Dual endothelin receptor antagonism prevents remodeling of resistance arteries in diabetesThis article is one of a selection of papers published in the two-part special issue entitled 20 Years of Endothelin Research.
Bibliographic record
Abstract
Vascular remodeling, characterized by extracellular matrix deposition and increased media-to-lumen (M/L) ratio, contributes to the development of microvascular complications in diabetes. We have previously shown in type 2 diabetic Goto-Kakizaki (GK) rats that selective ETA receptor blockade prevents medial thickening of mesenteric arteries via regulation of matrix metalloproteases (MMP), whereas selective ETB receptor blockade augments this thickening. The goal of this study was to determine the effect of combined ETA and ETB receptor blockade on resistance vessel remodeling. Vessel structure, MMP activity, and extracellular matrix proteins were assessed in control Wistar and diabetic GK rats treated with vehicle or bosentan (100 mg/kg per day) for 4 weeks (n = 7-9 per group). Bosentan completely prevented the increase in M/L ratio and MMP-2 activity in diabetes but paradoxically increased M/L ratio and MMP activation in control animals. Collagenase (MMP-13) activity and protein levels were significantly decreased in diabetes. Accordingly, collagen deposition was augmented in GK rats. Dual ET receptor antagonism improved enzyme activity and normalized MMP-13 levels in diabetic animals but blunted MMP-13 activity in control animals. In summary, current findings suggest that diabetes-mediated remodeling of resistance arteries is prevented by dual blockade of ETA and ETB receptors and that the relative role of ET receptors in the regulation of vascular structure differs in the control and disease states.
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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".