The importance of decreased Cl <sup>‐</sup> channel activity in preventing K <sup>+</sup> ‐induced force depression at the onset of muscle activity (1102.17)
Bibliographic record
Abstract
Interstitial [K + ] reaches 10‐12 mM even in non‐fatiguing muscle activity. At low stimulation frequencies (1‐90 Hz) and 37°C, peak force is potentiated at these [K + ], and force depression in mouse EDL muscle occurs only when [K + ] exceeds 13 mM. Partial decreases in Cl ‐ channel activity allows for force recovery at elevated [K + ] while complete block of Cl ‐ channel activity worsens the K + ‐induced force depression. The objective of this study was to determine the optimal level of Cl ‐ channel activity or conductance (GCl). Increasing K + from 4.7 (control) to 13 mM reduced peak force by 75%. Subsequent exposure to 9‐AC, a ClC‐1 Cl ‐ channel blocker, allowed for an increase in force being 25%, 26% and 43% at 6.5, 10 and 20 µM 9‐AC, respectively. At 40 and 100 µM 9‐AC, the force increases were smaller, being 15% and 9%, respectively; i.e., an optimum effect was observed at 20 µM 9‐AC. Interestingly, at 20 µM 9‐AC, the GCl is expected to be about 30% of the normal GCl at rest. More importantly, there is evidence for a 70% decrease in GCl at the onset of a muscle activity. We therefore suggest that this decrease in GCl at the onset of exercise play a critical role in preventing K + , which rapidly reaches 10‐12 mM, to depress membrane excitability and force generation and in fact maintain the K + ‐induced force thereby improving muscle performance. Grant Funding Source : CIHR
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".