<i>In vivo</i> effects of cytokines on pancreatic β‐cells in models of type I diabetes dependent on CD4<sup>+</sup> T lymphocytes
Bibliographic record
Abstract
CD4(+) T cells can actively kill beta-cells in type I diabetes as well as help CD8(+) T cells become cytolytic. Cytokines have the potential to kill beta-cells, or upregulate Fas on beta-cells, and increase their susceptibility to FasL. We investigated the direct effects of cytokines on beta-cells in perforin-deficient non-obese diabetic (NOD) mice and NOD4.1 TCR transgenic mice, two models in which CD8(+) T cells play a less dominant role. Inhibiting the effects of cytokines by the overexpression of suppressor of cytokine signalling-1 (SOCS1) in beta-cells did not reduce diabetes or insulitis in perforin-deficient NOD, NOD4.1 or interleukin (IL)-1 receptor-deficient NOD4.1 mice. SOCS1 overexpression prevented Fas upregulation on NOD4.1 beta-cells, but did not prevent islet destruction because SOCS1 transgenic islets were killed when grafted into NOD4.1.scid mice. Likewise, Fas-deficient NOD.lpr islets were destroyed in NOD4.1 mice. Although blocking the effects of interferon (IFN)gamma on beta-cells did not affect diabetes in NOD4.1 mice, global deficiency of IFNgammaR2 reduced diabetes and insulitis, suggesting that IFNgamma is involved in CD4(+) T-cell activation or migration. Our data show that beta-cells under attack by CD4(+) T cells are not destroyed by the effects of cytokines including IFNgamma and IL-1 or Fas-dependent cytotoxicity.
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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 | 0.000 |
| 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".