Magnetic Resonance Imaging Tracking of Ferumoxytol-Labeled Human Neural Stem Cells: Studies Leading to Clinical Use
Bibliographic record
Abstract
Numerous stem cell-based therapies are currently under clinical investigation, including the use of neural stem cells (NSCs) as delivery vehicles to target therapeutic agents to invasive brain tumors. The ability to monitor the time course, migration, and distribution of stem cells following transplantation into patients would provide critical information for optimizing treatment regimens. No effective cell-tracking methodology has yet garnered clinical acceptance. A highly promising noninvasive method for monitoring NSCs and potentially other cell types in vivo involves preloading them with ultrasmall superparamagnetic iron oxide nanoparticles (USPIOs) to enable cell tracking using magnetic resonance imaging (MRI). We report here the preclinical studies that led to U.S. Food and Drug Administration approval for first-in-human investigational use of ferumoxytol to label NSCs prior to transplantation into brain tumor patients, followed by surveillance serial MRI. A combination of heparin, protamine sulfate, and ferumoxytol (HPF) was used to label the NSCs. HPF labeling did not affect cell viability, growth kinetics, or tumor tropism in vitro, and it enabled MRI visualization of NSC distribution within orthotopic glioma xenografts. MRI revealed dynamic in vivo NSC distribution at multiple time points following intracerebral or intravenous injection into glioma-bearing mice that correlated with histological analysis. Preclinical safety/toxicity studies of intracerebrally administered HPF-labeled NSCs in mice were also performed, and they showed no significant clinical or behavioral changes, no neuronal or systemic toxicities, and no abnormal accumulation of iron in the liver or spleen. These studies support the clinical use of ferumoxytol labeling of cells for post-transplant MRI visualization and tracking.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".