The Need for Harmonization in Detection of Human Noroviruses in Food
Bibliographic record
Abstract
Noroviruses (NoV) have been recognized worldwide as a leading cause of foodborne gastroenteritis over the last decade. A broad range of foods- shellfish, fresh produce, and ready-to-eat/catered foods-has been implicated in NoV foodborne outbreaks. The recognition of NoV as an important food pathogen has been aided by the development of sensitive molecular methods for detection of the NoV genome. However, despite advances, NoV detection is still hampered by several limitations. First, NoV detection can often only be implemented by expert laboratories due to the complexity of the virus extraction step, which in most protocols is cumbersome and labor-intensive. Moreover, a very wide selection of automated methods for virus extraction from foods is available, so selection of an adequate method is not straightforward. On the other hand, automated systems have been made available or the RNA purification and real-time RT-PCR (RT-qPCR) is considered the gold standard for detection of NoV. Second, correct interpretation of real-time PCR results is often difficult. From a technical point of view, the interpretation of the often nonsigmoidal amplification curves remains difficult, even for experts. From a food safety perspective, interpretation of very high Cq (or Ct) values-and thus, of low viral genomic copy numbers-is not straightforward, as RT-(q)PCR merely detects the presence of viral genomic material that is not necessarily linked to the presence of infectious viral particles. Despite efforts, both limitations have not completely resolved thus far. Harmonization may be a first step to comprehend and deal with these limitations. The current review provides an overview of a number of validated methods that have been published by food safety and other authorities.
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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 | 0.180 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".