Sex‐related differences in motor unit behavior are influenced by myosin heavy chain during high‐ but not moderate‐intensity contractions
Bibliographic record
Abstract
Abstract Aims Motor unit recruitment and firing rate patterns of the vastus lateralis (VL) have not been compared between sexes during moderate‐ and high‐intensity contraction intensities. Additionally, the influence of fiber composition on potential sex‐related differences remains unquantified. Methods Eleven males and 11 females performed 40% and 70% maximal voluntary contractions (MVCs). Surface electromyographic (EMG) signals recorded from the VL were decomposed. Recruitment thresholds (RTs), MU action potential amplitudes (MUAPAMP), initial firing rates (IFRs), mean firing rates (MFRs), and normalized EMG amplitude (N‐EMGRMS) at steady torque were analyzed. Y‐intercepts and slopes were calculated for MUAPAMP, IFR, and MFR versus RT relationships. Type I myosin heavy chain isoform (MHC) was determined with muscle biopsies. Results There were no sex‐related differences in MU characteristics at 40% MVC. At 70% MVC, males exhibited greater slopes (p = 0.002) for the MUAPAMP, whereas females displayed greater slopes (p = 0.001–0.007) for the IFR and MFR versus RT relationships. N‐EMGRMS at 70% MVC was greater for females (p < 0.001). Type I %MHC was greater for females (p = 0.006), and was correlated (p = 0.018–0.031) with the slopes for the MUAPAMP, IFR, and MFR versus RT relationships at 70% MVC (r = −0.599–0.585). Conclusion Both sexes exhibited an inverse relationship between MU firing rates and recruitment thresholds. However, the sex‐related differences in MU recruitment and firing rate patterns and N‐EMGRMS at 70% MVC were likely due to greater type I% MHC and smaller twitch forces of the higher threshold MUs for the females. Evidence is provided that muscle fiber composition may explain divergent MU behavior between sexes.
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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) | 0.000 | 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) | 0.003 | 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".