Effects of pharmacological adrenergic and vagal modulation on fractal heart rate dynamics
Bibliographic record
Abstract
Breakdown of short-term fractal-like behaviour of HR indicates an increased risk for adverse cardiovascular events and mortality, but the pathophysiological background for altered fractal HR dynamics is not known. Our aim was to study the effects of pharmacological modulation of autonomic function on fractal correlation properties of heart rate (HR) variability in healthy subjects. Short-term fractal scaling exponent (alpha1) along with spectral components of HR variability were analysed during the following pharmacological interventions in healthy subjects: (i) noradrenaline (NE) infusion (n=22), (ii) NE infusion after phentolamine (PHE) (n=8), (iii) combined NE + adrenaline (EPI) infusion (n=12), (iv) vagal blockade with high dose of atropine (n=10), (v) and vagal activation by low dose of atropine (n=10). Then alpha1 decreased progressively during the incremental doses of NE (from 0.85 +/- 0.250 to 0.55 +/- 0.23, P<0.0001). NE also decreased the average HR (P<0.001) and increased the high frequency spectral power (P<0.001). Vagal blockade with atropine increased the alpha1 value (from 0.82 +/- 0.22 to 1.24 +/- 0.41, P<0.05). Combined NE + EPI infusion and vagal activation with a low dose atropine did not result in any changes in alpha1, and alpha-adrenergic blockade by PHE did not completely reverse the effects of NE on alpha1. Increased levels of circulating NE result in reduction of short-term correlation properties of HR dynamics. The results suggest that coactivation of cardiac vagal outflow at the time of high levels of a circulating sympathetic transmitter explains the breakdown of fractal-like behaviour of human HR dynamics.
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) | 0.000 | 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) | 0.000 | 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".