Evidence map›Paper›PMID 40670998›Full record

ArticleBMC veterinary research2025

Optimization and application of an immuoperoxidase monolayer assay for the detection of PRRSV.

Peng Li, Chunxiao Guan, Liping Wang, Huiting Li, Guopeng Sun, Jinjiao He, Xingyou Liu, Xuannian Wang

Abstract read
In one paragraph

Article in BMC veterinary research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

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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

1 citing paper in PubMed.

  1. 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

8 authors.

Peng Li *School of Biological Engineering, Xinxiang University, Xinxiang, 453003, China.
Chunxiao Guan *School of Biological Engineering, Xinxiang University, Xinxiang, 453003, China.
Liping WangSchool of Biological Engineering, Xinxiang University, Xinxiang, 453003, China.
Huiting LiSchool of Biological Engineering, Xinxiang University, Xinxiang, 453003, China.
Guopeng SunSchool of Biological Engineering, Xinxiang University, Xinxiang, 453003, China.
Jinjiao HeSchool of Biological Engineering, Xinxiang University, Xinxiang, 453003, China.
Xingyou LiuSchool of Biological Engineering, Xinxiang University, Xinxiang, 453003, China. lxingyou@sohu.com.
Xuannian WangSchool of Biological Engineering, Xinxiang University, Xinxiang, 453003, China. wangxuannian@163.com.

Funding

The Modern Agricultural Industry Technology System in Henan Province HARS2212G3The Scientific and Technological Breakthrough Foundation of Henan Province 242102110061
6 · The paper itself

Abstract

backgroundPorcine reproductive and respiratory syndrome (PRRS), caused by PRRS virus (PRRSV), is a highly contagious disease with high morbidity and mortality that affects the global swine industry. Despite efforts to control it, there is still a widespread dissemination of PRRSV with obvious genetic variations in swine population, resulting in huge economic losses annually. Consequently, accurate laboratory diagnosis is crucial for the rapid confirmation of PRRSV infections.

resultsAn immunoperoxidase monolayer assay (IPMA) was developed for the specific and sensitive detection of PRRSV based on a broad-spectrum anti-PRRSV monoclonal antibody (mAb) 28F6. The mAb 28F6-based IPMA could specifically detect PRRSV and possessed no cross-reactions with CSFV, PCV2, and PEDV. Sensitivity analysis showed that the limit of detection of the IPMA reached 10

conclusionsThe IPMA could meet the demand for the specific and sensitive detection of PRRSV, which is helpful for accurate monitoring and early warning of PRRSV infections.

Indexed as

Porcine Reproductive and Respiratory SyndromePorcine respiratory and reproductive syndrome virusAnimalsAntibodies, MonoclonalAntibodies, ViralImmunoenzyme TechniquesReproducibility of ResultsSensitivity and SpecificitySwineAntibodies, MonoclonalAntibodies, ViralImmunoperoxidase monolayer assayMonoclonal antibodyPRRSVSpecific detection

Identifiers

PMID40670998
PMCPMC12265252

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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.