Regulated Cell Death Seen through the Lens of Islet Transplantation
Bibliographic record
Abstract
Clinical islet transplantation effectively restores euglycemia and corrects glycosylated hemoglobin in labile type 1 diabetes mellitus (T1DM). Despite marked improvements in islet transplantation outcomes, acute islet cell death remains a substantial obstacle that compromises long-term engraftment outcomes. Multiple organ donors are routinely required to achieve insulin independence. Therapeutic agents that ameliorate cell death and/or control injury-related inflammatory cascades offer potential to improve islet transplant success. Apoptotic cell death has been identified as a major contributor to cellular demise and therapeutic strategies that subvert initiation and consequences of apoptotic cell death have shown promise in pre-clinical models. Indeed, in numerous pathologies and diseases apoptosis has been the most extensively described form of regulated cell death. However, recent identification of novel, alternative regulated cell death pathways in other disease states and solid organ transplantation suggest that these additional pathways may also have substantial relevance in islet transplantation. These regulated, non-apoptotic cell death pathways exhibit distinct biochemical characteristics but have yet to be fully characterized within islet transplantation. We review herein the various regulated cell death pathways and highlight their relative potential contributions to islet viability, engraftment failure and islet dysfunction.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
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".