Ultrasonographic measurements of fascicle length overestimate adaptations in serial sarcomere number
Bibliographic record
Abstract
Ultrasound-derived measurements of muscle fascicle length (FL) are often used to infer increases (chronic stretch or training) or decreases (muscle disuse or aging) in serial sarcomere number (SSN). Whether FL adaptations measured via ultrasound can truly approximate SSN adaptations has not been investigated. We casted the right hindlimb of 15 male Sprague-Dawley rats in a dorsiflexed position (i.e., stretched the plantar flexors) for 2 weeks, with the left hindlimb serving as a control. Ultrasound images of the soleus, lateral gastrocnemius (LG), and medial gastrocnemius (MG) were obtained with the ankle at 90° and full dorsiflexion for both hindlimbs pre and post-cast. Following post-cast ultrasound measurements, legs were fixed in formalin with the ankle at 90°, then muscles were dissected and fascicles were teased out for measurement of sarcomere lengths via laser diffraction and calculation of SSN. Ultrasound detected an 11% increase in soleus FL, a 12% decrease in LG FL, and an 8-11% increase in MG FL for proximal fascicles and at full dorsiflexion. These adaptations were partly reflected by SSN adaptations, with a 6% greater soleus SSN in the casted leg than the un-casted leg, but no SSN differences for the gastrocnemii. Weak relationships were observed between ultrasonographic measurements of FL and measurements of FL and SSN from dissected fascicles. Our results showed that ultrasound-derived FL measurements can overestimate an increase in SSN by ∼5%. Future studies should be cautious when concluding a large magnitude of sarcomerogenesis from ultrasound-derived FL measurements, and may consider applying a correction factor. NEW FINDINGS: What is the central question of this study? Measurements of muscle fascicle length via ultrasound are often used to infer changes in serial sarcomere number, such as increases following chronic stretch or resistance training, and decreases with ageing: does ultrasound-derived fascicle length accurately depict adaptations in serial sarcomere number? What is the main finding and its importance? Ultrasound detected an ∼11% increase in soleus fascicle length, but measurements on dissected fascicles showed the actual serial sarcomere number increase was only ∼6%; therefore, measurements of ultrasound-derived fascicle length can overestimate serial sarcomere number adaptations by as much as 5%.
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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 | 0.001 | 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) | 0.001 | 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".