Capillary reconstruction in skin and blood vessels by tissue engineering
Bibliographic record
Abstract
An important aspect of engineered tissues is the addition of the microvasculature. Preexisting capillaries within the construct could enhance engraftment and allow the survival of thicker tissues. We first reconstructed in vitro a human capillary‐like network in a sponge using dermal fibroblasts, endothelial cells and keratinocytes. The spontaneous formation of capillary‐like structures in an extracellular matrix (ecm) was promoted. The vascularization of these skin constructs was accelerated after transplantation. We have shown that the production of completely biologic endothelialized tissue is feasible. The self‐assembly approach we developed allows the production of a tissue from cells only (no exogenous ecm or biomaterials). The self‐assembly approach consists of culturing mesenchymal cells (fibroblasts or smooth muscle cells) in the presence of ascorbic acid in order that cells produce and organize a dense ecm. The sheets are then superposed to reconstruct a dermis or rolled over a mandrel to produce a tubular structure such as a tissue‐engineered blood vessel (TEVB, 5mm i.d.). Capillary‐like structures may be produced by adding endothelial cells. In this manner, a tissue‐engineered capillary network forms in the TE‐dermis or in the adventitial layer of the TEVB. In conclusion, our tissue‐engineered models allowed us to progress towards the reconstruction of microvasculature in vitro.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".