Evidence map›Paper›PMID 34406945›Full record

ArticleIEEE transactions on nanobioscience2022

Rapid Detection of Anti-SARS-CoV-2 Antibody Using a Selenium Nanoparticle-Based Lateral Flow Immunoassay.

Chunxia Chen, Hangzhan Hu, Xiaoquan Li, Zhi Zheng, Zhizeng Wang, Xuance Wang, Peiming Zheng, Facai Cui, Gang Li, Yaohui Wang and 2 more

Open access · hybridAbstract read
In one paragraph

Article in IEEE transactions on nanobioscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
3.9field-weighted citation impact, top 6% 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

32 citing papers in PubMed, 53 citations in OpenAlex.

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  19. Recent Progress on Rapid Lateral Flow Assay-Based Early Diagnosis of COVID-19.Frontiers in bioengineering and biotechnology · 2022
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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.

Chunxia Chen
Hangzhan Hu
Xiaoquan Li
Zhi Zheng
Zhizeng Wang
Xuance Wang
Peiming Zheng
Facai Cui
Gang Li
Yaohui Wang
Zhigang Liu
Yuanfang Ma
Henan University · CNHenan Provincial People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus disease 2019 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 is highly transmissible. Early and rapid testing is necessary to effectively prevent and control the outbreak. Detection of SARS-CoV-2 antibodies with lateral flow immunoassay can achieve this goal. In this study, SARS-CoV-2 nucleoprotein (NP) was expressed and purified. We used the selenium nanoparticle as the labeling probe coupled with the NP to prepare an antibody (IgM and IgG) detection kit. The detection limit, cross reaction, sensitivity and specificity of the kit is verified. Separate detection of IgM and IgG, such as in this assay, was performed in order to reduce mutual interference and improve the accuracy of the test results.The final purity of NP was 91.83%. Selenium nanoparticle and NP successfully combined with stable effect. The LOD of the kit was 20 ng/mL for anti-NP IgG and 60 ng/mL for anti-NP IgM, respectively. The kit does not cross reaction with RF. The sensitivity of the kit was 94.74% and the specificity was 96.23%. The assay kit does not require any special device for reading the results and the readout is a simple color change that can be evaluated with the naked eye. This kit is suitable for rapid and real-time detection of the SARS-CoV-2 antibody IgG and IgM.

Indexed as

COVID-19NanoparticlesSeleniumHumansImmunoassayImmunoglobulin MSARS-CoV-2Sensitivity and SpecificityImmunoglobulin MSelenium

Identifiers

PMID34406945
PMCPMC8905607
OpenAlexW3112840252

What OpenQuestion holds

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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.