Brain transplantation of genetically engineered human neural stem cells globally corrects brain lesions in the mucopolysaccharidosis type VII mouse
Bibliographic record
Abstract
In the present study, we investigated the feasibility of using human neural stem cells (NSCs) in the treatment of diffuse central nervous system (CNS) alterations in a murine model of mucopolysaccharidosis VII (MPS VII), a lysosomal storage disease caused by a genetic defect in the beta-glucuronidase gene. An immortalized NSC line derived from human fetal telencephalon was genetically engineered to overexpress beta-glucuronidase and transplanted into the cerebral ventricles of neonatal MPS VII mouse. Transplanted human NSCs were found to integrate and migrate in the host brain and to produce large amount of beta-glucuronidase. Brain contents of the substrates of beta-glucuronidase were reduced to nearly normal levels, and widespread clearing of lysosomal storage was observed in the MPS VII mouse brain at 25 days posttransplantation. The number of engrafted cells decreased markedly after the transplantation, and it appears that the major cause of the cell death was not the immune response of the host but apoptotic cell death of grafted human NSCs. Results showed that human NSCs would serve as a useful gene transfer vehicle for the treatment of diffuse CNS lesions in human lysosomal storage diseases and are potentially applicable in the treatment of patients suffering from neurological disorders.
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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".