Effect of Vasopeptidase Inhibition on Myocardial and Vascular Collagen in Stroke Prone Spontaneously Hypertensive Rats.
Bibliographic record
Abstract
34 Left ventricular remodeling in hypertension is associated with cardiac interstitial and perivascular collagen deposition. The vasopeptidase inhibitor omapatrilat improves left ventricular remodeling in experimental heart failure. We hypothesized that omapatrilat would induce a regression of ventricular and vascular fibrosis in hypertension. We therefore investigated the effect of vasopeptidase inhibition on collagen deposition in heart and aorta of stroke prone spontaneously hypertensive rats (SHRSP). Ten-week old SHRSP were treated orally for 10 weeks with omapatrilat (40mg/kg/d). Collagen fibers in heart and descending thoracic aorta were stained with Sirius red. Collagen density was defined as the ratio of the area stained by Sirius red to the area of the studied field. After 10 weeks, systolic blood pressure was significantly (P<0.01) reduced in omapatrilat-treated vs untreated SHRSP. Collagen density was significantly decreased in the subendocardial myocardium of omapatrilat-treated vs untreated SHRSP (to 3.55 vs 7.53%, respectively, P<0.05) and in mid-myocardium (to 4.38 vs 7.92% respectively, P<0.01). Aortic collagen content decreased in omapatrilat-treated vs untreated SHRSP (to 22.7 vs 46.3×10 3 μm/mm section, respectively, P<0.05). In conclusion, in addition to being a potent anti-hypertensive agent, omapatrilat significantly improves ventricular and arterial structure in SHRSP with regression of collagen deposition.
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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) | 0.001 | 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".