Effect of blood flow and muscle contraction on noradrenaline spillover in the canine gracilis muscle
Bibliographic record
Abstract
Many authors have reported that, during exercise, noradrenaline spillover increases and fractional extraction decreases. It has been suggested that the increase in blood flow to active muscles may contribute to these effects. Muscle contraction also causes changes in many factors that may affect noradrenaline spillover and fractional extraction. In this experiment, we studied the effect of muscle contraction and blood flow on noradrenaline and adrenaline spillover and fractional extraction in the in situ canine gracilis muscle. The low intensity stimulation protocol enabled us to have muscle contractions without any effect on the local concentration of noradrenaline, as measured by microdialysis, and noradrenaline spillover. Fractional extraction of both noradrenaline and adrenaline was unaffected by increasing blood flow three and four times its resting value. In addition, noradrenaline spillover was increased by the higher blood flow, from 188 to 452 pg x min(-1) at rest and from 246 to 880 pg x min(-1) during stimulation. Stimulation of muscle contraction caused a significant increase in fractional extraction of noradrenaline and a nonsignificant increase in adrenaline extraction. In addition, an adrenaline spillover was observed in certain conditions. In light of our results, it seems that blood flow may not be the main factor decreasing fractional extraction of noradrenaline during exercise. However, blood flow could contribute to the increase in noradrenaline spillover observed in the active muscles during exercise.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".