Expression of myogenic regulatory factors is not different between whole muscle and isolated human muscle satellite cells following eccentric contractions
Bibliographic record
Abstract
Skeletal muscle satellite cells (SC) play an important role in muscle repair following injury. The regulation of SC activity is governed by myogenic regulatory factors (MRF), including MyoD, Myf5, myogenin and MRF4. The mRNA expression of these MRF in humans following muscle damage has been predominately measured in whole muscle homogenate. Whether the temporal expression of MRF in whole muscle homogenate accurately reflects SC specific expression of MRF remains largely unknown. Fourteen young men (21±0.5 yrs) performed 300 unilateral eccentric contractions (180 deg·s − 1 ) of the knee extensors on a Biodex dynamometer. Percutaneous muscle biopsies from the vastus lateralis were taken prior to (Pre) and 48h post‐exercise. Fluorescence‐activated cell sorting (FACS) analysis was utilized to purify a population of NCAM + muscle SC from the whole muscle homogenate. 48h post‐eccentric exercise, MyoD mRNA expression increased in whole muscle homogenate (~2.0‐fold, p<0.05) and in isolated SC (~2.2‐fold, p<0.05). Myf5 mRNA expression increased in whole muscle homogenate (~3.0‐fold, p<0.05) and in isolated SC (~3.2‐fold, p<0.05). Myogenin mRNA expression increased in whole muscle homogenate (~1.9‐fold, p<0.05) and in isolated SC (~12.0‐fold, p<0.05). MRF4 mRNA expression was not increased in 48h post‐exercise whole muscle homogenate or in isolated SC (p>0.05). These results suggest that the temporal mRNA expression of MRF in a population of isolated muscle SC accurately reflects the expression observed in whole muscle homogenate. Whole muscle homogenate represents a reliable proxy for the temporal expression of MRF in human muscle satellite cells. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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".