γδ T Cells Are Essential Effectors of Type 1 Diabetes in the Nonobese Diabetic Mouse Model
Bibliographic record
Abstract
γδ T cells, a lineage of innate-like lymphocytes, are distinguished from conventional αβ T cells in their Ag recognition, cell activation requirements, and effector functions. γδ T cells have been implicated in the pathology of several human autoimmune and inflammatory diseases and their corresponding mouse models, but their specific roles in these diseases have not been elucidated. We report that γδ TCR(+) cells, including both the CD27(-)CD44(hi) and CD27(+)CD44(lo) subsets, infiltrate islets of prediabetic NOD mice. Moreover, NOD CD27(-)CD44(hi) and CD27(+)CD44(lo) γδ T cells were preprogrammed to secrete IL-17, or IFN-γ upon activation. Adoptive transfer of type 1 diabetes (T1D) to T and B lymphocyte-deficient NOD recipients was greatly potentiated when γδ T cells, and specifically the CD27(-) γδ T cell subset, were included compared with transfer of αβ T cells alone. Ab-mediated blockade of IL-17 prevented T1D transfer in this setting. Moreover, introgression of genetic Tcrd deficiency onto the NOD background provided robust T1D protection, supporting a nonredundant, pathogenic role of γδ T cells in this model. The potent contributions of CD27(-) γδ T cells and IL-17 to islet inflammation and diabetes reported in this study suggest that these mechanisms may also underlie human T1D.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".