Modulation of Angiogenic Potential of Collagen Matrices by Covalent Incorporation of Heparin and Loading with Vascular Endothelial Growth Factor
Bibliographic record
Abstract
One of the prominent shortcomings of matrices for tissue engineering is their poor ability to support angiogenesis. We report here on experiments to enhance the angiogenic properties of collagen matrices. Our aim is to achieve this goal by covalently incorporating heparin into collagen matrices and by physically immobilizing angiogenic vascular endothelial growth factor (VEGF) to the heparin. The immobilization of heparin was performed with 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide (EDC) and N-hydroxysuccinimide (NHS). Carboxyl groups on the heparin are activated to succinimidyl esters, which react with amino functions on the collagen to zero length cross-links. This modification leads--in addition to the incorporation of heparin--to gross changes in in vitro degradation behavior and water-binding capacity. As a first approach to testing angiogenic capabilities, endothelial cells were exposed to nonmodified and heparinized collagen matrices. This exposure leads to an increase in endothelial cell proliferation. The increase can be further enhanced by loading the (heparinized) collagen matrices with VEGF. Evaluation of the angiogenic potential of heparinized matrices was further investigated by exposing them to the chorioallantoic membrane of chicken embryos and to the subcutaneous tissue of rats. Both approaches show that heparinized matrices have substantially increased angiogenic potential. In particular, the loading of heparinized matrices with VEGF invokes a further increase in angiogenic potential. It is apparent that the physical binding of VEGF to heparin allows for a release that is beneficial to angiogenesis. By varying the heparin and EDC/NHS concentrations during the modification process and by varying the loading with VEGF, the angiogenic potential as well as the degradation behavior can be adapted to obtain matrices that fulfill specific angiogenic requirements in the field of tissue engineering.
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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".