Effect of Thioketal Antioxidants on Islet Cell Transplantation
Bibliographic record
Abstract
Abstract Pancreatic islet transplantation is an effective strategy for restoring glucose regulation for highly selected patients with type 1 diabetes. However, an undesirable level of islet cell death is caused by oxidative stress that occurs during islet isolation, culture, and transplantation. The loss of islets throughout the transplantation procedure often necessitates multiple human donors per recipient to achieve insulin independence. Administration of exogenous antioxidants has shown promise in both preserving islet cell viability and functionality post‐transplantation. Herein, thioketal (TK) antioxidant is evaluated using neonatal porcine islets and Beta‐TC‐6 cells exposed to H 2 O 2 for changes in membrane integrity, oxygen consumption rates, and lipid peroxidation. Diabetic BALB/c mice are transplanted with a marginal dose of syngeneic islets, ±48‐h TK pre‐treatment, under the kidney capsule. Graft function is measured by nonfasting blood glucose and glucose tolerance testing. It is found that 200 µM of TK was not cytotoxic, reduced reactive oxygen species‐mediated oxidative islet damage, preserved in vitro islet cell functionality, and facilitated superior murine syngeneic marginal islet mass engraftment. These results demonstrate that the antioxidant attributes of TK reduce the deleterious effects of reactive oxygen species experienced in vitro and enhance marginal islet mass transplant outcomes.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".