“Smart” baroreception along the aortic arch, with reference to essential hypertension
Bibliographic record
Abstract
Beat-to-beat regulation of heart rate is dependent upon sensing of local stretching or local "disortion" by aortic baroreceptors. Distortions of the aortic wall are due mainly to left ventricular output and to reflected waves arising from the arterial tree. Distortions are generally believed to be useful in cardiac control since stretch receptors or aortic baroreceptors embedded in the adventitia of the aortic wall, transduce the distortions to cardiovascular neural reflex pathways responsible for beat-to-beat regulation of heart rate. Aortic neuroanatomy studies have also found a continuous strip of mechanosensory neurites spread along the aortic inner arch. Although their purpose is now unknown, such a combined sensing capacity would allow measurement of the space and time dependence of inner arch wall distortions due, among other things, to traveling waves associated with pulsatile flow in an elastic tube. We call this sensing capability--"smart baroreception." In this paper we use an arterial tree model to show that the cumulative effects of wave reflections, from many sites far downstream, have a surprisingly pronounced effect on the pressure distribution in the root segment of the tree. By this mechanism global hemodynamics can be focused by wave reflections back to the aortic arch, where they can rapidly impact cardiac control via smart baroreception. Such sensing is likely important to maintain efficient heart function. However, alterations in the arterial tree due to aging and other natural processes can lead in such a system to altered cardiac control and essential hypertension.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".