The Effect of PPAR Agonists on the Migration of Mature and Immature Eosinophils
Bibliographic record
Abstract
PPARγ agonists can either enhance or inhibit eosinophil migration, which is a sum of directional migration (chemotaxis) and random cell movement (chemokinesis). To date, the effects of PPAR agonists on chemokinesis have not been examined. This study investigates the effects of PPARα, δ, and γ agonists on eosinophil migration and chemokinesis. Eosinophils purified from blood of atopic donors were preincubated with rosiglitazone (PPARγ agonist), GW9578 (PPARα agonist), GW501516 (PPARδ agonist), or diluent. The effects of PPAR agonists were examined on eosinophil chemokinesis, eotaxin-induced migration of eosinophils, and migration of IL-5Rα+ CD34+ cells. Expressions of CCR3, phospho-p38, phospho-ERK, and calcium release were also measured in eosinophils after rosiglitazone treatment. Low concentrations of rosiglitazone, but not GW9578 or GW501516, increased chemokinesis of eosinophils (P = 0.0038), and SDF-1α-induced migration of immature eosinophils (P = 0.0538). Rosiglitazone had an effect on eosinophil calcium flux but had no effect on expression of CCR3 or phosphorylation of p38 or ERK. In contrast, high concentrations of rosiglitazone inhibited eosinophil migration (P = 0.0042). The effect of rosiglitazone on eosinophil migration and chemokinesis appears to be through modification of calcium signaling, which alludes to a novel PPAR-mediated mechanism to modulate eosinophil function.
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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 | 0.001 |
| 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".