Rate‐dependent changes of twitch force duration in rat cardiac trabeculae: a property of the contractile system
Bibliographic record
Abstract
1. We examined the mechanisms for rate-dependent changes in twitch force duration by simultaneously measuring force and [Ca2+]i in rat cardiac trabeculae. 2. Peak force decreased when the rate of stimulation was increased from 0.2 to 0.5 Hz, whilst it increased from 1 to 2 Hz. Over the same range of frequencies, peak [Ca2+]i transients increased monotonically, whilst both force and [Ca2+]i transient duration were abbreviated. 3. Changes in peak force or peak [Ca2+]i transients were not responsible for the changes in force or [Ca2+]i transient duration. 4. The changes in twitch force and [Ca2+]i transient duration were completed roughly within one beat following an abrupt change in the rate of stimulation. 5. Rate-dependent changes resembled those observed with isoproterenol (isoprenaline) application. However, kinase inhibitors (i.e. K252-a, H-89, KN-62 and KN-93) had no effect on the rate-dependent changes of twitch force and [Ca2+]i transient kinetics, suggesting that protein kinase A (PKA), protein kinase PKG) and Ca2+-calmodulin-dependent protein kinase II (CaM/kinase II) were not responsible for these kinetic changes. 6. Despite the changes in twitch force and [Ca2+]i transient kinetics, the rate-limiting step for the rate-dependent force relaxation still resides at the level of the contractile proteins. 7. Our results suggest that rate-dependent changes in force and [Ca2+]i transients do not depend on PKA or CaM/kinase II activity but might result from intrinsic features of the contractile and/or Ca2+-handling proteins.
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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".