Prevalence and Molecular Characterization of the Hepatitis E Virus in Retail Pork Products Marketed in Canada
Bibliographic record
Abstract
Infection with the hepatitis E virus (HEV) is very common worldwide. HEV causes acute viral hepatitis with approximately 20 million cases per year. While HEV genotypes 1 and 2 cause large waterborne and foodborne outbreaks with a significant mortality in developing countries, genotypes 3 and 4 are more prevalent in developed countries with transmission being mostly zoonotic. In North America and Europe, HEV has been increasingly detected in swine, and exposure to pigs and pork products is considered to be the primary source of infection. Therefore we set out to investigate the prevalence of HEV in retail pork products available in Canada, by screening meal-size portions of pork pâtés, raw pork sausages, and raw pork livers. The presence of the HEV genomes was determined by RT-PCR and viral RNA was quantified by digital droplet PCR. Overall, HEV was detected in 47% of the sampled pork pâtés and 10.5% of the sampled raw pork livers, but not in the sampled pork sausages, and sequencing confirmed that all HEV strains belonged to genotype 3. Further phylogenetic analysis revealed that except for one isolate that clusters with subtype 3d, all isolates belong to subtype 3a. Amino acid variations between the isolates were also observed in the sequenced capsid region. In conclusion, the prevalence of HEV in pâtés and raw pork livers observed in this study is in agreement with the current HEV distribution in pork products reported in other developed countries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".