Contractile properties of muscle fibers from the deep and superficial digital flexors of horses
Bibliographic record
Abstract
Equine digital flexor muscles have independent tendons but a nearly identical mechanical relationship to the main joint they act upon. Yet these muscles have remarkable diversity in architecture, ranging from long, unipennate fibers ("short" compartment of DDF) to very short, multipennate fibers (SDF). To investigate the functional relevance of the form of the digital flexor muscles, fiber contractile properties were analyzed in the context of architecture differences and in vivo function during locomotion. Myosin heavy chain (MHC) isoform fiber type was studied, and in vitro motility assays were used to measure actin filament sliding velocity (V(f)). Skinned fiber contractile properties [isometric tension (P(0)/CSA), velocity of unloaded shortening (V(US)), and force-Ca(2+) relationships] at both 10 and 30°C were characterized. Contractile properties were correlated with MHC isoform and their respective V(f). The DDF contained a higher percentage of MHC-2A fibers with myosin (heavy meromyosin) and V(f) that was twofold faster than SDF. At 30°C, P(0)/CSA was higher for DDF (103.5 ± 8.75 mN/mm(2)) than SDF fibers (81.8 ± 7.71 mN/mm(2)). Similarly, V(US) (pCa 5, 30°C) was faster for DDF (2.43 ± 0.53 FL/s) than SDF fibers (1.20 ± 0.22 FL/s). Active isometric tension increased with increasing Ca(2+) concentration, with maximal Ca(2+) activation at pCa 5 at each temperature in fibers from each muscle. In general, the collective properties of DDF and SDF were consistent with fiber MHC isoform composition, muscle architecture, and the respective functional roles of the two muscles in locomotion.
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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) | 0.000 | 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".