Genetic Manipulation of Human Embryonic Stem Cells: A System to Study Early Human Development and Potential Therapeutic Applications
Bibliographic record
Abstract
The successful derivation of human embryonic stem cell (hESC) lines by Thomson and colleagues [Thomson et al., 1998] provided a new area of investigation in both regenerative medicine and early human development. Fundamental study of the molecular and cellular mechanisms responsible for normal lineage development will rely on reproducible protocols to direct the differentiation of hESCs into specific lineages of interest and genetically manipulate both hESCs and their derivatives. Identifying standards for maintenance of hESCs, methods for controlled differentiation and genetic manipulation of hESCs and their derivatives will provide a foundation to explore their potential therapeutic use in cell and gene therapy. In the present review, our goal is to outline the latest advances in the field with particular focus on how hESCs and their derivatives can be genetically altered, how this may be useful in better understanding the cellular and molecular events of lineage differentiation, and how deregulation of these cellular processes may lead to abnormal development and disease.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".