Interstitial adenosine but not nitric oxide concentrations are elevated during systemic hypoxia in rat skeletal muscle
Bibliographic record
Abstract
During systemic hypoxia it is generally accepted that nitric oxide (NO) and adenosine (Ado) contribute to peripheral vasodilatation in skeletal muscle. To examine this, microdialysis fibers were inserted into the gastrocnemius muscle of anesthetized rats (n = 16) and perfused with ringers (control), 60 μM ATP (Ado precursor) or 500 μM AOPCP (5′ectonucleotidase inhibitor) at a rate of 5 μL/min during rest and hypoxia (10.5% O 2 ). During all perfusions and conditions no changes in interstitial NO concentrations were observed. Interstitial Ado levels during ringers perfusion were 0.04±0.01 μM at rest and increased to 0.07±0.01 μM during hypoxia (P<0.05). The perfusion of ATP alone increased (P<0.05) interstitial Ado levels from 0.04±0.01 μM to 0.12±0.02 μM at rest and hypoxia further increased (P<0.05) Ado levels to 0.18±0.02 μM. Perfusion of AOPCP reduced interstitial Ado to undetectable levels throughout the experiment, confirming inhibition of the 5′ectonucleotidase and subsequently Ado production. These data clearly demonstrate that hypoxia and alterations in Ado concentrations do not directly affect NO concentrations. In particular, when Ado production was inhibited, NO did not compensate for the loss of this vasodilator, even during hypoxia. Furthermore, interstitial Ado levels were elevated during hypoxia and appear to play a key role in interstitially mediated vasodilation. Supported by NSERC.
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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 | 0.001 |
| 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".