Angiopoietin‐1 but not angiopoietin‐2 induces IL‐8 synthesis and release from human neutrophils
Bibliographic record
Abstract
Angiopoietins (Ang1 and Ang2) can promote neutrophil chemotactic activities by activating Tie2 receptor. Moreover, pretreatment with Ang1 or Ang2 enhances the chemotactic effect of interleukin‐8 (IL‐8). We wanted to assess if angiopoietins can promote IL‐8 protein synthesis and release from human neutrophils. Only a treatment with Ang1 at 10 −8 M induced a significant increase of IL‐8 protein synthesis (3.6‐fold) and release (5.5‐fold), whereas the combination of Ang1 and Ang2 induced the same effect as Ang1 alone. Moreover, IL‐8 mRNA production was also increased (4.7‐fold) as compared to PBS. Cycloheximide, a protein synthesis inhibitor, reduced Ang1‐mediated IL‐8 protein synthesis and release by up to 96 and 92% respectively, whereas IL‐8 mRNA was increased by up to 18‐fold compared to PBS‐treated neutrophils. Actinomycin D, an mRNA synthesis inhibitor, reduced Ang1‐mediated IL‐8 mRNA and protein synthesis only within the first hour of treatment by 54 and 52% respectively. Using specific kinase inhibitors, we observed that Ang1‐driven IL‐8 mRNA and protein increase is p42/44 MAPK dependent and independent from p38 MAPK and PI3K activity. In summary, Ang1 (10 −8 M) induces IL‐8 mRNA synthesis, as well as IL‐8 protein synthesis and release, through the activation of p42/44 MAPK. 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) | 0.002 | 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".