Evidence map›Paper›PMID 32954173›Full record

ArticleACS omega2020

Highly Sensitive Fluorescence-Linked Immunosorbent Assay for the Determination of Human IgG in Serum Using Quantum Dot Nanobeads and Magnetic Fe

Jiaping Guo, Yameng Wang, Shanshan Niu, Hongping Li, Yongmei Tian, Songcheng Yu, Fei Yu, Yongjun Wu, Li-E Liu

Open access · goldAbstract read
In one paragraph

Article in ACS omega, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.0field-weighted citation impact, top 14% 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

12 citing papers in PubMed, 28 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Quantum dots against SARS-CoV-2: diagnostic and therapeutic potentials.Journal of chemical technology and biotechnology (Oxford, Oxfordshire : 1986) · 2022
    Review
  6. Nanotechnology: A Potential Weapon to Fight against COVID-19.Particle & particle systems characterization : measurement and description of particle properties and behavior in powders and other disperse systems · 2022
    Review
  7. Review
  8. Rapid detection of SARS-CoV-2: The gradual boom of lateral flow immunoassay.Frontiers in bioengineering and biotechnology · 2022
    Review
  9. Review
  10. Review
  11. Article
  12. 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 1 institution in 1 country.

Jiaping GuoCollege of Public Health, Zhengzhou University, Zhengzhou 450001, China.
Yameng WangCollege of Public Health, Zhengzhou University, Zhengzhou 450001, China.
Shanshan NiuSchool of Chemical Engineering, Zhengzhou University, Zhengzhou 450001, China.
Hongping LiSchool of Chemical Engineering, Zhengzhou University, Zhengzhou 450001, China.
Yongmei TianCollege of Public Health, Zhengzhou University, Zhengzhou 450001, China.
Songcheng YuCollege of Public Health, Zhengzhou University, Zhengzhou 450001, China.
Fei YuCollege of Public Health, Zhengzhou University, Zhengzhou 450001, China.
Yongjun WuCollege of Public Health, Zhengzhou University, Zhengzhou 450001, China.
Li-E LiuCollege of Public Health, Zhengzhou University, Zhengzhou 450001, China.
Zhengzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study is to establish a new method with high sensitivity, accuracy, and stability for the determination of human IgG and then expand it to analyze severe acute respiratory syndrome corona virus 2 (SARS-CoV-2)-specific IgM and IgG, which is of great significance for the screening and diagnosis of COVID-19. In this study, the magnetic Fe

Identifiers

PMID32954173
PMCPMC7495760
OpenAlexW3082283693

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.