Angiotensin II AT<sub>2</sub> receptor inhibits smooth muscle cell migration via fibronectin cell production and binding
Bibliographic record
Abstract
To explore the vascular function of the angiotensin II (ANG II) AT(2) receptor subtype (AT(2)R), we generated a vascular smooth muscle cell (SMC) line expressing the AT(2)R (SMC-vAT(2)). The involvement of AT(2)R in the motility response of SMCs was examined in SMC-vAT(2) cells and their controls (SMC-v) cultured on either laminin or fibronectin matrix proteins with the agarose drop technique. All experiments were conducted in the presence of a saturating concentration of losartan to inactivate the AT(1)R subtype. Under basal conditions, both cell lines migrated outside drops, but on laminin only. Treatment with ANG II significantly inhibited the migration of SMC-vAT(2) but not SMC-v cells, and this effect was prevented by the AT(2)R antagonist CGP-42112A. The decreased migration of SMC-vAT(2) was not associated with changes in cell growth, cytoskeleton stiffness, or smooth muscle actin, desmin, and tenascin expression. However, it was correlated with increased synthesis and binding of fibronectin. Both responses were prevented by incubation with selective AT(2)R antagonists. Addition of GRGDTP peptide, which prevents cell attachment of fibronectin, reversed the AT(2)R inhibitory effect on SMC-vAT(2) migration. These results suggest that activated ANG II AT(2)R inhibits SMC migration via cellular fibronectin synthesis and associated cell binding.
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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 | 0.001 |
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".