Central vs. peripheral determinants of sympathetic neural recruitment: insights from static handgrip exercise and postexercise circulatory occlusion
Bibliographic record
Abstract
Sympathetic outflow is modified during acute homeostatic stress through increased firing of low-threshold axons, recruitment of latent axons, and synaptic delay modifications. However, the role of central mechanisms versus peripheral reflex control over sympathetic recruitment remains unknown. Here, we examined sympathetic discharge patterns during fatiguing static handgrip (SHG) exercise and postexercise circulatory occlusion (PECO) to study the central vs. peripheral reflex elements of sympathetic neural coding. Muscle sympathetic nerve activity (MSNA; microneurography) was measured in six males (25 ± 3 yr) at baseline (3 min) and during 5 min of SHG exercise completed at 20% maximal voluntary contraction. Isolation of the peripheral metaboreflex component was achieved by PECO for 3 min. Action potential (AP) patterns were studied using wavelet-based methodology. Compared with baseline, total MSNA increased by minute 3 of SHG, remaining elevated throughout the duration of exercise and PECO (all P < 0.05). The AP content per burst increased above baseline by minute 4 of SHG (Δ4 ± 2), remaining elevated at minute 5 (Δ6 ± 4) and PECO (Δ4 ± 4; all P < 0.05). Similarly, total AP clusters increased by minute 4 of SHG (Δ5 ± 5) and remained elevated at minute 5 (Δ6 ± 3) and PECO (Δ7 ± 5; all P < 0.01), indicating recruitment of latent subpopulations. Finally, the AP cluster size-latency profile was shifted downward during minutes 4 (-32 ± 22 ms) and 5 (-49 ± 17 ms; both P < 0.05) of SHG but was not different than baseline during PECO (P > 0.05). Our findings suggest that central perceptual factors play a specific role in the synaptic delay aspect of sympathetic discharge timing, whereas peripheral reflex mechanisms affect recruitment of latent axons.
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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 | 0.000 |
| 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".