Initial steps of the pathogenesis of the infection caused by<i>Streptococcus suis</i>: fighting against nonspecific defenses
Bibliographic record
Abstract
Interactions between a bacterial pathogen and its potentially susceptible host are initiated with the colonization step. During respiratory/oral infection, the pathogens must compete with the normal microflora, resist defense mechanisms of the local mucosal immunity, and finally reach, adhere, and breach the mucosal epithelial cell barrier in order to induce invasive disease. This is the case during infection by the swine and zoonotic pathogen Streptococcus suis, which is able to counteract mucosal barriers to induce severe meningitis and sepsis in swine and in humans. The initial steps of the pathogenesis of S. suis infection has been a neglected area of research, overshadowed by studies on the systemic and central nervous phases of the disease. In this Review article, we provide for the first time, an exclusive focus on S. suis colonization and the potential mechanisms involved in S. suis establishment at the mucosa, as well as the mechanisms regulating mucosal barrier breakdown. The role of mucosal immunity is also addressed. Finally, we demystify the extensive list of putative adhesins and virulence factors reported to be involved in the initial steps of pathogenesis by S. suis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".