Effect of Fibroblast Growth Factor-2 on Melanocyte Proliferation in Tissue-Engineered Skin Substitutes
Bibliographic record
Abstract
Burn patients treated with tissue-engineered skin substitutes (TESs) often experience pigmentation irregularities, including hypopigmentation and pigmentation spots. These issues are thought to stem from the reduced presence of melanocytes through dilution during TES manufacturing. To address this, we hypothesized that supplementing epithelial cell cultures-primarily composed of keratinocytes but also containing melanocytes-with Fibroblast Growth Factor-2 (FGF-2), a known promoter of melanocyte proliferation, could enhance melanocyte growth. This would potentially increase their numbers in TESs and improve pigmentation outcomes. Our findings indicate that FGF-2, at an optimal dose of 0.2 nM, effectively maintains melanocyte numbers in 2D cultures and epithelial cell cultures through the first passage. Importantly, this treatment does not interfere with keratinocyte proliferation or differentiation, nor does it affect TES integrity. However, FGF-2 supplementation alone did not increase the proportion of melanocytes in epithelial cultures beyond the first passage or in TESs. In summary, while FGF-2 supports melanocyte growth in culture, its addition alone was insufficient to significantly improve TES pigmentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".