<i>Streptococcus suis</i>, an important pig pathogen and emerging zoonotic agent—an update on the worldwide distribution based on serotyping and sequence typing
Bibliographic record
Abstract
Streptococcus suis is an important pathogen causing economic problems in the pig industry. Moreover, it is a zoonotic agent causing severe infections to people in close contact with infected pigs or pork-derived products. Although considered sporadic in the past, human S. suis infections have been reported during the last 45 years, with two large outbreaks recorded in China. In fact, the number of reported human cases has significantly increased in recent years. In this review, we present the worldwide distribution of serotypes and sequence types (STs), as determined by multilocus sequence typing, for pigs (between 2002 and 2013) and humans (between 1968 and 2013). The methods employed for S. suis identification and typing, the current epidemiological knowledge regarding serotypes and STs and the zoonotic potential of S. suis are discussed. Increased awareness of S. suis in both human and veterinary diagnostic laboratories and further establishment of typing methods will contribute to our knowledge of this pathogen, especially in regions where complete and/or recent data is lacking. More research is required to understand differences in virulence that occur among S. suis strains and if these differences can be associated with specific serotypes or STs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".