The Role of microRNAs in Animal Cell Reprogramming
Bibliographic record
Abstract
Our concept of cell reprogramming and cell plasticity has evolved since John Gurdon transferred the nucleus of a completely differentiated cell into an enucleated Xenopus laevis egg, thereby generating embryos that developed into tadpoles. More recently, induced expression of transcription factors, oct4, sox2, klf4, and c-myc has evidenced the plasticity of the genome to change the expression program and cell phenotype by driving differentiated cells to the pluripotent state. Beyond these milestone achievements, research in artificial cell reprogramming has been focused on other molecules that are different than transcription factors. Among the candidate molecules, microRNAs (miRNAs) stand out due to their potential to control the levels of proteins that are involved in cellular processes such as self-renewal, proliferation, and differentiation. Here, we review the role of miRNAs in the maintenance and differentiation of mesenchymal stem cells, epimorphic regeneration, and somatic cell reprogramming to induced pluripotent stem cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".