Cutting Edge: Increased IL-17–Secreting T Cells in Children with New-Onset Type 1 Diabetes
Bibliographic record
Abstract
CD4(+)FOXP3(+) regulatory T cells are essential for immune tolerance, and murine studies suggest that their dysfunction can lead to type 1 diabetes (T1D). Human studies assessing regulatory T cell dysfunction in T1D have relied on analysis of FOXP3-expressing cells. Recently, distinct subsets of CD4(+)FOXP3(+) T cells with differing function were identified. Notably, CD45RA(-)CD25(int)FOXP3(low) T cells lack suppressive function and secrete the proinflammatory cytokine IL-17. Therefore, we evaluated whether the relative fractions of CD4(+)FOXP3(+) subsets are altered in new-onset T1D subjects. We report that children with new-onset T1D have an increased proportion of CD45RA(-)CD25(int)FOXP3(low) cells that are not suppressive and secrete significantly more IL-17 than other FOXP3(+) subsets. Moreover, these T1D subjects had a higher proportion of both CD4(+) and CD8(+) T cells that secrete IL-17. The bias toward IL-17-secreting T cells in T1D suggests a role for this proinflammatory cytokine in the pathogenesis of disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".