Eotaxin promotes eosinophil transmigration via the activation of the plasminogen-plasmin system
Bibliographic record
Abstract
The effect of eotaxin, a potent eosinophil chemotactic factor, on eosinophil transmigration through a reconstituted basal membrane (Matrigel) was evaluated. Eotaxin induced significant eosinophil transmigration in the presence of 10% fetal bovine serum (FBS) and interleukin-5. Its effect was optimal at 0.01 microM, and it plateaued at 18 h. Eotaxin's effect was greater with eosinophils from asthmatic subjects (61.1 +/- 3.4%) than with eosinophils from normal subjects (38.7 +/- 4.2%) (P < 0.001). Inhibition of metalloproteinases decreased eotaxin-induced transmigration by < or = 10.4%, whereas inhibition of the plasminogen-plasmin system decreased eotaxin's effect by < or = 44.4% (P = 0.0002). Moreover, eotaxin-induced transmigration was largely diminished in medium with low concentrations of serum [0.5% FBS: 6.1 +/- 2.4%; 10% FBS: 40.2 +/- 5.8% (P = 0.0001)] but returned to its initial level with the addition of plasminogen (2 U/mL) to 0.5% FBS (43.1 +/- 6.5%). These data show that eotaxin is an efficient promoter of eosinophil transmigration in vitro, that it is more potent with cells from asthmatics than with normal cells, and that its effect depends predominantly on the activation of the plasminogen-plasmin system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".