Human Neural Stem Cells Over-Expressing VEGF Provide Neuroprotection, Angiogenesis and Functional Recovery in Mouse Stroke Model
Bibliographic record
Abstract
BACKGROUND: Intracerebral hemorrhage (ICH) is a lethal stroke type. As mortality approaches 50%, and current medical therapy against ICH shows only limited effectiveness, 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 induce behavioral recovery in rat ICH model, and that combined administration of NSCs and vascular endothelial growth factor (VEGF) results in improved structural and functional outcome from cerebral ischemia. METHODS AND FINDINGS: We postulated that human NSCs overexpressing VEGF transplanted into cerebral cortex overlying ICH lesion could provide improved survival of grafted NSCs, increased angiogenesis and behavioral recovery in mouse ICH model. ICH was induced in adult mice by unilateral injection of bacterial collagenase into striatum. HB1.F3.VEGF human NSC line produced an amount of VEGF four times higher than parental F3 cell line in vitro, and induced behavioral improvement and 2-3 fold increase in cell survival at two weeks and eight weeks post-transplantation. CONCLUSIONS: Brain transplantation of F3 human NSCs over-expressing VEGF near ICH lesion sites provided differentiation and survival of grafted human NSCs and renewed angiogenesis of host brain and functional recovery of ICH animals. These results suggest a possible application of the human neural stem cell line, which is genetically modified to over-express VEGF, as a therapeutic agent for ICH-stroke.
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) | 0.000 | 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 | 0.000 |
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".