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 both in vitro and 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 both in vitro and in patients with SLE. Our study provides additional evidence for HCQ as a treatment for SLE.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".