Current Advances and Travails in Islet Transplantation
Bibliographic record
Abstract
The successful demonstration that insulin-producing β-cells can be isolated (in the form of cell clusters called islets containing β and other endocrine and nonendocrine cells) from a recently deceased donor's pancreas, then transplanted into subjects with type 1 diabetes, and thereby restore, at least temporarily, insulin-independent normoglycemia has firmly established the important “proof of concept.” Even so, worldwide efforts to advance the therapy for widespread applicability have served to focus attention on the hurdles yet to clear. This review will briefly describe the present state of the art and succinctly define the research problems being attacked along with some recent advances that demonstrate significant progress. Since Paul Lacy's early rodent experiments in the 1960s established that pancreatic islets could be isolated from one animal and transplanted into a diabetic recipient to restore normoglycemia (1), investigators have pursued efforts to develop the therapy for clinical use. After years of development in various animal models and efforts to improve human islet isolation techniques (see [2–4] for reviews with a historical perspective), the first patient achieving short-term insulin independence was reported by the group at Washington University in St. Louis. That advance was based on new islet isolation technology utilizing islets pooled from several donors, intensive insulin treatment in the peritransplant period, and induction immunosuppression with antithymocyte globulin (ATG) to avoid glucocorticoid therapy (5). The development of new immunosuppressive drugs that allowed patients to remain off glucocorticoid therapy while awaiting subsequent islet infusions (because most recipients require islets from two or more donors) enabled the group in Edmonton to optimize the clinical islet transplantation procedure (6). The approach allowed the group to conclude that about 12,000 islet equivalents per recipient body weight (in kilograms) was required to restore insulin-independent normoglycemia (6) and sparked intense international interest and effort. Current …
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".