Advances and Challenges in Islet Transplantation: Islet Procurement Rates and Lessons Learned from Suboptimal Islet Transplantation
Bibliographic record
Abstract
The initial step in successful islet transplantation is procurement of healthy donor islets. Given the limited number of donor pancreata selected for islet isolation and that islets from multiple donors are typically required to obtain insulin independence, it is critical to improve pancreas procurement rates and yield of islets for transplantation. Islets are delicate microorgans that are susceptible to apoptosis, hypoxia, and ischemia during isolation, culture, and the peritransplant period. Once the islets are engrafted, both prompt revascularization and protection from beta-cell death and graft rejection are key to secure long-term survival and function. To facilitate the engraftment of more robust islets suitable for combating the challenging isolation period and proinflammatory transplantation milieu, numerous approaches have been employed to prevent beta-cell dysfunction and death including immune modulation, prevention of apoptosis and hypoxia, as well as stimulation of growth factors, angiogenesis, and reinnervation. In addition to briefly discussing islet isolation procedures, procurement rates, and islet transplantation, the relevant literature pertaining to successful suboptimal islet transplantation is reviewed to provide insight into potential approaches to balance the limited supply of available donor islets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".