Relationship between size and latency of action potentials in human muscle sympathetic nerve activity
Bibliographic record
Abstract
We employed a novel action potential detection and classification technique to study the relationship between the recruitment of sympathetic action potentials (i.e., neurons) and the size of integrated sympathetic bursts in human muscle sympathetic nerve activity (MSNA). Multifiber postganglionic sympathetic nerve activity from the common fibular nerve was collected using microneurography in 10 healthy subjects at rest and during activation of sympathetic outflow using lower body negative pressure (LBNP). Burst occurrence increased with LBNP. Integrated burst strength (size) varied from 0.22 ± 0.07 V at rest to 0.28 ± 0.09 V during LBNP. Sympathetic burst size (i.e., peak height) was directly related to the number of action potentials within a sympathetic burst both at baseline (r = 0.75 ± 0.13; P < 0.001) and LBNP (r = 0.75 ± 0.12; P < 0.001). Also, the amplitude of detected action potentials within sympathetic bursts was directly related to the increased burst size at both baseline (r = 0.59 ± 0.16; P < 0.001) and LBNP (r = 0.61 ± 0.12; P < 0.001). In addition, the number of detected action potentials and the number of distinct action potential clusters within a given sympathetic burst were correlated at baseline (r = 0.7 ± 0.1; P < 0.001) and during LBNP (r = 0.74 ± 0.03; P < 0.001). Furthermore, action potential latency (i.e., an inverse index of neural conduction velocity) was decreased as a function of action potential size at baseline and LBNP. LBNP did not change the number of action potentials and unique clusters per sympathetic burst. It was concluded that there exists a hierarchical pattern of recruitment of additional faster conducting neurons of larger amplitude as the sympathetic bursts become stronger (i.e., larger amplitude bursts). This fundamental pattern was evident at rest and was not altered by the level of baroreceptor unloading applied in this study.
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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 | 0.003 |
| 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".