Role of neutrophil elastase in LTB<sub>4</sub>‐induced neutrophil transmigration <i>in vivo</i> assessed with a specific inhibitor and neutrophil elastase deficient mice
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The serine protease neutrophil elastase (NE) appears to regulate inflammatory responses at multiple levels but its role in leukocyte transmigration in vivo remains unclear. The present study aimed to address this issue by using both an NE inhibitor (ONO-5046) and NE deficient (NE(-/-)) mice. EXPERIMENTAL APPROACH: A number of inflammatory mediators (LTB(4), KC and PAF) were investigated in vitro for their ability to stimulate the release and the surface expression of NE by neutrophils. In addition, the role of NE in leukocyte migration elicited by topical LTB(4) was investigated in vivo in mouse cremasteric venules as observed by intravital microscopy. KEY RESULTS: Amongst the mediators tested in vitro, LTB(4) was found to be a highly potent and efficacious inducer of NE cell surface expression on murine neutrophils. Furthermore, in wild-type mice (WT), LTB(4)-induced leukocyte transmigration was reduced by intravenous ONO-5046 (66% inhibition), an effect that appeared to occur at the level of the perivascular basement membrane. Interestingly, LTB(4)-induced responses were normal in NE(-/-) mice and, while ONO-5046 had no inhibitory effect in these animals, the broad-spectrum serine protease inhibitor aprotinin suppressed leukocyte transmigration in both WT and NE(-/-) mice. CONCLUSIONS AND IMPLICATIONS: The findings demonstrate the potent ability of LTB(4) to induce cell-surface expression of NE and provide evidence for the involvement of NE in LTB(4)-induced neutrophil transmigration in vivo. The results also suggest the existence of compensatory mechanisms in NE(-/-) mice, highlighting the added value of investigating pharmacological blockers in parallel with genetic deletion.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".