Epigenetic of Somatic Cells Reprogramming
Bibliographic record
Abstract
Embryonic stem cells (ESCs) is a highly undifferentiated pluripotent cells, can differentiate into various cells. Stem cell therapy is an opportunity of the human regenerative medicine, and also a focus of medical research. While the source of embryonic stem cells is controversial in ethics, which indirectly promotes the technology development of somatic cell reprogramming and induced pluripotent stem cells (iPSCs). Although iPSCs can produce healthy mature individuals, there are still some limitations, such as the success rate of iPSCs is very low and the problem of tumorigenicity. ESCs and iPSCs have difference in gene expression level and the potential mechanism of epigenetic regulation. In the process of cell differentiation and development, DNA sequence has no change, which means the specific epigenetic modifications (DNA methylation, histone modifications, miRNA and lncRNA) plays an important role in the pluripotent maintenance and differentiation of ESCs and iPSCs. In this review, we discussed the regulation effect of various epigenetic modifications in the pluripotent maintenance and differentiation of ESCs and iPSCs, and the relationship between epigenetic modifications and somatic reprogramming is also discussed. We think, based on the various omics data produced by next generation sequencing, developing bioinformatics methods will greatly promote the stem cell research and its clinical application.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".