Length-dependent twitch contractile characteristics of skeletal muscle
Bibliographic record
Abstract
The length dependence of force development of mammalian skeletal muscles was evaluated during twitch, double-pulse, and tetanic contractions, and the relation between muscle length and the time-dependent characteristics of twitch and double-pulse contractions were determined. In situ isometric contractions of the rat gastrocnemius muscle were analyzed at seven different lengths, based on a reference length at which the maximal response to double-pulse contractions occurred (Lopt-2P). Twitch and double-pulse contractions were analyzed for developed tension (DT), contraction time (tC), average rate of force development (DT-tC(-1)), half-relaxation time (t50%R), peak rate of relaxation (DT x dtmin(-1)), and 90%-relaxation time (t90%R). Considering the length at which maximal tetanic DT occurred to be the optimal length (Lo-TET), the peak DT for twitch contractions and double-pulse contractions was observed at Lo-TET + 0.75 mm (p < 0.05) and Lo-TET + 0.1 mm (p > 0.05), respectively. When measured at the length for which maximal twitch and double-pulse contractions were obtained, tetanic DT was 95.2 +/- 3 and 99.0 +/- 2% of the maximal value, respectively. These observations suggest that double-pulse contractions are more suitable for setting length for experimental studies than twitch contractions. Twitch and double-pulse contraction tC were 15.53 +/- 1.14 and 25.0 +/- 0.6 ms, respectively, at Lopt-2P, and increased above Lopt-2P and decreased below Lopt-2P. Twitch t50%R was 12.18 +/- 0.90 ms at Lopt-2P, and increased above Lopt-2P and below Lopt-2P. Corresponding changes for double-pulse contractions were greater. Stretching the muscle leads to slower twitch contractions and double-pulse contractions, but the mechanisms of this change in time course remain unclear.
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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 | 0.000 | 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".