In vivo serial evaluation of superparamagnetic iron‐oxide labeled stem cells by off‐resonance positive contrast
Bibliographic record
Abstract
MRI is emerging as a diagnostic modality to track iron-oxide-labeled stem cells. This study investigates whether an off-resonance (OR) pulse sequence designed to generate positive contrast at 1.5T can assess the location, quantity, and viability of delivered stem cells in vivo. Using mouse embryonic stem cell transfected with luciferase reporter gene (luc-mESC), multimodality validation of OR signal was conducted to determine whether engraftment parameters of superparamagnetic iron-oxide labeled luc-mESC (SPIO-luc-mESC) could be determined after cell transplantation into the mouse hindlimb. A significant increase in signal- and contrast-to-noise of the SPIO-luc-mESC was achieved with the OR technique when compared to a gradient recalled echo (GRE) sequence. A significant correlation between the quantity of SPIO-luc-mESC and OR signal was observed immediately after transplantation (R(2) = 0.74, P < 0.05). The assessment of transplanted cell viability by bioluminescence imaging (BLI) showed a significant increase of luciferase activities by day 16, while the MRI signal showed no difference. No significant correlation between BLI and MRI signals of cell viability was observed. In conclusion, using an OR sequence the precise localization and quantitation of SPIO-labeled stem cells in both space and time were possible. However, the OR sequence did not allow evaluation of cell viability.
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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) | 0.001 | 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".