Normal Human Epithelial Cells Regulate the Size and Morphology of Tissue-Engineered Capillaries
Bibliographic record
Abstract
The survival of thick tissues/organs produced by tissue engineering requires rapid revascularization after grafting. Although capillary-like structures have been reconstituted in some engineered tissues, little is known about the interaction between normal epithelial cells and endothelial cells involved in the in vitro angiogenic process. In the present study, we used the self-assembly approach of tissue engineering to examine this relationship. An endothelialized tissue-engineered dermal substitute was produced by adding endothelial cells to the tissue-engineered dermal substitute produced by the self-assembly approach. The latter consists in culturing fibroblasts in the medium supplemented with serum and ascorbic acid. A network of tissue-engineered capillaries (TECs) formed within the human extracellular matrix produced by dermal fibroblasts. To determine whether epithelial cells modify TECs, the size and form of TECs were studied in the endothelialized tissue-engineered dermal substitute cultured in the presence or absence of epithelial cells. In the presence of normal keratinocytes from skin, cornea or uterine cervix, endothelial cells formed small TECs (cross-sectional area estimated at less than 50 microm(2)) reminiscent of capillaries found in the skin's microcirculation. In contrast, TECs grown in the absence of epithelial cells presented variable sizes (larger than 50 microm(2)), but the addition of keratinocyte-conditioned media or exogenous vascular endothelial growth factor induced their normalization toward a smaller size. Vascular endothelial growth factor neutralization inhibited the effect of keratinocyte-conditioned media. These results provide new direct evidence that normal human epithelial cells play a role in the regulation of the underlying TEC network, and advance our knowledge in tissue engineering for the production of TEC networks 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 | 0.000 |
| 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".