Gene expression profiling discerns molecular pathways elicited by ligand signaling to enhance the specification of embryonic stem cells into skeletal muscle lineage
Bibliographic record
Abstract
Regulation of lineage specification and differentiation in embryonic stem (ES) cells can be achieved through the activation of endogenous signaling, an avenue for potential application in regenerative medicine. During vertebrate development, retinoic acid (RA) plays an important role in body axis elongation and mesoderm segmentation in that graded exposure to RA provides cells with positional identity and directs commitment to specific tissue lineages. Nevertheless, bexarotene, a clinically approved rexinoid, enhances the specification and differentiation of ES cells into skeletal myocytes more effectively than RA. Thus profiling the transcriptomes of ES cells differentiated with bexarotene or RA permits the identification of different genetic targets and signaling pathways that may contribute to the difference of bexarotene and RA in efficiency of myogenesis. Interestingly, bexarotene induces the early expression of a myogenic progenitor marker, Meox1 , while the expression of many RA targets is also enhanced by bexarotene. Several signaling molecules involved in the progression of myogenic specification and commitment are differentially regulated by bexarotene and RA, suggesting that early targets of rexinoid allow the coordinated regulation of molecular events which leads to efficient myogenic differentiation in ES 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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".