Early Cell Fate Decisions of Human Embryonic Stem Cells and Mouse Epiblast Stem Cells Are Controlled by the Same Signalling Pathways
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human embryonic stem cells have unique value for regenerative medicine, as they are capable of differentiating into a broad variety of cell types. Therefore, defining the signalling pathways that control early cell fate decisions of pluripotent stem cells represents a major task. Moreover, modelling the early steps of embryonic development in vitro may provide the best approach to produce cell types with native properties. Here, we analysed the function of key developmental growth factors such as Activin, FGF and BMP in the control of early cell fate decisions of human pluripotent stem cells. This analysis resulted in the development and validation of chemically defined culture conditions for achieving specification of human embryonic stem cells into neuroectoderm, mesendoderm and into extra-embryonic tissues. Importantly, these defined culture conditions are devoid of factors that could obscure analysis of developmental mechanisms or render the resulting tissues incompatible with future clinical applications. Importantly, the growth factor roles defined using these culture conditions similarly drove differentiation of mouse epiblast stem cells derived from post implantation embryos, thereby reinforcing the hypothesis that epiblast stem cells share a common embryonic identity with human pluripotent stem cells. Therefore the defined growth factor conditions described here represent an essential step toward the production of mature cell types from pluripotent stem cells in conditions fully compatible with clinical use ant also provide a general approach for modelling the early steps of mammalian embryonic development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it