Molecular serotyping of clinical strains of<i>Haemophilus (Glaesserella) parasuis</i>brings new insights regarding Glässer’s disease outbreaks in Brazil
Bibliographic record
Abstract
Glässer’s disease (GD) is an important infectious disease of swine caused byHaemophilus (Glaesserella) parasuis. Vaccination with inactivated whole cell vaccines is the major approach for prevention ofH. parasuisinfection worldwide, but the immunity induced is predominantly against the specific polysaccharide capsule. As a consequence, the available vaccines may not induce adequate protection against the field strains, when the capsules present in the vaccine strains are different from those in strains isolated from the farms. Therefore, it is crucial to mapH. parasuisserovars associated with regional outbreaks so that appropriate bacterin vaccines can be developed and distributed for prevention of infection. In this study, 459H. parasuisfield strains isolated from different Glässer’s disease outbreaks that occurred in 10 different Brazilian States were analyzed for serotype using PCR-based approaches. Surprisingly, non-typeable (NT) strains were the second most prevalent group of field strains and along with serovars 4, 5 and 1 comprised more than 70% of the isolates. A PCR-based approach designed to amplify the entire polysaccharide capsule locus revealed 9 different band patterns in the NT strains, and 75% of the NT strains belonged to three clusters, suggesting that a number of new serovars are responsible for a substantial proportion of disease. These results indicate that commercially available vaccines in Brazil do not cover the most prevalentH. parasuisserovars associated with GD.
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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 | 0.000 |
| 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) | 0.001 | 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".