The effects of training in hyperoxia vs. normoxia on skeletal muscle enzyme activities and exercise performance
Bibliographic record
Abstract
Inspiring a hyperoxic (H) gas permits subjects to exercise at higher power outputs while training, but there is controversy as to whether this improves skeletal muscle oxidative capacity, maximal O(2) consumption (Vo(2 max)), and endurance performance to a greater extent than training in normoxia (N). To determine whether the higher power output during H training leads to a greater increase in these parameters, nine recreationally active subjects were randomly assigned in a single-blind fashion to train in H (60% O(2)) or N for 6 wk (3 sessions/wk of 10 x 4 min at 90% Vo(2 max)). Training heart rate (HR) was maintained during the study by increasing power output. After at least 6 wk of detraining, a second 6-wk training protocol was completed with the other breathing condition. Vo(2 max) and cycle time to exhaustion at 90% of pretraining Vo(2 max) were tested in room air pre- and posttraining. Muscle biopsies were sampled pre- and posttraining for citrate synthase (CS), beta-hydroxyacyl-coenzyme A dehydrogenase (beta-HAD), and mitochondrial aspartate aminotransferase (m-AsAT) activity measurements. Training power outputs were 8% higher (17 W) in H vs. N. However, both conditions produced similar improvements in Vo(2 max) (11-12%); time to exhaustion (approximately 100%); and CS (H, 30%; N, 32%), beta-HAD (H, 23%; N, 21%), and m-AsAT (H, 21%; N, 26%) activities. We conclude that the additional training stimulus provided by training in H was not sufficient to produce greater increases in the aerobic capacity of skeletal muscle and whole body Vo(2 max) and exercise performance compared with training in N.
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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) | 0.002 | 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".