A simple magnetic nanoparticles-based viral RNA extraction method for efficient detection of SARS-CoV-2

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Abstract

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The ongoing outbreak of the novel coronavirus disease 2019 (COVID-19) originating from Wuhan, China, draws worldwide concerns due to its long incubation period and strong infectivity. Although RT-PCR-based molecular diagnosis techniques are being widely applied for clinical diagnosis currently, timely and accurate diagnosis are still limited due to labour intensive and time-consuming operations of these techniques. To address the issue, herein we report the synthesis of poly (amino ester) with carboxyl groups (PC)-coated magnetic nanoparticles (pcMNPs), and the development of pcMNPs-based viral RNA extraction method for the sensitive detection of COVID-19 causing virus, the SARS-CoV-2. This method combines the lysis and binding steps into one step, and the pcMNPs-RNA complexes can be directly introduced into subsequent RT-PCR reactions. The simplified process can purify viral RNA from multiple samples within 20 min using a simple manual method or an automated high-throughput approach. By identifying two different regions ( ORFlab and N gene) of viral RNA, a 10-copy sensitivity and a strong linear correlation between 10 and 10 5 copies of SARS-CoV-2 pseudovirus particles are achieved. Benefitting from the simplicity and excellent performances, this new extraction method can dramatically reduce the turn-around time and operational requirements in current molecular diagnosis of COVID-19, in particular for the early clinical diagnosis.

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  1. SciScore for 10.1101/2020.02.22.961268: (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
    The oligonucleotide primers and probes were synthesized by GENEWIZ (Suzhou, China), and corresponding sequences are shown in Table 1.
    GENEWIZ
    suggested: (GENEWIZ, RRID:SCR_003177)

    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:
    • No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

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