Fiber type differences in O <sub>2</sub> cost of force development during fatigue in isolated single fibers
Bibliographic record
Abstract
One purported contributor to the VO 2 slow component is that some lower threshold motor units may fatigue but continue to utilize O 2 , resulting in uncoupling of oxidative phosphorylation and force generation. The present study utilized intact isolated single myocytes of differing fatigue resistance to investigate the relationship between fatigue, tension development, and aerobic metabolism (assessed by intracellular PO 2 ; P i O 2 ) during a progressive electrically stimulated tetanic contraction protocol. Single Xenopus fibers were grouped by contraction frequency required to elicit fatigue to 60% of initial force into 0.5 Hz (fast fatiguing; FF) and 1 Hz (slow fatiguing; SF) subgroups. Phosphorescence quenching was used to determine ΔP i O 2 (a proxy for VO 2 ) and developed isometric tension was monitored to allow calculation of the time‐tension integral (TxT). Absolute peak force (p = 0.06) and peak ΔP i O 2 (p = 0.36) were not different between groups, but time to 60% of peak was significantly (p < 0.05) longer in SF vs. FF. Prior to fatigue, both ΔP i O 2 and TxT rose proportionally with contraction frequency in SF and FF ‐ therefore ΔP i O 2 /TxT was identical between groups. At fatigue, TxT fell dramatically in both groups, but ΔP i O 2 decreased a proportionate amount only in the FF group, resulting in an increase in ΔP i O 2 /TxT in the SF relative to the pre‐fatigue condition. These data show that more fatigue resistant fibers better maintain aerobic metabolism when they fatigue, resulting in an increased O 2 cost of contractions that could contribute to the VO 2 slow component in whole body exercise. Supported by NIH AR 40155, NSERC RPG238805, CIHR and Parker B. Francis Foundation
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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) | 0.002 | 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".