Contractile properties of human motor units in health, aging, and disease
Bibliographic record
Abstract
The primary function of skeletal muscle is to produce force for postural control and movement. Although the contractile properties of the whole muscle are useful functional indicators, they do not accurately reflect the heterogeneity of the constituent motor units (MUs) and their changes in health and disease. However, data on the contractile properties of human MUs, in comparison to other animal species, are relatively sparse. This, in part, is due to greater methodological challenges of in vivo studies of MUs in the human. The purpose of this review is to critically appraise the methods used in humans; to describe the normative data from different muscle groups; to discuss differences between data from healthy humans and other animal species; and, last, to characterize changes of the MU contractile properties in aging, disease, and in response to intervention. Because the spike-triggered averaging technique can only be used to study the twitch properties, other methods were subsequently developed to measure a wider range of contractile properties. Although there is general agreement between human data and those from other animal species, major differences do exist. Potential reasons for these discrepancies include true biological differences, but differences in the techniques used may also be responsible. Although limited, measurement of MU contractile properties in humans has provided insight into the changes associated with aging and motoneuronal diseases and provides a means of gauging their adaptive capacity for training and immobilization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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 | 0.002 |
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".