Cyclo‐oxygenase‐2 mediated prostaglandin release regulates blood flow in connective tissue during mechanical loading in humans
Bibliographic record
Abstract
Mechanical loading is known to increase connective tissue blood flow of human tendons and to cause local release of vasodilatory substances. The present study investigated the importance of prostaglandins (PG) formed by cyclo-oxygenase isoforms (COX-1 and 2) for the exercise-related increase in blood flow in connective tissue. Healthy individuals (n = 24, age: 23-31 years) underwent 30 min of intermittent, isometric, plantarflexion with both calf muscles either without (n = 6, Control, C) or with blockade of PG formation, either COX-2 specific (n = 10, Celecoxib 2 x 100 mg day-1 for 3 days prior to the experiment) or COX unspecific (n = 8, indomethacin 100 mg (12 and 1 h pre-experiment) and acetyl salicylic acid 500 mg day-1 for 3 days pre-experiment). Prostaglandin E2 (PGE2) concentration was determined by microdialysis and blood flow by 133Xe washout. In C, interstitial PGE2 rose from (0.8 +/- 0.2 (rest) to 1.4 +/- 0.5 ng ml-1 (exercise), P < 0.05), whereas during unspecific COX inhibition, tissue PGE2 was completely inhibited at rest and during exercise. COX-2 specific blockade did not inhibit tissue PGE2 at rest, but totally abolished the exercise induced increase. Blood flow was similar in the three groups at rest (P > 0.05), whereas the increase in flow with exercise was reduced by 35 and 43 % with COX-2 specific blockade (3.2 +/- 0.7 to 6.1 +/- 1.5 ml (100 g tissue)-1 min-1 or COX unspecific blockade (3.0 +/- 0.8 to 7.6 +/- 1.6), respectively, compared to C (2.7 +/- 0.8 to 10.2 +/- 2.0)(P < 0.05). The findings indicate that COX-2 specific mechanisms are responsible for the exercise-induced increase in prostaglandin synthesis, and that increase in tissue prostaglandin plays an important role for blood flow in peritendinous connective tissue during physical loading in vivo.
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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".