A Simple Colorimetric Molecular Detection of Novel Coronavirus (COVID-19), an Essential Diagnostic Tool for Pandemic Screening

  1. Pazhanimuthu Annamalai
  2. Madhu Kanta
  3. Pazhanivel Ramu
  4. Baskar Ravi
  5. Kokilavani Veerapandian
  6. Rengarajan Srinivasan

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Abstract

The recent outbreak of the newly emerged novel coronavirus (SARS-CoV-2) presents a big challenge for public health laboratories as virus isolates are not available while there is an increasing evidence that the epidemic is more widespread than initially thought, as well as spreading internationally across borders through travellers does already happen warranting a methodology for the rapid detection of the infection to control SARS-CoV-2. Aim: We intended to develop and deploy a robust and rapid diagnostic methodology using LAMP assay for use in point of care settings to detect SARS-COV-2 infection. Methodology: In the present study, we have developed a validated rapid diagnostic procedure to detect SARS-CoV-2 using LAMP assay, its design relying on isothermal amplification of the nucleic acids of the SARS-CoV-2. Results: The LAMP assay developed detects SARS-CoV-2 infection rapidly with high sensitivity and reliability. The data generated by LAMP assay were comparable and at par with the data generated by real-time PCR method. Conclusion: The present study demonstrates that the LAMP assay developed was a rapid, reliable, sensitive and cost effective method to detect SARS-CoV-2 infection in a point of care as well as in laboratory settings.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.10.20060293: (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

    No key resources detected.

    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.
    • Thank you for including a protocol registration statement.

    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.

  2. Version published to 10.1101/2020.04.10.20060293v1 on medRxiv