Non‐invasive detection of transplanted pancreatic islets
Bibliographic record
Abstract
Type 1 diabetes (insulin-dependent, IDDM) results in immune-mediated destruction of pancreatic beta cells, which leads to a deficiency in insulin secretion and as a result, to hyperglycaemia. Keeping blood glucose levels under tight control represents the most effective way either to prevent the onset or to reduce the progression of the chronic complications of IDDM. At present, pancreatic islet transplantation is emerging as the most promising clinical modality, which can stop diabetes progression without increasing the incidence of hypoglycaemic events. Although early results of clinical trials using the Edmonton Protocol and its variations are very encouraging, it is still unclear how long the islets will survive and how often the transplantation procedure will be successful. In order to monitor transplantation efficiency and graft survival, reliable non-invasive imaging methods are critically needed. If such methods are introduced clinically, essential information regarding the location, function and viability of transplanted islets can be obtained repeatedly and non-invasively. This review will focus on the latest advancements in the field of in vivo imaging of islet transplantation and describe various islet labelling and imaging techniques. In addition, we will critically look into limitations and obstacles currently present on the way to successful clinical implementation of this approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".