Effect of sympathetic nerve blockade on low‐frequency oscillations of forearm and leg skin blood flow in healthy humans
Bibliographic record
Abstract
OBJECTIVE: To Examine the effect of inhibiting sympathetic function on cutaneous vasomotion in the forearm and leg. METHODS: Intradermal microdialysis fibers were placed in the forearm and leg, one as an untreated control (lactated Ringer's) and the other perfused with bretylium tosylate to block sympathetic nerves. Skin blood flow was monitored using laser Doppler flowmetry. Baseline was collected for 10 minutes before local skin temperature was increased to 42°C. Spectral analysis was performed using a Morlet wavelet. RESULTS: Bretylium tosylate increased skin blood flow during baseline in the forearm (d=1.6, P<.05) and leg (d=0.5, P<.05) and decreased skin blood flow at both sites during both the initial peak (d≥1.0, P<.05) and plateau (d≥0.8, P<.05). Treatment with bretylium tosylate reduced wavelet amplitude associated with neural activity during baseline in the forearm (d=1.6, P<.05) and leg (d=0.9, P<.05). This reduction in wavelet amplitude at bretylium tosylate-treated sites was also observed during the initial vasodilation to local heating in both the forearm (d=1.6, P<.05) and leg (d=1.4, P<.05) and during the sustained vasodilation in both the forearm (d=1.6, P<.05) and leg (d=1.2, P<.05). CONCLUSIONS: Our data support that the frequency band (0.021-0.052 Hz) associated with neurogenic activity appears to be correct having a large sympathetic component.
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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".