How Biomaterials Can Influence Various Cell Types in the Repair and Regeneration of the Heart after Myocardial Infarction
Bibliographic record
Abstract
The healthy heart comprises many different cell types that work together to preserve optimal function. However, in a diseased heart the function of one or more cell types is compromised which can lead to many adverse events, one of which is myocardial infarction (MI). Immediately after MI, the cardiac environment is characterized by excessive cardiomyocyte death and inflammatory signals leading to the recruitment of macrophages to clear the debris. Proliferating fibroblasts then invade, and a collagenous scar is formed to prevent rupture. Better functional restoration of the heart is not achieved due to the limited regenerative capacity of cardiac tissue. To address this, biomaterial therapy is being investigated as an approach to improve regeneration in the infarcted heart, as they can possess the potential to control cell function in the infarct environment and limit the adverse compensatory changes that occur post-MI. Over the past decade, there has been considerable research into the development of biomaterials for cardiac regeneration post-MI; and various effects have been observed on different cell types depending on the biomaterial that is applied. Biomaterial treatment has been shown to enhance survival, improve function, promote proliferation, and guide the mobilization and recruitment of different cells in the post-MI heart. This review will provide a summary on the biomaterials developed to enhance cardiac regeneration and remodeling post-MI with a focus on how they control macrophages, cardiomyocytes, fibroblasts, and endothelial cells. A better understanding of how a biomaterial interacts with the different cell types in the heart may lead to the development of a more optimized biomaterial therapy for cardiac regeneration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".