Effects of blebbistatin and Ca<sup>2+</sup> concentration on force produced during stretch of skeletal muscle fibers
Bibliographic record
Abstract
When activated muscle fibers are stretched at low speeds [≤ 2 optimal length (L(o))/s], force increases in two phases, marked by a change in slope [critical force (P(c))] that happens at a critical sarcomere length extension (L(c)). Some studies attribute P(c) to the number of attached cross bridges before stretch, while others attribute it to cross bridges in a pre-power-stroke state. In this study, we reinvestigated the mechanisms of forces produced during stretch by altering either the number of cross bridges attached to actin or the cross-bridge state before stretch. Two sets of experiments were performed: 1) activated fibers were stretched by 3% L(o) at speeds of 1.0, 2.0, and 3.0 L(o)/s in different pCa(2+) (4.5, 5.0, 5.5, 6.0), or 2) activated fibers were stretched by 3% L(o) at 2 L(o)/s in pCa(2+) 4.5 containing either 5 μM blebbistatin(+/-) or its inactive isomer (+/+). All stretches started at a sarcomere length (SL) of 2.5 μm. When fibers were activated at a pCa(2+) of 4.5, P(c) was 2.47 ± 0.11 maximal force developed before stretch (P(o)) and decreased with lower concentrations of Ca(2+). L(c) was not Ca(2+) dependent; the pooled experiments provided a L(c) of 14.34 ± 0.34 nm/half-sarcomere (HS). P(c) and L(c) did not change with velocities of stretch. Fibers activated in blebbistatin(+/-) showed a higher P(c) (2.94 ± 0.17 P(o)) and L(c) (16.30 ± 0.38 nm/HS) than control fibers (P(c) 2.31 ± 0.08 P(o); L(c) 14.05 ± 0.63 nm/HS). The results suggest that forces produced during stretch are caused by both the number of cross bridges attached to actin and the cross bridges in a pre-power-stroke state. Such cross bridges are stretched by large amplitudes before detaching from actin and contribute significantly to the force developed during stretch.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".