Trimodal Cell Tracking In Vivo: Combining Iron- and Fluorine-Based Magnetic Resonance Imaging with Magnetic Particle Imaging to Monitor the Delivery of Mesenchymal Stem Cells and the Ensuing Inflammation
Bibliographic record
Abstract
The therapeutic potential of mesenchymal stem cells (MSCs) is limited, as many cells undergo apoptosis following administration. In addition, the attraction of immune cells (predominately macrophages) to the site of implantation can lead to MSC rejection. We implemented a trimodal imaging technique to monitor the fate of transplanted MSCs and infiltrating macrophages in vivo. MSCs were labeled with an iron oxide nanoparticle (ferumoxytol) and then implanted within the hind limb muscle of 10 C57BI/6 mice. Controls received unlabeled MSCs (n = 5). A perfluorocarbon agent was administered intravenously for uptake by phagocytic macrophages in situ; 1 and 12 days later, the ferumoxytol-labeled MSCs were detected by proton (1H) magnetic resonance imaging (MRI) and magnetic particle imaging (MPI). Perfluorocarbon-labeled macrophages were detected by fluorine-19 (19F) MRI. 1H/19F MRI was acquired on a clinical scanner (3 T) using a dual-tuned surface coil and balanced steady-state free precession (bSSFP) sequence. The measured volume of signal loss and MPI signal declined over 12 days, which is consistent with the death and clearance of iron-labeled MSCs. 19F signal persisted over 12 days, suggesting the continuous infiltration of perfluorocarbon-labeled macrophages. Because MPI and 19F MRI signals are directly quantitative, we calculated estimates of the number of MSCs and macrophages present over time. The presence of MSCs and macrophages was validated with histology following the last imaging session. This is the first study to combine the use of iron- and fluorine-based MRI with MPI cell 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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".