Effects of Glucose Toxicity and Islet Purity on In Vivo Magnetic Resonance Imaging of Transplanted Pancreatic Islets
Bibliographic record
Abstract
BACKGROUND: Pancreatic islet transplantation has recently emerged as a powerful clinical modality to restore normoglycemia in diabetic patients. Despite the success of the Edmonton protocol, these patients still experience a significant islet loss immediately after transplantation. Noninvasive magnetic resonance imaging (MRI) allows for longitudinal monitoring of graft loss providing that islets are labeled with a magnetically "visible" contrast agent. To fully interpret the imaging data, it is critical to investigate factors normally present during clinical transplantation and influencing MRI of transplanted islets. METHODS: Here, we focused on both the effect of hyperglycemia and the effect of contaminating nonendocrine tissue, which is always present in islet preparations, on MRI imaging of islet grafts. Human pancreatic islets labeled with Feridex were transplanted in diabetic and healthy animals. Separate groups of animals were transplanted with Feridex-labeled pure and nonpure (50% islets and 50% nonendocrine tissue) preparations. The fate of the graft in all groups was monitored by in vivo MRI. RESULTS: We found that diabetic animals with transplanted islets showed a significantly higher rate of islet death than their healthy counterparts on in vivo MR images. Interestingly, transplantation of islets contaminated with nonendocrine tissue did not have any significant influence on MR images, presumably because of a low labeling rate of this tissue and a fast rate of its disappearance after transplantation. CONCLUSIONS: We believe that this study serves as yet another step on our way to clinical use of in vivo imaging of islet transplantation.
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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".