Evidence map›Paper›PMID 31552611›Full record

ArticleVirologica Sinica2020

Development and Evaluation of a Universal and Supersensitive NS1-Based Luciferase Immunosorbent Assay to Detect Zika Virus-Specific IgG.

Tianyu Wang, Ying Zhan, De Wu, Zhihai Chen, Wei Wu, Yao Deng, Wenling Wang, Wenjie Tan, Shixing Tang

Open access · greenAbstract readEvaluation Study
In one paragraph

Article in Virologica Sinica, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.7field-weighted citation impact, top 25% 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

8 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Seroepidemiology ofFrontiers in microbiology · 2021
    Article
  8. 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

9 authors at 4 institutions in 1 country.

Tianyu WangGuangdong Provincial Key Laboratory of Tropical Disease Research, Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, 510515, China.
Ying ZhanNHC Key Laboratory of Biosafety, National Institute for Viral Disease Control and Prevention, China CDC, Beijing, 102206, China.
De WuKey Laboratory for Repository and Application of Pathogenic Microbiology, Research Center for Pathogens Detection Technology of Emerging Infectious Diseases, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China.
Zhihai ChenThe National Clinical Key Department of Infectious Disease, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China.
Wei WuNHC Key Laboratory of Biosafety, National Institute for Viral Disease Control and Prevention, China CDC, Beijing, 102206, China.
Yao DengNHC Key Laboratory of Biosafety, National Institute for Viral Disease Control and Prevention, China CDC, Beijing, 102206, China.
Wenling WangNHC Key Laboratory of Biosafety, National Institute for Viral Disease Control and Prevention, China CDC, Beijing, 102206, China.
Wenjie TanNHC Key Laboratory of Biosafety, National Institute for Viral Disease Control and Prevention, China CDC, Beijing, 102206, China. tanwj28@163.com.ORCID http://orcid.org/0000-0002-5963-1136
Shixing TangGuangdong Provincial Key Laboratory of Tropical Disease Research, Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, 510515, China. tamgshixing@smu.edu.cn.ORCID http://orcid.org/0000-0001-9637-1874
Chinese Center For Disease Control and Prevention · CNCapital Medical University · CNGuangdong Provincial Center for Disease Control and Prevention · CNSouthern Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Zika virus (ZIKV) causes rash, moderate fever, conjunctivitis, and arthralgia, and has serious connection with neurological complications; therefore, it is a major threat to public health. A rapid and supersensitive method for detecting anti-ZIKV antibodies in humans and animals is thus urgently required. Here, we report an NS1-based luciferase immunosorbent assay (LISA), developed to detect ZIKV-specific IgG. Fusion proteins including a reporter Nano-luciferase (NLuc) and various fragments of ZIKV NS1 protein were expressed in 293 T cells. LISA was performed using the above cell lysates containing the expressed fusion proteins. Sample panels of humans and animals infected with ZIKV were examined for sensitivity of LISA, relative to those of ZIKV RT-PCR, commercial NS1-based ELISA, and micro-neutralization (MN) assays. Specificity and potential cross-reactivity were also evaluated using various convalescent serum samples derived from patients infected with dengue virus (DENV), Japanese encephalitis virus (JEV), and hepatitis C virus (HCV). Results indicated the optimal antigenic domain for anti-ZIKV IgG detection was located within 172-352 amino acids (aa) of ZIKV NS1 protein. NS1-based LISA performs better than commercial ELISA in anti-ZIKV IgG detection. LISA was shown to be at least fourfold more sensitive than commercial ELISA, and could detect anti-ZIKV IgG in various animal hosts without the need of species-specific labeled antibody. This novel assay is potentially useful for the rapid and sensitive detection of anti-ZIKV IgG in human and animal samples.

Indexed as

AnimalsAntibodies, ViralCross ReactionsEnzyme-Linked Immunosorbent AssayHEK293 CellsHumansImmunoglobulin GImmunosorbentsLuciferasesSensitivity and SpecificityViral Fusion ProteinsViral Nonstructural ProteinsZika VirusZika Virus InfectionAntibodies, ViralImmunoglobulin GImmunosorbentsLuciferasesNS1 protein, zika virusViral Fusion ProteinsViral Nonstructural ProteinsDetectionIgGLuciferase immunosorbent assayNS1Zika virus (ZIKV)

Identifiers

PMID31552611
PMCPMC7035413
OpenAlexW2975068038

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.