Considering Cause and Effect of Immune Cell Aging on Cardiac Repair after Myocardial Infarction
Bibliographic record
Abstract
The importance of the immune system for cardiac repair following myocardial infarction is undeniable; however, the complex nature of immune cell behavior has limited the ability to develop effective therapeutics. This limitation highlights the need for a better understanding of the function of each immune cell population during the inflammatory and resolution phases of cardiac repair. The development of reliable therapies is further complicated by aging, which is associated with a decline in cell and organ function and the onset of cardiovascular and immunological diseases. Aging of the immune system has important consequences on heart function as both chronic cardiac inflammation and an impaired immune response to cardiac injury are observed in older individuals. Several studies have suggested that rejuvenating the aged immune system may be a valid therapeutic candidate to prevent or treat heart disease. Here, we review the basic patterns of immune cell behavior after myocardial infarction and discuss the autonomous and nonautonomous manners of hematopoietic stem cell and immune cell aging. Lastly, we identify prospective therapies that may rejuvenate the aged immune system to improve heart function such as anti-inflammatory and senolytic therapies, bone marrow transplant, niche remodeling and regulation of immune cell differentiation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".