Clinical Magnetic Resonance Imaging of Pancreatic Islet Grafts After Iron Nanoparticle Labeling
Bibliographic record
Abstract
There is a crucial need for noninvasive assessment tools after cell transplantation. This study investigates whether a magnetic resonance imaging (MRI) strategy could be clinically applied to islet transplantation. The purest fractions of seven human islet preparations were labeled with superparamagnetic iron oxide particles (SPIO, 280 microg/mL) and transplanted into four patients with type 1 diabetes. MRI studies (T2*) were performed prior to and at various time points after transplantation. Viability and in vitro and in vivo functions of labeled islets were similar to those of control islets. All patients could stop insulin after transplantation. The first patient had diffuse hypointense images on her baseline liver MRI, typical for spontaneous high iron content, and transplant-related modifications could not be observed. The other three patients had normal intensity on pretransplant images, and iron-loaded islets could be identified after transplantation as hypointense spots within the liver. In one of them, i.v. iron therapy prevented subsequent visualization of the spots because of diffuse hypointense liver background. Altogether, this study demonstrates the feasibility and safety of MRI-based islet graft monitoring in clinical practice. Iron overload (spontaneous or induced) represents the major obstacle to the technique.
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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 | 0.000 | 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".