Effects of Hypercapnia on Myocardial Blood Flow in Healthy Human Subjects
Bibliographic record
Abstract
Elevation of the end-tidal partial pressure of CO2 (PETco2) increases cerebral and myocardial blood flow (MBF), suggesting that it may be a suitable alternative to pharmacologic stress or exercise for myocardial perfusion imaging. The purpose of this study was to document the pharmacodynamics of CO2 for MBF using prospective end-tidal targeting to precisely control arterial Pco2 and PET to measure the outcome variable, MBF. Methods: Ten healthy men underwent serial 82Rb PET/CT imaging. Imaging was performed at rest and during 6-min hypercapnic plateaus (baseline; PETco2 at 50, 55, and 60 mm Hg; repeat of PETco2 at 60 mm Hg; and repeat of baseline). MBF was measured using 82Rb injected 3 min after the beginning of hypercapnia and a 1-tissue-compartment model with flow-dependent extraction correction. Results were compared with those obtained during an adenosine stress test (140 μg/kg/min). Results: Baseline PETco2 was 38.9 ± 0.8 (mean ± SD) mm Hg (range, 35–43 mm Hg). All PETco2 targets were sustained, with SDs of less than 1.5 mm Hg. Heart rate, systolic blood pressure, rate × pressure product, and respiratory frequency increased with progressive hypercapnia. MBF increased significantly at each level of hypercapnia to 1.92-fold over baseline (0.86 ± 0.24 vs. 0.45 ± 0.08 mL/min/g; P = 0.002) at a PETco2 of 60 mm Hg. MBF after the administration of adenosine was significantly greater than that with the maximal hypercapnic stimulus (2.00 vs. 0.86 mL/min/g; P < 0.0001). Conclusion: To our knowledge, this study is the first to assess the response of MBF to different levels of hypercapnia in healthy humans with PET. MBF increased with increasing levels of hypercapnia; MBF at a PETco2 of 60 mm Hg was double that at baseline.
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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".