Evaluation of the Persistence of Infectious Human Noroviruses on Food Surfaces by Using Real-Time Nucleic Acid Sequence-Based Amplification
Bibliographic record
Abstract
Noroviruses (NoV) are the major cause of nonbacterial gastroenteritis. However, there is no published study to ascertain their survival on foodstuffs which are directly related to human health risk. In the present study, we developed a rapid, simple, and sensitive real-time nucleic acid sequence-based amplification (NASBA) combined with an enzymatic treatment for distinguishing infectious from noninfectious human NoV. The developed method was validated using spiked ready-to-eat food samples. When feline calicivirus (FCV) was used as a NoV surrogate in the preliminary assays, it appeared more sensitive to heat inactivation and enzymatic pretreatment than the human NoV. This suggests that FCV may not be an ideal model for studying NoV. Our results reveal clearly that the developed enzymatic pretreatment/real-time NASBA combination successfully distinguished the infectious from heat-inactivated NoV. Moreover, we demonstrate that NoV survived for at least 10 days on refrigerated ready-to-eat foods, such as lettuce and turkey. However, the survival rate was higher on turkey than on lettuce, probably because of their different surface natures. The approach developed in this study may be suitable for more in-depth studies of the persistence and inactivation of human NoV and may be applied to other nonculturable RNA viruses. Moreover, the evaluation of infectious NoV survival provided valuable information concerning its persistence on ready-to-eat food.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".