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A new assay for quantitative detection of hepatitis A virus
Stockholm University, Faculty of Science, Department of Mathematics.
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Number of Authors: 82021 (English)In: Journal of Virological Methods, ISSN 0166-0934, E-ISSN 1879-0984, Vol. 288, article id 114010Article in journal (Refereed) Published
Abstract [en]

Hepatitis A virus (HAV) is mainly transmitted via contaminated food or water or through person-to-person contact. Here, we describe development and evaluation of a reverse transcription droplet digital PCR (RTddPCR) and reverse transcription real-time PCR (RT-qPCR) assay for detection of HAV in food and clinical specimens. The assay was evaluated by assessing limit of detection, precision, matrix effects, sensitivity and quantitative agreement. The 95 % limit of detection (LOD95 %) was 10 % higher for RT-ddPCR than for RTqPCR. A Bayesian model was used to estimate precision on different target concentrations. From this, we found that RT-ddPCR had somewhat greater precision than RT-qPCR within runs and markedly greater precision between runs. By analysing serum from naturally infected persons and a naturally contaminated food sample, we found that the two methods agreed well in quantification and had comparable sensitivities. Tests with artificially contaminated food samples revealed that neither RT-ddPCR nor RT-qPCR was severely inhibited by presence of oysters, raspberries, blueberries or leafy-green vegetables. For this assay, we conclude that RT-qPCR should be considered if rapid, qualitative detection is the main interest and that RT-ddPCR should be considered if precise quantification is the main interest. The high precision of RT-ddPCR allows for detection of small changes in viral concentration over time, which has direct implications for both food control and clinical studies.

Place, publisher, year, edition, pages
2021. Vol. 288, article id 114010
Keywords [en]
Hepatitis A virus, Digital PCR, Real-time PCR, Reverse transcription, Validation, Food-borne virus
National Category
Microbiology in the medical area Biological Sciences
Identifiers
URN: urn:nbn:se:su:diva-190034DOI: 10.1016/j.jviromet.2020.114010ISI: 000604174900004PubMedID: 33152410OAI: oai:DiVA.org:su-190034DiVA, id: diva2:1530969
Available from: 2021-02-24 Created: 2021-02-24 Last updated: 2022-02-25Bibliographically approved

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Karlsson, Måns

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