Effect of training intensity on muscle lactate transporters and lactate threshold of cross‐country skiers
Bibliographic record
Abstract
The training intensity may affect the monocarboxylate transporters MCT1 and MCT4 in skeletal muscle. Therefore, 20 elite cross-country skiers (11 men and nine women) trained hard for 5 months at either moderate (MIG, 60-70% of VO2max) or high intensity (HIG, 80-90%). The lactate threshold, several performance parameters, and the blood lactate concentration (cLa) after exhausting treadmill running were also determined. Muscle biopsies taken from the vastus lateralis muscle before and after the training period were analysed for the two MCTs and for muscle fibre types and six enzymes. The concentration of MCT1 did not change for HIG (P=0.3) but fell for MIG (-12 +/- 3%, P=0.01); the training response differed between the two groups (P=0.05). The concentration of MCT4 did not change during the training period (P > 0.10). The concentration of the two MCTs did not differ between the two sexes (P=0.9). The running speed at the lactate threshold rose for HIG (+3.2 +/- 0.9%, P=0.003), while no change was seen for MIG (P=0.54); the training response differed between the two groups (P=0.04). The cLa after long-lasting exhausting treadmill running correlated with the concentration of MCT1 (rs=0.69, P=0.002), but not with that of MCT4 (rs=0.2, P=0.2). There were no other significant correlations between the concentrations of the two MCTs and the performance parameters, muscle fibre types, or enzymes (r < or = 0.36, P > 0.10). Thus, the training response differed between MIG and HIG both in terms of performance and of the effect on MCT1. Training at high intensity may be more effective for cross-country skiers. Finally, MCT1 may be important for releasing lactate to the blood during long-lasting exercise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".