Human neural stem cells genetically modified to overexpress brain‐derived neurotrophic factor promote functional recovery and neuroprotection in a mouse stroke model
Bibliographic record
Abstract
Intracerebral hemorrhage (ICH) is a lethal stroke type; mortality approaches 50%, and current medical therapy against ICH shows only limited effectiveness, so an alternative approach is required, such as stem cell-based cell therapy. Previously we have shown that intravenously transplanted human neural stem cells (NSCs) selectively migrate to the brain and promote functional recovery in rat ICH model, and others have shown that intracerebral infusion of brain-derived neurotrophic factor (BDNF) results in improved structural and functional outcome from cerebral ischemia. We postulated that human NSCs overexpressing BDNF transplanted into cerebral cortex overlying ICH lesion could provide improved survival of grafted NSCs and increased angiogenesis and behavioral recovery in mouse ICH model. ICH was induced in adult mice by injection of bacterial collagenase into striatum. The HB1.F3.BDNF (F3.BDNF) human NSC line produces sixfold higher amounts of BDNFF over the parental F3 cell line in vitro, induces behavioral improvement, and produces a threefold increase in cell survival at 2 weeks and 8 weeks posttransplantation. Brain transplantation of human NSCs overexpressing BDNF provided differentiation and survival of grafted human NSCs and renewed angiogenesis of host brain and functional recovery of ICH animals. These results indicate that the F3.BDNF human NSCs should be of great value as a cellular source for experimental studies involving cellular therapy for human neurological disorders, including ICH.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".