Transplantation of GABAergic Cells Derived from Bioreactor-Expanded Human Neural Precursor Cells Restores Motor and Cognitive Behavioral Deficits in a Rodent Model of Huntington's Disease
Bibliographic record
Abstract
Huntington's disease (HD) is a neurodegenerative disorder that is characterized by progressive dementia, choreiform involuntary movements, and emotional deterioration. Neuropathological features include the progressive degeneration of striatal γ-aminobutyric acid (GABA) neurons. New therapeutic approaches, such as the transplantation of human neural precursor cells (hNPCs) to replace damaged or degenerated cells, are currently being investigated. The aim of this study was to investigate the potential for utilizing telencephalic hNPCs expanded in suspension bioreactors for cell restorative therapy in a rodent model of HD. hNPCs were expanded in a hydrodynamically controlled and homogeneous environment under serum-free conditions. In vitro analysis revealed that the bioreactor-expanded telencephalic (BET)-hNPCs could be differentiated into a highly enriched population of GABAergic neurons. Behavioral assessments of unilateral striatal quinolinic acid-lesioned rodents revealed a significant improvement in motor and memory deficits following transplantation with GABAergic cells differentiated from BET-hNPCs. Immunohistochemical analysis revealed that transplanted BET-hNPCs retained a GABAergic neuronal phenotype without aberrant transdifferentiation or tumor formation, indicating that BET-hNPCs are a safe source of cells for transplantation. This preclinical study has important implications as the transplantation of GABAergic cells derived from predifferentiated BET-hNPCs may be a safe and feasible cell replacement strategy to promote behavioral recovery in HD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".