Calmodulin kinase II accelerates L‐type Ca<sup>2+</sup> current recovery from inactivation and compensates for the direct inhibitory effect of [Ca<sup>2+</sup>]<sub>i</sub> in rat ventricular myocytes
Bibliographic record
Abstract
Some studies report that the positive relationship between L-type Ca(2+) current (I(Ca-L)) and frequency in cardiac myocytes is mainly due to a direct negative feedback of sarcoplasmic reticulum Ca(2+) release on I(Ca-L) inactivation while others provide evidence for activation of calmodulin kinase II (CaMKII). To further elucidate the role of endogenous CaMKII activity, the CaMKII inhibitory peptides, autocamtide-2 relating inhibitory peptide (AIP) and myristoylated AIP were applied using conventional and perforated patch-clamp methods. AIP inhibited the normal adaptive increase in I(Ca-L) in response to abrupt increase in pacing frequency from 0.05 to 2 Hz. The positive I(Ca-L)-frequency relationship was reversed by AIP and the inhibitory effect of AIP was significantly exaggerated at fast pacing rates. The onset of inactivation of I(Ca-L) was not altered by AIP. After thapsigargin treatment, AIP slowed recovery from inactivation of I(Ca-L) and this effect was exaggerated during fast pacing. Buffering of [Ca(2+)](i) by BAPTA and EGTA accelerated recovery of I(Ca-L) from inactivation, and BAPTA partly eliminated the effect of AIP on the recovery. We conclude that: (1) [Ca(2+)](i) directly slows I(Ca-L) recovery from inactivation; and (2) Ca(2+)-dependent endogenous CaMKII activity accelerates the I(Ca-L) recovery. Thus, at fast heart rates, elevated [Ca(2+)](i) activates endogenous CaMKII and compensates for its direct inhibitory effect on I(Ca-L) recovery from inactivation. Dynamic activity of endogenous CaMKII enhances the positive I(Ca-L)-frequency relationship.
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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 | 0.001 |
| 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".