99mTc-Based Imaging of Transplanted Neural Stem Cells and Progenitor Cells
Bibliographic record
Abstract
UNLABELLED: Cell therapy for neurologic disorders will benefit significantly from progress in methods of noninvasively imaging cell transplants. The success of current cell therapy has varied, in part because of differences in cell sources, differences in transplantation procedures, and lack of understanding of cell fate after transplantation. Standardization of transplantation procedures will progress with noninvasive imaging. In turn, in vivo imaging will enhance our understanding of neural transplant biology and improve therapeutic outcomes. The goal of this study was to determine the effect of a (99m)Tc-based probe on neural stem and progenitor cell transplants and validate the SPECT images of the transplanted cells. METHODS: We previously developed a method to label neural stem and progenitor cells with (99m)Tc to visualize these cells in the brain with SPECT. The cells were initially labeled with a permeation peptide carrying a chelate for (99m)Tc. The proliferation and differentiation characteristics of the labeled cells were studied in tissue culture. In parallel experiments, the labeled cells were stereotactically injected into the rat brain, and the site of transplantation was verified with histochemistry and phosphorimaging. RESULTS: The accuracy of the transplant location obtained by SPECT was confirmed by comparison with phosphorimages and histologic sections of the brain. The labeling did, however, decrease the proliferative capacity of the neural stem and progenitor cells. CONCLUSION: The labeling technique described here can be used to standardize the location of cell transplants in the brain and quantify the number of transplanted cells. However, a (99m)Tc-based probe can decrease the cellular proliferation of neural progenitor 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".