Glutathione Ethyl Ester Supplementation during Pancreatic Islet Isolation Improves Viability and Transplant Outcomes in a Murine Marginal Islet Mass Model
Bibliographic record
Abstract
BACKGROUND: The success of pancreatic islet transplantation still faces many challenges, mainly related to cell damage during islet isolation and early post-transplant. The increased generation of reactive oxygen species (ROS) during islet isolation and the consumption of antioxidant defenses appear to be an important pathway related to islet damage. METHODOLOGY/PRINCIPAL FINDINGS: In the present study we evaluated whether supplementation of glutathione-ethyl-ester (GEE) during islet isolation could improve islet viability and transplant outcomes in a murine marginal islet mass model. We also cultured human islets for 24 hours in standard CMRL media with or without GEE supplementation. Supplementation of GEE decreased the content of ROS in isolated islets, leading to a decrease in apoptosis and maintenance of islet viability. A higher percentage of mice transplanted with a marginal mass of GEE treated islets became euglycemic after transplant. The supplementation of 20 mM GEE in cultured human islets significantly reduced the apoptosis rate in comparison to untreated islets. CONCLUSIONS/SIGNIFICANCE: GEE supplementation was able to decrease the apoptosis rate and intracellular content of ROS in isolated islets and might be considered a potential intervention to improve islet viability during the isolation process and maintenance in culture before islet transplantation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".