Evidence map›Paper›PMID 36816861›Full record

ArticleVeterinary research forum : an international quarterly journal2023

Rapid and visual detection of an isolated and identified goose parvovirus (GPV) strain by a loop-mediated isothermal amplification assay.

Jun-Ting Liu, Yan-Hui Chen, Yi-Feng Pei, Qian Yu, Ruth Afumba, Hao Dong

Open access · greenAbstract read
In one paragraph

Article in Veterinary research forum : an international quarterly journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-weighted citation impact, top 37% 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

5 citing papers in PubMed, 6 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 1 institution in 1 country.

Jun-Ting LiuDepartment of Microbiology, Faculty of Life Sciences, Jilin Agricultural University, Changchun, China.
Yan-Hui ChenDepartment of Microbiology, Faculty of Life Sciences, Jilin Agricultural University, Changchun, China.
Yi-Feng PeiDepartment of Microbiology, Faculty of Life Sciences, Jilin Agricultural University, Changchun, China.
Qian YuDepartment of Microbiology, Faculty of Life Sciences, Jilin Agricultural University, Changchun, China.
Ruth AfumbaDepartment of Microbiology, Faculty of Life Sciences, Jilin Agricultural University, Changchun, China.
Hao DongDepartment of Microbiology, Faculty of Life Sciences, Jilin Agricultural University, Changchun, China.
Jilin Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gosling plague caused by goose parvovirus (GPV), a highly infectious septic disease with high mortality, has caused substantial loss in the waterfowl industry. A method for the rapid detection of GPV is needed. In this study, we isolated the virus strain of GPV in May 2020 and applied it to the loop-mediated isothermal amplification (LAMP) assay. We designed five sets of primers for the goose parvovirus VP3 gene by LAMP. The GV-1 primer set was selected to detect GPV sensitively and rapidly. LAMP was more sensitive compared to PCR. In addition, the LAMP method could complete detection within 60 min which was faster than the PCR assay. The LAMP provided a convenient and effective experimental method for detection of GPV for inspection and quarantine departments and health care units in China, and it is expected to become a simple and routine detection method, especially suitable for goose farms.

Indexed as

Goose parvovirusGosling plagueLoop-mediated isothermal amplification

Identifiers

PMID36816861
PMCPMC9906619
OpenAlexW4321612200

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.