Dynamics of blood lactate concentration during recovery period of short duration‐high intensity exercise in acute hypoxia or normoxia
Bibliographic record
Abstract
We studied dynamics of blood lactate concentration during recovery period of short duration‐high intensity exercise in acute hypoxia or normoxia. University track and field athletes were recruited for single‐blind randomized crossover designed study. All subjects completed wingate test in both acute hypoxia (FiO 2 =0.145) and normoxia (FiO 2 =0.21). Two wingate tests were separated at least 48 hours. After 10 min cycling warm‐up (100 W), subjects took 5 min rests and then conducted 30 sec wingate tests. After the wingate test, subjects of both conditions took 30 min rests in normoxia. Blood samplings were conducted 1 min before and, 0.5, 1, 2, 3, 4, 5, 7, 10, 20 and 30 min after the wingate tests to examine the blood lactate concentration. Peak power output (PPO), mean power output (MPO) and fatigue index (FI) were not different in both condition (PPO; 984.3±49.3 W and 971.0±32.1 W, MPO; 674.3±37.4 W and 680.0±36.3 W, FI; 49.2±7.1% and 49.6±5.2%, normoxia and hypoxia respectively). The AUC of blood lactate, when examined pre to 10 min values, was significantly higher in acute hypoxia than normoxia (p<0.05). The rates of reduction of blood lactate concentration was higher in hypoxia than normoxia(p<0.05, 0.42±0.02 and 0.28±0.03 mmol/L/min, respectively). These results suggested that muscle glycogen breakdown was higher in acute hypoxia than normoxia in spite of same wingate test performances. It also suggests that lactate oxidation may be higher in acute hypoxic group when resting at normoxia. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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".