Persistently active interferon‐γ pathway and expansion of T‐bet<sup>+</sup> B cells in a subset of patients with childhood‐onset systemic lupus erythematosus
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) is an autoimmune disease causing significant morbidity and mortality, despite important improvements in its management in the last decades. The objective of this work is to investigate the role of IFN-γ in the pathogenesis of childhood-onset systemic lupus erythematosus (cSLE), evaluating the crosstalk between IFN-α and IFN-γ and the expression of T-bet, a transcription factor induced by IFN-γ, in B cells of patients with cSLE. Expression levels of both IFN-α and IFN-γ-induced genes were upregulated in patients with cSLE. We found increased serum levels of CXCL9 and CXCL10 in patients with cSLE. Type I IFN score decreased with initiation of immunosuppressive treatment; conversely, type II IFN score and levels of CXCL9 were not significantly affected by immunosuppressive treatment. Type II IFN score and CXCL9 were significantly higher in patients with lupus nephritis. We observed the expansion of a population of naïve B cells expressing T-bet in a cluster of patients with cSLE. IFN-γ, but not IFN-α, induced the expression of T-bet in B cells. Our data suggest that IFN-γ is hyperactive in cSLE, especially in patients with lupus nephritis, and it is not modulated by therapy. Our data reinforce the potential of IFN-γ as a therapeutic target in SLE.
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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".