Limited Interactions between Streptococcus Suis and Haemophilus Parasuis in In Vitro Co-Infection Studies
Bibliographic record
Abstract
Streptococcus suis and Haemophilus parasuis are normal inhabitants of the porcine upper respiratory tract but are also among the most frequent causes of disease in weaned piglets worldwide, causing inflammatory diseases such as septicemia, meningitis and pneumonia. Using an in vitro model of infection with tracheal epithelial cells or primary alveolar macrophages (PAMs), it was possible to determine the interaction between S. suis serotype 2 and H. parasuis strains with different level of virulence. Within H. parasuis strains, the low-virulence F9 strain showed higher adhesion levels to respiratory epithelial cells and greater association levels to PAMs than the high-virulence Nagasaki strain. Accordingly, the low-virulence F9 strain induced, in general, higher levels of pro-inflammatory cytokines than the virulent Nagasaki strain from both cell types. In general, S. suis adhesion levels to respiratory epithelial cells were similar to H. parasuis Nagasaki strain. Yet, S. suis strains induced a significantly lower level of pro-inflammatory cytokine expression from epithelial cells and PAMs than those observed with both H. parasuis strains. Finally, this study has shown that, overall and under the conditions used in the present study, S. suis and H. parasuis have limited in vitro interactions between them and use probably different host receptors, regardless to their level of virulence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".