Abrogation of ICOS/ICOS ligand costimulation in NOD mice results in autoimmune deviation toward the neuromuscular system
Bibliographic record
Abstract
NOD mice spontaneously develop insulin-dependent diabetes around 10-40 wk of age. Numerous immune gene variants contribute to the autoimmune process. However, genes that direct the autoimmune response toward β cells remain ill defined. In this study, we provide evidence that the Icos and Icosl genes contribute to the diabetes process. Protection from diabetes in ICOS(-/-) and ICOSL(-/-) NOD mice was unexpectedly associated with the development of an autoimmune disorder of the neuro-muscular system, characterized by myositis, sensory ganglionitis and, to a reduced extent, inflammatory infiltrates in the CNS. This syndrome was reproduced upon adoptive transfer of CD4(+) and CD8(+) T cells from diseased donors to naïve NOD.scid recipients. Our data further show that protection from diabetes results from defective activation of autoimmune diabetogenic effector T cells in ICOS(-/-) NOD mice, whereas acceleration of diabetes in BDC2.5 ICOS(-/-) NOD mice is induced by a dominant defect in Treg. Taken together, our findings indicate that costimulation signals play a key role in regulating immune tolerance in peripheral tissues and that the ICOS/ICOSL costimulatory pathway influences the balance between Treg and diabetogenic effector T cells.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".