Development of an RNA Extraction Protocol for Norovirus from Raw Oysters and Detection by qRT-PCR and Droplet-Digital RT-PCR
Bibliographic record
Abstract
Foodborne viruses such as norovirus and hepatitis A virus cause frequent outbreaks associated with the consumption of raw or undercooked oysters. Viral particles are bioaccumulated in the oyster's digestive glands, making RNA extraction and RT-PCR detection difficult due to the complex nature of the food matrix and the presence of RT-PCR inhibitors. Herein, we have developed a viral RNA extraction protocol from raw oysters using murine norovirus (MNV) as a surrogate for human noroviruses. The method combines lysis in Tri-Reagent reagent, followed by RNA extraction using Direct-Zol purification columns and lithium chloride precipitation. Viral load quantification was performed by both qRT-PCR and droplet-digital RT-PCR. We have demonstrated that this method can efficiently remove RT-PCR inhibitors, and is sensitive enough to reliably detect viral contamination at 25 PFU/0.2 g. We have also compared the efficiency of this method with the ISO 15216-1:2017 method and Method E developed by Quang and colleagues, and observed significantly higher efficiency compared with the ISO 15216-1 method and comparable efficiency with Method E, with less steps, and shorter hands-on time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".