Angiopoietin‐1 but not angiopoietin‐2 promotes neutrophil viability: Role of interleukin‐8 and platelet‐activating factor
Bibliographic record
Abstract
We reported that the angiopoietins (Ang1 and Ang2) induce platelet activating factor (PAF) synthesis from endothelial cells and neutrophils. Since PAF can promote neutrophil viability, we addressed whether Ang1 and/or Ang2 could modulate neutrophil survival. Viability was assessed by flow cytometry using apoptotic and necrotic markers. Basal neutrophil viability from 0 to 24 hours post‐isolation decreased from 98% to ≈50%. Treatment with pro‐survival mediators such as interleukin‐8 (IL‐8; 25 nM) and PAF (100 nM) increased neutrophil viability by 22 and 35% (raising it from 56 to 69 and 76%) respectively. Treatment with Ang1 (0.001– 10 nM) increased neutrophil viability by up to 40%, while Ang2 had no effect. Combination of IL‐8 or PAF with Ang1 (10 nM) further increased neutrophil viability by 44 and 67% respectively. We also observed that Ang1, but not Ang2, can promote IL‐8 release and that a pretreatment of the neutrophils with blocking anti‐IL‐8 antibodies inhibited the pro‐survival effect of IL‐8 and Ang1 by 82 and 66% respectively. Pretreatment with a PAF receptor antagonist did abrogate PAF pro‐survival activity, without affecting Ang1‐induced neutrophil viability. Our data are the first one to report Ang1 pro‐survival activity on neutrophils, which is mainly driven through IL‐8 release. This work was supported by the Canadian Institutes of Health Research and the Heart and Stroke Foundation of Quebec.
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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) | 0.001 | 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".