Direct evidence of leukocyte adhesion in arterioles by angiotensin II
Bibliographic record
Abstract
Although leukocytes adhere in arteries in various vascular diseases, to date no endogenous proinflammatory molecule has been identified to initiate leukocyte adhesion in the arterial vasculature. This study was undertaken to assess angiotensin II (Ang II)-induced leukocyte adhesion in arterioles in vivo. Rats received intraperitoneal injections of Ang II; 4 hours later, leukocyte recruitment in mesenteric microcirculation was examined using intravital microscopy. Ang II (1 nM) produced significant arteriolar leukocyte adhesion of mononuclear cells. Using function-blocking monoclonal antibodies (mAbs) against different rat cell adhesion molecules (CAMs), we discovered that this effect was dependent on P-selectin and beta(2)-integrin. In postcapillary venules, Ang II also induced leukocyte infiltration, which was reduced by P-selectin and by beta(2)- and alpha(4)-integrin blockade. Interestingly, neutrophils were the primary cells recruited in venules. Although beta(2)-integrin expression in peripheral leukocytes of Ang II-treated animals was not altered, it was increased in peritoneal cells. Immunohistochemical studies revealed increased P-selectin, E-selectin, intercellular cell adhesion molecule-1 (ICAM-1), and vascular cell adhesion molecule-1 (VCAM-1) expression in response to Ang II in arterioles and venules. These findings provide the first evidence that Ang II causes leukocyte adhesion to the arterial endothelium in vivo at physiologically relevant doses. Therefore, Ang II may be a key molecule in cardiovascular diseases in which leukocyte adhesion to the arteries is a characteristic feature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".