Novel cardioprotection strategies for the aged heart: evidence from pre‐clinical studies
Bibliographic record
Abstract
The incidence of cardiovascular disease is rising as the population ages. This has led to an increase in the need to perform cardiac surgery in older patients. However, aged hearts are particularly susceptible to reperfusion injury following periods of myocardial ischaemia that occur during cardiac surgery. Indeed, older adults experience myocardial dysfunction and reduced survival post-surgery compared to younger people and certain groups, including older women and frail older adults, are at particular risk. This highlights the need to design cardioprotective strategies specifically for the ageing heart. Cardioprotection during surgery is often accomplished by perfusing the heart with chemical arresting agents, known as cardioplegic solutions. New protective strategies have been developed and tested in animal models, where cardioplegic solutions have been modified by changing their temperature, chemical components and/or the frequency of delivery. In addition, drugs designed to activate cardioprotective mechanisms or to inhibit mechanisms involved in injury have been added to improve the efficacy of these solutions. However, most experimental studies have developed and optimized cardioplegic solutions in hearts from younger male animals. This review discusses pre-clinical models used to optimize cardioplegic solutions, with an emphasis on the few studies that have used hearts from older animals. Pharmacologic agents that have been shown to enhance the benefits of cardioplegia in younger hearts and could, in theory, protect vulnerable older hearts are also considered. We emphasize the need to conduct studies in frail older animals of both sexes to facilitate translation of laboratory-based observations to the clinic.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".