Angiopoietin like‐2 stimulates leukocytes adhesion to the native aortic endothelium in LDLr−/−; hApoB100+/+ mice
Bibliographic record
Abstract
Angiopoietin like‐2 (Angptl2), which circulating levels are increased in patients with coronary artery disease, could contribute to chronic inflammation. We hypothesized that the pro‐inflammatory effect of Angptl2 contributes to atherogenesis by stimulating leukocyte adhesion in atherosclerotic mice (LDLr −/− ; hApoB 100 +/+ , ATX). In C57Bl/6 control mice (WT), Angptl2 levels increased with age (P<0.05) in the plasma and the aorta. In ATX mice, levels were higher (P<0.05) than in WT mice both in plasma (3 folds) and the aorta (2 folds), and they further rose with age (P<0.05). In vitro, basal endothelial levels of TNF‐α and IL‐6 gene expression were higher in ATX compared to WT mice; stimulation with Angptl2 (100 nM) further raised their expression in both WT and ATX mice. Ex vivo, Angptl2 stimulated the adhesion of leukocytes on the native endothelium of the aorta of ATX mice (P<0.05), but not WT mice. Leukocytes adhesion on the endothelium was due to a rise (P<0.05) in the abundance of adhesion molecules (mRNA and protein) and the surface of endothelial cells (ICAM‐1) and leukocytes (Mac‐1, L‐selectin and PSGL‐1). Antibodies against P‐selectin or ICAM‐1 prevented (P<0.05) leukocyte adhesion in ATX mice. Our data suggest that Angptl2 could significantly contribute to the atherosclerotic process by stimulating pro‐inflammatory cytokines secretion and leukocytes adhesion.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".