Sildenafil improves microvascular O<sub>2</sub> delivery-to-utilization matching and accelerates exercise O<sub>2</sub> uptake kinetics in chronic heart failure
Bibliographic record
Abstract
Nitric oxide (NO) can temporally and spatially match microvascular oxygen (O(2)) delivery (Qo(2mv)) to O(2) uptake (Vo(2)) in the skeletal muscle, a crucial adjustment-to-exercise tolerance that is impaired in chronic heart failure (CHF). To investigate the effects of NO bioavailability induced by sildenafil intake on muscle Qo(2mv)-to-O(2) utilization matching and Vo(2) kinetics, 10 males with CHF (ejection fraction = 27 ± 6%) undertook constant work-rate exercise (70-80% peak). Breath-by-breath Vo(2), fractional O(2)extraction in the vastus lateralis {∼deoxygenated hemoglobin + myoglobin ([deoxy-Hb + Mb]) by near-infrared spectroscopy}, and cardiac output (CO) were evaluated after sildenafil (50 mg) or placebo. Sildenafil increased exercise tolerance compared with placebo by ∼20%, an effect that was related to faster on- and off-exercise Vo(2) kinetics (P < 0.05). Active treatment, however, failed to accelerate CO dynamics (P > 0.05). On-exercise [deoxy-Hb + Mb] kinetics were slowed by sildenafil (∼25%), and a subsequent response "overshoot" (n = 8) was significantly lessened or even abolished. In contrast, [deoxy-Hb + Mb] recovery was faster with sildenafil (∼15%). Improvements in muscle oxygenation with sildenafil were related to faster on-exercise Vo(2) kinetics, blunted oscillations in ventilation (n = 9), and greater exercise capacity (P < 0.05). Sildenafil intake enhanced intramuscular Qo(2mv)-to-Vo(2) matching with beneficial effects on Vo(2) kinetics and exercise tolerance in CHF. The lack of effect on CO suggests that improvement in blood flow to and within skeletal muscles underlies these effects.
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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) | 0.001 | 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".