In vivo Sarcomere Lengths and Sarcomere Elongations Are Not Uniform across an Intact Muscle
Bibliographic record
Abstract
Sarcomere lengths have been a crucial outcome measure for understanding and explaining basic muscle properties and muscle function. Sarcomere lengths for a given muscle are typically measured at a single spot, often in the mid-belly of the muscle, and at a given muscle length. It is then assumed implicitly that the sarcomere length measured at this single spot represents the sarcomere lengths at other locations within the muscle, and force-length, force-velocity, and power-velocity properties of muscles are often implied based on these single sarcomere length measurements. Although, intuitively appealing, this assumption is yet to be supported by systematic evidence. The objective of this study was to measure sarcomere lengths at defined locations along and across an intact muscle, at different muscle lengths. Using second harmonic generation (SHG) imaging technique, sarcomere patterns in passive mouse tibialis anterior (TA) were imaged in a non-contact manner at five selected locations (“proximal,” “distal,” “middle,” “medial,” and “lateral” TA sites) and at three different lengths encompassing the anatomical range of motion of the TA. We showed that sarcomere lengths varied substantially within small regions of the muscle and also for different sites across the entire TA. Also, sarcomere elongations with muscle lengthening were non-uniform across the muscle, with the highest sarcomere stretches occurring near the myotendinous junction. We conclude that muscle mechanics derived from sarcomere length measured from a small region of a muscle may not well-represent the sarcomere length and associated functional properties of the entire 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".