The concept of skeletal muscle memory: Evidence from animal and human studies
Bibliographic record
Abstract
Within the current paradigm of the myonuclear domain theory, it is postulated that a linear relationship exists between muscle fibre size and myonuclear content. The myonuclear domain is kept (relatively) constant by adding additional nuclei (supplied by muscle satellite cells) during muscle fibre hypertrophy and nuclear loss (by apoptosis) during muscle fibre atrophy. However, data from recent animal studies suggest that myonuclei that are added to support muscle fibre hypertrophy are not lost within various muscle atrophy models. Such myonuclear permanence has been suggested to constitute a mechanism allowing the muscle fibre to (re)grow more efficiently during retraining, a phenomenon referred to as "muscle memory." The concept of "muscle memory by myonuclear permanence" has mainly been based on data attained from rodent experimental models. Whether the postulated mechanism also holds true in humans remains largely ambiguous. Nevertheless, there are several studies in humans that provide evidence to potentially support or contradict (parts of) the muscle memory hypothesis. The goal of the present review was to discuss the evidence for the existence of "muscle memory" in both animal and human models of muscle fibre hypertrophy as well as atrophy. Furthermore, to provide additional insight in the potential presence of muscle memory by myonuclear permanence in humans, we present new data on previously performed exercise training studies. Finally, suggestions for future research are provided to establish whether muscle memory really exists in humans.
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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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".