Irradiated Human Dermal Fibroblasts Are as Efficient as Mouse Fibroblasts as a Feeder Layer to Improve Human Epidermal Cell Culture Lifespan
Bibliographic record
Abstract
A fibroblast feeder layer is currently the best option for large scale expansion of autologous skin keratinocytes that are to be used for the treatment of severely burned patients. In a clinical context, using a human rather than a mouse feeder layer is desirable to reduce the risk of introducing animal antigens and unknown viruses. This study was designed to evaluate if irradiated human fibroblasts can be used in keratinocyte cultures without affecting their morphological and physiological properties. Keratinocytes were grown either with or without a feeder layer in serum-containing medium. Our results showed that keratinocytes grown either on an irradiated human feeder layer or irradiated 3T3 cells (i3T3) can be cultured for a comparable number of passages. The average epithelial cell size and morphology were also similar. On the other hand, keratinocytes grown without a feeder layer showed heavily bloated cells at early passages and stop proliferating after only a few passages. On the molecular aspect, the expression level of the transcription factor Sp1, a useful marker of keratinocytes lifespan, was maintained and stabilized for a high number of passages in keratinocytes grown with feeder layers whereas Sp1 expression dropped quickly without a feeder layer. Furthermore, gene profiling on microarrays identified potential target genes whose expression is differentially regulated in the absence or presence of an i3T3 feeder layer and which may contribute at preserving the growth characteristics of these cells. Irradiated human dermal fibroblasts therefore provide a good human feeder layer for an effective expansion of keratinocytes in vitro that are to be used for clinical purposes.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".