Pluripotency Crossroads: Junction of Transcription Factors, Epigenetic Mechanisms, MicroRNAs, and Long Non-coding RNAs
Bibliographic record
Abstract
Embryonic stem cells (ESCs) are derived from inner cell mass (ICM) and have the potency to differentiate into three germ layers (ectoderm, endoderm, and mesoderm). This potency of ESCs, called pluripotency, is critical for maintaining stemness. Transcriptional regulatory circuitry preserving stemness consists of transcription factors (TFs), epigenetic mechanisms, microRNAs (miRNAs or miRs), and long non-coding RNAs (lncRNAs). In this circuitry, components assist each other to activate essential genes for maintaining pluripotency and suppressing lineage-specific genes. TFs act directly by binding to their binding sites in the genome or indirectly by activating another gene (such as a miR), epigenetic mechanisms play their role by providing an activatory or inhibitory context for transcription, miRNAs regulate gene expression at the post-transcriptional level, and lncRNAs act as a scaffold function for epigenetic elements, regulating gene expression in ESCs. All these factors create a crossroad and collaborate to sustain stemness in the ESCs. Herein, we explain the role of each member in this circuitry and demonstrate the significance of the crossroad for keeping stemness.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".