Modulation of Force Development by Na<sup>+</sup>, K<sup>+</sup>, Na<sup>+</sup> K<sup>+</sup> Pump and K<sub>ATP</sub> Channel During Muscular Activity
Bibliographic record
Abstract
Extracellular K+ concentration increases during exercise and especially during fatigue development. It has been proposed that K+ is an important factor in the etiology of skeletal muscle fatigue because it suppresses membrane excitability and eventually force development. Based on the effect of K+, it has then been proposed the Na+ K+ pump reduces increases in extracellular K+ concentration while the ATP-sensitive K+ channel (KATP channel) allows for rapid increases in extracellular K+ to suppress force development when ATP levels start to fall or when the levels of metabolic end-products become high. However, recent studies have now demonstrated that an increase in extracellular K+ concentration can be advantageous to muscle during exercise because it not only stimulates vasodilation and the exercise pressor reflex, but it also potentiates force development when the Na+ concentration gradient is maintained. A new hypothesis is therefore proposed in which the Na+ K+ pump is important in maintaining the Na+ concentration gradient (and not the K+ concentration gradient as previously suggested), while the activation of KATP channels is important to increase the K+ efflux and extracellular concentration. This situation then optimizes the development of force during exercise. Another hypothesis is proposed in which more KATP channels are activated while the activity of the Na+ K+ pump is reduced when ATP levels start to decrease to allow for an accumulation of intracellular Na+ and further increases in extracellular K+ concentration. These concentration changes then reduce membrane excitability and force development (i.e., fatigue) to protect muscle against large ATP depletion and function impairment.
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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".