Brain Transplantation of Immortalized Human Neural Stem Cells Promotes Functional Recovery in Mouse Intracerebral Hemorrhage Stroke Model
Bibliographic record
Abstract
We have generated stable, immortalized cell lines of human NSCs from primary human fetal telencephalon cultures via a retroviral vector encoding v-myc. HB1.F3, one of the human NSC lines, expresses a normal human karyotype of 46, XX, and nestin, a cell type-specific marker for NSCs. F3 has the ability to proliferate continuously and differentiate into cells of neuronal and glial lineage. The HB1.F3 human NSC line was used for cell therapy in a mouse model of intracerebral hemorrhage (ICH) stroke. Experimental ICH was induced in adult mice by intrastriatal administration of bacterial collagenase; 1 week after surgery, the rats were randomly divided into two groups so as to receive intracerebrally either human NSCs labeled with beta-galactosidase (n = 31) or phosphate-buffered saline (PBS) (n = 30). Transplanted NSCs were detected by 5-bromo-4-chloro-3-indolyl-beta-d-galactoside histochemistry or double labeling with beta-galactosidase (beta-gal) and mitogen-activated protein (MAP)2, neurofilaments (both for neurons), or glial fibrillary acidic protein (GFAP) (for astrocytes). Behavior of the animals was evaluated for period up to 8 weeks using modified Rotarod tests and a limb placing test. Transplanted human NSCs were identified in the perihematomal areas and differentiated into neurons (beta-gal/MAP2(+) and beta-gal/NF(+)) or astrocytes (beta-gal/GFAP(+)). The NSC-transplanted group showed markedly improved functional performance on the Rotarod test and limb placing after 2-8 weeks compared with the control PBS group (p < .001). These results indicate that the stable immortalized human NSCs are a valuable source of cells for cell replacement and gene transfer for the treatment of ICH and other human neurological disorders. Disclosure of potential conflicts of interest is found at the end of this article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.002 | 0.001 |
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".