Severe COVID-19 is characterised by inflammation and immature myeloid cells early in disease progression

  1. Liam Townsend
  2. Adam H. Dyer
  3. Aifric Naughton
  4. Sultan Imangaliyev
  5. Jean Dunne
  6. Rachel Kiersey
  7. Dean Holden
  8. Aoife Mooney
  9. Deirdre Leavy
  10. Katie Ridge
  11. Jamie Sugrue
  12. Mubarak Aldoseri
  13. Jo Hannah Kelliher
  14. Martina Hennessy
  15. Declan Byrne
  16. Paul Browne
  17. Christopher L. Bacon
  18. Catriona Doyle
  19. Ruth O’Riordan
  20. Anne-Marie McLaughlin
  21. Ciaran Bannan
  22. Ignacio Martin-Loeches
  23. Arthur White
  24. Rachel M. McLoughlin
  25. Colm Bergin
  26. Nollaig M. Bourke
  27. Cliona O’Farrelly
  28. Niall Conlon
  29. Clíona Ní Cheallaigh

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Abstract

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  1. Version published to 10.1016/j.heliyon.2022.e09230
  2. ScreenIT

    SciScore for 10.1101/2021.09.01.21262953: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: James’s Hospital Joint Research Ethics Committee (reference REC 2020-03).
    Consent: Informed consent was obtained from all participants.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Naive (CD27+) and Effector (CD27-) T cells were characterised using CD27, CD45RA and CD197 antibodies.
    CD27+
    suggested: (Leica Biosystems Cat# NCL-CD27, RRID:AB_563539)
    CD27, CD45RA
    suggested: None
    CD197
    suggested: None
    Software and Algorithms
    SentencesResources
    Methods Details: Immunophenotyping was carried out on fresh whole EDTA treated blood and samples were analysed on a FACS Canto II Flow Cytometer (BD San Jose USA), using BD DIVA v8 and FLO Jo v10 software.
    BD DIVA
    suggested: None
    All data analysis, visualizations and modelling were performed using open source packages available in Python 3.7.51, 52 For univariate analysis, a FDR corrected t-test was applied on power transformed numerical variables.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your data.

    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.
    • Thank you for including a protocol registration statement.

    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.

  3. Version published to 10.1101/2021.09.01.21262953v1 on medRxiv