Effects of fiber type on force depression after active shortening in skeletal muscle
Bibliographic record
Abstract
The aim of this study was to investigate force depression in Type I and Type II muscle fibers. Experiments were performed using skinned fibers from rabbit soleus and psoas muscles. Force depression was quantified after active fiber shortening from an average sarcomere length (SL) of 3.2µ m to an average SL of 2.6 µm at an absolute speed of 0.115f iber length/s and at a relative speed corresponding to 17% of the unloaded shortening velocity (V0) in each type of fibers. Force decay and mechanical work during shortening were also compared between fiber types. After mechanical testing, each fiber was subjected to myosin heavy chain (MHC) analysis in order to confirm its type (Type I expressing MHC I, and Type II expressing MHC IId). Type II fibers showed greater steady-state force depression after active shortening at a speed of 0.115 fiber length/s than Type I fibers (14.5±1.5% versus 7.8±1.7%). Moreover, at this absolute shortening speed, Type I fibers showed a significantly greater rate of force decay during shortening and produced less mechanical work than Type II fibers. When active shortening was performed at the same relative speed (17% V0), the difference in force depression between fiber types was abolished. These results suggest that no intrinsic differences were at the origin of the disparate force depressions observed in Type I and Type II fibers when actively shortened at the same absolute speed, but rather their distinct force-velocity relationships.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".