Inhibition of human and rabbit arterial smooth muscle cell migration mediated by the kinin B<sub>1</sub> receptor: role of receptor density and released mediators
Bibliographic record
Abstract
Bradykinin (BK)-related peptides are suspected to negatively influence diverse functions in vascular smooth muscle cells (SMCs), notably via stimulation of the inducible B1 receptor (B1R), and have been shown to inhibit the migration of rat SMCs. The present study had several objectives: (i) to test whether B1R mediates the inhibition of migration of arterial SMCs from additional species (the human and the rabbit); (ii) whether B1R density influences this action and whether autocrine NO or prostanoid release modulate it; and (iii) the possible signaling interaction between the B1R and phosphatidylinositol-3 kinase (PI-3K) has been addressed. The peptidase resistant B1R agonist Sar-[D-Phe8]des-Arg9-BK (10 nmol/L - 1 micromol/L) was an inhibitor of migration in human or rabbit arterial SMCs in a wound closure assay, more effectively if the medium composition allowed a high B1R expression (20% fetal bovine serum (FBS) + interleukin-1beta (IL-1beta) in human SMCs, 10% FBS in rabbit cells). The effect of the B1R agonist on motility was abrogated by a B1R antagonist, B-9858, but not by the B2R antagonist Hoe 140; a peptidase-resistant B2R agonist, [Phe8Psi(CH2-NH)-Arg9]BK, had a marginal or no effect on migration. Sar-[D-Phe8]des-Arg9-BK (1 micromol/L) did not significantly influence SMC proliferation. The B1R-mediated inhibition of SMC migration was not affected by pharmacological inhibition of the nitric oxide synthases or cyclooxygenases-1 or -2, but was correlated to an inhibition of PI-3K in both types of SMCs. The inhibition of SMC migration mediated by the kinin B1R is likely independent from NO or prostanoid release, applicable to several species, and correlated to receptor density and the inhibition of PI-3K.
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 | 0.000 |
| 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".