Evidence map›Paper›PMID 32636460›Full record

ArticleScientific reports2020

Sensitive and rapid lateral-flow assay for early detection of subclinical mammary infection in dairy cows.

Mohanned Naif Alhussien, Ajay Kumar Dang

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
3.6field-weighted citation impact, top 8% 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

9 citing papers in PubMed, 25 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

2 authors at 2 institutions in 2 countries.

Mohanned Naif AlhussienAnimal Production Division, Agricultural College, Aleppo University, Aleppo, Syrian Arab Republic. mohannadalhussien@gmail.com.ORCID http://orcid.org/0000-0002-3833-9953
Ajay Kumar DangLactation and Immuno-Physiology Laboratory, ICAR-National Dairy Research Institute, Karnal, Haryana, 132 001, India.ORCID http://orcid.org/0000-0002-7306-5302
National Dairy Research Institute · INUniversity of Aleppo · SY

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detection of subclinical mastitis (SCM) in its initial stage can save great economic losses, improve milk quality and animal welfare. We have developed a semiquantitative lateral flow assay for the detection of SCM in dairy cows targeting myeloperoxidase (MPO) enzyme of milk neutrophils. A competitive immunoassay format was used, and colloidal gold nanoparticles (GNP) were prepared and used as a labelling agent. Monoclonal anti-MPO antibodies were used and assessed for its quality by enzyme-linked immunosorbent assay and dot blot. Conjugation method for GNP and anti-MPO antibodies was standardised, and the conjugate was placed over the conjugate pad. MPO coupled with a carrier protein (OVA) and the species-specific secondary antibodies were placed on test and control lines, respectively. The developed assay was verified with 75 milk samples collected from healthy, SCM and clinical mastitis cows. It displayed a high sensitivity as it could detect MPO as low as 1.5 ng/ml, an accuracy greater than 97% and showed no crossreactivity when crosschecked with other milk proteins. The developed assay can be used as an alternative for SCM diagnostic tests where lab structure are available for obtaining the lysate of milk SCC.

Indexed as

AnimalsAntibodies, MonoclonalAsymptomatic InfectionsCattleEnzyme-Linked Immunosorbent AssayFemaleGoldMastitis, BovineMetal NanoparticlesMilkNeutrophilsPeroxidaseSensitivity and SpecificityAntibodies, MonoclonalGoldPeroxidase

Identifiers

PMID32636460
PMCPMC7341798
OpenAlexW3038367820

What OpenQuestion holds

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LicenceCC BY
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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.