Somatic cell transformation into stem cell-like cells induced by different microenvironments
Bibliographic record
Abstract
Development of induced pluripotent stem cell (iPSC) technology introduced a novel way to derive pluripotent stem cells, but the genetic manipulation required to generate iPSCs may lead to uncontrolled tumorigenesis of the established cells and thus limit clinical feasibility of the technology. Numerous attempts have been made to date, and alternative reprogramming of somatic cells to reactivate cellular plasticity after differentiation has been suggested. As a result, it had become clear that cell-to-cell interactions and specific acellular environments can be utilized for somatic cell reprogramming. In our previous studies, embryonic stem cell (ESC)-like cells could be derived from transforming ovarian cells and fetal fibroblasts by cell-to-cell interaction or specific cell-mediated microenvironmental factor(s). This cellular event was induced without undertaking genetic manipulation of progenitor cells. Several differences were found between the cellular properties of niche-induced, ESC-like cells and those of genetically manipulated iPSCs and the referenced ESCs. Thus, we provided evidence that terminally differentiated somatic cells either acquire pluripotency-like activity or possess cellular and genetic plasticity under a specific microenvironment and/or cell-to-cell interaction. In this minireview, we discuss derivation of stem cell-like cells under specific microenvironmental conditions in terms of technical perspectives and limitations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".