Stimulation characteristics that determine arteriolar dilation in skeletal muscle
Bibliographic record
Abstract
To determine the skeletal muscle stimulation parameters that are most important in establishing vasodilation in the microvasculature, I tested whether arteriolar diameter during 2 min of repetitive, short-duration, tetanic skeletal muscle contractions increased with changes in stimulus frequency, stimulation train duration, and contraction frequency. To test this, the diameter of transverse arterioles approximately perpendicular to small bundles of cremaster muscle fibers in situ of anesthetized Golden Syrian hamsters was used as a bioassay system. Arteriolar diameter was measured before and during different stimulation patterns that consisted of a contraction frequency [6, 12, or 24 contractions per minute (cpm)], a stimulation train duration (250, 500, or 750 ms) and a stimulus frequency (4, 8, 10, 15, 20, 30, 40, 60, and 80 Hz). The magnitude of the dilation significantly increased with stimulus frequency but not in a simple linear manner. The average rate of increase was 0.32 +/- 0.02 microm/Hz from 4 to 20 Hz and 0.09 +/- 0.02 microm/Hz from 30 to 80 Hz. The magnitude of the dilation increased significantly with the contraction frequency where the dilation at 6 cpm was significantly smaller than the dilation at 24 cpm across all stimulus frequencies. Changing the train duration from 250 to 750 ms did not significantly affect the magnitude of the dilation. These observations suggest that stimulation parameters are important in determining the magnitude of the microvascular dilation and that the magnitude of the dilation was dependent on both the contraction frequency and stimulus frequency but was independent of train duration.
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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) | 0.001 | 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".