Comparison of seven commercial RT-PCR diagnostic kits for COVID-19

  1. Puck B. van Kasteren
  2. Bas van der Veer
  3. Sharon van den Brink
  4. Lisa Wijsman
  5. Jørgen de Jonge
  6. Annemarie van den Brandt
  7. Richard Molenkamp
  8. Chantal B.E.M. Reusken
  9. Adam Meijer

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.1016/j.jcv.2020.104412
  2. ScreenIT

    SciScore for 10.1101/2020.04.22.056747: (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

    Software and Algorithms
    SentencesResources
    We determined the slope by linear regression in GraphPad Prism and defined the required levels for PCR efficiency (E) and R2 as >95% and >0.95, respectively.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Next, we ran four replicates of a 2-fold dilution series (diluted in yeast carrier RNA in water) to determine the LOD95% by Probit analysis using SPSS Statistics (IBM, version 24).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    • No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
    • 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.

  3. Version published to 10.1101/2020.04.22.056747v1 on bioRxiv