Glycogenin protein level in muscle fiber types of stimulated rodent hind limb following changes in glycogen content
Bibliographic record
Abstract
Glycogenin (GN) may be degraded, may stay bound to partly‐catabolized glycogen or may form unglycosylated GN when glycogen content decreases. Oxidative and glycolytic muscle fibers may respond differently. One hind limb (40 Female Sprague Dawley rats) was stimulated to contract for 40 min to lower glycogen content. The soleus (SOL), red (RG) and white (WG) gastrocnemius muscles from unstimulated and stimulated limbs were examined for total and unglycosylated GN protein levels. Muscle tissue (corresponding unstimulated and stimulated) was grouped (n=6) by post‐stimulation glycogen content (WGI <40; WGII <100; RGI <100, RGII >100; SOL >100 μmol▸g dry wt. −1 ). GN increased (P≤0.05) in WGI (66%), RGI (33%), and RGII (100%), but was unchanged in WGII and SOL. In WGII, GN decreased in a low‐spin myofibrillar pellet fraction and increased in the soluble fraction (no net change). Unglycosylated GN was detected in stimulated W and RG muscles (up to 25% of total GN) and was most abundant in WGI (lowest glycogen content). In summary, no correlation existed between GN level and glycogen content post‐stimulation either across fiber types or within a fiber type. No net degradation of GN occurred in either W or RG muscles in response to significant reduction in glycogen content; new GN was always synthesized. Pre‐existing GN is likely retained in smaller granules or as unglycosylated protein. Funded by NSERC of Canada.
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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".