The potential benefit of non-purified islets preparations for islet transplantation
Bibliographic record
Abstract
Since the advent of islet transplantation, there has been a significant emphasis on the importance of islet purity despite an inevitable associated loss of islet mass during the purification process. One of the key elements of the 'Edmonton Protocol' for islet transplantation published in 2000 was an emphasis on the need for sequential transplants of highly purified islets (averaging 24% beta cell purity) and the close correlation between the numbers of islets transplanted and the success of the procedure. However, the emphasis on islet purity may warrant further consideration as auto transplantation of non-purified islets currently provides the most successful insulin independence rates within the field of islet transplantation. While the role of auto and allo immunity could contribute to the differences in the success rates it is clear that within the clinical setting, significant acinar and ductal contamination is well tolerated. However, one could go further and hypothesize that extra-insular tissue including acinar tissue, ductal tissue, peri-pancreatic lymph nodes and vascular tissue actually confer an advantage to islet survival/function and may even contribute to the insulin secreting capacity of the graft post transplant. As such this review will assess the influence of extra-insular pancreatic tissue on the results of islet transplantation based on published evidence and will also explore the possibility that non-islet pancreatic cells are capable of differentiating into a beta cell phenotype in vivo contributing to an ongoing regeneration of endocrine mass during the period following transplantation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".