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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".