Mechanisms of excitation-contraction uncoupling relevant to activity-induced muscle fatigueThis paper is one of a selection of papers published in this Special Issue, entitled 14th International Biochemistry of Exercise Conference – Muscles as Molecular and Metabolic Machines, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
If the free [Ca2+] in the cytoplasm of a skeletal muscle fiber is raised substantially for a period of seconds to minutes or to high levels just briefly, it leads to disruption of the normal excitation-contraction (E-C) coupling process and a consequent long-lasting decrease in force production. It appears that the disruption to the coupling occurs at the triad junction, where the voltage-sensor molecules (dihydropyridine receptors) normally interact with and open the Ca2+ release channels (ryanodine receptors) in the adjacent sarcoplasmic reticulum (SR). This disruption results in inadequate release of SR Ca2+ upon stimulation. Such E-C uncoupling may underlie the long-duration low-frequency fatigue that can occur after various types of exercise, as well as possibly being a contributing factor to the muscle weakness in certain muscle diseases. The process or processes causing the disruption of the coupling between the voltage sensors and the release channels is not known with certainty, but might be associated with structural changes at the triad junction, possibly caused by activation of the Ca2+-dependent protease, micro-calpain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".