Acellular bioscaffolds redirect cardiac fibroblasts and promote functional tissue repair in rodents and humans with myocardial injury
Bibliographic record
Abstract
Coronary heart disease is a leading cause of death. Tissue remodeling and fibrosis results in cardiac pump dysfunction and ischemic heart failure. Cardiac fibroblasts may rebuild damaged tissues when prompted by suitable environmental cues. Here, we use acellular biologic extracellular matrix scaffolds (bioscaffolds) to stimulate pathways of muscle repair and restore tissue function. We show that acellular bioscaffolds with bioinductive properties can redirect cardiac fibroblasts to rebuild microvascular networks and avoid tissue fibrosis. Specifically, when human cardiac fibroblasts are combined with bioactive scaffolds, gene expression is upregulated and paracrine mediators are released that promote vasculogenesis and prevent scarring. We assess these properties in rodents with myocardial infarction and observe bioscaffolds to redirect fibroblasts, reduce tissue fibrosis and prevent maladaptive structural remodeling. Our preclinical data confirms that acellular bioscaffold therapy provides an appropriate microenvironment to stimulate pathways of functional repair. We translate our observations to patients with coronary heart disease by conducting a first-in-human observational cohort study. We show that bioscaffold therapy is associated with improved perfusion of infarcted myocardium, reduced myocardial scar burden, and reverse structural remodeling. We establish that clinical use of acellular bioscaffolds is feasible and offers a new frontier to enhance surgical revascularization of ischemic heart muscle.
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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".