Preservation of Motor Unit Number Estimates (MUNEs) in Masters Runners is Muscle Dependent
Bibliographic record
Abstract
A contributing factor to the loss of muscle mass and strength associated with adult aging is the reduction in the number of functioning motor units (MUs). Previously, we reported life-long competitive runners (∼66 yrs) have greater numbers of functioning MUs in a leg muscle (tibialis anterior) compared with recreationally active age-matched controls (Power et al. 2010). This finding suggests chronic exposure to high levels of physical activity may maintain MU health into the seventh decade of life. However, it is unknown whether this finding is muscle-dependent (chronically exercised muscle is affected), or whether the effects can be extrapolated to other muscle groups due to an overall neuro-protective effect. PURPOSE: Thus, the purpose was to estimate the number of functioning MUs in the biceps brachii (upper limb muscle not directly loaded by running) of 8 young (∼27 yrs) and 6 old (∼70 yrs) recreationally active men and to compare these groups with 9 master runners (∼67 yrs). METHODS: Decomposition-enhanced spike-triggered averaging was used to collect surface and intramuscular electromyography signals from the biceps brachii during 30-s sustained elbow flexion contractions at 10% of maximum voluntary isometric contraction (MVC). Ratios of averaged surface MU EMG potentials to the whole muscle electrical potential (M-wave) were calculated to estimate the number of functioning MUs in the biceps brachii muscle. RESULTS: The estimated number of functioning MUs were lower in the master runners (185 ± 66 MUs) and old (145 ± 76 MUs) men compared with the young (357 ± 133 MUs), with no difference between the old and masters runners. CONCLUSION: In contrast to our earlier observations in the tibilais anterior muscle, the numbers of functioning MUS were lower in the biceps brachii of both older groups than the young men. This suggests that the positive effects of exercise on MU survival may be dependent upon direct and chronic activation of a specific MU pool. Supported by NLCAHR and NSERC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".