Heparin Augmentation Enhances Bioactive Properties of Acellular Extracellular Matrix Scaffold
Bibliographic record
Abstract
Extracellular matrix (ECM) maintains a reservoir of bioactive growth factors and matricellular proteins that provide bioinductive effects on local cells that influence phenotype and behaviors. Bioactive acellular ECM scaffolds can be used therapeutically to stimulate adaptive tissue repair. Fibroblast growth factor-2 (FGF-2) attenuates transforming growth factor-β1 (TGF-β1)-mediated cardiac fibrosis. Heparin glycosaminoglycan can influence FGF-2 bioactivity and could be leveraged to enhance tissue engineering strategies. We explored the effects of heparin on FGF-2 enhancement of bioactive ECM scaffold biomaterials for its antifibrotic effect on attenuating human cardiac myofibroblast activation. Increasing heparin concentration at a fixed concentration of FGF-2 markedly increased the amount of FGF-2 retained and eluted by ECM scaffolds. To explore synergistic bioinductive effects of heparin and FGF-2, collagen gel contraction assay using human cardiac myofibroblasts was performed in vitro. Myofibroblast activation was induced by profibrotic cytokine, TGF-β1. FGF-2 and heparin in combination reduced human cardiac myofibroblast-mediated collagen gel contraction to a greater extent than FGF-2 alone. These observations were confirmed for both human atrial and human ventricular cardiac fibroblasts. Cell death was not different between groups. In summary, heparin is an effective adjuvant to enhance FGF-2 loading and elution of acellular ECM scaffold biomaterials. Heparin increases the bioactive effects of FGF-2 in attenuating human cardiac myofibroblast activation in response to profibrotic TGF-β1. These data may inform tissue engineering strategies for myocardial repair to prevent fibrosis.
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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".