Genomic characterization of noroviruses from an outbreak associated with oysters
Bibliographic record
Abstract
Human noroviruses are the leading cause of non-bacterial shellfish-associated gastroenteritis. In 2022, a multi-jurisdictional norovirus outbreak associated with contaminated oysters occurred that involved hundreds of illnesses. Here, we conducted genetic analysis on 30 clinical samples associated with this oyster outbreak. We first determined the capsid genotypes by Sanger sequencing and viral titers by droplet-digital reverse transcription PCR. Multiple genotypes were identified in this outbreak, which could indicate contamination with wastewaters. The majority of samples belonged to GII.3[P12], followed by GII.2[P16], GII.17[P17], and GII.4 Sydney[P16]. We next proceeded with whole-genome sequencing and obtained full genomes for 19 samples. Phylogenetic analysis revealed that some of the isolates showed high similarity with the sequences isolated from the United States related to the same outbreak. We also analyzed amino acid variations in the sequenced genomes and found that overall the GII.3[P12] isolates have lower variations compared to other genotypes.IMPORTANCENorovirus outbreaks associated with contaminated shellfish occur frequently. Whole-genome sequencing (WGS) could play a critical role in understanding and controlling norovirus outbreaks as it allows for source attribution, tracking transmission pathways, and detecting recurrent or linked outbreaks. Here, we described how the data obtained by WGS were employed for understanding transmission patterns and norovirus epidemiology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".