The influence of longitudinal muscle fascicle growth on mechanical function
Bibliographic record
Abstract
Skeletal muscle has the remarkable ability to remodel and adapt, such as the increase in serial sarcomere number (SSN) or fascicle length (FL) observed after overstretching a muscle. This type of remodeling is termed longitudinal muscle fascicle growth, and its impact on biomechanical function has been of interest since the 1960s due to its clinical applications in muscle strain injury, muscle spasticity, and sarcopenia. Despite simplified hypotheses on how longitudinal muscle fascicle growth might influence mechanical function, existing literature presents conflicting results partly due to a breadth of methodologies. The purpose of this review is to outline what is currently known about the influence of longitudinal muscle fascicle growth on mechanical function and suggest future directions to address current knowledge gaps and methodological limitations. Various interventions indicate longitudinal muscle fascicle growth can increase the optimal muscle length for active force, but whether the whole force-length relationship widens has been less investigated. Future research should also explore the ability for longitudinal fascicle growth to broaden the torque-angle relationship's plateau region, and the relation to increased force during shortening. Without a concurrent increase in intramuscular collagen, longitudinal muscle fascicle growth also reduces passive tension at long muscle lengths; further research is required to understand whether this translates to increased joint range of motion. Finally, some evidence suggests longitudinal fascicle growth can increase maximum shortening velocity and peak isotonic power; however, there has yet to be direct assessment of these measures in a neurologically intact model of longitudinal muscle fascicle growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".