Impact of Capsid and Genomic Integrity Tests on Norovirus Extraction Recovery Rates
Bibliographic record
Abstract
Human norovirus (HuNoV) is the leading pathogen responsible for food-borne illnesses. However, both infectious and non-infectious HuNoV can be detected by RT-qPCR. This study evaluated the efficiency of different capsid integrity treatments coupled with RT-qPCR or a long-range viral RNA (long RT-qPCR) detection to reduce the recovery rates of heat inactivated noroviruses and fragmented RNA. The three capsid treatments evaluated (RNase, the intercalating agent PMAxx and PtCl4) reduced the recovery of heat inactivated HuNoV and murine norovirus (MNV) spiked on lettuce, when combined with the ISO 15216-1:2017 extraction protocols. However, PtCl4 also reduced non-heat-treated noroviruses recovery as estimated by RT-qPCR. The PMAxx and RNase treatments had a similar effect on MNV only. The most efficient approaches, the RNase and PMAxx treatments, reduced the heat-inactivated HuNoV recovery rates estimated using RT-qPCR by 2 and >3 log, respectively. The long RT-qPCR detection approach also reduced the recovery rates of heat inactivated HuNoV and MNV by 1.0 and 0.5 log, respectively. Since the long-range viral RNA amplification could be applied to verify or confirm RT-qPCR results, it also provides some advantages by reducing the risk of false positive HuNoV results.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".