Mechanisms Underlying Resistance of Pancreatic Islets from ALR/Lt Mice to Cytokine-Induced Destruction
Bibliographic record
Abstract
Nuclear and mitochondrial genomes combine in ALR/Lt mice to produce systemically elevated defenses against free radical damage, rendering these mice resistant to immune-mediated pancreatic islet destruction. We analyzed the mechanism whereby isolated islets from ALR mice resisted proinflammatory stress mediated by combined cytokines (IL-1beta, TNF-alpha, and IFN-gamma) in vitro. Such damage entails both superoxide and NO radical generation, as well as peroxynitrite, resulting from their combination. In contrast to islets from other mouse strains, ALR islets expressed constitutively higher glutathione reductase, glutathione peroxidase, and higher ratios of reduced to oxidized glutathione. Following incubation with combined cytokines, islets from control strains produced significantly higher levels of hydrogen peroxide and NO than islets from ALR mice. Nitrotyrosine was generated in NOD and C3H/HeJ islets but not by ALR islets. Western blot analysis showed that combined cytokines up-regulated the NF-kappaB inducible NO synthase in NOD-Rag and C3H/HeJ islets but not in ALR islets. This inability of cytokine-treated ALR islets to up-regulate inducible NO synthase and produce NO correlated both with reduced kinetics of IkappaB degradation and with markedly suppressed NF-kappaB p65 nuclear translocation. Hence, ALR/Lt islets resist cytokine-induced diabetogenic stress through enhanced dissipation and/or suppressed formation of reactive oxygen and nitrogen species, impaired IkappaB degradation, and blunted NF-kappaB activation. Nitrotyrosylation of beta cell proteins may generate neoantigens; therefore, resistance of ALR islets to nitrotyrosine formation may, in part, explain why ALR mice are resistant to type 1 diabetes when reconstituted with a NOD immune system.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".