Can Myogenic Tone Protect Endothelial Function? Integrating Myogenic Activation and Dilator Reactivity for Cerebral Resistance Arteries in Metabolic Disease
Bibliographic record
Abstract
The obese Zucker rat (OZR) manifests multiple risk factors for impaired cerebrovascular function, including hypertension and insulin resistance although how they combine to produce integrated vascular function is unclear. As studies have suggested that myogenic activation (MA) severity for middle cerebral arteries (MCAs) may be proportional to hypertension severity, we hypothesized that MA will negatively correlate with dilator reactivity in OZR. MA of MCA from OZR was divided into low, medium, and high based on the slope of MA, while MCA reactivity and vascular metabolite bioavailability were assessed in all groups. Endothelium-dependent dilation of MCA in OZR was attenuated and correlated with the MA slope. Treatment of OZR MCA with TEMPOL (antioxidant) improved dilation in low or medium MA groups, but had less impact on high MA. Alternatively, treatment with gadolinium to normalize MA in OZR had reduced impact on dilator reactivity in MCA from low and medium MA groups, but improved responses in the high group. Treatment with both agents resulted in dilator responses that were comparable across all groups. These results suggest that, under conditions with stronger MA, endothelial function may receive some protection despite the environment, potentially from the ability of MCA to reduce wall tension despite increased pressure.
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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 | 0.001 | 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".