Tissue-Engineered Skin Preserving the Potential of Epithelial Cells to Differentiate into Hair After Grafting
Bibliographic record
Abstract
The aim of this study was to evaluate whether tissue-engineered skin produced in vitro was able to sustain growth of hair follicles in vitro and after grafting. Different tissues were designed. Dissociated newborn mouse keratinocytes or newborn mouse hair buds (HBs) were added onto dermal constructs consisting of a tissue-engineered cell-derived matrix elaborated from either newborn mouse or adult human fibroblasts cultured with ascorbic acid. After 7-21 days of maturation at the air-liquid interface, no hair was noticed in vitro. Epidermal differentiation was observed in all tissue-engineered skin. However, human fibroblast-derived tissue-engineered dermis (hD) promoted a thicker epidermis than mouse fibroblast-derived tissue-engineered dermis (mD). In association with mD, HBs developed epithelial cyst-like inclusions presenting outer root sheath-like attributes. In contrast, epidermoid cyst-like inclusions lined by a stratified squamous epithelium were present in tissues composed of HBs and hD. After grafting, pilo-sebaceous units formed and hair grew in skin elaborated from HBs cultured 10-26 days submerged in culture medium in association with mD. However, the number of normal hair follicles decreased with longer culture time. This hair-forming capacity after grafting was not observed in tissues composed of hD overlaid with HBs. These results demonstrate that epithelial stem cells can be kept in vitro in a permissive tissue-engineered dermal environment without losing their potential to induce hair growth after grafting.
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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".