The Catchlike Property in Skeletal Muscle: Influence of Contraction and Muscle‐Type on Augmented Contractile Function
Bibliographic record
Abstract
In slow and fast motor units of skeletal muscle, discharge rates are modulated to match and preserve target force levels. Although relatively constant patterns in firing rates are most common, two or more closely-spaced pulses at the onset of activation may appear in both human and mammalian skeletal muscles. The outcome of this aberration in motor unit firing pattern has been shown to increase both the rate and extent of force development; an effect referred to as the catchlike property (CLP). The purpose of this study was to explore the contraction (i.e. isometric vs. concentric) and muscle-type (fast vs. slow twitch) dependence of the CLP in extensor digitorum longus (EDL) and soleus muscles of the mouse (n=10 for all data). The addition of a single pulse 10ms at the onset of an otherwise constant frequency train (CFT) was utilized to augment force and work output of the contractile event, referred to as the catchlike-inducing train (CLT). When compared to the CFT, the peak force during the CLT of soleus muscles was increased to 1.15±0.03 and 1.39±0.03 during isometric (ISO) and concentric (CON) contractions, respectively. In EDL muscles, however, forces were increased to 1.47±0.05 and 1.20±0.04 for ISO and CON contractions; demonstrating a muscle and contraction-type dependence (p <0.05). Similarly, the work done and/or force-time integral increased to 1.34±0.03 and 1.21±0.03 (soleus), and 1.43±0.05 and 1.18±0.02 (EDL), for ISO and CON contractions, respectively. However, when normalized for the number of pulses within each contraction, no net increase in work was evident. Therefore, the catch-like property may serve primarily as a mechanism for augmenting initial force levels rather than as a modulator of contractile efficiency within skeletal muscle.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".