The interaction of Ca<sup>2+</sup> with sarcomeric proteins: role in function and dysfunction of the heart
Bibliographic record
Abstract
The hallmarks of the normal heartbeat are both rapid onset of contraction and rapid relaxation as well as an inotropic response to both increased end-diastolic volume and increased heart rate. At the microscopic level, Ca(2+) plays a crucial role in normal cardiac contraction. This paper reviews the cycle of Ca(2+) fluxes during the normal heartbeat, which underlie the coupling between excitation and contraction and permit a highly synchronized action of cardiac sarcomeres. Length dependence of the response of the regulatory sarcomeric proteins mediates the Frank-Starling Law of the heart. However, Ca(2+) transport may go astray in heart disease such as in congestive heart failure, and both jeopardize systole and diastole and triggering arrhythmias. The interaction between weak and strong segments in nonuniform cardiac muscle allows partial preservation of force of contraction but may further lead to mechanoelectric feedback or reverse excitation-contraction coupling mediating an early diastolic Ca(2+) transient caused by the rapid force decrease during the relaxation phase. These rapid force changes in nonuniform muscle may cause arrhythmogenic Ca(2+) waves to propagate by the activation of neighboring sarcoplasmic reticulum by diffusing Ca(2+) ions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".