Magnetic microspheres can be used for magnetic particle imaging of cancer cells arrested in the mouse brain
Bibliographic record
Abstract
Purpose Magnetic particle imaging (MPI) is a new imaging modality that sensitively and specifically detects superparamagnetic iron oxide nanoparticles (SPIOs). MRI cell tracking with SPIOs has very high sensitivity, but low specificity and quantification is difficult. MPI could overcome these limitations. There are no reports of micron‐sized iron oxide particles (MPIO) for cell tracking by MPI. Therefore, the goal was to evaluate if MPIO can be used for in vivo detection and quantification of cancer cells distributed in the mouse brain by MPI. Methods In the first experiment mice were injected with either 2.5 × 10 5 or 5.0 × 10 5 MPIO‐labeled cancer cells and MPI was performed ex vivo. In a second experiment, mice received either 2.5 × 10 5 or 5.0 × 10 4 MPIO‐labeled cells and MPI was performed in vivo. In a third experiment, mice were injected with 5.0 × 10 4 cells, labeled with either MPIO or ferucarbotran, and MPI was performed in vivo. Results MPIO‐labeled cells were visible in all MPI images of the mouse brain. The MPI signal and iron content measurements were greater for brains of mice that were injected with higher numbers of MPIO‐labeled cells. Ferucarbotran‐labeled cells were not detected in the brain by MPI. Conclusion This is the first example of the use of MPIO for cell tracking with MPI. With an intracardiac cell injection, ~15% of cells will arrest in the brain vasculature. For our lowest cell injection of 5.0 × 10 4 cells, this was ~10 000 cells, distributed throughout the brain.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".