A Collagen–Chitosan Hydrogel for Endothelial Differentiation and Angiogenesis
Bibliographic record
Abstract
Cell therapy for the treatment of cardiovascular disease has been hindered by low cell engraftment, poor survival, and inadequate phenotype and function. In this study, we added chitosan to a previously developed injectable collagen matrix, with the aim of improving its properties for cell therapy and neovascularization. Different ratios of collagen and chitosan were mixed and chemically crosslinked to produce hydrogels. Swell and degradation assays showed that chitosan improved the stability of the collagen hydrogel. In culture, endothelial cells formed significantly more vascular-like structures on collagen–chitosan than collagen-only matrix. While the differentiation of circulating progenitor cells to CD31+ cells was equal on all matrices, vascular endothelial-cadherin expression was increased on the collagen–chitosan matrix, suggesting greater maturation of the endothelial cells. In addition, the collagen–chitosan matrix supported a significantly greater number of CD133+ progenitor cells than the collagen-only matrix. In vivo, subcutaneously implanted collagen–chitosan matrices stimulated greater vascular growth and recruited more von Willebrand factor (vWF+) and CXCR4+ endothelial/angiogenic cells than the collagen-only matrix. These results indicate that the addition of chitosan can improve the physical properties of collagen matrices, and enhance their ability to support endothelial cells and angiogenesis for use in cardiovascular tissue engineering applications.
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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".