Tissue engineering of skin and cornea
Bibliographic record
Abstract
Human beings are greatly preoccupied with the unavoidable nature of aging. While the biological processes of senescence and aging are the subjects of intense investigations, the molecular mechanisms linking aging with disease and death are yet to be elucidated. Tissue engineering offers new models to study the various processes associated with aging. Using keratin 19 as a stem cell marker, our studies have revealed that stem cells are preserved in human skin reconstructed by tissue engineering and that the number of epithelial stem cells varies according to the donor's age. As with skin, human corneas can also be engineered in vitro. Among the epithelial cells used for reconstructing skin and corneas, significant age-dependent variations in the expression of the transcription factor Sp1 were observed. Culturing skin epithelial cells with a feeder layer extended their life span in culture, likely by preventing Sp1 degradation in epithelial cells, therefore demonstrating the pivotal role played by this transcription factor in cell proliferation. Finally, using the human tissue-engineered skin as a model, we linked Hsp27 activation with skin differentiation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".