Motor Unit Survival in Lifelong Runners Is Muscle Dependent
Bibliographic record
Abstract
UNLABELLED: A contributing factor to the loss of muscle mass and strength with adult aging is the reduction in the number of functioning motor units (MUs). Recently, we reported that lifelong competitive runners (master runners = ~66 yr) had greater numbers of MUs in a leg muscle (tibialis anterior) than age-matched recreationally active controls. This suggested that long-term exposure to high levels of physical activity may limit the loss of MU numbers with adult aging. However, it is unknown if this finding is the result of long-term activation of the specifically exercised motoneuron pool (i.e., tibialis anterior) or an overall systemic neuroprotective effect of high levels of physical activity. PURPOSE: The purpose was to estimate the number of functioning MUs (MUNEs) in the biceps brachii (an upper body muscle not directly loaded by running) of nine young (27 ± 5 yr) and nine old (70 ± 5 yr) men and nine lifelong competitive master runners (67 ± 4 yr). METHODS: Decomposition-enhanced spike-triggered averaging was used to measure surface and intramuscular EMG signals during elbow flexion at 10% of maximum voluntary isometric contraction. RESULTS: Derived MUNEs were lower in the biceps brachii of runners (185 ± 69 MUs) and old men (133 ± 69 MUs) than the young (354 ± 113 MUs), but the old and master runners were similar. CONCLUSIONS: Although there were no significant differences in MUNE between both older groups in the biceps brachii muscle, with the number of subjects tested here, we cannot eliminate the possibility of some whole-body neuroprotective effect. However, when compared with the remote biceps muscle, a greater influence on age-related spinal motoneuron survival was found in a chronically activated MN pool specific to the exercised muscle.
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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".