Neuronal Regulation of Aortic Valve Cusps
Bibliographic record
Abstract
Heart valves have long been considered exclusively passive structures that open and close in response to changes in transvalvular pressure during the cardiac cycle. Although this is partly true, recent evidence suggests that valves are far more sophisticated structures. Microscopic examination of heart valves reveals a complex network of endothelial cells, interstitial cells, an extracellular matrix and a rich network of intrinsic nerves. The distribution of these nerve networks varies between the four valves, but is remarkably conserved between species. The present review will focus mainly on aortic valve innervation for several reasons: it is most commonly involved in disease processes, it lies in a unique hemodynamic environment and is exposed to extreme mechanical forces. These nerves are likely to play a significant role in the modulation of aortic valve structure and function and its adaptation to different hemodynamic and humoral conditions. The objectives of this review are first to describe the anatomy of aortic valve innervation, then detail the functional significance of innervation to the valve and finally make the case for the clinical relevance of understanding the neural control of aortic valves and its potential pharmacologic implications.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".