TOWARD DEVELOPMENT OF IMAGING MODALITIES FOR ISLETS AFTER TRANSPLANTATION: INSIGHTS FROM THE NATIONAL INSTITUTES OF HEALTH WORKSHOP ON BETA CELL IMAGING
Bibliographic record
Abstract
BACKGROUND: Pancreatic islet transplantation can provide insulin independence and near normal glucose control in selected patients with type 1 diabetes mellitus. However, in most cases, achieving insulin independence necessitates the use of at least two donor pancreases per recipient and the rate of insulin independence may decline after transplantation. To better understand the fate of transplanted islets and the relationship between transplanted islet mass, graft function, and overall glucose homeostasis, an accurate and reproducible method of imaging islets in vivo is needed. METHODS: Recent advances in noninvasive imaging techniques such as magnetic resonance imaging, positron emission tomography, and other imaging modalities show great promise as potential tools to monitor islet number, mass, and function in the clinical setting. A recent international workshop, "Imaging the Pancreatic Beta Cell," sponsored by the National Institute of Biomedical Imaging and Bioengineering, the National Institute of Diabetes and Digestive and Kidney Diseases, and the Juvenile Diabetes Research Foundation International focused on these emerging efforts to develop novel ways of imaging pancreatic beta cells in vivo. RESULTS: Potential clinically applicable techniques include the use of directed magnetic resonance contrast agents such as lanthanides (Ln(3+)) and manganese (Mn(2+)) or magnetic resonance imaging probes such as superparamagnetic iron oxide nanoparticles. Potential techniques for positron emission tomography imaging include the use of beta cell-specific antibodies, or pharmacologic agents such as glyburide analogs, or d-mannoheptulose. Optical imaging techniques are also being used to evaluate various aspects of beta cell metabolism including intracellular Ca(2+) flux, glucokinase activity, and insulin granular exocytosis. CONCLUSIONS: The consensus among investigators at the imaging workshop was that an accurate and reproducible in vivo measure of functional islet mass is critically needed to further the strides that have been made in both islet transplantation and diabetes research as a whole. Such measures would potentially allow the assessment of islet engraftment and the early recognition of graft loss, leading to greater improvements in islet graft survival and function.
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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 | 0.026 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".