Rapid detection and serovar identification of common <i>Salmonella enterica</i> serovars in Canada using a new pyrosequencing assay
Bibliographic record
Abstract
Serotyping of Salmonella enterica subsp. enterica is a critical step for foodborne salmonellosis investigation. To identify Salmonella enterica subsp. enterica serovars, we have developed a new assay based on a triplex polymerase chain reaction (PCR) with pyrosequencing for amplicon confirmation and phylogenetic discrimination of strains. The top 54 most prevalent serovars of S. enterica in Canada were examined with a total of 23 single-nucleotide polymorphisms (SNPs) and (or) single-nucleotide variations (SNVs) located on 3 genes (fliD, sopE2, and spaO). Seven of the most common serovars, Newport, Typhi, Javiana, Infantis, Thompson, Heidelberg, and Enteritidis, were successfully distinguished from the other serovars based on their unique SNP-SNV combinations. The remaining serovars, including Typhimurium, ssp I:4,[5],12:i:-, and Saintpaul, were further divided into 47 subgroups that demonstrate the relatedness to phylogenetic classifications of each serovar. This pyrosequencing assay is not only cost-effective, rapid, and user-friendly, but also provides phylogenetic information by analyzing 23 selected SNPs. With the added layer of confidence in the PCR results and the accuracy and speed of pyrosequencing, this novel method would benefit the food industry and provides a tool for rapid outbreak investigation through quick detection and identification of common S. enterica serovars in Canada.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".