Optimization of Storage Temperature for Retention of Undifferentiated Cell Character of Cultured Human Epidermal Cell Sheets
Bibliographic record
Abstract
Cultured epidermal cell sheets (CES) containing undifferentiated cells are useful for treating skin burns and have potential for regenerative treatment of other types of epithelial injuries. The undifferentiated phenotype is therefore important for success in both applications. This study aimed to optimize a method for one-week storage of CES for their widespread distribution and use in regenerative medicine. The effect of storage temperatures 4 °C, 8 °C, 12 °C, 16 °C, and 24 °C on CES was evaluated. Analyses included assessment of viability, mitochondrial reactive oxygen species (ROS), membrane damage, mitochondrial DNA (mtDNA) integrity, morphology, phenotype and cytokine secretion into storage buffer. Lowest cell viability was seen at 4 °C. Compared to non-stored cells, ABCG2 expression increased between temperatures 8-16 °C. At 24 °C, reduced ABCG2 expression coincided with increased mitochondrial ROS, as well as increased differentiation, cell death and mtDNA damage. P63, C/EBPδ, CK10 and involucrin fluorescence combined with morphology observations supported retention of undifferentiated cell phenotype at 12 °C, transition to differentiation at 16 °C, and increased differentiation at 24 °C. Several cytokines relevant to healing were upregulated during storage. Importantly, cells stored at 12 °C showed similar viability and undifferentiated phenotype as the non-stored control suggesting that this temperature may be ideal for storage of CES.
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".