Stimulation of T cell autoreactivity by anomalous expression of NKG2D and its MIC ligands in rheumatoid arthritis
Bibliographic record
Abstract
Effector T cell responses can be modulated by competing positive or negative signals transduced by natural killer (NK) cell receptors. This raises the possibility that dominant T cell stimulation might promote autoimmune reactions. In rheumatoid arthritis (RA), the severity of autoimmune and inflammatory joint disease correlates with large numbers of CD4+CD28- T cells, which are scarce in healthy individuals. For poorly defined reasons, these T cells are autoreactive, implying that they may contribute to disease manifestations. CD4+CD28- T cells in peripheral blood and synovial tissue of RA patients were found to express NKG2D, a costimulatory receptor that is absent on normal CD4 T cells. NKG2D was induced by tumor necrosis factor alpha and IL-15, which are abundant in inflamed synovia and RA patient sera. RA synoviocytes aberrantly expressed the stress-inducible MIC ligands of NKG2D, which stimulated autologous CD4+CD28- T cell cytokine and proliferative responses. Peripheral blood serum samples of RA patients contained substantial amounts of synoviocyte-derived soluble MICA, which failed to induce down-modulation of NKG2D because of the opposing activity of tumor necrosis factor alpha and IL-15. These results suggest that a profound dysregulation of NKG2D and its MIC ligands may cause autoreactive T cell stimulation, thus promoting the self-perpetuating pathology in RA and possibly other autoimmune diseases.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".