Exercise and fatigue: integrating the role of K+, Na+ and Cl− in the regulation of sarcolemmal excitability of skeletal muscle
Bibliographic record
Abstract
Abstract Perturbations in K + have long been considered a key factor in skeletal muscle fatigue. However, the exercise-induced changes in K + intra-to-extracellular gradient is by itself insufficiently large to be a major cause for the force decrease during fatigue unless combined to other ion gradient changes such as for Na + . Whilst several studies described K + -induced force depression at high extracellular [K + ] ([K + ] e ), others reported that small increases in [K + ] e induced potentiation during submaximal activation frequencies, a finding that has mostly been ignored. There is evidence for decreased Cl − ClC-1 channel activity at muscle activity onset, which may limit K + -induced force depression, and large increases in ClC-1 channel activity during metabolic stress that may enhance K + induced force depression. The ATP-sensitive K + channel (K ATP channel) is also activated during metabolic stress to lower sarcolemmal excitability. Taking into account all these findings, we propose a revised concept in which K + has two physiological roles: (1) K + -induced potentiation and (2) K + -induced force depression. During low-moderate intensity muscle contractions, the K + -induced force depression associated with increased [K + ] e is prevented by concomitant decreased ClC-1 channel activity, allowing K + -induced potentiation of sub-maximal tetanic contractions to dominate, thereby optimizing muscle performance. When ATP demand exceeds supply, creating metabolic stress, both K ATP and ClC-1 channels are activated. K ATP channels contribute to force reductions by lowering sarcolemmal generation of action potentials, whilst ClC-1 channel enhances the force-depressing effects of K + , thereby triggering fatigue. The ultimate function of these changes is to preserve the remaining ATP to prevent damaging ATP depletion.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".