Improved viability of stem cell transplants in animal models of Parkinson's disease
Bibliographic record
Abstract
Cell-based therapy with the replacement of midbrain dopamine neurons is viewed as a potential method of treating Parkinson's disease (PD).However, many scientific, technical, as well as ethical issues remain, including source of the dopamine cells, methods of delivery, survival of cells, and potential long-term benefit of such an approach.Human pluripotent stem cells (PSCs) have been suggested as a potential source of dopamine cells that may overcome some of these concerns.Differentiation of PSCs into midbrain dopamine cells has been achieved in vitro, but, to date, viable dopamine neurons from human PSCs have been unsuccessful in vivo, and concerns around teratomas exist.The recent study by Kriks, et al. in Nature reports on the improved efficacy and potential safety of human PSCs by use of a novel differentiation protocol to improve specificity of dopamine cells for more efficient grafting.1 Several factors are known to be important for midbrain floor-plate dopamine cell development, including a floor-plate transcription marker, FOXA2 and LMX1A (a roof-plate marker), both of which are induced by the activation of sonic hedgehog signaling.The investigators used a variety of small-molecule activators, including CHIR, a glycogen synthase kinase-3 beta inhibitor, to improve the neurogenic conversion of PSC-derived midbrain floor-plate cells toward
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".