Epigenetic Conversion as a Safe and Simple Method to Obtain Insulin-secreting Cells from Adult Skin Fibroblasts
Bibliographic record
Abstract
Regenerative medicine requires new, fully functional cells that are delivered to patients in order to repair degenerated or damaged tissues. When such cells are not readily available, they can be obtained using different approaches that include, among the many, reprogramming and trans-differentiation, with advantages and limitations that are specific of the different techniques. Here a new strategy for the conversion of an adult mature fibroblast into an insulin-secreting cell, arbitrarily designated as epigenetic converted cells (EpiCC), is described. The method has been developed, based on the increasing understanding of the mechanisms controlling epigenetic regulation of cell fate and differentiation. In particular, the first step uses an epigenetic modifier, namely 5-aza-cytidine, to drive adult cells into a "highly permissive" state. It then takes advantage of this brief and reversible window of epigenetic plasticity, to re-address cells toward a different lineage. The approach is designated "epigenetic cell conversion". It is a simple and robust way to obtain an efficient, controlled and stable cellular inter-lineage switch. Since the protocol does not involve the use of any gene transfection, it is free of viral vectors and does not involve a stable pluripotent state, it is highly promising for translational medicine applications.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".