Detection of Foodborne Viruses in Dates Using ISO 15216 Methodology
Bibliographic record
Abstract
Foodborne viruses such as human norovirus (HuNoV) and hepatitis A virus (HAV) are the major causes of foodborne illnesses worldwide. These viruses have a low infectious dose and are persistent in the environment and food for weeks. Ready-to-eat (RTE) low moisture foods (LMFs) undergo minimal pathogen reduction processes. In recent years, multiple foodborne HAV outbreaks involving hundreds of individuals were associated with the consumption of dates, indicating that they could be important vehicles for foodborne infection. There is no standard method for the extraction and detection of foodborne viruses from dates, but herein we have compared the efficiency of three different protocols based on the ISO 15216 method in the extraction of murine norovirus (MNV) from whole Medjool dates and successfully employed the best performing method in the extraction of HAV, HuNoV GI, and GII and determined the limit of detection (LOD95) of 61, 148, and 184 genomic equivalent (gEq) per 25 g, respectively. Finally, we tested the adopted method on various varieties of dates including pitted ones and reported the detection of HuNoV GI and GII from four naturally contaminated date varieties. This ISO 15216 protocol could be employed for surveillance purposes and outbreak management related to dates.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".