Combined Angiotensin II Type 1 and Type 2 Receptor Blockade on Vascular Remodeling and Matrix Metalloproteinases in Resistance Arteries
Bibliographic record
Abstract
We investigated the role of angiotensin II type 1 (AT1) and AT2 receptors, matrix metalloproteinases (MMPs), and extracellular matrix (ECM) components involved in vascular remodeling of resistance arteries induced by angiotensin II (Ang II). Sprague-Dawley rats received Ang II (120 ng/kg per minute SC) +/- the AT1 antagonist losartan (10 mg/kg per day PO), the AT1/AT2 antagonist Sar1-Ile8-Ang II (Sar-Ile; 10 microg/kg per minute SC), or hydralazine (25 mg/kg per day PO) for 7 days. Structure and mechanical properties of small mesenteric arteries were evaluated on a pressurized myograph. Ang II increased growth index (+21%), which was partially decreased by losartan (-11%) and abrogated by Sar-Ile. Hydralazine markedly increased growth index (+32%) despite systolic blood pressure (BP) lowering, suggesting a BP-independent effect of Ang II on vascular growth. Elastic modulus was increased by Sar-Ile compared with Ang II and control. Vascular type I collagen was reduced (P<0.05), whereas fibronectin increased significantly with Sar-Ile. Vascular tissue inhibitor of metalloproteinase-2 binding to MMP-2 was abrogated by Sar-Ile, but MMP-2 activity was significantly increased compared with losartan, Ang II, and controls. Thus, AT1 blockade exerted antigrowth effects and reduced stiffness of small resistance arteries by decreasing nonelastic fibrillar components (collagen and fibronectin). Concomitant AT1/AT2 blockade prevented growth, reduced collagen type I and elastin deposition but increased vascular stiffness, fibronectin, and MMP-2 activity. These results demonstrate opposing roles of AT1 receptors that increase fibronectin and vascular stiffness and AT2 receptors that decrease MMP-2 and increase elastin. Changes in vascular wall mechanics, ECM deposition, and MMP activity are thus modulated differentially by Ang II receptors.
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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) | 0.001 | 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".