Hypercapnia is essential to reduce the cerebral oxidative metabolism during extreme apnea in humans
Bibliographic record
Abstract
The cerebral metabolic rate of oxygen (CMRO 2 ) is reduced during apnea that yields profound hypoxia and hypercapnia. In this study, to dissociate the impact of hypoxia and hypercapnia on the reduction in CMRO 2 , 11 breath-hold competitors completed three apneas under: (a) normal conditions (NM), yielding severe hypercapnia and hypoxemia, (b) with prior hyperventilation (HV), yielding severe hypoxemia only, and (c) with prior 100% oxygen breathing (HX), yielding the greatest level of hypercapnia, but in the absence of hypoxemia. The CMRO 2 was calculated from the product of cerebral blood flow (ultrasound) and the radial artery-jugular venous oxygen content difference (cannulation). Secondary measures included net-cerebral glucose/lactate exchange and nonoxidative metabolism. Reductions in CMRO 2 were largest in the HX condition (−44 ± 15%, p < 0.05), with the most severe hypercapnia (PaCO 2 = 58 ± 5 mmHg) but maintained oxygen saturation. The CMRO 2 was reduced by 24 ± 27% in NM ( p = 0.05), but unchanged in the HV apnea where hypercapnia was absent. A net-cerebral lactate release was observed at the end of apnea in the HV and NM condition, but not in the HX apnea (main effect p < 0.05). These novel data support hypercapnia/pH as a key mechanism mediating reductions in CMRO 2 during apnea, and show that severe hypoxemia stimulates lactate release from the brain.
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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 | 0.001 |
| 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".