First detection of SARS-CoV-2 in untreated wastewaters in Italy

  1. Giuseppina La Rosa
  2. Marcello Iaconelli
  3. Pamela Mancini
  4. Giusy Bonanno Ferraro
  5. Carolina Veneri
  6. Lucia Bonadonna
  7. Luca Lucentini
  8. Elisabetta Suffredini

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Abstract

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  1. Version published to 10.1016/j.scitotenv.2020.139652
  2. ScreenIT

    SciScore for 10.1101/2020.04.25.20079830: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Sequences were identified in terms of the closest homology sequence using BLAST https://blast.ncbi.nlm.nih.gov/Blast.cgi. All Italian SARS-CoV-2 genome sequences available at the time of analysis were retrieved from Gisaid (https://www.gisaid.org/) for comparison with study sequences, using the MEGA X software (Kumar et al., 2018).
    BLAST
    suggested: (BLASTX, RRID:SCR_001653)
    MEGA X
    suggested: None

    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.
    • 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 10.1101/2020.04.25.20079830v2 on medRxiv
  4. Version published to 10.1101/2020.04.25.20079830v1 on medRxiv