Regulation of bone blood flow in humans: The role of nitric oxide, prostaglandins, and adenosine
Bibliographic record
Abstract
The mechanisms that regulate bone blood flow (BBF) in humans are largely unknown. Animal studies suggest that nitric oxide (NO) could be involved, and in this study, we investigated the effects of inhibition of nitric oxide synthase (NOS) alone and in combination with inhibition of cyclooxygenase (COX) enzyme, thus prostaglandin (PG) synthesis on femoral bone marrow blood flow by positron emission tomography in healthy young men at rest and during one‐leg dynamic exercise. In an additional group of healthy men, the role of adenosine (ADO) in the regulation of BBF during exercise was investigated by use of an adenosine receptor blocker (aminophylline). Inhibitors were directly infused into the femoral artery. Resting BBF was 1.1 ± 0.4 mL 100 g−1min−1 and increased to almost sixfold in response to exercise (6.3 ± 1.5 mL 100 g−1 min−1). Inhibition of NOS reduced BBF at rest to 0.7 ± 0.3 mL 100 g−1 min−1 (P = .036), but did not affect BBF significantly during exercise (5.5 ± 1.4 mL 100 g−1 min−1, P = .25). On the other hand, while combined NOS and COX inhibition did not cause any further reduction of blood flow at rest (0.6 ± 0.2 mL 100 g−1min−1), the combined blockade reduced BBF during exercise by ~21%, to 5.0 ± 1.8 mL 100 g−1 min−1 (P = .014). Finally, the ADO inhibition during exercise reduced BBF from 5.5 ± 1.9 mL 100 g−1 min−1 to 4.6 ± 1.2 mL 100 g−1 min−1 (P = .045). In conclusion, our results support the view that NO is involved in controlling bone marrow blood flow at rest, and NO, PG, and ADO play important roles in controlling human BBF 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 | 0.000 |
| 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 | 0.001 | 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".