Selective in vivo antagonism of endothelin receptors in transforming growth factor-β1 transgenic mice that mimic the vascular pathology of Alzheimer’s diseaseThis article is one of a selection of papers published in the two-part special issue entitled 20 Years of Endothelin Research.
Bibliographic record
Abstract
Increased levels of transforming growth factor-beta1 (TGF-beta1) induce a vascular pathology that shares similarities with that seen in Alzheimer's disease, and which possibly contributes to the cognitive decline. In aged transgenic mice that overexpress TGF-beta1 (TGF mice), we previously found reduced dilatory function and selectively impaired endothelin-1 (ET-1)-induced contraction. Here we studied the effects of chronic treatments with selective ETA (ABT-627) or ETB (A-192621) receptor antagonist on cerebrovascular reactivity, cerebral perfusion, or memory performance. The dilatory deficit of TGF mice was not improved by either treatment, but both ET-1 contraction and basal nitric oxide (NO) production were distinctly altered. Although ABT-627 was devoid of any effect in TGF mice, it virtually abolished the ET-1-induced contraction and NO release in wild-type (WT) littermates. In contrast, A-192621 only acted upon TGF mice with full recovery of ET-1 contraction and baseline NO synthesis. TGF mice, treated or not, had no cognitive deficit in the Morris water maze, nor did ABT-627-treated WT controls despite severely impaired vasoreactivity. These findings confirm that ETA receptors primarily mediate the ET-1-induced contraction. Further, they suggest that ETB receptors play a detrimental role in conditions of increased TGF-beta1 and that vascular dysfunction does not inevitably lead to cognitive deficit.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".