Exercise‐induced increase in interstitial bradykinin and adenosine concentrations in skeletal muscle and peritendinous tissue in humans
Bibliographic record
Abstract
Bradykinin is known to cause vasodilatation in resistance vessels and may, together with adenosine, be an important regulator of tissue blood flow during exercise. Whether tissue concentrations of bradykinin change with exercise in skeletal muscle and tendon-related connective tissue has not yet been established. Microdialysis (molecular mass cut-off 5 kDa) was performed simultaneously in calf muscle and peritendinous Achilles tissue at rest and during 10 min periods of incremental (0.75 W, 2 W, 3.5 W and 4.75 W) dynamic plantar flexion exercise in 10 healthy individuals (mean age 27 years, range 22-33 years). Interstitial bradykinin and adenosine concentrations were determined using an internal reference to determine relative recovery ([2,3,prolyl-3,4-(3)H(N)]-bradykinin and [2-(3)H]-adenosine). Bradykinin and adenosine recovery were closely related and in the range of 30-50 %. The interstitial concentration of bradykinin rose in response to exercise both in skeletal muscle (from 23.1 +/- 4.9 nmol l(-1) to 110.5 +/- 37.9 nmol l(-1); P < 0.05) and in the peritendinous tissue (from 27.7 +/- 7.8 nmol l(-1) to 105.0 +/- 37.9 nmol l(-1); P < 0.05). In parallel, the adenosine concentration increased both in muscle (from 0.48 +/- 0.07 micromol l(-1) to 1.59 +/- 0.35 micromol l(-1); P < 0.05) and around the tendon (from 0.33 +/- 0.03 micromol l(-1) to 0.86 +/- 0.16 micromol l(-1); P < 0.05). In conclusion, the data show that muscular activity increases the interstitial concentrations of bradykinin and adenosine in both skeletal muscle and the connective tissue around its adjacent tendon. These findings support a role for bradykinin and adenosine in exercise-induced hyperaemia in skeletal muscle and suggest that bradykinin and adenosine are potential regulators of blood flow in peritendinous tissue.
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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".