Bibliographic record
Abstract
Parkinson’s disease (PD) is referred to as a neurodegenerative disease which is a disease that targets specific brain regions and is characterized by neuronal death. PD is believed to be caused by the loss of nerve cells in the substantia nigra (SN), a dopamine releasing area (Dickson, 2012). Current treatments are directed at alleviating pain symptoms and slowing down the progression of disease, however, no cure currently exists. Recent advances in stem cell therapies raise new possibilities to treat neurodegenerative diseases. Stem cells have the ability to differentiate into neural cells, and thus, could potentially be used to restore neurogenesis and neuroplasticity (Lunn et al., 2011). There exist several cell types that can be applied in therapy including embryonic stem cells (ESCs), neural stem cells (NSCs), induced pluripotent stem cells (iPSCs), and mesenchymal stem cells (MSCs). PD which has localized neural degeneration to the SN may serve as a better model for stem cell therapy and displays greater success when compared to other neurodegenerative diseases that spread to several brain regions (Vasic et al., 2019). This review aims to discuss the several approaches used in stem cell therapy as well as the current challenges and shortcomings of this cell-based therapy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.002 |
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".