Evidence map›Paper›PMID 33273714›Full record

ArticleNature biomedical engineering2020

Rapid lateral flow immunoassay for the fluorescence detection of SARS-CoV-2 RNA.

Daming Wang, Shaogui He, Xiaohui Wang, Youqin Yan, Jianzhong Liu, Shimin Wu, Shiguo Liu, Yang Lei, Min Chen, Li Li and 9 more

Open access · hybridAbstract read
PubMed Publisher
In one paragraph

Article in Nature biomedical engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
113citing papers in PubMed, 2 pooled it
15.8field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

113 citing papers in PubMed, 2 syntheses or guidelines pooled it, 296 citations in OpenAlex.

  1. Pooled it
  2. Rapid, point-of-care antigen tests for diagnosis of SARS-CoV-2 infection.The Cochrane database of systematic reviews · 2022
    Pooled it
  3. Article
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  6. Article
  7. Article
  8. Article
  9. High-sensitivity, protein-independent detection of dsDNA sequences.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  10. Review
  11. Article
  12. An immunoassay based on polyHRP signal amplification for HPV16/18 E6 RNA detection with anti-DNA-RNA hybrid antibody.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2025
    Article
  13. Article
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53 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors at 8 institutions in 2 countries.

Daming Wang *Academy for Engineering & Technology, Fudan University, Shanghai, China. wangdm@sibet.ac.cn.ORCID http://orcid.org/0000-0001-5572-5090
Shaogui He *Xiamen Huaxia University, Xiamen, China.
Xiaohui WangInstitute of Health Service and Transfusion Medicine, Beijing, China. lovechina1980@163.com.ORCID http://orcid.org/0000-0003-3091-0219
Youqin YanWuhan No. 7 Hospital, Wuhan, China.
Jianzhong LiuWuhan No. 7 Hospital, Wuhan, China.
Shimin WuGeneral Hospital of the Yangtze River Shipping, Wuhan, China.
Shiguo LiuThe Third People's Hospital of Hubei Province, Wuhan, China.
Yang LeiAnbio (Xiamen) Biotechnology, Xiamen, China.
Min ChenXiamen Center for Disease Control and Prevention, Xiaman, China.
Li LiXiamen Center for Disease Control and Prevention, Xiaman, China.
Jieli ZhangAnbio (Xiamen) Biotechnology, Xiamen, China.
Liwei ZhangAnbio (Xiamen) Biotechnology, Xiamen, China.
Xiao HuAnbio (Xiamen) Biotechnology, Xiamen, China.
Xinhui ZhengAnbio (Xiamen) Biotechnology, Xiamen, China.
Jiawei BaiAnbio (Xiamen) Biotechnology, Xiamen, China.
Yulong ZhangInstitute of Health Service and Transfusion Medicine, Beijing, China.
Yitong ZhangDepartment of Biochemistry and Molecular Biology, Harbin Medical University, Harbin, China.
Mingxuan SongSuzhou Institute of Biomedical Engineering and Technology (SIBET), Chinese Academy of Sciences, Suzhou, China.
Yuguo TangSuzhou Institute of Biomedical Engineering and Technology (SIBET), Chinese Academy of Sciences, Suzhou, China. tangyg@sibet.ac.cn.ORCID http://orcid.org/0000-0001-9668-6841
Chinese Academy of Sciences · CNHarbin Medical University · CNWuhan No. 7 Hospital · CNWuhan University · CNFudan University · CNInstitute for Transfusion Medicine · USXiamen University · CNXian Center for Disease Control and Prevention · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the need for rapid and accurate nucleic acid detection at the point of care. Here, we report an amplification-free nucleic acid immunoassay, implemented on a lateral flow strip, for the fluorescence detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in less than one hour. The assay uses DNA probes that are designed to bind to the conserved open reading frame 1ab (ORF1ab), envelope protein (E) and the nucleocapsid (N) regions of the SARS-CoV-2 genome, and a fluorescent-nanoparticle-labelled monoclonal antibody that binds to double-stranded DNA-RNA hybrids. In a multi-hospital randomized double-blind trial involving 734 samples (593 throat swabs and 141 sputum) provided by 670 individuals, the assay achieved sensitivities of 100% and specificities of 99% for both types of sample (ground truth was determined using quantitative PCR with reverse transcription). The inexpensive amplification-free detection of SARS-CoV-2 RNA should facilitate the rapid diagnosis of COVID-19 at the point of care.

Indexed as

Antibodies, MonoclonalCOVID-19FluorescenceHumansImmunoassayMolecular Diagnostic TechniquesNucleic Acid Amplification TechniquesPandemicsPoint-of-Care SystemsRNA, ViralSARS-CoV-2Sensitivity and SpecificityAntibodies, MonoclonalRNA, Viral

Identifiers

PMID33273714
OpenAlexW3108584707

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.