Do multiple ionic interactions contribute to skeletal muscle fatigue?
Bibliographic record
Abstract
During intense exercise or electrical stimulation of skeletal muscle the concentrations of several ions change simultaneously in interstitial, transverse tubular and intracellular compartments. Consequently the functional effects of multiple ionic changes need to be considered together. A diminished transsarcolemmal K(+) gradient per se can reduce maximal force in non-fatigued muscle suggesting that K(+) causes fatigue. However, this effect requires extremely large, although physiological, K(+) shifts. In contrast, moderate elevations of extracellular [K(+)] ([K(+)](o)) potentiate submaximal contractions, enhance local blood flow and influence afferent feedback to assist exercise performance. Changed transsarcolemmal Na(+), Ca(2+), Cl(-) and H(+) gradients are insufficient by themselves to cause much fatigue but each ion can interact with K(+) effects. Lowered Na(+), Ca(2+) and Cl(-) gradients further impair force by modulating the peak tetanic force-[K(+)](o) and peak tetanic force-resting membrane potential relationships. In contrast, raised [Ca(2+)](o), acidosis and reduced Cl(-) conductance during late fatigue provide resistance against K(+)-induced force depression. The detrimental effects of K(+) are exacerbated by metabolic changes such as lowered [ATP](i), depleted carbohydrate, and possibly reactive oxygen species. We hypothesize that during high-intensity exercise a rundown of the transsarcolemmal K(+) gradient is the dominant cellular process around which interactions with other ions and metabolites occur, thereby contributing to fatigue.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".