Evaluation of a Serum-Free Medium for Human Epithelial and Stromal Cell Culture
Bibliographic record
Abstract
Over the past decade, growing demand from many domains (research, cosmetics, pharmaceutical industries, etc.) has given rise to significant expansion of the number of in vitro cell cultures. Despite the widespread use of fetal bovine serum, many issues remain. Among them, the whole constitution of most serums remains unknown and is subject to significant variations. Furthermore, the presence of potential contamination and xenogeny elements is challenging for clinical applications, while limited production is an obstacle to the growing demand. To circumvent these issues, a Serum-Free Medium (SFM) has been developed to culture dermal and vesical fibroblasts and their corresponding epithelial cells, namely, keratinocytes and urothelial cells. To assess the impact of SFM on these cells, proliferation, clonogenic and metabolic assays have been compared over three passages to conditions associated with the use of a classic Fetal Bovine Serum-Containing Medium (FBSCM). The results showed that the SFM enabled fibroblast and epithelial cell proliferation while maintaining a morphology, cell size and metabolism similar to those of FBSCM. SFM has repeatedly been found to be better suited for epithelial cell proliferation and clonogenicity. Fibroblasts and epithelial cells also showed more significant mitochondrial metabolism in the SFM compared to the FBSCM condition. However, the SFM may need further optimization to improve fibroblast proliferation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".