Working under pressure: coronary arteries and the endothelin system
Bibliographic record
Abstract
Endogenous endothelin-1-dependent (ET-1) tone in coronary arteries depends on the balance between ET(A) and ET(B) receptor-mediated effects and on parameters such as receptor distribution and endothelial integrity. Numerous studies highlight the striking functional interactions that exist between nitric oxide (NO) and ET-1 in the regulation of vascular tone. Many of the cardiovascular complications associated with cardiovascular risk factors and aging are initially attributable, at least in part, to endothelial dysfunction characterized by a dysregulation between NO and ET-1. The contribution of the imbalance between smooth muscle ET(A/B) and endothelial ET(B) receptors to this process is poorly understood. An increased contribution of ET-1 that is associated with a proportional decrease in that of NO accompanies the development of coronary endothelial dysfunction, coronary vasospasm, and atherosclerosis. These data form the basis for the rationale of testing therapeutic approaches counteracting ET-1-induced cardiovascular dysfunction. It remains to be determined whether the beneficial role of endothelial ET(B) receptors declines with age and risk factors for cardiovascular diseases, revealing smooth muscle ET(B) receptors with proconstricting and proinflammatory activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".