Cutaneous Mechanoreceptor Feedback from the Hand and Foot Can Modulate Muscle Sympathetic Nerve Activity
Bibliographic record
Abstract
Stimulation of high threshold mechanical nociceptors on the skin can modulate efferent sympathetic outflow. Whether low threshold mechanoreceptors from glabrous skin are similarly capable of modulating autonomic outflow is unclear. Therefore, the purpose of this study was to examine the effects of cutaneous afferent feedback from the hand palm and foot sole on efferent muscle sympathetic nerve activity (MSNA). Fifteen healthy young participants (9 male; 25 ± 3 years [range: 22-29]) underwent microneurographic recording of multi-unit MSNA from the right fibular nerve during 2 minutes of baseline and 2 minutes of mechanical vibration (150Hz, 220μm peak-to-peak) applied to the left hand or foot. Each participant completed three trials of both hand and foot stimulation, each separated by 10 minutes. MSNA burst frequency decreased similarly during the two minutes of both hand (20.8 ± 8.9 vs. 19.3 ± 8.6 bursts/minute [ -8%], p=0.035) and foot (21.0 ± 8.3 vs. 19.5 ± 8.3 bursts/minute [ -8%], p=0.048) vibration but did not alter normalized mean burst amplitude or area (All p>0.05). Larger reductions in burst frequency were observed during the first 10 seconds (onset) of both hand (20.8 ± 8.9 vs. 17.0 ± 10.4 [ -25%], p<0.001) and foot (21.0 ± 8.3 vs. 18.3 ± 9.4 [ -16%], p=0.035) vibration, in parallel with decreases in normalized mean burst amplitude (hand: 0.45 ± 0.06 vs. 0.36 ± 0.14% [ -19%], p=0.03; foot: 0.47 ± 0.07 vs. 0.34 ± 0.19% [ -27%], p=0.02) and normalized mean burst area (hand: 0.42 ± 0.05 vs. 0.32 ± 0.12% [ -25%], p=0.003; foot: 0.47 ± 0.05 vs. 0.34 ± 0.16% [ -28%], p=0.01). These results demonstrate that tactile feedback from the hands and feet can influence efferent sympathetic outflow to skeletal muscle.
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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 | 0.001 |
| 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) | 0.003 | 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".