Human Embryonic Stem Cells: Isolation, Maintenance, and Differentiation
Bibliographic record
Abstract
The isolation of pluripotent human embryonic stem (hES) cells having the capacity to differentiate in vitro to numerous cell types generated much excitement and promise in the field of regenerative medicine. However, along with great enthusiasm came hot controversy for stem cell research and researchers alike because available hES cell lines were isolated from "excess" embryos from in vitro fertilization clinics. Despite ethical and political debates, the methods and protocols to study diverse lineages are developing. Furthermore, strategies using specific growth factor combinations, cell-cell and cell-extracellular matrix induction systems are being explored for directed differentiation along a desired lineage. However, there is a great need to characterize the mechanisms that control self-renewal and differentiation and a necessity to improve methodologies and develop new purification protocols for the potential future clinical application of hES cells. After the scientific and political obstacles are overcome, it is anticipated that the hES cell field will make a tremendous difference in conditions, such as burn traumas and diabetic foot ulcers, as well a number of degenerative diseases such as Parkinson's disease, type 1 diabetes, rheumatoid arthritis, and myocardial infarction. In this introductory chapter, we will summarize and review recent progress in the field of hES cell differentiation protocols and discuss some of the current issues surrounding hES cell research.
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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".