Evidence map›Paper›PMID 40498381›Full record

ArticleVirus genes2025

Development and application of a real-time fluorescent RAA assay for H9 subtype avian influenza viruses.

Wanting Zhou, Xiaoqi Li, Jie Tian, Shuo Liu, Cheng Peng, Jinping Li, Guangyu Hou, Jizhe Yang, Wenming Jiang, Hualei Liu

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Article in Virus genes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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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

10 authors.

Wanting ZhouChina Animal Health and Epidemiology Center, Qingdao, China.
Xiaoqi LiChina Animal Health and Epidemiology Center, Qingdao, China.
Jie TianChina Animal Health and Epidemiology Center, Qingdao, China.
Shuo LiuChina Animal Health and Epidemiology Center, Qingdao, China.
Cheng PengChina Animal Health and Epidemiology Center, Qingdao, China.
Jinping LiChina Animal Health and Epidemiology Center, Qingdao, China.
Guangyu HouChina Animal Health and Epidemiology Center, Qingdao, China.
Jizhe YangChina Animal Health and Epidemiology Center, Qingdao, China.
Wenming JiangChina Animal Health and Epidemiology Center, Qingdao, China. civcul@163.com.
Hualei LiuChina Animal Health and Epidemiology Center, Qingdao, China. liuhualei@cahec.cn.

Funding

This work was supported by Shandong Provincial Key Research and Development 2022CXGC010606
6 · The paper itself

Abstract

The H9N2 subtype of avian influenza virus (AIV) is a critical pathogen responsible for avian infectious diseases, inducing respiratory symptoms in poultry and exhibiting high susceptibility to coinfections, which complicates clinical diagnosis. In this study, we designed specific primers and probes targeting the hemagglutinin (HA) gene of H9N2 AIV and developed a real-time fluorescent reverse transcription recombinase-aided isothermal amplification (RT-RAA) assay through systematic optimization of reaction components and conditions. The established method demonstrated exclusive reactivity with the H9N2 subtype, yielding negative results for all other tested viral pathogens, thereby showcasing high specificity. Analytical sensitivity testing revealed the capability to detect as low as 13.5 copies/μL of H9N2 viral RNA, indicating superior sensitivity. To further validate the practical application of this method, a total of 48 clinical samples, comprising oropharyngeal and cloacal swabs, were tested using the developed RT-RAA assay. The results revealed a positivity rate of 79%, reflecting strong diagnostic performance. This study presents a rapid RT-RAA assay characterized by high specificity and sensitivity, offering a robust technical platform for the immediate identification of H9N2 AIV and facilitating epidemiological surveillance in avian populations.

Indexed as

Influenza A Virus, H9N2 SubtypeInfluenza in BirdsMolecular Diagnostic TechniquesNucleic Acid Amplification TechniquesPoultry DiseasesAnimalsBirdsChickensHemagglutinin Glycoproteins, Influenza VirusPoultryRecombinasesRNA, ViralSensitivity and SpecificityHemagglutinin Glycoproteins, Influenza VirusRecombinasesRNA, ViralAvian influenza virusH9 N2 subtypeReverse transcription recombinase-aided isothermal amplification

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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.