The Future of Cell-Based Transplantation Therapies for Neurodegenerative Disorders
Bibliographic record
Abstract
Parkinson's disease is a common neurodegenerative disease with a lifetime incidence of 2.5% and a prevalence of at least 2% in individuals over 70 years old. Patients can be effectively treated with drugs that target the dopaminergic nigro-striatal pathway, but over time the efficacy of these medications is limited by the development of profound motor fluctuations and dyskinesias. This has prompted the search for alternative treatments, including the use of cell replacement therapies. Over the last decade, human fetal nigral transplants have demonstrated that dopaminergic neurons can survive and provide clinical benefit for patients with Parkinson's disease. However, there are clearly ethical concerns and a limit to the supply of this tissue as well as more recently anxieties over side effects. As a result, alternative sources of tissue have been investigated, and one such source are stem cells, which provide an attractive renewable tissue supply. In this review, we will discuss the current state-of-the-art and the characteristics of Parkinson's disease that increase its attraction as a target of stem cell therapy against results of current clinical trials using fetal neural grafts. Then we will discuss the various types and sources of stem cells, and some early transplantation results in animal models of Parkinson's disease. Finally we will discuss the prospect of using stem cells to deliver drugs and neurotrophic factors involved in neuroprotective and neuroreparative strategies in Parkinson's disease and other neurodegenerative conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".