COMPARISON OF TECHNIQUES TO MEASURE MUSCLE GLYCOGEN USE
Bibliographic record
Abstract
Most researchers use muscle biopsies to assess muscle glycogen utilisation. An alternative method involves partitioning oxidation of the various endogenous and exogenous glucose sources with a 13C glucose tracer. PURPOSE To compare measurements of muscle glycogen utilisation during exercise using biopsy and biochemical analyses with the 13C technique. METHODS Eight trained cyclists (23 ± 5 yrs, 75.4 ± 8.8 kg, 59.6 ± 4.1 ml.min-1.kg-1; peak power 345 ± 44 W; lean body mass (DEXA) 62.91 ± 8.24 kg) conducted two 75 min exercise trials at 80% VO2max and 50% VO2max. Vastus lateralis biopsies were taken before and after exercise. To prime the bicarbonate pool a small amount of 13C glucose (150 ml; 2 g, +400% d VPDB) was consumed prior to warm-up (30 min at 30% VO2max) and at 15 min. Enriched CHO drinks (150–175 ml; 2 g CHO, +400% d VPDB) were consumed every 15 min during 75 min of 80% VO2max and 50% VO2max cycling. Blood glucose and breath CO2 samples were collected for 13C enrichment analysis with mass spectrometry. Biochemical determination of muscle glycogen concentration was combined with calculated adipose tissue free leg skeletal muscle mass from DEXA (17.27 ± 3.37 kg) to derive rate of glycogen utilisation. RESULTS There was a strong correlation between the rates of total body muscle glycogen utilisation using the 13C technique and that derived from the biochemical and DEXA data (R2 = 0.75, P <0.001). Rates of glycogen utilization were similar with biochemical and 13C techniques, resp. (50% VO2max: 1.2 ± 0.4; 1.4 ± 0.3; 80% VO2max: 2.8 ± 0.9; 3.0 ± 0.7 g.min-1). CONCLUSION The 13C technique gives a valid measure of muscle glycogen utilisation.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".