Hemoglobin and Streptococcus suis cell wall act in synergy to potentiate the inflammatory response of monocyte-derived macrophages
Bibliographic record
Abstract
Streptococcus suis, a major swine pathogen world-wide, can trigger macrophages to secrete large amounts of proinflammatory cytokines, which increase the permeability of the blood-brain barrier. In this study, we hypothesized that hemoglobin may potentiate the inflammatory response of human macrophages stimulated with a S. suis cell-wall preparation. Monocyte-derived macrophages were stimulated with the S. suis cell-wall preparation in the presence or absence of human hemoglobin, and the secretion of interleukin-1beta (IL-1beta), tumor necrosis factor-alpha (TNF-alpha), IL-6, and IL-8 was analyzed by enzyme-linked immunosorbent assays. The cell-wall preparation induced dose-dependent IL-1beta, TNF-alpha, IL-6, and IL-8 responses in macrophages. Hemoglobin potentiated the cell-wall induced inflammatory response, resulting in a significantly higher secretion of all the cytokines. The S. suis cell-wall preparation in combination with hemoglobin activated macrophage intracellular kinases involved in inflammatory signaling pathways. In conclusion, hemoglobin, which may be released in vivo by the action of S. suis suilysin on red blood cells, contributes to raising the levels of pro-inflammatory mediators by acting in synergy with S. suis cell-wall components. This phenomenon may contribute to the development and the severity of meningitis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".