Development of Magnetic Particle Imaging (MPI) for Cell Tracking and Detection
Bibliographic record
Abstract
Abstract Introduction Magnetic particle imaging (MPI) is a new imaging modality that sensitively and specifically detects superparamagnetic iron oxide nanoparticles (SPIONs) within a sample. SPION-based MRI cell tracking has very high sensitivity, but low specificity and quantification of iron labeled cells is difficult. MPI cell tracking could overcome these challenges. Methods MDM-AB-231BR cells labeled with MPIO, mice were intracardially injected with either 2.5 × 10 5 or 5.0 × 10 5 cells. MRI was performed in vivo the same day at 3T using a bSSFP sequence. After mice were imaged ex vivo with MPI. In a second experiment Mice received an intracardiac injection of either 2.5 × 10 5 or 5 × 10 4 MPIO-labeled 231BR cells. In a third experiment, mice were injected with 5 × 10 4 4T1BR cells, labelled with either MPIO or the SPION Vivotrax. MRI and MPI was performed in vivo. Results Signal from MPI and signal voids from MRI both showed more iron content in mice receiving an injection of 5.0 × 10 5 cells than the 2.5 × 10 5 injection. In the second experiment, Day 0 MRI showed signal voids and MPI signal was detected in all mouse brains. The MPI signal and iron content measured in the brains of mice that were injected with 2.5 × 10 5 cells were approximately four times greater than in brains injected with 5 × 10 4 cells. In the third experiment, in vivo MRI was able to detect signal voids in the brains of mice injected with Vivotrax and MPIO, although voids were fainter in Vivotrax labeled cells. In vivo MPI signal was only detectable in mice injected with MPIO-labeled cells. Conclusion This is the first example of the use of MPIO for cell tracking with MPI. With an intracardiac cell injection, approximately 15% of the injected cells are expected to arrest in the brain vasculature. For our lowest cell injection of 5.0 × 10 4 cells this is ∼10000 cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".