Bulge-Derived Epithelial Cells Isolated from Human Hair Follicles Using Enzymatic Digestion or Explants Result in Comparable Tissue-Engineered Skin
Bibliographic record
Abstract
Hair follicle stem cells, located in the bulge region of the outer root sheath, are multipotent epithelial stem cells capable of differentiating into epidermal, sebaceous gland, and hair shaft cells. Efficient culturing of these cells is crucial for advancements in dermatology, regenerative medicine, and skin model development. This investigation aimed to develop a protocol for isolating enriched bulge-derived epithelial cells from scalp specimens to produce tissue-engineered substitutes. The epithelium, including hair follicles, was separated from the dermis using thermolysin, followed by microdissection of the bulge region. Epithelial stem cells were isolated using enzymatic dissociation to create a single-cell suspension and compared with the direct explant culture and a benchmark method which isolates cells from the epidermis and pilosebaceous units. After 8 days of culture, the enzymatic digestion of microdissected bulges yielded 5.3 times more epithelial cells compared to explant cultures and proliferated faster than the benchmark method. Cells cultured from all methods exhibited comparable morphology and growth rates. The fully stratified epidermis of tissue-engineered skin was similar, indicating comparable differentiation potential. This enzymatic digestion method improved early-stage cell recovery and expansion while maintaining keratinocyte functionality, offering an efficient hair bulge cell-extraction technique for tissue engineering and regenerative medicine 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".