The O<sub>2</sub>cost of the tension-time integral in isolated single myocytes during fatigue
Bibliographic record
Abstract
One proposed explanation for the Vo(2) slow component is that lower-threshold motor units may fatigue and develop little or no tension but continue to use O(2), thereby resulting in a dissociation of cellular respiration from force generation. The present study used intact isolated single myocytes with differing fatigue resistance profiles to investigate the relationship between fatigue, tension development, and aerobic metabolism. Single Xenopus skeletal muscle myofibers were allocated to a fast-fatiguing (FF) or a slow-fatiguing (SF) group, based on the contraction frequency required to elicit a fall in tension to 60% of peak. Phosphorescence quenching of a porphyrin compound was used to determine Delta intracellular Po(2) (Pi(O(2)); a proxy for Vo(2)), and developed isometric tension was monitored to allow calculation of the time-integrated tension (TxT). Although peak DeltaPi(O(2)) was not different between groups (P = 0.36), peak tension was lower (P < 0.05) in SF vs. FF (1.97 +/- 0. 17 V vs. 2. 73 +/- 0.30 V, respectively) and time to 60% of peak tension was significantly longer in SF vs. FF (242 +/- 10 s vs. 203 +/- 10 s, respectively). Before fatigue, both DeltaPi(O(2)) and TxT rose proportionally with contraction frequency in SF and FF, resulting in DeltaPi(O(2))/TxT being identical between groups. At fatigue, TxT fell dramatically in both groups, but DeltaPi(O(2)) decreased proportionately only in the FF group, resulting in an increase in DeltaPi(O(2))/TxT in the SF group relative to the prefatigue condition. These data show that more fatigue-resistant fibers maintain aerobic metabolism as they fatigue, resulting in an increased O(2) cost of contractions that could contribute to the Vo(2) slow component seen in whole body 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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".