Systemic vascular effects of acute electrical baroreflex stimulation
Bibliographic record
Abstract
We intended to determine if acute baroreflex activation therapy (BAT) increases venous capacitance and aortic conductance. BAT is effective in resistant hypertension, but its effect on the systemic vasculature is poorly understood. Left ventricular (LV) and aortic pressures and subdiaphragmatic aortic and caval flows (ultrasonic) were measured in six anesthetized dogs. Changes in abdominal blood volume (Vabdominal) were estimated as the integrated difference in abdominal aortic inflow and caval outflow. An electrode was implanted on the right carotid sinus. Data were measured during control and BAT. Next, sodium nitroprusside (SNP) was infused and BAT was subsequently added. Finally, angiotensin II (ANG II) was infused, and three increased BAT currents were added. We found that BAT decreased mean aortic pressure (PAo) by 22.5 ± 1.3 mmHg (P < 0.001) and increased aortic conductance by 16.2 ± 4.9% (P < 0.01) and Vabdominal at a rate of 2.2 ± 0.6 ml·kg(-1)·min(-1) (P < 0.01). SNP decreased PAo by 17.4 ± 0.7 mmHg (P < 0.001) and increased Vabdominal at a rate of 2.2 ± 0.7 ml·kg(-1)·min(-1) (P < 0.05). During the SNP infusion, BAT decreased PAo further, by 26.0 ± 2.1 mmHg (P < 0.001). ANG II increased PAo by 40.4 ± 3.5 mmHg (P = 0.001). When an increased BAT current was added, PAo decreased to baseline (P < 0.01) while aortic conductance increased from 62.3 ± 5.2% to 80.2 ± 3.3% (P < 0.05) of control. Vabdominal increased at a rate of 1.8 ± 0.9 ml·kg(-1)·min(-1) (P < 0.01), reversing the ANG II effects. In conclusion, BAT increases arterial conductance, decreases PAo, and increases venous capacitance even in the presence of powerful vasoactive drugs. Increasing venous capacitance may be an important effect of BAT in hypertension.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".