Application of Bioengineered Materials in the Surgical Management of Heart Failure
Bibliographic record
Abstract
The epicardial surface of the heart is readily accessible during cardiac surgery and presents an opportunity for therapeutic intervention for cardiac repair and regeneration. As an important anatomic niche for endogenous mechanisms of repair, targeting the epicardium using decellularized extracellular matrix (ECM) bioscaffold therapy may provide the necessary environmental cues to promote functional recovery. Following ischemic injury to the heart caused by myocardial infarction (MI), epicardium derived progenitor cells (EPDCs) become activated and migrate to the site of injury. EPDC differentiation has been shown to contribute to endothelial cell, cardiac fibroblast, cardiomyocyte, and vascular smooth muscle cell populations. Post-MI, it is largely the activation of cardiac fibroblasts and the resultant dysregulation of ECM turnover which leads to maladaptive structural cardiac remodeling and loss of cardiac function. Decellularized ECM bioscaffolds not only provide structural support, but have also been shown to act as a bioactive reservoir for growth factors, cytokines, and matricellular proteins capable of attenuating maladaptive cardiac remodeling. Targeting the epicardium post-MI using decellularized ECM bioscaffold therapy may provide the necessary bioinductive cues to promote differentiation toward a pro-regenerative phenotype and attenuate cardiac fibroblast activation. There is an opportunity to leverage the clinical benefits of this innovative technology with an aim to improve the prognosis of patients suffering from progressive heart failure. An enhanced understanding of the utility of decellularized ECM bioscaffolds in epicardial repair will facilitate their growth and transition into clinical practice. This review will provide a summary of decellularized ECM bioscaffolds being developed for epicardial infarct repair in coronary artery bypass graft (CABG) surgery.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".