Evidence map›Paper›PMID 32543298›Full record

Trial reportEmerging microbes & infections2020

Development of an automatic integrated gene detection system for novel severe acute respiratory syndrome-related coronavirus (SARS-CoV2).

Yuchang Li, Jing Li, Ying Zhang, Lizhong Dai, Lin Li, Juan Liu, Sen Zhang, Xiaoyan Wu, Yi Hu, Chenfeng Qin and 2 more

Open access · goldAbstract readClinical Trial
In one paragraph

Trial report in Emerging microbes & infections, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
0.6field-weighted citation impact, top 19% 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

17 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Review
  12. Review
  13. Development of a quantitative one-step multiplex RT-qPCR assay for the detection of SARS-CoV-2 in a biological matrix.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2021
    Article
  14. Article
  15. Facile biosensors for rapid detection of COVID-19.Biosensors & bioelectronics · 2020
    Review
  16. Article
  17. An alternate prospect in detecting presymptomatic and asymptomatic COVID-19 carriers through odor differentiation by HeroRATs.Journal of veterinary behavior : clinical applications and research : official journal of : Australian Veterinary Behaviour Interest Group, International Working Dog Breeding Association
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 2 institutions in 1 country.

Yuchang LiState Key Laboratory of Pathogen and Biosecurity, The Academy of Millitary Medical Science Institute of Microbiology and Epidemiology, Beijing, People's Republic of China.
Jing LiState Key Laboratory of Pathogen and Biosecurity, The Academy of Millitary Medical Science Institute of Microbiology and Epidemiology, Beijing, People's Republic of China.
Ying ZhangState Key Laboratory of Pathogen and Biosecurity, The Academy of Millitary Medical Science Institute of Microbiology and Epidemiology, Beijing, People's Republic of China.
Lizhong DaiSansure Biotech Inc., Changsha, People's Republic of China.
Lin LiState Key Laboratory of Pathogen and Biosecurity, The Academy of Millitary Medical Science Institute of Microbiology and Epidemiology, Beijing, People's Republic of China.
Juan LiuSansure Biotech Inc., Changsha, People's Republic of China.
Sen ZhangState Key Laboratory of Pathogen and Biosecurity, The Academy of Millitary Medical Science Institute of Microbiology and Epidemiology, Beijing, People's Republic of China.
Xiaoyan WuState Key Laboratory of Pathogen and Biosecurity, The Academy of Millitary Medical Science Institute of Microbiology and Epidemiology, Beijing, People's Republic of China.
Yi HuState Key Laboratory of Pathogen and Biosecurity, The Academy of Millitary Medical Science Institute of Microbiology and Epidemiology, Beijing, People's Republic of China.
Chenfeng QinState Key Laboratory of Pathogen and Biosecurity, The Academy of Millitary Medical Science Institute of Microbiology and Epidemiology, Beijing, People's Republic of China.
Tao JiangState Key Laboratory of Pathogen and Biosecurity, The Academy of Millitary Medical Science Institute of Microbiology and Epidemiology, Beijing, People's Republic of China.
Xiaoping KangState Key Laboratory of Pathogen and Biosecurity, The Academy of Millitary Medical Science Institute of Microbiology and Epidemiology, Beijing, People's Republic of China.ORCID https://orcid.org/0000-0002-0587-399X
Academy of Military Medical Sciences · CNSansure Biotech (China) · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In December 2019, Wuhan, China suffered a serious outbreak of a novel coronavirus infectious disease (COVID) caused by novel severe acute respiratory syndrome-related coronavirus (SARS-CoV 2). To quickly identify the pathogen, we designed and screened primer sets, and established a sensitive and specific qRT-PCR assay for SARS-CoV 2; the lower limit of detection (LOD) was 14.8 (95% CI: 9.8-21) copies per reaction. We combined this qRT-PCR assay with an automatic integration system for nucleic acid extraction and amplification, thereby establishing an automatic integrated gene detection system (AIGS) for SARS-CoV 2. Cross reactive analysis performed in 20 other respiratory viruses and 37 nasopharyngeal swabs confirmed a 100% specificity of the assay. Using two fold diluted SARS-CoV 2 culture, the LOD of AIGS was confirmed to be 365 copies/ml (95% CI: 351-375), which was Comparable to that of conventional q RT-PCR (740 copies/ml, 95% CI: 689-750). Clinical performances of AIGS assay were assessed in 266 suspected COVID-19 clinical respiratory tract samples tested in parallel with a commercial kit. The clinical sensitivity of the AIGS test was 97.62% (95% CI: 0.9320-0.9951) based on the commercial kit test result, and concordance analysis showed a high agreement in SARS-CoV-2 detection between the two assays, Pearson R was 0.9623 (95% CI: 0.9523-0.9703). The results indicated that this AIGS could be used for rapid detection of SARS-CoV 2. With the advantage of simple operation and less time consuming, AIGS could be suitable for SARS-CoV2 detection in primary medical institutions, thus would do a great help to improve detection efficiency and control the spread of COVID-19.

Indexed as

Automation, LaboratoryBetacoronavirusChinaCoronavirus InfectionsCOVID-19DNA PrimersHumansLimit of DetectionPandemicsPneumonia, ViralReal-Time Polymerase Chain ReactionRNA, ViralSARS-CoV-2Sensitivity and SpecificityVirus CultivationDNA PrimersRNA, Viralautomatic integrated gene detection systemCOVID-19qRT-PCRrapid detectionSARS-CoV2

Identifiers

PMID32543298
PMCPMC7473122
OpenAlexW3035348393

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.