Lower Respiratory Tract Myeloid Cells Harbor SARS-Cov-2 and Display an Inflammatory Phenotype

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

No abstract available

Article activity feed

  1. SciScore for 10.1101/2020.08.11.20171967: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Upon the informed consent of the patient’s legally authorized representative, patients were enrolled in the University of Pittsburgh Acute Lung Injury Registry and Biospecimen Repository (IRB# PRO10110387), which has been described elsewhere.10,11 Longitudinal samples were collected on days 5 and 10 after enrollment.
    Randomizationnot detected.
    BlindingCell differentials were performed by manual counts of 200 nucleated cells by three separate authors who were blinded to the others’ results – the mean result of the counts was utilized.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Colloidal gold conjugated secondary antibodies used were goat anti-mouse 18 nm and goat anti-rabbit 6 nm (Jackson ImmunoResearch).
    anti-mouse
    suggested: None
    anti-rabbit
    suggested: None
    Software and Algorithms
    SentencesResources
    Tomographic tilt-series and large-area montaged over-views were acquired using the SerialEM software package (Mastronarde, 2005).
    SerialEM
    suggested: (SerialEM, RRID:SCR_017293)
    Tomographic data were calculated, analyzed and modeled using the IMOD software package (Kremer et al., 1996; Mastronarde, 2008) on MacPro and iMac Pro computers (Apple, Inc, Cupertino, CA)
    IMOD
    suggested: (IMOD, RRID:SCR_003297)
    Imaris (Bitplane) was used to generate a surface rendered image that illustrated the areas of co-localization in this dataset.
    Imaris
    suggested: (Imaris, RRID:SCR_007370)

    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.