A Noninvasive Test For Estimating Type I Myosin Heavy Chain Expression In Women Using Mechanomyography
Bibliographic record
Abstract
PURPOSE: To determine if mechanomyographic amplitude (MMGRMS)-torque relationships could estimate type I percent myosin heavy chain expression (%MHC) of the vastus lateralis (VL) in sedentary women. METHODS: Fifteen healthy women (mean ± SD; age = 21.3 ± 5.3 yrs) volunteered for this study. Subjects performed 3 isometric maximal voluntary contractions (MVCs) of the knee extensors on an isokinetic dynamometer. The highest torque output determined the target torque levels for the subsequent randomly ordered isometric trapezoidal muscle actions at 30% and 70% MVC. An MMG sensor was placed on the VL. Simple linear regression models were fit to the log-transformed MMGRMS-torque relationships for the linear increasing and decreasing segments. MMGRMS was averaged for the steady torque segment. After testing, muscle biopsies were taken from the VL. %MHC was analyzed using SDS-PAGE. Pearson’s product moment correlation coefficients determined relationships among type I %MHC expression and each MMG variable (6 total). Sequential multiple-regression procedures determined if a predictive model for type I %MHC of the VL could be developed with the MMG variables significantly correlated with type I %MHC. Alpha was set at 0.05. RESULTS: Type I %MHC was correlated with the b terms from the MMGRMS-torque relationships for the linearly increasing segments at 70% MVC (p = 0.003; r = -0.72) and MMGRMS for the steady torque segments at 30% (p = 0.008; r = -0.65) and 70% MVC (p = 0.040; r = -0.54). No other relationships existed (p > 0.05). For the regression model, correlated variables were added in order of significance. The addition of each variable significantly added to the model (p < 0.05) and overall accounted for 81.2% of the variance in type I %MHC (Table 1). CONCLUSION: MMGRMS may provide a noninvasive method for estimating type I %MHC of the VL in untrained women. Future research should investigate the utility of this model in other populations.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".