Effect of age on cardiac excitation–contraction coupling
Bibliographic record
Abstract
1. Cardiovascular diseases most commonly occur in the elderly and are a frequent cause of disability or death. However, the effect of age itself on cardiac function is not well understood. 2. Studies in both human and animal hearts indicate that contractile function is unaffected by age while at rest. However, the ability to increase cardiac contractile force during strenuous activities, such as exercise, declines with age. 3. Similar findings have been observed in individual ventricular myocytes isolated from aged hearts. When myocytes are stimulated with beta-adrenoceptor agonists or rapid pacing frequencies, aged cells show a much smaller increase in peak contractions and Ca(2+) transients than young adult cells. In addition, contractions and Ca(2+) transients are prolonged in aged cells compared with younger cells under these conditions. 4. These observations suggest that the age-related decline in cardiac contractile function originates at the cellular level and may reflect modifications in processes involved in excitation-contraction (EC) coupling. 5. Biochemical studies have shown that there are age-related modifications in the expression, regulation and function of a number of proteins essential to EC coupling in the heart. 6. Functional studies indicate that these changes in EC coupling proteins disrupt Ca(2+) homeostasis and contribute to decrease in peak contraction and prolongation of contraction duration observed in myocytes from aged hearts. 7. The present review describes modifications in cardiac contractile function that occur in the ageing heart and evaluates underlying alterations in the EC coupling pathway that may be responsible for this decline in contractile function in ageing.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".