Crystal digital RT-PCR for the detection and quantification of norovirus and hepatitis A virus RNA in frozen raspberries
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".