Abstract 15881: Functional Benefits of CCN1-Collagen Matrix for the Treatment of Myocardial Infarction
Bibliographic record
Abstract
Background: Matricellular proteins are non-structural ECM proteins with important wound healing effects. They regulate cell function through interactions with cell surface receptors, enzymes and structural proteins such as collagen. The aim of this study was to examine the effects of matricellular protein CCN1 delivery in a mouse myocardial infarction (MI) model using a collagen matrix. CCN1 is pro-angiogenic and restricts fibrosis, while the matrix promotes cell function and directly interacts with CCN1. Methods/Results: Experimental MI was induced by LAD ligation in mice. At 1wk post-MI mice were randomized to receive intramyocardial injection of PBS, CCN1, collagen matrix or CCN1-matrix. Cardiac function was assessed at 1 and 3 wk post-treatment. At 1wk, LVEF was superior in the CCN1 (48.1±3.3%) and CCN1-matrix (41.9±3.5%) groups compared to PBS (34.6±2.8%; p<0.03). At 4wk, LVEF of the CCN1-matrix treated hearts improved to 49.7±2.1%, while CCN1 (38.0±2.6%) and matrix (38.7±1.6%) were better than PBS (28.9±2.9%) but no better than at 1wk (p<0.01). In vitro, CCN1-matrix offered protective effects to cardiomyocytes as there was a 39.3% increase in their survival under stress conditions (serum and O2 deprivation) compared to matrix-cultured cells (p=0.02). Cardiac fibroblasts responded to culture on the CCN1-matrix with a 2.3±0.7 and 1.6±0.2 fold reduction in proliferation compared to TCPS (p=0.008) and matrix (p=0.03) culture, respectively. Furthermore, the collagenase activity of CCN1-matrix cultured fibroblasts was reduced compared to those cultured on TCPS or matrix. Circulating angiogenic cells (CACs), which play a role in neovascularization post-MI, responded to CCN1-matrix treatment with a 2.2±0.2 fold increase in proliferation (p=0.001), a 1.8±0.2 fold increase in migration towards VEGF (p=0.001) and promoted greater tubule network formation (5.1±0.6 fold; p=0.007) in an angiogenic assay compared to matrix-cultured CACs. Conclusion: We demonstrate a novel approach to treat MI using a CCN1-collagen matrix. CCN1 treatment improved LVEF at 1wk post-MI, but this persisted only when CCN1 was delivered with the matrix. These data suggest that CCN1 has a positive effect on the MI heart but required the matrix for sustained benefit.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".