Signalling pathways that regulate human eosinophil migration
Bibliographic record
Abstract
Rationale: To initiate eosinophil migration in vitro, 5‐oxo‐6,8,11,14‐eicosatetraenoic acid (5‐oxo‐ETE), a potent eosinophil chemotactic factor, activates proteolysis, notably by promoting matrix metalloproteinase (MMP)‐9 secretion. Hypothesis : We postulated that protein kinase C (PKC) and mitogen‐activated protein kinase/extracellular signal‐regulated kinase (MAPK/ERK) are involved in 5‐oxo‐ETE‐induced eosinophil migration through extracellular matrix by increasing MMP‐9 secretion. Methods : Purified peripheral blood eosinophils were pre‐incubated with or without different selective PKC inhibitors (isoforms α β: Ro‐31‐8425, isoform δ: Rottelin or PKC ζ blocking peptide), ERK inhibitor (PD98054) or p38 MAPK inhibitor (SB203580) for 30 min at 37°C, followed by addition of 5‐oxo‐ETE. Migration assays using Matrigel, a reconstituted basement membrane, was assessed, and rapid MMP‐9 release (within an hour) was evaluated by zymography. Results and Conclusion : All of these inhibitors decreased by 30 to 90% 5‐oxo‐ETE‐induced eosinophil migration through Matrigel. However, only PKC δ and ERK signalling pathway inhibitors reduced rapid release of MMP‐9, suggesting that the other kinases studied could act on MMP‐9 synthesis, generation of other proteases, adherence or cell motility. More experiments are needed to clarify the mechanisms that regulate eosinophil migration.
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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) | 0.004 | 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".