Assessing static and dynamic sympathetic transduction using microneurography
Bibliographic record
Abstract
The relationship between sympathetic nerve activity and the vasculature has been of great interest due to its potential role in various cardiovascular-related diseases. This relationship, termed "sympathetic transduction," has been quantified using several different laboratory and analytical techniques. The most common method is to assess the association between relative changes in muscle sympathetic nerve activity, measured via microneurography, and physiological outcomes (e.g., blood pressure, total peripheral resistance, blood flow, etc.) in response to a sympathetic stressor (e.g., exercise, cold stress, orthostatic stress). This approach, however, comes with its own caveats. For instance, elevations in blood pressure and heart rate during a sympathetic stressor can have an independent impact on muscle sympathetic nerve activity. Another assessment of sympathetic transduction was developed by Wallin and Nerhed in 1982, where alterations in blood pressure and heart rate were assessed immediately following bursts of muscle sympathetic nerve activity at rest. This approach has since been characterized and further innovated by others, including the breakdown of consecutive burst sequences (e.g., singlet, doublet, triplet, and quadruplet), and burst height (quartile analysis) on specific vascular outcomes (e.g., blood pressure, blood flow, vascular resistance). The purpose of this review is to provide an overview of the literature that has assessed sympathetic transduction using microneurography and various sympathetic stressors (static sympathetic transduction) and using the same or similar approach established by Wallin and Nerhed at rest (dynamic neurovascular transduction). Herein, we discuss the overlapping literature between these two methodologies and highlight the key physiological questions that remain.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".