Postexercise serum hepcidin response to repeated sprint exercise under normoxic and hypoxic conditions
Bibliographic record
Abstract
We determined the effects of repeated sprint exercise under normoxic and hypoxic conditions on serum hepcidin levels. Ten male athletes (age: 20.9 ± 0.3 years; height: 175.7 ± 6.0 cm; weight: 67.3 ± 6.3 kg) performed 2 exercise trials under normoxic (NOR; fraction of inspiratory oxygen (FiO2): 20.9%) or hypoxic conditions (HYPO; FiO2: 14.5%). The exercise consisted of 3 sets of 5 × 6 s of maximal pedaling (30-s rest periods between sprints, 10-min rest periods between sets). Blood samples were collected before exercise, immediately after exercise, and 1 and 3 h after exercise. Serum hepcidin levels were significantly elevated after exercise in both trials (both P < 0.01), with no significant difference between the trials. The postexercise blood lactate levels were significantly higher in the HYPO than the NOR (P < 0.05). Both trials caused similar increases in plasma interleukin-6 and serum iron levels (P < 0.001), with no significant difference between the trials. A significant interaction (trial × time) was apparent in terms of serum erythropoietin (EPO) levels (P = 0.003). The EPO level was significantly higher in the HYPO than the NOR at 3 h after exercise (P < 0.05). In conclusion, repeated sprint exercise significantly increased serum hepcidin levels to similar extent in 2 trials, despite differences in the inspired oxygen concentrations during both the exercise and the 3-h postexercise period.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".