Cardiovascular signatures of COVID-19 predict mortality and identify barrier stabilizing therapies

  1. Dakota Gustafson
  2. Michelle Ngai
  3. Ruilin Wu
  4. Huayun Hou
  5. Alice Carvalhal Schoffel
  6. Clara Erice
  7. Serena Mandla
  8. Filio Billia
  9. Michael D. Wilson
  10. Milica Radisic
  11. Eddy Fan
  12. Uriel Trahtemberg
  13. Andrew Baker
  14. Chris McIntosh
  15. Chun-Po S. Fan
  16. Claudia C. dos Santos
  17. Kevin C. Kain
  18. Kate Hanneman
  19. Paaladinesh Thavendiranathan
  20. Jason E. Fish
  21. Kathryn L. Howe

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Abstract

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  1. Version published to 10.1016/j.ebiom.2022.103982
  2. ScreenIT

    SciScore for 10.1101/2022.02.08.22270636: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: All participants who were 18 years of age or older, provided either direct written informed consent or were consented into the study by a lawfully entitled substitute decision-maker on behalf of a participant when lacking the capacity to make the decision.
    IRB: The study, and consenting, was conducted in accordance with protocols approved by the Research Ethics Board (REB) of the University Health Network (REB#: 20-5453.6; Cardiovascular Disease and Outcomes among Patients with SARS-CoV-2 Infection During Admission and Post-Discharge [The COVID study]) or St.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Patients testing negative by nasopharyngeal SARS-CoV-2 polymerase chain reaction had immunological history (i.e., antibody reactivity) ascertained through spike antigen cross-reactivity using a Federal Drug Administration approved enzyme-linked immunosorbent sandwich assay (Online Figure II).
    i.e.
    suggested: None
    Software and Algorithms
    SentencesResources
    Transcriptomic data (i.e., messenger RNA and miRNA sequencing) have been deposited at the Gene Expression Omnibus and the RStudio analysis pipeline is contained within the Online Data Supplement.
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    MiRNA pathway analysis was conducted using BioCarta/KEGG/Reactome databases and tested for enrichment by a hypergeometric test with adjustment for multiple comparisons using the Benjamini-Hochberg FDR, with P ≤0.05 considered to be statistically enriched in a gene set of interest.
    BioCarta/KEGG/Reactome
    suggested: None
    Data were analyzed with GraphPad Prism 9.0.0 for MacOS (GraphPad Software, Inc., La Jolla, CA, USA;
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Final figures were assembled for publication purposes using Adobe Illustrator (v25.4.1).
    Adobe Illustrator
    suggested: (Adobe Illustrator, RRID:SCR_010279)

    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: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04747782RecruitingCOVID-19 Longitudinal Biomarkers in Lung Injury

    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.

  3. Version published to 10.1101/2022.02.08.22270636v1 on medRxiv