Detection and Sequencing of Multiple Human Norovirus Genotypes from Imported Frozen Raspberries Linked to Outbreaks in the Province of Quebec, Canada, in 2017
Bibliographic record
Abstract
Human noroviruses are among the main causes of acute gastroenteritis worldwide. Frozen raspberries have been linked to several norovirus food-related outbreaks. However, the extraction of norovirus RNA from frozen raspberries remains challenging. Recovery yields are low and PCR inhibitors limit the sensitivity of the detection methodologies. In 2017, 724 people from various regions of the Province of Quebec, Canada, were infected by noroviruses and the outbreak investigation pointed to frozen raspberries as a putative source. A new magnetic silica bead approach was used for the extraction of viruses from different outbreak samples. The RNA extracts were tested by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and five samples were confirmed positive for norovirus by RT-qPCR amplicon sequencing. A multiplex long-range two-step RT-PCR approach was developed to amplify norovirus ORF2 and ORF3 capsid genes from the positive frozen raspberry RNA extracts and other sequencing strategies were also explored. These capsid genes were sequenced by Next-Generation Sequencing. Phylogenetic analyses confirmed the presence of multiple genotypes (GI.3, GI.6, and GII.17) and intra-genotype variants in some of the frozen raspberry samples. Variants of genotype GI.3 and GI.6 had 100% homology with sequences from patient samples. Similar strains were also reported in previous outbreaks. Confirmation approaches based on sequencing the norovirus capsid genes using Next-Generation Sequencing can be applied at trace level contaminations and could be useful to assess risk and assist in source tracking.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".