An indirect role for NK cells in a CD4<sup>+</sup> T‐cell‐dependent mouse model of type I diabetes
Bibliographic record
Abstract
CD8(+) T cells kill pancreatic β-cells in a cell-cell contact-dependent mechanism in the non-obese diabetic mouse. CD4(+) T lymphocytes are also able to kill pancreatic β-cells, but they do not directly contact β-cells and may use another cell type as the actual cytotoxic cell. Natural killer (NK) cells could have this role but it is uncertain whether they are cytotoxic towards β-cells. Therefore, the requirement for NK cells in β-cell destruction in the CD4-dependent T-cell antigen receptor transgenic NOD4.1 mice was examined. NK cells failed to kill β-cells in vitro, even in the absence of major histocompatibility complex class I. We observed only 9.7±1.1% of islet infiltrating NK cells from NOD4.1 mice expressing the degranulation marker CD107a. Diabetogenic CD4(+) T cells transferred disease to NODscid.IL2Rγ(-/-) mice lacking NK cells, indicating that NK cells do not contribute to β-cell death in vitro or in vivo. However, depletion of NK cells reduced diabetes incidence in NOD4.1 mice, suggesting that NK cells may help to maintain the right environment for cytotoxicity of effector cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".