Comparison of RNA Extraction Methods for the Detection of a Norovirus Surrogate in Ready-to-Eat Foods
Bibliographic record
Abstract
Four nucleic acid extraction methods were evaluated for the purpose of quantifying a norovirus surrogate (murine norovirus [MNV-1]) concentrated from different food samples. Simple (strawberries and lettuce) and complex (sliced turkey breast, soft-shell clams, and potato salad) food matrices were inoculated with a viral suspension containing high (4×10(5) PFU) or low (4×10(3) PFU) numbers of viral particles. MNV-1 was eluted using either the Pulsifier™ or repetitive pipetting. The four methods were based on using magnetic silica (MiniMAG), non-magnetic silica (bioMérieux Basic kit), silica membrane (Qiagen kit), and phenol (TriReagent) for RNA extraction. The greatest recovery of viral RNA from simple matrices was obtained using magnetic silica for both inoculation levels. For strawberries, the addition of pectinase during the elution step improved RNA recovery when the Pulsifier was used with silica membrane extraction and when repetitive pipetting was used with magnetic silica extraction. In the case of complex matrices, the extraction of high or low numbers of MNV-1 was highest overall using magnetic silica. The exception was soft-shell clams with a high viral load, in which the greatest recovery was obtained with the phenol-based method. In general, magnetic silica was the most effective for extracting both high and low numbers of MNV-1 particles from a wide range of foods.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".