Tracking the Fate of Stem Cell Implants with Fluorine-19 MRI
Bibliographic record
Abstract
BACKGROUND: In this study we used cellular magnetic resonance imaging (MRI) to detect mesenchymal stem cells (MSC) labeled with a Fluorine-19 (19F) agent. 19F-MRI offers unambiguous detection and in vivo quantification of labeled cells. METHODS: We investigated two common stem cell transplant mouse models: an immune competent, syngeneic transplant model and an immune compromised, xenograft transplant model. 19F labelled stem cells were implanted intramuscularly into the hindlimb of healthy mice. The transplant was then monitored for up to 17 days using 19F-MRI, after which the tissue was excised for fluorescence microscopy and immunohistochemisty. RESULTS: Immediately following transplantation, 19F-MRI quantification correlated very well with the expected cell number in both models. The 19F signal decreased over time in both models, with a more rapid decrease in the syngeneic model. By endpoint, only 2/7 syngeneic mice had any detectable 19F signal. In the xenograft model, all mice had detectable signal at endpoint. Fluorescence microscopy and immunohistochemistry were used to show that the 19F signal was related to the presence of bystander labeled macrophages, and not original MSC. CONCLUSIONS: Our results show that 19F-MRI is an excellent tool for verifying the delivery of therapeutic cells early after transplantation. However, in certain circumstances the transfer of cellular label to other bystander cells may confuse interpretation of the long-term fate of the transplanted 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".