Endothelial cells modulate eosinophil surface markers and mediator release
Bibliographic record
Abstract
Migration from blood to tissue modulates eosinophil function, possibly through interactions with endothelial cells. The effects of contact with and migration through endothelial cells on eosinophil expression of surface markers and release of leukotriene C4 were evaluated. A small proportion (2.6%) of eosinophils spontaneously migrated through endothelial cell monolayers. Activation of endothelial cells by interleukin (IL)-4 or IL-1beta slightly increased this migration (to 12.4%), which became much greater when a chemoattractant was placed in the lower chamber (84.3%). However, the chemotactic effect was downregulated by pretreating endothelial cells with interferon gamma (IFN-gamma; 63.1%). At baseline, 5% of eosinophils expressed CD69; this increased to 30.7% in culture on untreated endothelial cells and to 50.9% on IL-1beta-pretreated endothelial cells. This effect was mediated through intercellular adhesion molecule-1/CD11b interaction. Eosinophil migration through endothelial cells further increased CD69 expression to 63.9% and also increased CD35 expression from 83.3 to 91.3%. Upon stimulation, eosinophils that had migrated through endothelial cells produced more leukotriene C4 than control cells (872.4 and 103.9 pg x mL(-1), respectively). Endothelial cell pretreatment with IL-4 or IL-1beta further increased leukotriene C4 release (1,789.1 and 2,895.1 pg x mL(-1), respectively), whereas pretreatment with IFN-gamma decreased it (293.7 pg x mL(-1)). These data show that in vitro interactions with endothelial cells upregulate eosinophil membrane receptor expression and mediator release and that these effects are differently modulated by T-helper cell type 1 and 2 cytokines. These eosinophil modulations may play an important role in asthma pathogenesis.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".