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Hepatitis A virus subgenotyping based on RT-qPCR assays

Overview of attention for article published in BMC Microbiology, November 2014
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Title
Hepatitis A virus subgenotyping based on RT-qPCR assays
Published in
BMC Microbiology, November 2014
DOI 10.1186/s12866-014-0296-1
Pubmed ID
Authors

Coralie Coudray-Meunier, Audrey Fraisse, Camélia Mokhtari, Sandra Martin-Latil, Anne-Marie Roque-Afonso, Sylvie Perelle

Abstract

BackgroundThe hepatitis A virus (HAV) is the most frequent cause of viral hepatitis worldwide and is recognized as one of the most widespread foodborne pathogens. HAV genotypes and subtypes differ in their geographic distribution and the incidence of HAV infection varies considerably among countries, and is particularly high in areas with poor sanitation and hygiene. Phylogenetic analyses are traditionally used in clinical microbiology for tracing the geographic origin of HAV strains. In food microbiology, this approach is complicated by the low contamination levels of food samples. To date, real-time reverse-transcription PCR has been one of the most promising detection methods due to its sensitivity, specificity and ability to deliver quantitative data in food samples, but it does not provide HAV subtyping information.ResultsSix subtype-specific RT-qPCR assays were developed for human HAV. The limit of detection of HAV was 50 genome copies / assay for subtype IIB, 500 genome copies / assay for IA, IB, IIA and IIIB and 5000 genome copies / assay for IIIA. The specificity of the assays was evaluated by testing reference isolates and in vitro HAV RNA transcripts. No significant cross reactivity was observed. Subtyping results concordant with sequencing analysis were obtained from 34/35 clinical samples. Co-infection with a minor strain of a different subtype was suggested in 5 cases and a recombinant event in one case.ConclusionsThese RT-qPCR assays may be particularly useful for accurately tracing HAV in low-level contaminated samples such as food matrices but also to allow co-infection identification in human samples.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Unknown 30 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 29%
Researcher 5 16%
Student > Bachelor 4 13%
Student > Postgraduate 3 10%
Student > Ph. D. Student 3 10%
Other 6 19%
Unknown 1 3%
Readers by discipline Count As %
Medicine and Dentistry 6 19%
Agricultural and Biological Sciences 5 16%
Engineering 3 10%
Biochemistry, Genetics and Molecular Biology 3 10%
Immunology and Microbiology 3 10%
Other 7 23%
Unknown 4 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 November 2014.
All research outputs
#3,690,828
of 4,553,834 outputs
Outputs from BMC Microbiology
#757
of 911 outputs
Outputs of similar age
#113,821
of 145,274 outputs
Outputs of similar age from BMC Microbiology
#40
of 46 outputs
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So far Altmetric has tracked 911 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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