<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 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) | 0.001 | 0.001 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".