Absolute quantification and degradation evaluation of SARS-CoV-2 RNA by droplet digital PCR

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Quantifying SARS-CoV-2 infectivity, formulating well-calibrated public-health policy, and managing the safety of workplaces would all be facilitated by precise measurement of the extent to which SARS-CoV-2 RNA is present in an intact form in biological specimens and human environments. We describe assays that use digital PCR in nanoliter droplets (droplet digital PCR) to measure these properties. Such assays could be broadly deployed to inform COVID-19 epidemiology, measure symptomatic and asymptomatic infectivity, and help manage the safety of environments in which people live, move, and work.

Article activity feed

  1. SciScore for 10.1101/2020.06.24.20139584: (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.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Negative control: RNA extracted from HEK 293 (human embryonic kidney) cells.
    HEK 293
    suggested: CLS Cat# 300192/p777_HEK293, RRID:CVCL_0045)

    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.

    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.