Effects of combined AT1 receptor antagonist/NEP inhibitor on vascular remodeling and cardiac fibrosis in SHRSP
Bibliographic record
Abstract
BACKGROUND: The association of an angiotensin-converting enzyme inhibitor (ACEI) with a neutral endopeptidase inhibitor (NEPI) has potent blood pressure (BP) lowering action, but is associated with side-effects. We evaluated the effects of combining an angiotensin II type 1 (AT1) receptor blocker (ARB, valsartan) and a NEPI (CGS 25354) in comparison with a dual ACEI/NEPI (CGS 30440) in stroke-prone spontaneously hypertensive rats (SHRSP). METHODS AND RESULTS: Ten-week-old SHRSP were treated with valsartan (10 mg/kg per day), valsartan + CGS 25354 (100 mg/kg per day), CGS 25354, CGS 30440 (10 mg/kg per day) or hydralazine (25 mg/kg per day) for 10 weeks. Mesenteric resistance arteries were studied on a pressurized myograph, whereas cardiac effects were assessed by histology and immunohistochemistry. BP of SHRSP was lowered by combined valsartan/NEPI and ACEI/NEPI slightly more than valsartan, whereas NEPI was ineffective. Valsartan, valsartan/NEPI and ACEI/NEPI normalized resistance artery relaxation responses to acetylcholine, and significantly decreased media/lumen ratio and collagen deposition. All treatments decreased vascular NAD(P)H oxidase-mediated superoxide production. Valsartan/NEPI and ACEI/NEPI decreased media/lumen ratio of intramyocardial coronary arteries, while valsartan alone had no effect. Valsartan/NEPI and ACEI/NEPI increased vascular matrix metalloproteinase-2 activity, and decreased tissue inhibitors of metalloproteinase-2 activity and macrophage infiltration. CONCLUSION: Combined valsartan/NEPI was almost as effective as a dual ACEI/NEPI in lowering BP and improving vascular remodeling in SHRSP. These findings suggest the potential therapeutic value of combining ARB and NEPI in the treatment of hypertension.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".