Pulmonary capillary surface area in supine exercising humans: demonstration of vascular recruitment
Bibliographic record
Abstract
In exercising humans, cardiac output (CO) increases, with minor increases in pulmonary artery pressure (PAP). It is unknown if the CO is accommodated via distention of already perfused capillaries or via recruitment of nonconcomitantly perfused pulmonary capillaries. Ten subjects (9 female) performed symptom-limited exercise. Six had resting mean PAP (PAPm) <20 mmHg, and four had PAPm between 21 and 24 mmHg. The first-pass pulmonary circulatory metabolism of [3H]benzoyl-Phe-Ala-Pro (BPAP) was measured at rest and at peak exercise, and functional capillary surface area (FCSA) was calculated. Data are means ± SD. Mean pulmonary arterial pressure rose from 18.8 ± 3.3 SD mmHg to 28.5 ± 4.6 SD mmHg, CO from 6.4 ± 1.6 to 13.4 ± 2.9 L/min, and pulmonary artery wedge pressure from 14 ± 3.3 to 19.5 ± 5 mmHg (all P ≤ 0.001). Percent BPAP metabolism fell from 74.7 ± 0.1% to 67.1 ± 0.1%, and FCSA/body surface area (BSA) rose from 2,939 ± 640 to 5,018 ± 1,032 mL·min−1·m−2 (all P < 0.001). In nine subjects, the FCSA/BSA-to-CO relationship suggested principally capillary recruitment and not distention. In subject 10, a marathon runner, resting CO and FCSA/BSA were high, and increases with exercise suggested distention. Exercising humans demonstrate pulmonary capillary recruitment and distention. At moderate resting CO, increasing blood flow causes principally recruitment while, based on one subject, when exercise begins at high CO, further increases appear to cause distention. Our findings clarify an important physiologic question. The technique may provide a means for further understanding exercise physiology, its limitation in pulmonary hypertension, and responses to therapy.
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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 | 0.001 |
| 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".