Recent Advances in the Development of Stem‐Cell‐Derived Dopaminergic Neuronal Transplant Therapies for Parkinson's Disease
Bibliographic record
Abstract
The last decade has seen exciting advances in the development of potential stem cell-based therapies for Parkinson's disease (PD), which have used different types of stem cells as starting material. These cells have been developed primarily to replace dopamine-producing neurons in the substantia nigra that are progressively lost in the disease process. The aim is to largely restore lost motor functions, whilst not ever being curative. We discuss cell-based strategies that will have to fulfill important criteria to become effective and competitive therapies for PD. These criteria include reproducibly producing sufficient numbers of cells with an authentic substantia nigra dopamine neuron A9 phenotype, which can integrate into the host brain after transplantation and form synapses (considered crucial for long-term functional benefits). Furthermore, it is essential that transplanted cells exhibit no, or only very low levels of, proliferation without tumor formation at the site of grafting. Cumulative research has shown that stem cell-based approaches continue to have great potential in PD, but key questions remain to be answered. Here, we review the most recent progress in research on stem cell-based dopamine neuron replacement therapy for PD and briefly discuss what the immediate future might hold. © 2021 International Parkinson and Movement Disorder Society.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".