Neuromuscular Activity Determines, at Least in Part, the Motoneuron, Nerve and Muscle Properties Under Normal Conditions and After Nerve Injury
Bibliographic record
Abstract
Whether pattern or amount of daily activity determines neuromuscular properties is the focus of this review. The fast-to-slow conversion of many properties of fast-twitch muscles, by stimulating their nerves electrically with the continuous low-frequency pattern typical of slow motoneurons, argued that muscle properties are determined by their pattern of activity. However, the composition of the motor units (MUs) in almost all muscles is heterogeneous, with the MUs grouped into slow, fast-fatigue-resistant and fast-fatigable types that match corresponding histochemical fiber types. Nonetheless, their contractile forces lie on a continuum, with MUs recruited into activity in order of their size. This ‘size principle’ of MU organization and function applies in normally innervated and reinnervated muscles and, importantly, begs the question of whether it is the amount rather than the pattern of the MU activation that determines their properties. Experimental evidence that uniform daily amounts of ~<0.5, 5%, and 50% ES, converted motoneuron, nerve, and muscle properties to one physiological and histochemical type, argued in favor of the amount of activity determining MU properties. Yet, that the properties were not confined to the expected narrow range argued that factors other than the pattern and/or amount of neuromuscular activity must be considered. These include the progressive increase in the synaptic inputs onto motoneurons. The range of the effects of endurance and intermittent exercise programs on healthy subjects and those suffering nerve injuries and disease is also consistent with the argument that factors other than pattern or amount of neuromuscular activity should be investigated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".