The contributions of extracellular matrix and sarcomere properties to passive muscle stiffness in cerebral palsy
Bibliographic record
Abstract
Abstract Cerebral palsy results from an upper motor neuron lesion and has significant effects on skeletal muscle stiffness throughout the body. The increased stiffness that occurs is partly a result of changes in the microstructural components of muscle. In particular, alterations in extracellular matrix, sarcomere length, fibre diameter, and fat content have been reported; however, experimental studies have shown wide variability in the degree to which each component is altered. Many studies have reported alterations in the extracellular matrix, while others have reported no changes. A consistent finding throughout the literature is increased sarcomere length in cerebral palsy muscle. Often more than one component is altered, making it difficult to determine the individual effects on stiffness. The purpose of this study is to use a modeling approach to isolate individual effects of microstructural alterations that typically occur during cerebral palsy on whole muscle behavior; in particular, the extracellular matrix volume fraction, stiffness, and sarcomere length. These microstructural effects can be captured using a three dimensional model of muscle. We found that the extracellular matrix volume fraction has a larger effect on stiffness compared to sarcomere length, even when coupled with decreased extracellular matrix stiffness. Additionally, the effects of sarcomere length in passive stiffness are mitigated by the increased extracellular matrix volume fraction. Using this model, we can achieve a better understanding of the possible combinations of microstructural changes that can occur during cerebral palsy. Developing these insights into diseased muscle tissue will help to direct future clinical and experimental procedures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".