A preclinical assessment of the non-heart beating donor pancreas for islet transplantation
Bibliographic record
Abstract
The results of islet transplantation have been significantly improved in recent years following major advances made by the Edmonton group in Canada. These included transplanting fresh islets from more than one donor combined with a new less diabetogenic immunosuppressive protocol. Clinical islet transplant programs will be limited by the declining numbers of organ donors. Non-heart beating donors have been used to expand the renal donor pool, however the pancreas in particularly susceptible to warm ischaemia and may therefore not be suitable for islet transplantation.;The aim of this thesis was to evaluate the use of islets from non-heart beating donors in pre-clinical animal models. Different preservation methods were used including new and old preservation solutions along with the two-layer method (TLM) to attempt rodent pancreas resuscitation. However, even a short period of warm ischaemia led to poor islet yields and viability. A proportion of this work was to examine the use of ADP:ATP ratio as a potential viability test to estimate the degree of warm ischaemic damage.;Pulsatile machine perfusion has been a promising method for kidney preservation. A Waters RM3 perfusion machine was compared to both conventional cold storage and TLM for porcine pancreas preservation. Unfortunately, islet fragmentation and poor islet yields were a problem following machine perfusion suggesting that cold storage should remain the gold standard preservation method. In conclusion, the use of the non-heart beating donor pancreas for islet transplantation still remains a problem until more effective preservation methods are developed.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".