Toward Engineering a Novel Transplantation Site for Human Pancreatic Islets
Bibliographic record
Abstract
slet transplantation is a promising therapy for treatment of type 1 diabetes.A real breakthrough was reported when the Edmonton protocol was introduced in 2000.This protocol induced insulin independence in diabetic patients for 1 year (1).Although these clinical islet transplantations demonstrated the application of the technique, the long-term function of the islet grafts was not that successful.After 2 years, less than 50% of the patients remained insulin independent.Five years following transplantation, this declined to just 10% (1).In recent years, some groups demonstrated remarkable progress in islet transplantation outcomes (2), and experienced groups have been able to produce insulin independence after transplantation of islets from a single donor by controlling all known parameters for optimal islet donation (3).Recently, a number of groups have focused on the identification of factors determining success or failure of islet grafts.Various signs point to the transplantation site as a major factor in graft failure.The majority of islet transplantation is currently accomplished by the infusion of islets into the liver via the portal vein.Several alternative sites were investigated in animals and humans for efficacy as transplantation sites for islets, but none adequately accommodated islet engraftment.A rather novel approach that has been investigated recently is the engineering of an artificial site by using biopolymeric scaffolds.These scaffolds facilitate revascularization and allow adequate glucose sensing and insulin release.Recent developments in this area are reviewed in this article because of their potential clinical application.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".