Imaging Islets Labeled With Magnetic Nanoparticles at 1.5 Tesla
Bibliographic record
Abstract
We have developed a magnetic resonance imaging (MRI) technique for imaging Feridex (superparamagnetic iron oxide [SPIO])-labeled islets of Langerhans using a standard clinical 1.5-Tesla (T) scanner and employing steady-state acquisition imaging sequence (3DFIESTA). Both porcine and rat islets were labeled with SPIO by a transfection technique using a combination of poly-l-lysine and electroporation. Electron microscopy demonstrated presence of SPIO particles within the individual islet cells, including beta-cells and particles trapped between cell membranes. Our labeling method produced a transfection rate of 860 pg to 3.4 ng iron per islet, dependent on the size of the islet. The labeling procedure did not disrupt either the function or viability of the islets. In vitro 3DFIESTA magnetic resonance images of single-labeled islets corresponded with their optical images. In vivo T2*-weighted scan using 1.5 T detected as few as 200 SPIO-labeled islets transplanted under rat kidney capsule, which correlated with immunohistochemistry of the transplant for insulin and iron. Ex vivo 3DFIESTA images of kidneys containing 200, 800 or 2,000 SPIO-labeled islet isografts showed good correlation between signal loss and increasing numbers of islets. These data provide evidence that islets can be labeled with SPIO and imaged using clinically available 1.5- T MRI.
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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) | 0.001 | 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 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".