Do multiple ionic interactions contribute to skeletal muscle fatigue?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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 | 0.001 |
| 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 it