COVID-19 Serology Control Panel Using the Dried-Tube Specimen Method

  1. William J. Windsor
  2. Vijaya Knight
  3. Patricia A. Merkel
  4. Molly M. Lamb
  5. Heidi R. Tucker
  6. Kyle Carson
  7. Kelly M. Howard
  8. Jennifer L. Yates
  9. Mario L. Santiago
  10. Mary K. McCarthy
  11. Thomas E. Morrison
  12. Ross M. Kedl
  13. Ashley Frazer-Abel
  14. Kejun Guo
  15. Gillian Andersen
  16. Leah Huey
  17. Bradley S. Barrett
  18. Jessica M. Colón-Franco
  19. William T. Lee
  20. May C. Chu

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Abstract

The dried-tube specimen (DTS) procedure was used to develop the COVID-19 serology control panel (CSCP). The DTS offers the benefit of shipping materials without a cold chain, allowing for greater access without deterioration of material integrity. Samples in the panel were sourced from COVID-19 convalescent persons from March to May 2020. The immunoglobulin subtypes (total Ig, IgM, and IgG) and their respective reactivity to severe acute respiratory syndrome coronavirus 2 nucleocapsid, spike, and receptor-binding domain antigens of the samples were delineated and compared with the WHO International Standard to elucidate the exact binding antibody units of each CSCP sample and ensure the CSCP provides adequate reactivity for different types of serological test platforms. We distribute the CSCP as a kit with five coded tubes to laboratories around the world to be used to compare test kits for external quality assurance, for harmonizing laboratory testing, and for use as training materials for laboratory workers.

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  1. Version published to 10.4269/ajtmh.21-1036
  2. Version published to 10.1101/2021.07.07.21260101v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.07.07.21260101: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot 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 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.

  4. Version published to 10.1101/2021.07.07.21260101v1 on medRxiv