Hydroxychloroquine Inhibits the Differentiation of Th17 Cells in Systemic Lupus Erythematosus
Bibliographic record
Abstract
Objective. Hydroxychloroquine (HCQ) is a commonly used medicine for the treatment of systemic lupus erythematosus (SLE), and Th17 cells are closely related to the pathogenesis of SLE. However, the role and mechanism of HCQ on Th17 cell differentiation in SLE is not clearly understood. Here, we investigate the effect of HCQ on Th17 cell differentiation bothin vitroand in patients with SLE. Methods. Twenty-five patients with SLE were divided into 2 treatment groups: prednisone alone and HCQ plus prednisone. Interleukin 17 (IL-17) expression was analyzed by ELISA and real-time (RT)-PCR. Th17 were measured in patients with SLE by flow cytometry before and after HCQ treatment.In vitro, naive T cells were cultured in Th17-inducing conditions with or without HCQ. Cell differentiation and IL-17 expression were analyzed. Finally, transcriptome sequencing identified differential gene expression between naive T cells and induced Th17 cells. Results. In patients, HCQ plus prednisone treatment inhibited IL-17 production, gene expression, and Th17 cell differentiation.In vitro, HCQ inhibited Th17 cell proliferation and differentiation, as well as IL-17 production. Five microRNA were significantly different in Th17 cells compared with naive T cells, and HCQ treatment reversed this effect.In vivo, microRNA-590 (miR-590) was verified and was significantly decreased in Th17 cells, compared with naive T cells from lupus-prone mice. Moreover, miR-590 was increased in patients treated with HCQ plus prednisone. Conclusion. HCQ inhibited Th17 cell differentiation and IL-17 production bothin vitroand in patients with SLE. Our study provides additional evidence for HCQ as a treatment for 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".