Substantial working muscle glycerol turnover during two-legged cycle ergometry
Bibliographic record
Abstract
We combined tracer and arteriovenous (a-v) balance techniques to evaluate the effects of exercise and endurance training on leg triacylglyceride turnover as assessed by glycerol exchange. Measurements on an exercising leg were taken to be a surrogate for working skeletal muscle. Eight men completed 9 wk of endurance training [5 days/wk, 1 h/day, 75% peak oxygen consumption (Vo(2peak))], with leg glycerol turnover determined during two pretraining trials [45 and 65% Vo(2peak) (45% Pre and 65% Pre, respectively)] and two posttraining trials [65% of pretraining Vo(2peak) (ABT) and 65% of posttraining Vo(2peak) (RLT)] using [(2)H(5)]glycerol infusion, femoral a-v sampling, and measurement of leg blood flow. Endurance training increased Vo(2peak) by 15% (45.2 +/- 1.2 to 52.0 +/- 1.8 mlxkg(-1)xmin(-1), P < 0.05). At rest, there was tracer-measured leg glycerol uptake (41 +/- 8 and 52 +/- 15 micromol/min for pre- and posttraining, respectively) even in the presence of small, but significant, net leg glycerol release (-68 +/- 19 and -50 +/- 13 micromol/min, respectively; P < 0.05 vs. zero). Furthermore, while there was no significant net leg glycerol exchange during any of the exercise bouts, there was substantial tracer-measured leg glycerol turnover during exercise (i.e., simultaneous leg muscle uptake and leg release) (uptake, release: 45% Pre, 194 +/- 41, 214 +/- 33; 65% Pre, 217 +/- 79, 201 +/- 84; ABT, 275 +/- 76, 312 +/- 87; RLT, 282 +/- 83, 424 +/- 75 micromol/min; all P < 0.05 vs. corresponding rest). Leg glycerol turnover was unaffected by exercise intensity or endurance training. In summary, simultaneous leg glycerol uptake and release (indicative of leg triacylglyceride turnover) occurs despite small or negligible net leg glycerol exchange, and furthermore, leg glycerol turnover can be substantially augmented during exercise.
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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".