Longitudinal Magnetic Resonance Imaging Tracking of Transplanted Neural Progenitor Cells in the Spinal Cord Utilizing the Bright-Ferritin Mechanism
Bibliographic record
Abstract
Human neural progenitor cells (hNPCs) hold promise for treating spinal cord injury. Studies to date have focused on improving their regenerative potential and therapeutic effect. Equally important is ensuring successful delivery and engraftment of hNPCs at the injury site. Unfortunately, no current imaging solution for cell tracking is compatible with long-term monitoring in vivo. The objective of this study was to apply a novel bright-ferritin magnetic resonance imaging (MRI) mechanism to track hNPC transplants longitudinally and on demand in the rat spinal cord. We genetically modified hNPCs to stably overexpress human ferritin. Ferritin-overexpressing (FT) hNPCs labeled with 0.2 mM manganese provided significant T1-induced bright contrast on in vitro MRI, with no adverse effect on cell viability, morphology, proliferation, and differentiation. In vivo, 2 M cells were injected into the cervical spinal cord of Rowett nude rats. MRI employed T1-weighted acquisitions and T1 mapping on a 3 T scanner. Conventional short-term cell tracking was performed using exogenous Mn labeling prior to cell transplantation, which displayed transient bright contrast on MRI 1 day after cell transplantation and disappeared after 1 week. In contrast, long-term cell tracking using bright-ferritin allowed on-demand signal recall upon Mn supplementation and precise visualization of the surviving hNPC graft. In fact, this new cell tracking technology identified 7 weeks post-transplantation as the timepoint by which substantial hNPC integration occurred. Spatial distribution of hNPCs on MRI matched that on histology. In summary, bright-ferritin provides the first demonstration of long-term, on-demand, high-resolution, and specific tracking of hNPCs in the rat spinal cord.
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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) | 0.000 | 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".