Recruitment of Ca<sup>2+</sup> release channels by calcium‐induced Ca<sup>2+</sup> release does not appear to occur in isolated Ca<sup>2+</sup> release sites in frog skeletal muscle
Bibliographic record
Abstract
Ca(2+) release from the sarcoplasmic reticulum (SR) in skeletal muscle in response to small depolarisations (e.g. to -60 mV) should be the sum of release from many isolated Ca(2+) release sites. Each site has one SR Ca(2+) release channel activated by its associated T-tubular voltage sensor. The aim of this study was to evaluate whether it also includes neighbouring Ca(2+) release channels activated by Ca-induced Ca(2+) release (CICR). Ca(2+) release in frog cut muscle fibres was estimated with the EGTA/phenol red method. The fraction of SR Ca content ([Ca(SR)]) released by a 400 ms pulse to -60 mV (denoted f(Ca)) provided a measure of the average Ca(2+) permeability of the SR associated with the pulse. In control experiments, f(Ca) was approximately constant when [Ca(SR)] was 1500-3000 microM (plateau region) and then increased as [Ca(SR)] decreased, reaching a peak when [Ca(SR)] was 300-500 microM that was 4.8 times larger on average than the plateau value. With 8 mM of the fast Ca(2+) buffer BAPTA in the internal solution, f(Ca) was 5.0-5.3 times larger on average than the plateau value obtained before adding BAPTA when [Ca(SR)] was 300-500 microM. In support of earlier results, 8 mM BAPTA did not affect Ca(2+) release in the plateau region. At intermediate values of [Ca(SR)], BAPTA resulted in a small, if any, increase in f(Ca), presumably by decreasing Ca inactivation of Ca(2+) release. Since BAPTA never decreased f(Ca), the results indicate that neighbouring channels are not activated by CICR with small depolarisations when [Ca(SR)] is 300-3000 microM.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".