Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19

  1. Mustafa Buyukozkan
  2. Sergio Alvarez-Mulett
  3. Alexandra C. Racanelli
  4. Frank Schmidt
  5. Richa Batra
  6. Katherine L. Hoffman
  7. Hina Sarwath
  8. Rudolf Engelke
  9. Luis Gomez-Escobar
  10. Will Simmons
  11. Elisa Benedetti
  12. Kelsey Chetnik
  13. Guoan Zhang
  14. Edward Schenck
  15. Karsten Suhre
  16. Justin J. Choi
  17. Zhen Zhao
  18. Sabrina Racine-Brzostek
  19. He S. Yang
  20. Mary E. Choi
  21. Augustine M.K. Choi
  22. Soo Jung Cho
  23. Jan Krumsiek

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Abstract

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  1. Version published to 10.1016/j.isci.2022.104612
  2. ScreenIT

    SciScore for 10.1101/2021.07.19.21260776: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variableChildren (less than 18 years old) and pregnant women (confirmed by a positive beta-HCG test and/or medical records) were excluded.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data not available within WC-CEDAR were manually extracted and recorded in REDCap.
    REDCap
    suggested: (REDCap, RRID:SCR_003445)
    Each metabolite and protein was annotated with pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database(35).
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    A Gaussian Graphical Model (GGM) based network was then constructed using the GeneNet algorithm(39) and drawing an edge for all partial correlations with an FDR smaller than 0.2.
    GeneNet
    suggested: (GeneNet, RRID:SCR_007678)

    Results from OddPub: Thank you for sharing your code and 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.
    • 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 10.1101/2021.07.19.21260776 on medRxiv