The effect of work cycle frequency on the potentiation of dynamic force in mouse fast twitch skeletal muscle
Bibliographic record
Abstract
The purpose of this study was to test the hypothesis that the potentiation of concentric twitch force during work cycles is dependent upon both the speed and direction of length change. Concentric and eccentric forces were elicited by stimulating muscles during the shortening and lengthening phases, respectively, of work cycles. Work cycle frequency was varied in order to vary the speed of muscle shortening and/or lengthening; all forces were measured as the muscle passed though optimal length (L(o)). Both concentric and eccentric force were assessed before (unpotentiated control) and after (potentiated) the application of a tetanic conditioning protocol known to potentiate twitch force output. The influence of the conditioning protocol on relative concentric force was speed dependent, with forces increased to 1.19±0.01, 1.25±0.01 and 1.30±0.01 of controls at 1.5, 3.3 and 6.9 Hz, respectively (all data N=9-10 with P<0.05). In contrast, the conditioning protocol had only a limited effect on eccentric force at these frequencies (range: 1.06±0.01 to 0.96±0.03). The effect of the conditioning protocol on concentric work (force × distance) was also speed dependent, being decreased at 1.5 Hz (0.84±0.01) and increased at 3.3 and 6.9 Hz (1.05±0.01 and 1.39±0.01, respectively). In contrast, eccentric work was not increased at any frequency (range: 0.88±0.02 to 0.99±0.01). Thus, our results reveal a hysteresis-like influence of activity-dependent potentiation such that concentric force and/or work were increased but eccentric force and/or work were not. These outcomes may have implications for skeletal muscle locomotor function in vivo.
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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 | 0.000 |
| 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".