Iron Labeling and Pre-Clinical MRI Visualization of Therapeutic Human Neural Stem Cells in a Murine Glioma Model
Bibliographic record
Abstract
BACKGROUND: Treatment strategies for the highly invasive brain tumor, glioblastoma multiforme, require that cells which have invaded into the surrounding brain be specifically targeted. The inherent tumor-tropism of neural stem cells (NSCs) to primary and invasive tumor foci can be exploited to deliver therapeutics to invasive brain tumor cells in humans. Use of the strategy of converting prodrug to drug via therapeutic transgenes delivered by immortalized therapeutic NSC lines have shown efficacy in animal models. Thus therapeutic NSCs are being proposed for use in human brain tumor clinical trials. In the context of NSC-based therapies, MRI can be used both to non-invasively follow dynamic spatio-temporal patterns of the NSC tumor targeting allowing for the optimization of treatment strategies and to assess efficacy of the therapy. Iron-labeling of cells allows their presence to be visualized and tracked by MRI. Thus we aimed to iron-label therapeutic NSCs without affecting their cellular physiology using a method likely to gain United States Federal Drug Administration (FDA) approval. METHODOLOGY: For human use, the characteristics of therapeutic Neural Stem Cells must be clearly defined with any pertubation to the cell including iron labeling requiring reanalysis of cellular physiology. Here, we studied the effect of iron-loading of the therapeutic NSCs, with ferumoxide-protamine sulfate complex (FE-Pro) on viability, proliferation, migratory properties and transgene expression, when compared to non-labeled cells. FE-Pro labeled NSCs were imaged by MRI at tumor sites, after intracranial administration into the hemisphere contralateral to the tumor, in an orthotopic human glioma xenograft mouse model. CONCLUSION: FE-Pro labeled NSCs retain their proliferative status, tumor tropism, and maintain stem cell character, while allowing in vivo cellular MRI tracking at 7 Tesla, to monitor their real-time migration and distribution at brain tumor sites. Of significance, this work directly supports the use of FE-Pro-labeled NSCs for real-time tracking in the clinical trial under development: "A Pilot Feasibility Study of Oral 5-Fluorocytosine and Genetically modified Neural Stem Cells Expressing Escherichia coli Cytosine Deaminase for Treatment of Recurrent High-Grade 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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".