Targeting Rat Brainstem Glioma Using Human Neural Stem Cells and Human Mesenchymal Stem Cells
Bibliographic record
Abstract
PURPOSE: Brainstem gliomas are usually inoperable and have a dismal prognosis. Based on the robust tropisms of neural stem cells (NSC) and mesenchymal stem cells (MSC) to brain tumors, we compared the tumor-tropic migratory capacities of these stem cells and evaluated the therapeutic potential of genetically engineered human NSCs encoding cytosine deaminase (CD) and IFNbeta against brainstem gliomas. EXPERIMENTAL DESIGN: The directed migratory capacities of NSCs and MSCs to brainstem glioma (F98) were evaluated both in vitro and in vivo. The human NSCs (HB1.F3) and various human MSCs, such as bone marrow-derived MSCs (HM3.B10), adipose tissue-derived MSCs, and umbilical cord blood-derived MSCs, were tested. Human fibroblast cells (HFF-1) were used as the negative control. As a proof of concept, the bioactivity of HB1.F3-CD-IFNbeta was analyzed with a cell viability assay, and animals with brainstem gliomas were injected with HB1.F3-CD-IFNbeta cells followed by systemic 5-fluorocytosine treatment. RESULTS: In an in vitro modified Transwell migration assay and in vivo stem cell injection into established brainstem gliomas in rats, all the stem cells showed a significant migratory capacity compared with that of the control (P < 0.01). Histologic analysis showed a 59% reduction in tumor volume in the HB1.F3-CD-IFNbeta-treated group (P < 0.05). Apoptotic cells were increased 2.33-fold in animals treated with HB1.F3-CD-IFNbeta compared with the respective control groups (P < 0.01). CONCLUSION: The brainstem glioma-tropic migratory capacities of MSCs from various sources were similar to those of NSCs. Genetically engineered NSCs show therapeutic efficacy against brainstem gliomas.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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 teacher head, 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".