<i>Streptococcus suis</i> Serotype 2, an Important Swine and Human Pathogen, Induces Strong Systemic and Cerebral Inflammatory Responses in a Mouse Model of Infection
Bibliographic record
Abstract
Streptococcus suis, an important swine and human pathogen, causes septic shock and meningitis. The pathogenesis of both systemic and CNS infections caused by S. suis is poorly understood. A hematogenous model of infection in CD1 mice was developed to study the systemic release of cytokines during the septic shock phase and the proinflammatory events in the CNS associated with this pathogen. Using a liquid array system, high levels of systemic TNF-alpha, IL-6, IL-12, IFN-gamma, CCL2, CXCL1, and CCL5 were observed 24 h after infection and might be responsible for the sudden death of 20% of animals. Infected mice that survived the early sepsis later developed clinical signs of meningitis and exhibited lesions in the meninges and in numerous regions of the brain, such as the cortex, hippocampus, thalamus, hypothalamus, and corpus callosum. Bacterial Ags were found in association with microglia residing only in the affected zones. In situ hybridization combined with immunocytochemistry showed transcriptional activation of TLR2 and TLR3 as well as CD14, NF-kappaB, IL-1beta, CCL2, and TNF-alpha, mainly in myeloid cells located in affected cerebral structures. Early transcriptional activation of TLR2, CD14, and inflammatory cytokines in the choroid plexus and cells lining the brain endothelium suggests that these structures are potential entry sites for the bacteria into the CNS. Our data indicate an important role of the inflammatory response in the pathogenesis of S. suis infection in mice. This experimental model may be useful for studying the mechanisms underlying sepsis and meningitis during bacterial infection.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".