Neuralized Mouse Embryonic Stem Cells Develop Neural Rosette-Like Structures in Response to Retinoic Acid and Produce Teratomas in the Brains of Syngeneic Mice
Bibliographic record
Abstract
Several induction protocols can direct differentiation of mouse embryonic stem cells (ESCs) to become neural cells. The B5 and B6 mouse ESC lines display different growth patterns in vitro, and when grown as adherent cultures, the B6 ESCs proliferated at a significantly lower rate than B5 ESCs. Remarkably, after a neural induction protocol that includes removal of LIF and addition of retinoic acid (RA), mature B6 embryoid bodies (EBs) displayed a unique neural rosette-like morphology. On Day 8 of neural induction, B6 EBs revealed mature neuronal markers localized primarily to cells in the center of the EBs and glial markers expressed both in centrally and peripherally located cells. In contrast to B5 cells, when neuralized Day 8 B6 EB cells were dissociated and transplanted into the left striatum of syngeneic C57BL/6 mouse brains, teratomas formed. In addition, teratomas established from undifferentiated B6 cells grew more rapidly and achieved larger volumes when compared to those produced by Day 8, neuralized B6 EBs. The slow growth rate of B6 cells in vitro may have contributed to incomplete neuralization, formation of neural rosette-like structures, and a propensity to form teratomas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".