Human Islet Isolation, Biobanking, and Distribution for Research
Bibliographic record
Abstract
Globally more than 400 million adults are living with diabetes, a condition of dysregulated metabolism characterized by high blood sugar levels and elevated risk for microvascular and macrovascular complications. Many of these individuals live with type 2 diabetes, resulting from a combined insulin resistance and dysfunction of insulin production by the pancreatic islets of Langerhans. Millions also live with type 1 diabetes, an autoimmune disease where the insulin producing islet cells are destroyed and circulating insulin is nearly absent. One treatment for type 1 diabetes that has emerged over the past several decades is the transplantation of cadaveric donor islets into the portal vein of the liver, and other potential novel sites. Alongside this has been the growth of research studies examining human islet function and dysfunction relevant to all forms of diabetes. Research studies using human islet tissue have contributed to our understanding of cellular function, disease genetics, tissue regeneration, metabolism, transplantation and tissue engineering, and has informed current progress in clinical trial of stem cell therapies for diabetes. This talk will briefly highlight progress in islet transplantation in Edmonton, and the relationship between this and the establishment of a human research islet isolation program to parallel clinical islet tissue processing. We will highlight some differences in tissue isolation for transplant and research, and will describe current efforts of the Alberta Diabetes Institute IsletCore (www.isletcore.ca) on supplying the international research community with research islet tissue, along with recent challenges faced during the COVID19 pandemic. Finally, some notable research outcomes in understanding islet physiology and improving islet transplantation will be discussed.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.083 | 0.093 |
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".