Production of a Self-Aligned Scaffold, Free of Exogenous Material, from Dermal Fibroblasts Using the Self-Assembly Technique
Bibliographic record
Abstract
Many pathologies of skin, especially ageing and cancer, involve modifications in the matrix alignment. Such tissue reorganization could have impact on cell behaviour and/or more global biological processes. Tissue engineering provides accurate study model by mimicking the skin and it allows the construction of versatile tridimensional models using human cells. It also avoids the use of animals, which gave sometimes nontranslatable results. Among the various techniques existing, the self-assembly method allows production of a near native skin, free of exogenous material. After cultivating human dermal fibroblasts in the presence of ascorbate during two weeks, a reseeding of these cells takes place after elevation of the resulting stroma on a permeable ring and culture pursued for another two weeks. This protocol induces a clear realignment of matrix fibres and cells parallel to the horizon. The thickness of this stretched reconstructed tissue is reduced compared to the stroma produced by the standard technique. Cell count is also reduced. In conclusion, a new, easy, and inexpensive method to produce aligned tissue free of exogenous material could be used for fundamental research applications in dermatology.
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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".