Quantification of muscle fiber strain during in vivo repetitive stretch-shortening cycles
Bibliographic record
Abstract
Muscles subjected to lengthening contractions exhibit evidence of subcellular disruption, arguably a result of fiber strain magnitude. Due to the difficulty associated with measuring fiber strains during lengthening contractions, fiber length estimates have been used to formulate relationships between the magnitude of injury and mechanical measures such as fiber strain. In such protocols, the series compliance is typically minimized by removing the distal tendon and/or preactivating the muscle. These in vitro and in situ experiments do not represent physiological contractions well where fiber strain and muscle strain may be disassociated; thus the mechanisms of in vivo muscle injury remain elusive. The purpose of this paper was to quantify fiber strains during lengthening contractions in vivo and assess the potential role of fiber strain in muscle injury following repetitive stretch-shortening cycles. Using intact New Zealand White rabbit dorsiflexors, fiber strain and joint torque were measured during 50 stretch-shortening cycles. We were able to show that fiber length changes are disassociated from muscle tendon unit length changes and that complex fiber dynamics during these cycles prevent easy estimates of fiber strains. In addition, fiber strains vary, depending on how they are defined, and vary from repetition to repetition, thereby further complicating the potential relationship between muscle injury and fiber strain. We conclude from this study that, during in vivo stretch-shortening cycles, the relationship between fiber strain and muscle injury is complex. This is due, in part, to temporal effects of repeated loading on fiber strain magnitude that may be explained by an increasing compliance of the contractile element as exercise progresses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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".