Evidence map›Paper›PMID 22894568›Full record

ArticleBMC veterinary research2012

Reverse transcription loop-mediated isothermal amplification assay for rapid detection of Bovine Rotavirus.

Zhixun Xie, Qing Fan, Jiabo Liu, Yaoshan Pang, Xianwen Deng, Zhiqin Xie, Xie Liji, Mazhar I Khan

Open access · goldAbstract readComparative Study
In one paragraph

Article in BMC veterinary research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.8field-weighted citation impact, top 28% 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

10 citing papers in PubMed, 17 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

8 authors at 2 institutions in 2 countries.

Zhixun XieDepartment of Biotechnology, Guangxi Veterinary Research Institute, 51 You Ai Road, Nanning, Guangxi, 530001, China. xiezhixun@126.com
Qing Fan
Jiabo Liu
Yaoshan Pang
Xianwen Deng
Zhiqin Xie
Xie Liji
Mazhar I Khan
Guangxi Veterinary Research Institute · CNUniversity of Connecticut · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBovine rotavirus (BRV) infection is common in young calves. This viral infection causes acute diarrhea leading to death. Rapid identification of infected calves is essential to control BRV successfully. Therefore development of simple, highly specific, and sensitive detection method for BRV is needed.

resultsA reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay was developed and optimized for rapid detection of BRV. Specific primer sets were designed to target the sequences of the VP6 gene of the neonatal calf diarrhea virus (NCDV) strain of BRV. The RT-LAMP assay was performed in a water bath for 60 minutes at 63°C, and the amplification products were visualized either directly or under ultraviolet light. This BRV specific RT-LAMP assay could detect 3.32 copies of subtype A BRV. No cross-reactions were detected with other bovine pathogens. The ability of RT-LAMP to detect bovine rotavirus was further evaluated with 88 bovine rectal swab samples. Twenty-nine of these samples were found to be positive for BRV using RT-LAMP. The BRV-specific-RT-LAMP results were also confirmed by real-time RT-PCR assay.

conclusionsThe bovine rotavirus-specific RT-LAMP assay was highly sensitive and holds promise as a prompt and simple diagnostic method for the detection of group A bovine rotavirus infection in young calves.

Indexed as

AnimalsCattleCattle DiseasesNucleic Acid Amplification TechniquesRNA, ViralRotavirusRotavirus InfectionsSensitivity and SpecificityRNA, Viral

Identifiers

PMID22894568
PMCPMC3599620
OpenAlexW2132170359

What OpenQuestion holds

Textmetadata
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.