Using a Neodymium Magnet to Target Delivery of Ferumoxide-Labeled Human Neural Stem Cells in a Rat Model of Focal Cerebral Ischemia
Bibliographic record
Abstract
Efficiency of targeted delivery of stem cells via transplantation by intravenous injection is limited because of rapid clearance. Thus, more effective, newer methods are required. We hypothesized that combining the use of ferumoxide labeling and magnetic fields could enhance targeted delivery of stem cells. The effects of a magnetic field on proliferation, viability, and differentiation of human neural stem cells (NSCs) were determined in culture, and the results indicated that the difference between control and cultures exposed to a magnetic field were insignificant. To assess migration in vitro, ferumoxide-labeled cells were seeded into a culture dish that had a neodymium magnet below its center, and the labeled NSCs were found to aggregate above the magnet. To investigate targeted delivery of NSCs in vivo, rats were separated into three groups: ischemia only (IO), ischemia with injection of ferumoxide-labeled cells (IC), and ischemia with injection of labeled cells and magnet exposure (ICM). Twenty-four hours after middle cerebral artery occlusion (MCAo), labeled human NSCs were injected into the tail vein. Seven days after MCAo, ICM rats had a larger number and greater distribution of Prussian blue-positive NSCs as compared with controls. In addition, infarct volume in ICM rats was significantly reduced. Our study suggests that this use of a magnetic field may be useful for improving the efficacy of targeted migration of stem cells in stem-based cell therapy in ischemic brain injury.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".