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Crystal digital RT-PCR for the detection and quantification of norovirus and hepatitis A virus RNA in frozen raspberries

2022· article· en· W4292559800 on OpenAlexafffund
Émilie Larocque, Valérie Lévesque, Dominic Lambert

Bibliographic record

VenueInternational Journal of Food Microbiology · 2022
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsCanadian Food Inspection Agency
FundersCanadian Food Inspection Agency
KeywordsNorovirusRNAVirologySerial dilutionBlowing a raspberryBiologyHepatitis a virusReal-time polymerase chain reactionDigital polymerase chain reactionMurine norovirusVirusDetection limitVirus quantificationMolecular biologyRNA extractionChemistryPolymerase chain reactionChromatographyGeneMedicineFood scienceBiochemistry

Abstract

fetched live from OpenAlex

Berries are important vehicles for norovirus (NoV) and hepatitis A virus (HAV) foodborne outbreaks. Sensitive and quantitative detection of these viruses in food samples currently relies on RT-qPCR, but remains challenging due to their low concentration and the presence of RT-qPCR inhibitors. Moreover, quantification requires a standard curve. In this study, crystal digital RT-PCR (RT-cdPCR) assays were adapted from RT-qPCR sets of primers and probe currently used in our diagnostic laboratory for the detection and precise quantification of norovirus genogroups I and II (NoV GI, GII) and hepatitis A virus (HAV) RNA in frozen raspberry samples. We selected assay conditions based on optimal separation of positive and negative droplets, and peak resolution. Using virus-specific in vitro RNA transcripts diluted in raspberry RNA extracts, we showed that all three RT-cdPCR assays were sensitive, and we estimated the 95 % detection limit at 9 copies per RT-cdPCR reaction for NoV GI, 3 for NoV GII, and 14 for HAV. Serial dilutions of the RNA transcripts showed excellent linearity over a range of four orders of magnitude. We achieved precise quantification (CV ≤ 35 %) of the RNA transcripts between runs down to 15-145 copies per reaction for NoV GI, <20 for NoV GII, and < 15 for HAV. The three RT-cdPCR assays also proved to be tolerant to inhibitors from frozen raspberries, although not as tolerant as the RT-qPCR assays in the case of NoV GI and HAV. We further evaluated the assays with inoculated frozen raspberry samples and compared their performance to that of the RT-qPCR assays. As compared to the corresponding RT-qPCR assays, the NoV GI and HAV RT-cdPCR assays showed a decreased qualitative sensitivity, while the NoV GII RT-cdPCR assay had an increased sensitivity. As for quantification, the NoV GI and NoV GII RT-cdPCR assays produced similar estimates of RNA copy number than their respective RT-qPCR assays, whereas for HAV, the RT-cdPCR assay produced lower estimates than the RT-qPCR assay. However, all the RT-cdPCR assays provided more precise quantitative measurements at low levels of contamination than the RT-qPCR assays. In conclusion, the potential of the RT-cdPCR assays in this study to detect viral RNA from frozen raspberries varied according to assay, but these RT-cdPCR assays should be considered for precise absolute quantification in difficult matrices such as frozen raspberries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.295
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations8
Published2022
Admission routes2
Has abstractyes

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