Evidence map›Paper›PMID 34830291›Full record

ArticleInternational journal of molecular sciences2021

Monoclonal Antibodies against Nucleocapsid Protein of SARS-CoV-2 Variants for Detection of COVID-19.

Ruei-Min Lu, Shih-Han Ko, Wan-Yu Chen, Yu-Ling Chang, Hsiu-Ting Lin, Han-Chung Wu

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.3field-weighted citation impact, top 16% 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

13 citing papers in PubMed, 23 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

Ruei-Min LuBiomedical Translation Research Center (BioTReC), Academia Sinica, Taipei 11529, Taiwan.ORCID 0000-0003-3792-5499
Shih-Han KoBiomedical Translation Research Center (BioTReC), Academia Sinica, Taipei 11529, Taiwan.
Wan-Yu ChenInstitute of Cellular and Organismic Biology, Academia Sinica, Taipei 11529, Taiwan.
Yu-Ling ChangBiomedical Translation Research Center (BioTReC), Academia Sinica, Taipei 11529, Taiwan.
Hsiu-Ting LinInstitute of Cellular and Organismic Biology, Academia Sinica, Taipei 11529, Taiwan.
Han-Chung WuBiomedical Translation Research Center (BioTReC), Academia Sinica, Taipei 11529, Taiwan.ORCID 0000-0002-5185-1169
Institute of Biomedical Sciences, Academia Sinica · TWInstitute of Cellular and Organismic Biology, Academia Sinica · TW

Funding

Ministry of Science and Technology, Taiwan [MOST- 108-3114-Y-001-002], [MOST-108-2823-8-001-001], and [MOST 109-3114-Y- 001-001]
6 · The paper itself

Abstract

Mitigation strategies of the coronavirus disease 2019 (COVID-19) pandemic have been greatly hindered by the continuous emergence of SARS-CoV-2 variants. New sensitive, rapid diagnostic tests for the wide-spectrum detection of viral variants are needed. We generated a panel of 41 monoclonal antibodies against the SARS-CoV-2 nucleocapsid protein (NP) by using mice hybridoma techniques. Of these mAbs, nine exhibited high binding activities and were applied in latex-based lateral flow immunoassays (LFIAs). The LFIAs utilizing NP-mAb-7 and -40 had the best sensitivity and lowest limit of detection: 8 pg for purified NP and 625 TCID

Indexed as

AdolescentAdultAgedAged, 80 and overAntibodies, MonoclonalAntigen-Antibody ReactionsCoronavirusCOVID-19Cross ReactionsFemaleHumansImmunoassayMaleMiddle AgedNucleocapsid ProteinsPoint-of-Care SystemsAntibodies, MonoclonalNucleocapsid ProteinsRNA, ViralantibodyCOVID-19LFIAnucleocapsid proteinrapid testSARS-CoV-2

Identifiers

PMID34830291
PMCPMC8623253
OpenAlexW3213952387

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.