Stem cells of the skin and cornea: their clinical applications in regenerative medicine
Bibliographic record
Abstract
PURPOSE OF REVIEW: The use of stem cells is of great interest for the treatment of various pathologies and ultimately for the restoration of organ function. Progress pointing towards future treatments of skin and corneal epithelial stem cell defects are reviewed, including the transplantation of living tissue-engineered substitutes. RECENT FINDINGS: This article focuses on substitutes optimized for permanent replacement of skin and cornea. New skin substitutes for burn care are currently under development. More complex tissue-engineered skin substitutes in which stroma, adipose tissue, capillaries, and neurons are combined with the epithelium are being developed. Some dermal/epidermal substitutes have been applied to the treatment of patients. Cultured corneal epithelial cells have been characterized and more complete corneal substitutes are being designed. Long-term clinical results on the transplantation of cultured corneal stem cells for the treatment of limbal stem cell deficiency have been reported. SUMMARY: Advances in tissue engineering for the development of substitutes that will benefit patients suffering from skin or corneal stem cell deficiencies are reviewed. These products are often a combination of cells, scaffolds and other factors. Key considerations in the development of corneal and skin substitutes for clinical applications are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".