Neural stem cell‐based treatment for neurodegenerative diseases
Bibliographic record
Abstract
Human neurodegenerative diseases such as Parkinson's disease (PD), Huntington's disease (HD), amyotrophic lateral sclerosis (ALS) and Alzheimer's disease (AD) are caused by a loss of neurons and glia in the brain or spinal cord. Neurons and glial cells have successfully been generated from stem cells such as embryonic stem cells (ESCs), mesenchymal stem cells (MSCs) and neural stem cells (NSCs), and stem cell-based cell therapies for neurodegenerative diseases have been developed. A recent advance in generation of a new class of pluripotent stem cells, induced pluripotent stem cells (iPSCs), derived from patients' own skin fibroblasts, opens doors for a totally new field of personalized medicine. Transplantation of NSCs, neurons or glia generated from stem cells in animal models of neurodegenerative diseases, including PD, HD, ALS and AD, demonstrates clinical improvement and also life extension of these animals. Additional therapeutic benefits in these animals can be provided by stem cell-mediated gene transfer of therapeutic genes such as neurotrophic factors and enzymes. Although further research is still needed, cell and gene therapy based on stem cells, particularly using neurons and glia derived from iPSCs, ESCs or NSCs, will become a routine treatment for patients suffering from neurodegenerative diseases and also stroke and spinal cord injury.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".