Recording Vagal Nerve Activity for the Control of an Artificial Heart System
Bibliographic record
Abstract
Monitoring cardiovascular control system information is important in considering the quality of life (QOL) of patients with artificial hearts. Natural heart circulation is controlled by an autonomic nervous system. Therefore, it is desirable to record autonomic nerve activity for the control of artificial heart systems. We directly recorded vagal nerve activity in long-term animal experiments. Six healthy adult goats were anesthetized with halothane inhalation, and thoracotomy was performed with the fourth rib resection during mechanical ventilation. Arterial blood pressure and right and left atrial pressures were continuously monitored with an inserted catheter. Cardiac output was measured by an electromagnetic flow meter attached to the ascending aorta. After the chest was closed, an incision was made in the left neck, and the left vagal nerve was separated. Stainless steel electrodes were inserted into the vagal nerve and fixed by a plasticizer. After the incision was closed, the goats were transferred to a cage and extubated after waking. Vagal nerve activity was measured using hemodynamic parameters when the animals were awake. Our results show that clear observation of autonomic nerve discharge was made through this experimental system for over 1 month. The tonus of the vagal nerve was significantly altered before body motion with hemodynamic changes, suggesting the possibility of prediction. These results suggest that information from autonomic nerves may help to control implantable artificial hearts or ventricular assist devices.
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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 | 0.000 |
| 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".