ArticleFood and environmental virology2026
Development of Fit-for-Purpose, High Quality Proficiency Samples for Interlaboratory Evaluation of RT-PCR Detection of HPAI H5N1 in Milk.
Article in Food and environmental virology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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15 authors.
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Abstract
The increased demand for highly pathogenic avian influenza (HPAI) testing in milk highlights the critical need for well-characterized samples that enable meaningful evaluation of current detection methods. In this study, we focus on the development and rigorous characterization of artificially contaminated spiked milk samples, forming the foundation for reliable assessment of assay performance. By using reverse transcriptase digital PCR (RT-dPCR), we obtained precise genome copy (GC) measurements of our virus stock and of spiked samples, producing accurate quantification even at low viral loads. In parallel, the U.S. Department of Agriculture National Veterinary Services Laboratories' (USDA-NVSL's) influenza A matrix gene (IAV M) real-time reverse transcriptase (rRT-PCR) assay was employed as the main detection assay, serving as a tool to validate the sample preparation methodology. The goal of this work is to show that robust, reproducible, and analytically traceable samples can be generated to support HPAI method evaluation in proficiency exercises (PEs) distributed nationwide. Low between-sample standard deviations, ranging from 0 to 0.10 rRT-PCR Ct values for replicate samples, fall below the theoretical threshold indicative of heterogeneity, along with RT-dPCR determinations of sample GCs affirm the homogeneity of the developed samples. Parallel analysis of corresponding milk-based and phosphate buffered saline (PBS)-based samples showed no indication of a matrix effect. No significant shifts of Ct value or of measured GCs were observed over time, proving samples to be stable for at least 28 days when stored at -80 °C. The defined assay sensitivity, expressed as the level of detection 50% (LOD
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