Evidence map›Paper›PMID 23305816›Full record

ArticleJournal of virological methods2013

Development of a real-time RT-PCR method for detection of porcine rubulavirus (PoRV-LPMV).

Sandra Cuevas-Romero, Anne-Lie Blomström, Arcelia Alvarado, Pablo Hernández-Jauregui, Francisco Rivera-Benitez, Humberto Ramírez-Mendoza, Mikael Berg

Open access · bronzeAbstract read
In one paragraph

Article in Journal of virological methods, 2013. 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
1.0field-weighted citation impact, top 23% 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, 10 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

7 authors at 2 institutions in 3 countries.

Sandra Cuevas-RomeroDivision of Virology, Department of Biomedical Sciences and Veterinary Public Health, Swedish University of Agricultural Sciences, Uppsala, Sweden. cuevas.julieta@inifap.gob.mx
Anne-Lie Blomström
Arcelia Alvarado
Pablo Hernández-Jauregui
Francisco Rivera-Benitez
Humberto Ramírez-Mendoza
Mikael Berg
Swedish University of Agricultural Sciences · SEUniversidad Nacional Autónoma de México · MX

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to provide a rapid and sensitive method for detection of the Porcine rubulavirus La Piedad-Michoacan-Mexico Virus (PoRV-LPMV), we have developed a specific real-time reverse transcriptase polymerase chain reaction assay. The detection of PoRV-LPMV, represents a diagnostic challenge due to the viral RNA being present in very small amounts in tissue samples. In this study, a TaqMan(®) real-time PCR assay was designed based on the phosphoprotein gene of PoRV-LPMV, to allow specific amplification and detection of viral RNA in clinical samples. Assay conditions for the primers and probe were optimized using infected PK15 cells and ten-fold serial dilutions of a plasmid containing the whole P-gene. The sensitivity of the developed TaqMan(®) assay was approximately 10 plasmid copies per reaction, and was shown to be 1000 fold better than a conventional nested RT-PCR. The performance of this real-time RT-PCR method enables studies of various aspects of PoRV-LPMV infection. Finally, the assay detects all current known variants of the virus.

Indexed as

AnimalsCell LineCyclophilinsGenome, ViralPhosphoproteinsPlasmidsReal-Time Polymerase Chain ReactionReverse Transcriptase Polymerase Chain ReactionRNA, ViralRubulavirusRubulavirus InfectionsSwineSwine DiseasesViral ProteinsCyclophilinsPhosphoproteinsP protein, Porcine rubulavirusRNA, ViralViral Proteins

Identifiers

PMID23305816
PMCPMC7113651
OpenAlexW2069350323

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.