Bradykinin facilitates noradrenaline spillover during contraction in the canine gracilis muscle
Bibliographic record
Abstract
Noradrenaline spillover from skeletal muscle vascular areas increases during exercise but the underlying mechanisms are not well understood. Muscle contraction itself causes changes in many factors that could affect noradrenaline spillover. For instance, it has been reported that bradykinin is synthesized in skeletal muscle areas during contraction. Because the B2 bradykinin receptor facilitates noradrenaline spillover, it may be involved in the increase associated with contraction. In this experiment, we studied the effect of bradykinin on noradrenaline spillover in the in situ canine gracilis muscle, using the specific B2 antagonist HOE 140. The drug did not modify noradrenaline spillover at rest, but did cause a significant decrease during muscle contraction, from 558 to 181 pg min(-1). As reported previously in the literature, fractional extraction of noradrenaline decreased during muscle contraction. This effect was independent of HOE 140 treatment. In light of our results, it seems that bradykinin formation during muscle contraction may play an important part in the observed increase in noradrenaline spillover but does not affect fractional extraction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".