PEX-Producing Human Neural Stem Cells Inhibit Tumor Growth in a Mouse Glioma Model
Bibliographic record
Abstract
A unique characteristic of neural stem cells is their capacity to track glioma cells that have migrated away from the main tumor mass into the normal brain parenchyma. PEX, a naturally occurring fragment of human metalloproteinase-2, acts as an inhibitor of glioma and endothelial cell proliferation, migration, and angiogenesis. In the present study, we evaluated the antitumor activity of PEX-producing human neural stem cells against malignant glioma. The HB1.F3 cell line (immortalized human neural stem cell) was transfected by a pTracer vector with PEX. The retention of the antiproliferative activity and migratory ability of PEX-producing HB1.F3 cells (HB1.F3-PEX) was confirmed in vitro. For the in vivo studies, DiI-labeled HB1.F3-PEX cells were stereotactically injected into established glioma tumor in nude mice. Tumor size was subsequently measured by magnetic resonance imaging and at the termination of the studies by histologic analysis including tumor volume, microvessel density, proliferation, and apoptosis rate. Histologic analysis showed that DiI-labeled HB1.F3-PEX cells migrate at the tumor boundary and cause a 90% reduction of tumor volume (P < 0.03). This reduction in tumor volume in animals treated with HB1.F3-PEX was associated with a significant decrease in angiogenesis (44.8%, P < 0.03) and proliferation (23.6%, P < 0.03). These results support the use of neural stem cells as delivery vehicle for targeting therapeutic genes against human glioma.
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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) | 0.001 | 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".