Review of the speculative role of co-infections in Streptococcus suis-associated diseases in pigs
Bibliographic record
Abstract
Streptococcus suis is one of the most important bacterial swine pathogens affecting post-weaned piglets, causing mainly meningitis, arthritis and sudden death. It not only results in severe economic losses but also raises concerns over animal welfare and antimicrobial resistance and remains an important zoonotic agent in some countries. The definition and diagnosis of S. suis-associated diseases can be complex. Should S. suis be considered a primary or secondary pathogen? The situation is further complicated when referring to respiratory disease, since the pathogen has historically been considered as a secondary pathogen within the porcine respiratory disease complex (PRDC). Is S. suis a respiratory or strictly systemic pathogen? S. suis is a normal inhabitant of the upper respiratory tract, and the presence of potentially virulent strains alone does not guarantee the appearance of clinical signs. Within this unclear context, it has been largely proposed that co-infection with some viral and bacterial pathogens can significantly influence the severity of S. suis-associated diseases and may be the key to understanding how the infection behaves in the field. In this review, we critically addressed studies reporting an epidemiological link (mixed infections or presence of more than one pathogen at the same time), as well as in vitro and in vivo studies of co-infection of S. suis with other pathogens and discussed their limitations and possibilities for improvement and proposed recommendations for future 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".