Bioactive Extracellular Matrix Scaffold Promotes Adaptive Cardiac Remodeling and Repair
Bibliographic record
Abstract
Structural cardiac remodeling after ischemic injury can induce a transition to heart failure from progressive loss of cardiac function. Cellular regenerative therapies are promising but face significant translational hurdles. Tissue extracellular matrix (ECM) holds the necessary environmental cues to stimulate cell-based endogenous myocardial repair pathways and promote adaptive remodeling toward functional recovery. Heart epicardium has emerged as an important anatomic niche for endogenous repair pathways including vasculogenesis and cardiogenesis. We show that acellular ECM scaffolds surgically implanted on the epicardium following myocardial infarction (MI) can attenuate structural cardiac remodeling and improve functional recovery. We assessed the efficacy of this strategy on post-MI functional recovery by comparing intact bioactive scaffolds with biologically inactivated ECM scaffolds. We confirm that bioactive properties within the acellular ECM biomaterial are essential for the observed functional benefits. We show that interaction of human cardiac fibroblasts with bioactive ECM can induce a robust cell-mediated vasculogenic paracrine response capable of functional blood vessel assembly. Fibroblast growth factor-2 is uncovered as a critical regulator of this novel bioinductive effect. Acellular bioactive ECM scaffolds surgically implanted on the epicardium post-MI can reprogram resident fibroblasts and stimulate adaptive pro-reparative pathways enhancing functional recovery. We introduce a novel surgical strategy for tissue repair that can be performed as an adjunct to conventional surgical revascularization with minimal translational challenges.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".