Promotion of Angiogenesis in Tissue Engineering: Developing Multicellular Matrices with Multiple Capacities
Bibliographic record
Abstract
One of the aims of tissue engineering is to be able to develop multi-tissue organs in the future. This requires the optimization of conditions for the differentiation of multiple cell types and maintenance of the differentiated phenotype within complex engineered tissues. The goal of this study was to develop prototype tissue engineered matrices to support the simultaneous growth of different cell types with a particular focus on the angiogenic process. We examined two different matrix compositions for the promotion of blood vessel and tube formation. A fibrin-based matrix with the addition of a combination of growth factors supported vascular growth and the invasion of inflammatory cells. Using this fibrin matrix, in combination with a collagen-based hydrogel, a simple in vitro model of the cornea with adjacent sclera was developed that was complete with innervation and vascular structures. In addition, we showed that collagen-based matrices were effective in delivering mononuclear endothelial progenitor cells to ischemic tissue in vivo, and allowing these cells to incorporate into vascular structures. It is anticipated that with further development, these matrices have potential for use as delivery matrices for cell transplantation and for in vitro study purposes of multiple cell types.
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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 | 0.001 | 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 | 0.001 |
| 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".