2 has led to a growing need for expansive serological testing. To address this, an EUA approved immunoassay for detection of antibodies to SARS-CoV-2 in venous serum samples was investigated for use with dried blood spot (DBS) samples. Results from self-collected DBS samples demonstrated a 98.1% categorical agreement to venous serum with a correlation (R) of 0.9600 while professionally collected DBS samples demonstrated a categorical agreement of 100.0% with a correlation of 0.9888 to venous serum. Additional studies were performed to stress different aspects of at-home DBS collection, including shipping stability, effects of interferences, and other sample-specific robustness studies. These studies demonstrated a categorical agreement of at least 95.0% and a mean bias less than ± 20.0%. Furthermore, the ability to track antibody levels following vaccination with the BioNTech/Pfizer vaccine was demonstrated with serial self-collected DBS samples from pre-dose (Day 0) out to 19 weeks.

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  1. SciScore for 10.1101/2021.11.19.21266532: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: All studies were performed under two Institutional Review Board approved protocols: SQNM-RND-103 (IRB number 520100174, study number 1269845) and SCMM-RND-402 (IRB number 520180046, study number 1270479). 2.3.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    These samples were generated by mixing venous serum (screened for SARS-CoV-2 antibodies) with packed red blood cells to create samples with 40% hematocrit.
    SARS-CoV-2
    suggested: None

    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 did not find any issues relating to the usage of bar graphs.


    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.

    Results from scite Reference Check: We found no unreliable references.


    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.