Angiostatin Inhibits Neutrophil Migration and Activation
Bibliographic record
Abstract
Dysregulated recruitment of activated neutrophils contributes to tissue damage, morbidity and mortality. Here, we studied the role of angiostatin (ANG), an anti-angiogenic molecule, in neutrophil recruitment and activation in vivo and in vitro. First, we showed lipid raft mediated surface and cytosolic localization of FITC-conjugated ANG in fMLP-activated but not resting mouse and human neutrophils. ANG co-localised with neutrophil α-tubulin, angiomotin and β3 integrin in response to fMLP. ANG inhibited the signal for reduced mitotracker dye, an index of neutrophil activation in suspended as well as adhered neutrophils in response to fMLP and LPS. ANG also inhibited the signal for phosphorylated forms of hsp-27, p38 and p44/42 MAPK that regulate neutrophil activation. Finally, ANG blocked formation of reactive oxygen species while activating caspase-3 and inducing apoptosis in LPS-activated neutrophils. In-vitro confocal microscopy on mouse as well as human neutrophils revealed that ANG abolished fMLP-induced polarisation of actin-rich leading edge. Intravital microscopy showed that ANG reduced leukocyte adhesion and emigration (p<0.05) and simultaneously increased rolling flux (p<0.05) in post-capillary venules in TNFα-treated cremaster muscles. We conclude that ANG is a novel inhibitor of neutrophil migration and activation. (Funding NSERC Discovery Grant) Grant Funding Source: NSERC Discovery Grant
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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) | 0.002 | 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".