Myotubes originating from single fast and slow satellite cells display similar patterns of AChE expression
Bibliographic record
Abstract
Slow- and fast-contracting skeletal muscles of both rats and mice display significant differences in their patterns of acetylcholinesterase (AChE) expression. Although neural influences are known to account for a large proportion of these differences, intrinsic variations between fast and slow myogenic precursor cells have been implicated. In the present study, we have capitalized on the use of Immorto transgenic mice to obtain single myogenic precursor cells isolated from either slow or fast muscle fibers and determined whether these cells generated myotubes that produced distinct patterns of AChE expression as observed in vivo between slow and fast muscles. These two myotube populations displayed similar cell-associated and secreted AChE enzyme activity as well as comparable levels of AChE transcripts. Both myotube populations also expressed nearly identical molecular form profiles. By contrast, AChE activity and transcript levels were approximately two- and fivefold greater in fast skeletal muscles compared with slow ones. Together, these findings indicate that differences in AChE expression between fast and slow muscles are not due to inherent differences in myogenic precursor cells, thereby suggesting that other factors, such as innervation, play a predominant role in establishing the distinct patterns of AChE expression in these muscle types.
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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) | 0.001 | 0.001 |
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".