In Vivo Imaging of Immune Rejection in Transplanted Pancreatic Islets
Bibliographic record
Abstract
As islet transplantation becomes an acceptable clinical modality for restoring normoglycemia in type 1 diabetic patients, there is a crucial need for noninvasive assessment of the fate of the grafts. In spite of the success of the Edmonton Protocol, a significant graft loss occurs due to immunological and nonimmunological events immediately after transplantation. Allogeneic rejection in graft recipients is one of the major reasons for islet death and graft failure. Therefore, monitoring the islet rejection using reliable noninvasive methods would significantly aid in clinical assessment of graft success. We have previously developed a method to detect transplanted islets noninvasively using magnetic resonance imaging (MRI). For this procedure, human pancreatic islets are labeled with an MRI contrast agent that enables their visualization on magnetic resonance images. In our present study, we not only detected labeled human islets in a preclinical intrahepatic model of human islet transplantation in mice but also showed that islet rejection can be monitored noninvasively and repeatedly in real time by MRI. In addition, in this study, we have adapted, for islet cell labeling, a Food and Drug Administration-approved commercially available contrast agent, Feridex, that is used clinically for liver imaging. We believe that this agent, in combination with our preclinical model of islet transplantation, will facilitate the transition of imaging immune rejection to clinical trials.
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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".