Lifelong exercise is associated with more homogeneous motor unit potential features across deep and superficial areas of vastus lateralis
Bibliographic record
Abstract
Motor unit (MU) expansion enables rescue of denervated muscle fibres helping to ameliorate age-related muscle atrophy, with evidence to suggest master athletes are more successful at this remodelling. Electrophysiological data has suggested MUs located superficially are larger than those located deeper within young muscle. However, the effects of ageing and exercise on MU heterogeneity across deep and superficial aspects of vastus lateralis (VL) remain unclear. Intramuscular electromyography was used to record individual MU potentials (MUPs) and near fibre MUPs (NFMs) from deep and superficial regions of the VL during 25% maximum voluntary contractions, in 83 males (15 young (Y), 17 young athletes (YA), 22 old (O) and 29 master athletes (MA)). MUP size and complexity were assessed using area and number of turns, respectively. Multilevel mixed effects linear regression models were performed to investigate the effects of depth in each group. MUP area was greater in deep compared with superficial MUs in Y (p<0.001) and O (p=0.012) but not in YA (p=0.071) or MA (p=0.653). MUP amplitude and NF MUP area were greater, and MUPs were more complex in deep MUPs from Y, YA and O (all p<0.05) but did not differ across depth in MA (all p>0.07). These data suggest MU characteristics differ according to depth within the VL which may be influenced by both ageing and exercise. A more homogenous distribution of MUP size and complexity across muscle depths in older athletes may be a result of a greater degree of age-related MU adaptations.
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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 | 0.000 |
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".