Heparin-Modified Collagen Gels for Controlled Release of Pleiotrophin: Potential for Vascular Applications
Bibliographic record
Abstract
A fast re-endothelialization, along with the inhibition of neointima hyperplasia, are crucial to reduce the failure of vascular bypass grafts. Implants modifications with molecules capable of speeding up the re-endothelialization process have been proposed over the last years. However, clinical trials of angiogenic factor delivery have been mostly disappointing, underscoring the need to investigate a wider array of angiogenic factors. In this work, a drug release system based on a type I collagen hydrogel has been proposed for the controlled release of Pleiotrophin (PTN), a cytokine known for its pro-angiogenetic effects. Heparin, in virtue of its ability to sequester, protect and release growth factors, has been used to better control the release of PTN. Performances of the PTN drug delivery system on endothelial (ECs) and smooth muscle cells (SMCs) have been investigated. Structural characterization (mechanical tests and immunofluorescent analyses of the collagen fibers) was performed on the gels to assess if heparin caused changes in their mechanical behavior. The release of PTN from the different gel formulations has been analyzed using a PTN-specific ELISA assay. Cell viability was evaluated with the Alamar Blue Cell Viability Assay on cells directly seeded on the gels (direct test) and on cells incubated with supernatant, containing the released PTN, obtained from the gels (indirect test). The effects of the different gels on the migration of both ECs and SMCs have been evaluated using a Transwell migration assay. Hemocompatibility of the gel has been assessed with a clotting/hemolysis test. Structural analyses showed that heparin did not change the structural behavior of the collagen gels. ELISA quantification demonstrated that heparin induced a constant release of PTN over time compared to other conditions. Both direct and indirect viability assays showed an increase in ECs viability while no effects were noted on SMCs. Cell migration results evidenced that the heparin/PTN-modified gels significantly increased ECs migration and decreased the SMCs one. Finally, heparin significantly increased the hemocompatibility of the collagen gels. In conclusion, the PTN-heparin-modified collagen here proposed can represent an added value for vascular medicine, able to ameliorate the biological performance, and integration of vascular grafts.
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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".