Physiological aspects of the subcellular localization of glycogen in skeletal muscle
Bibliographic record
Abstract
Glucose is stored in skeletal muscle fibers as glycogen, a branched-chain polymer observed in electron microscopy images as roughly spherical particles (known as β-particles of 10-45 nm in diameter), which are distributed in distinct localizations within the myofibers and are physically associated with metabolic and scaffolding proteins. Although the subcellular localization of glycogen has been recognized for more than 40 years, the physiological role of the distinct localizations has received sparse attention. Recently, however, studies involving stereological, unbiased, quantitative methods have investigated the role and regulation of these distinct deposits of glycogen. In this report, we review the available literature regarding the subcellular localization of glycogen in skeletal muscle as investigated by electron microscopy studies and put this into perspective in terms of the architectural, topological, and dynamic organization of skeletal muscle fibers. In summary, the distribution of glycogen within skeletal muscle fibers has been shown to depend on the fiber phenotype, individual training status, short-term immobilization, and exercise and to influence both muscle contractility and fatigability. Based on all these data, the available literature strongly indicates that the subcellular localization of glycogen has to be taken into consideration to fully understand and appreciate the role and regulation of glycogen metabolism and signaling in skeletal muscle. A full understanding of these phenomena may prove vital in elucidating the mechanisms that integrate basic cellular events with changing glycogen content.
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How this classification was reachedexpand
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) | 0.001 | 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 | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".