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      ddPCR: a more accurate tool for SARS-CoV-2 detection in low viral load specimens

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          ABSTRACT

          Quantitative real time PCR (RT-PCR) is widely used as the gold standard for clinical detection of SARS-CoV-2. However, due to the low viral load specimens and the limitations of RT-PCR, significant numbers of false negative reports are inevitable, which results in failure to timely diagnose, cut off transmission, and assess discharge criteria. To improve this situation, an optimized droplet digital PCR (ddPCR) was used for detection of SARS-CoV-2, which showed that the limit of detection of ddPCR is significantly lower than that of RT-PCR. We further explored the feasibility of ddPCR to detect SARS-CoV-2 RNA from 77 patients, and compared with RT-PCR in terms of the diagnostic accuracy based on the results of follow-up survey. 26 patients of COVID-19 with negative RT-PCR reports were reported as positive by ddPCR. The sensitivity, specificity, PPV, NPV, negative likelihood ratio (NLR) and accuracy were improved from 40% (95% CI: 27–55%), 100% (95% CI: 54–100%), 100%, 16% (95% CI: 13–19%), 0.6 (95% CI: 0.48–0.75) and 47% (95% CI: 33–60%) for RT-PCR to 94% (95% CI: 83–99%), 100% (95% CI: 48–100%), 100%, 63% (95% CI: 36–83%), 0.06 (95% CI: 0.02–0.18), and 95% (95% CI: 84–99%) for ddPCR, respectively. Moreover, 6/14 (42.9%) convalescents were detected as positive by ddPCR at 5–12 days post discharge. Overall, ddPCR shows superiority for clinical diagnosis of SARS-CoV-2 to reduce the false negative reports, which could be a powerful complement to the RT-PCR.

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          Most cited references16

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          A new coronavirus associated with human respiratory disease in China

          Emerging infectious diseases, such as severe acute respiratory syndrome (SARS) and Zika virus disease, present a major threat to public health 1–3 . Despite intense research efforts, how, when and where new diseases appear are still a source of considerable uncertainty. A severe respiratory disease was recently reported in Wuhan, Hubei province, China. As of 25 January 2020, at least 1,975 cases had been reported since the first patient was hospitalized on 12 December 2019. Epidemiological investigations have suggested that the outbreak was associated with a seafood market in Wuhan. Here we study a single patient who was a worker at the market and who was admitted to the Central Hospital of Wuhan on 26 December 2019 while experiencing a severe respiratory syndrome that included fever, dizziness and a cough. Metagenomic RNA sequencing 4 of a sample of bronchoalveolar lavage fluid from the patient identified a new RNA virus strain from the family Coronaviridae, which is designated here ‘WH-Human 1’ coronavirus (and has also been referred to as ‘2019-nCoV’). Phylogenetic analysis of the complete viral genome (29,903 nucleotides) revealed that the virus was most closely related (89.1% nucleotide similarity) to a group of SARS-like coronaviruses (genus Betacoronavirus, subgenus Sarbecovirus) that had previously been found in bats in China 5 . This outbreak highlights the ongoing ability of viral spill-over from animals to cause severe disease in humans.
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            High-Throughput Droplet Digital PCR System for Absolute Quantitation of DNA Copy Number

            Digital PCR enables the absolute quantitation of nucleic acids in a sample. The lack of scalable and practical technologies for digital PCR implementation has hampered the widespread adoption of this inherently powerful technique. Here we describe a high-throughput droplet digital PCR (ddPCR) system that enables processing of ∼2 million PCR reactions using conventional TaqMan assays with a 96-well plate workflow. Three applications demonstrate that the massive partitioning afforded by our ddPCR system provides orders of magnitude more precision and sensitivity than real-time PCR. First, we show the accurate measurement of germline copy number variation. Second, for rare alleles, we show sensitive detection of mutant DNA in a 100 000-fold excess of wildtype background. Third, we demonstrate absolute quantitation of circulating fetal and maternal DNA from cell-free plasma. We anticipate this ddPCR system will allow researchers to explore complex genetic landscapes, discover and validate new disease associations, and define a new era of molecular diagnostics.
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              RNA based mNGS approach identifies a novel human coronavirus from two individual pneumonia cases in 2019 Wuhan outbreak

              ABSTRACT From December 2019, an outbreak of unusual pneumonia was reported in Wuhan with many cases linked to Huanan Seafood Market that sells seafood as well as live exotic animals. We investigated two patients who developed acute respiratory syndromes after independent contact history with this market. The two patients shared common clinical features including fever, cough, and multiple ground-glass opacities in the bilateral lung field with patchy infiltration. Here, we highlight the use of a low-input metagenomic next-generation sequencing (mNGS) approach on RNA extracted from bronchoalveolar lavage fluid (BALF). It rapidly identified a novel coronavirus (named 2019-nCoV according to World Health Organization announcement) which was the sole pathogens in the sample with very high abundance level (1.5% and 0.62% of total RNA sequenced). The entire viral genome is 29,881 nt in length (GenBank MN988668 and MN988669, Sequence Read Archive database Bioproject accession PRJNA601736) and is classified into β-coronavirus genus. Phylogenetic analysis indicates that 2019-nCoV is close to coronaviruses (CoVs) circulating in Rhinolophus (Horseshoe bats), such as 98.7% nucleotide identity to partial RdRp gene of bat coronavirus strain BtCoV/4991 (GenBank KP876546, 370 nt sequence of RdRp and lack of other genome sequence) and 87.9% nucleotide identity to bat coronavirus strain bat-SL-CoVZC45 and bat-SL-CoVZXC21. Evolutionary analysis based on ORF1a/1b, S, and N genes also suggests 2019-nCoV is more likely a novel CoV independently introduced from animals to humans.
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                Author and article information

                Journal
                Emerg Microbes Infect
                Emerg Microbes Infect
                Emerging Microbes & Infections
                Taylor & Francis
                2222-1751
                7 June 2020
                2020
                : 9
                : 1
                : 1259-1268
                Affiliations
                [a ]State Key Laboratory of Virology, Renmin Hospital, Wuhan University , Wuhan, People’s Republic of China
                [b ]State Key Laboratory of Virology, Modern Virology Research Center, College of Life Sciences, Wuhan University , Wuhan, People’s Republic of China
                [c ]Department of Infectious Disease, Zhongnan Hospital, Wuhan University , Wuhan, People’s Republic of China
                [d ]Frontier Science Center for Immunology and Metabolism, Wuhan University , Wuhan, People’s Republic of China
                Author notes
                [CONTACT ] Yu Chen chenyu@ 123456whu.edu.cn
                [*]

                These authors contributed equally.

                Supplemental data for this article can be accessed at https://doi.org/10.1080/22221751.2020.1772678

                Author information
                https://orcid.org/0000-0003-2868-1816
                https://orcid.org/0000-0003-0678-4064
                https://orcid.org/0000-0003-1300-4652
                Article
                1772678
                10.1080/22221751.2020.1772678
                7448897
                32438868
                2514d3fd-33b6-4609-8b60-d5074b9ca053
                © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group, on behalf of Shanghai Shangyixun Cultural Communication Co., Ltd

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Figures: 3, Tables: 4, Equations: 0, References: 20, Pages: 10
                Categories
                Research Article
                Articles

                sars-cov-2,droplet digital pcr,rt-pcr,clinical detection,false negative

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