The interactive effects of K <sup>+</sup> and Cl <sup>−</sup> on force generation in skeletal muscles: impact at the onset of exercise
Bibliographic record
Abstract
It is now established that interstitial [K + ] ([K + ] int ) increases to 12–14 mM even during moderate exercise. Although it is known that such increases in [K + ] int contribute to the decrease in force during fatigue, recent studies suggest that at the onset of exercise small increases in [K + ] int actually potentiates force and K + ‐induced force depression is prevented by decreases in Cl − conductance (G Cl ). However, most studies were carried out at temperatures ranging from 25°–30°C or under very specific stimulation frequencies. The objective of this study was to document how the [K + ] int – force relationship is modulated at 37°C, at different frequencies and under different G Cl in order to better understand how muscle performance can be maximized at the onset of muscle activity. At a stimulation frequency allowing for maximal tetanic force, soleus muscle started to lose force at 10 and was completely lost by 13. EDL muscles were less sensitive to [K + ] int , such that loss of force started at 13 and was completely lost by 15mM. The critical [K + ] int required for tetanic force depression to start at 37°C is much higher than the critical [K + ] int reported at lower temperatures. Twitch force was potentiated by 80–100% as [K + ] int was elevated from 8–11 for soleus and from 8–13.5mM in EDL, while further increases in [K + ] int caused a gradual decline in twitch force. Potentiation up to 150% can also occur at frequencies of 1–30Hz in soleus, and 1–100Hz for EDL. The major effect of reducing G Cl was an increase in the critical [K + ] int that caused a depression of force.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".