The evolving nature of <i>Bordetella pertussis</i> in Ontario, Canada, 2009–2017: strains with shifting genotypes and pertactin deficiency
Bibliographic record
Abstract
This study examined the evolving nature of Bordetella pertussis in Ontario, Canada, by characterizing isolates for their genotypes and expression of pertactin (PRN). From 2009 to 2017, 413 B. pertussis were cultured from pertussis cases at the Public Health Ontario Laboratory. Their genotypes were determined by partial gene sequence analysis of their virulence and (or) vaccine antigens: filamentous haemagglutinin, PRN, fimbriae 3, and pertussis toxin, including the promoter region. Expression of PRN was measured by Western immunoblot. Two predominant genotypes, ST-1 and ST-2, were found throughout the study and were responsible for 47.5% and 46.3% of all case isolates, respectively. The prevalence of ST-1 appeared to fluctuate from 80.3% in 2009 to 20.0% in 2014 and 58.5% in 2017, while the prevalence of ST-2 changed from 18.4% in 2009 to 80.0% in 2014 and 26.2% in 2017. A PRN-deficient strain was first noted in 2011 (16.7%), and its prevalence increased to 70.8% in 2016 but decreased to 46.2% in 2017. More ST-2 (46.6%) than ST-1 (16.8%) strains were associated with PRN deficiency. Newer ST-21 and ST-22 found in 2015–2017 were uniformly PRN deficient. The impact of the evolving nature of B. pertussis on disease epidemiology requires further longitudinal studies.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".