Nasal host response-based screening for undiagnosed respiratory viruses: a pathogen surveillance and detection study

  1. Nagarjuna R Cheemarla
  2. Amelia Hanron
  3. Joseph R Fauver
  4. Jason Bishai
  5. Timothy A Watkins
  6. Anderson F Brito
  7. Dejian Zhao
  8. Tara Alpert
  9. Chantal B F Vogels
  10. Albert I Ko
  11. Wade L Schulz
  12. Marie L Landry
  13. Nathan D Grubaugh
  14. David van Dijk
  15. Ellen F Foxman

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Abstract

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  1. Version published to 10.1016/s2666-5247(22)00296-8
  2. ScreenIT

    SciScore for 10.1101/2020.06.04.20109306: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Human subjects: Residual nasopharyngeal samples from clinical testing were obtained from the Yale-New Haven Hospital Clinical Virology Laboratory and medical records were reviewed, data de-identified, and discovered positive cases were reported to health care providers according to IRB-approved protocol #2000027656 with oversight from the Yale Human Investigations Committee.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    CXCL10 measurements: Human CXCL10 was measured in duplicate for each sample using the R&D Human CXCL10/IP-10 DuoSet ELISA (Cat No: DY266) and concentrations were calculated from a standard curve on each plate according to manufacturer instructions using GraphPad Prism software.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    cDNA was used as input into a highly multiplexed amplicon generation approach for sequencing on the Oxford Nanopore Technologies MinION (ONT, Oxford, UK)30.
    Oxford Nanopore
    suggested: (Oxford Nanopore Technologies, RRID:SCR_003756)
    The RAMPART software from the ARTIC Network was used to monitor each sequencing run.
    RAMPART
    suggested: (Rampart, RRID:SCR_016742)

    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.

  3. Version published to 10.1101/2020.06.04.20109306v1 on medRxiv