Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning

  1. Jeany Delafiori
  2. Luiz Cláudio Navarro
  3. Rinaldo Focaccia Siciliano
  4. Gisely Cardoso de Melo
  5. Estela Natacha Brandt Busanello
  6. José Carlos Nicolau
  7. Geovana Manzan Sales
  8. Arthur Noin de Oliveira
  9. Fernando Fonseca Almeida Val
  10. Diogo Noin de Oliveira
  11. Adriana Eguti
  12. Luiz Augusto dos Santos
  13. Talia Falcão Dalçóquio
  14. Adriadne Justi Bertolin
  15. Rebeca Linhares Abreu-Netto
  16. Rocio Salsoso
  17. Djane Baía-da-Silva
  18. Fabiana G Marcondes-Braga
  19. Vanderson Souza Sampaio
  20. Carla Cristina Judice
  21. Fabio Trindade Maranhão Costa
  22. Nelson Durán
  23. Mauricio Wesley Perroud
  24. Ester Cerdeira Sabino
  25. Marcus Vinicius Guimarães Lacerda
  26. Leonardo Oliveira Reis
  27. Wagner José Fávaro
  28. Wuelton Marcelo Monteiro
  29. Anderson Rezende Rocha
  30. Rodrigo Ramos Catharino

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Abstract

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  1. Version published to 10.1021/acs.analchem.0c04497
  2. ScreenIT

    SciScore for 10.1101/2020.07.24.20161828: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    After machine leaning modeling, the presence of each discriminant m/z determined by the algorithm was confirmed in mass spectra using Xcalibur 3.0 software (Thermo, Bremen, Germany)
    Xcalibur
    suggested: (Thermo Xcalibur, RRID:SCR_014593)
    Molecule identification was proposed using METLIN (Scripps Center for Metabolomics, https://metlin.scripps.edu)
    https://metlin.scripps.edu
    suggested: (METLIN, RRID:SCR_010500)
    HMDB (
    HMDB
    suggested: (HMDB, RRID:SCR_007712)
    Biomarker pathway analysis and meaning were attributed based on Kegg database (Kyoto Encyclopedia of Genes and Genomes, https://www.genome.jp/kegg/) information and scientific literature.
    Kegg
    suggested: (KEGG, RRID:SCR_012773)

    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 10.1101/2020.07.24.20161828v1 on medRxiv