Virulence Factors Involved in the Pathogenesis of the Infection Caused by the Swine Pathogen and Zoonotic Agent <i>Streptococcus Suis</i>
Bibliographic record
Abstract
Streptococcus suis is a major swine pathogen responsible for important economic losses to the swine industry worldwide. It is also an emerging zoonotic agent of meningitis and streptococcal toxic shock-like syndrome. Since the recent recognition of the high prevalence of S. suis human disease in southeast and east Asia, the interest of the scientific community in this pathogen has significantly increased. In the last few years, as a direct consequence of these intensified research efforts, large amounts of data on putative virulence factors have appeared in the literature. Although the presence of some proposed virulence factors does not necessarily define a S. suis strain as being virulent, several cell-associated or secreted factors are clearly important for the pathogenesis of the S. suis infection. In order to cause disease, S. suis must colonize the host, breach epithelial barriers, reach and survive in the bloodstream, invade different organs, and cause exaggerated inflammation. In this review, we discuss the potential contribution of different described S. suis virulence factors at each step of the pathogenesis of the infection. Finally, we briefly discuss other described virulence factors, virulence factor candidates and virulence markers for which a precise role at specific steps of the pathogenesis of the S. suis infection has not yet been clearly established.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".