Oil immersed lossless total analysis system for integrated RNA extraction and detection of SARS-CoV-2

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Abstract

The COVID-19 pandemic exposed difficulties in scaling current quantitative PCR (qPCR)-based diagnostic methodologies for large-scale infectious disease testing. Bottlenecks include lengthy multi-step processes for nucleic acid extraction followed by qPCR readouts, which require costly instrumentation and infrastructure, as well as reagent and plastic consumable shortages stemming from supply chain constraints. Here we report an Oil Immersed Lossless Total Analysis System (OIL-TAS), which integrates RNA extraction and detection onto a single device that is simple, rapid, cost effective, and requires minimal supplies and infrastructure to perform. We validated the performance of OIL-TAS using contrived SARS-CoV-2 viral particle samples and clinical nasopharyngeal swab samples. OIL-TAS showed a 93% positive predictive agreement (n = 57) and 100% negative predictive agreement (n = 10) with clinical SARS-CoV-2 qPCR assays in testing clinical samples, highlighting its potential to be a faster, cheaper, and easier-to-deploy alternative for infectious disease testing.

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  1. SciScore for 10.1101/2020.09.30.20204842: (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
    Using a p20 pipette, 4 μL of nuclease-free water, 50% ethanol, and LAMP master mix were sequentially added to washing well 2, washing well 1 and the detection well, respectively in the same manner as the single output device (except different primer sets were used in the two output detection wells).
    LAMP
    suggested: (LAMP, RRID:SCR_001740)

    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.