Recording sympathetic nerve activity in conscious humans and other mammals: guidelines and the road to standardization
Bibliographic record
Abstract
Over the past several decades, studies of the sympathetic nervous system in humans, sheep, rabbits, rats, and mice have substantially increased mechanistic understanding of cardiovascular function and dysfunction. Recently, interest in sympathetic neural mechanisms contributing to blood pressure control has grown, in part because of the development of devices or surgical procedures that treat hypertension by manipulating sympathetic outflow. Studies in animal models have provided important insights into physiological and pathophysiological mechanisms that are not accessible in human studies. Across species and among laboratories, various approaches have been developed to record, quantify, analyze, and interpret sympathetic nerve activity (SNA). In general, SNA demonstrates "bursting" behavior, where groups of action potentials are synchronized and linked to the cardiac cycle via the arterial baroreflex. In humans, it is common to quantify SNA as bursts per minute or bursts per 100 heart beats. This type of quantification can be done in other species but is only commonly reported in sheep, which have heart rates similar to humans. In rabbits, rats, and mice, SNA is often recorded relative to a maximal level elicited in the laboratory to control for differences in electrode position among animals or on different study days. SNA in humans can also be presented as total activity, where normalization to the largest burst is a common approach. The goal of the present paper is to put together a summary of "best practices" in several of the most common experimental models and to discuss opportunities and challenges relative to the optimal measurement of SNA across species.Listen to this article's corresponding podcast at https://ajpheart.podbean.com/e/guidelines-for-measuring-sympathetic-nerve-activity/.
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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 | 0.085 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.007 |
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".