Survival, differentiation, and migration of bioreactor-expanded human neural precursor cells in a model of Parkinson disease in rats
Bibliographic record
Abstract
OBJECT: Fetal tissue transplantation for Parkinson disease (PD) has demonstrated promising results in experimental and clinical studies. However, the widespread clinical application of this therapeutic approach is limited by a lack of fetal tissue. Human neural precursor cells (HNPCs) are attractive candidates for transplantation because of their long-term proliferation activity. Furthermore, these cells can be reproducibly expanded in a standardized fashion in suspension bioreactors. In this study the authors sought to determine whether the survival, differentiation, and migration of HNPCs after transplantation depended on the region of precursor cell origin, intracerebral site of transplantation, and duration of their expansion. METHODS: Human neural precursor cells were isolated from the telencephalon, brainstem, ventral mesencephalon, and spinal cord of human fetuses 8-10 weeks of gestational age, and their differentiation potential characterized in vitro. After expansion in suspension bioreactors, the HNPCs were transplanted into the striatum and substantia nigra of parkinsonian rats. Histological analyses were performed 7 weeks posttransplantation. RESULTS: The HNPCs isolated from various regions of the neuraxis demonstrated diverse propensities to differentiate into astrocytes and neurons and could all successfully expand under standardized conditions in suspension bioreactors. At 7 weeks posttransplantation, survival and migration were significantly greater for HNPCs obtained from the more rostral brain regions. The HNPCs differentiated predominantly into astrocytes after transplantation into the striatum or substantia nigra regions, and thus no behavioral improvement was observed. CONCLUSIONS: Understanding the regional differences in HNPC properties is prerequisite to their application for PD cell restoration strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".