Cellular Imaging at 1.5 T: Detecting Cells in Neuroinflammation using Active Labeling with Superparamagnetic Iron Oxide
Bibliographic record
Abstract
The ability to visualize cell infiltration in experimental auto-immune encephalomyelitis (EAE), a well-known animal model for multiple sclerosis in humans, was investigated using a clinical 1.5-T magnetic resonance imaging (MRI) scanner, a custom-built, high-strength gradient coil insert, a 3-D fast imaging employing steady-state acquisition (FIESTA) imaging sequence and a superparamagnetic iron oxide (SPIO) contrast agent. An "active labeling" approach was used with SPIO administered intravenously during inflammation in EAE. Our results show that small, discrete regions of signal void corresponding to iron accumulation in EAE brain can be detected using FIESTA at 1.5 T. This work provides early evidence that cellular abnormalities that are the basis of diseases can be probed using cellular MRI and supports our earlier work which indicates that tracking of iron-labeled cells will be possible using clinical MR scanners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".