Extraction of human noroviruses from leafy greens and fresh herbs using magnetic silica beads
Bibliographic record
Abstract
Consumption of leafy greens and to a lesser extent fresh herbs has been associated with several foodborne outbreaks including human norovirus (HuNoV). However, the extraction and detection of viruses from these matrices present multiple challenges such as low recovery yields and relatively high PCR inhibition. A new magnetic silica bead based (MSB) extraction protocol was developed and used to recover norovirus from leafy greens and fresh herbs. The performance results were compared to the ISO 15216-1:2017 standard. The HuNoV GII.4 and GI.5 recovery yields from spiked lettuce using the MSB extraction protocol range from 33 to 82%. There was a good correlation between murine norovirus (MNV) and HuNoV recovery yields from fresh herbs and leafy greens. No reverse-transcriptase quantitative polymerase chain reaction (RT-qPCR) inhibition was detected from leafy green extracts using the MSB methodology. The selected commercial RT-qPCR detection kit had a major impact on RT-qPCR inhibition levels detected in the ISO 15216-1:2017 RNA extracts. RNase treatment was used to estimate genome recovery from HuNoV with intact capsids. This treatment resulted in similar HuNoV and MNV recovery yields. Between 2019 and 2020, the MSB protocol was used to conduct a survey of HuNoV in domestic and imported leafy greens and fresh herbs sold at retail in Canada. All of the 280 samples tested were negative. Overall, the use of MSB was shown to be an efficient approach to recover HuNoV from leafy greens and certain types of fresh herbs and to conduct surveys.
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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) | 0.001 | 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".