Hydroxychloroquine Suppresses Interferon-inducible Genes and B Cell Activating Factor in Patients With Incomplete and New-onset Systemic Lupus Erythematosus
Bibliographic record
Abstract
Objective. Hydroxychloroquine (HCQ) is commonly used as first-line treatment for systemic lupus erythematosus (SLE). Interferon (IFN)-inducible gene expression, IFN-γ–induced protein 10 (IP-10) and B cell activating factor (BAFF) are early mediators in SLE. The purpose of this study was to analyze the effects of HCQ on these factors. Methods. Patients with incomplete SLE (iSLE; antinuclear antibody titer ≥ 1:80, symptoms < 5 years, ≥ 1 objectified clinical American College of Rheumatology or SLE International Collaborating Clinics criteria), or new-onset, mild SLE were included when HCQ treatment was started for clinical reasons. Blood samples were taken at start and after 16 weeks. Three SLE-related IFN-inducible genes were measured in whole blood by real-time PCR, and an IFN score was calculated. Serum levels of IP-10 and BAFF were measured using ELISA. Results. In total, 9 patients were included: 7 with iSLE and 2 with new-onset SLE. The median SLE Disease Activity Index (SLEDAI) was 4. After 16 weeks of treatment with HCQ, the expression of IFN-inducible genes decreased in 8 of 9 patients, and the IFN-3 score decreased significantly (P = 0.012). There was a trend towards lower IP-10 levels (P = 0.055), and a significant decrease in BAFF levels (P = 0.023). Conclusion. HCQ suppresses IFN score and BAFF levels in patients with iSLE or new-onset SLE, and there is a trend towards lowering IP-10 levels. As these biomarkers are early mediators in SLE, this might support the hypothesis that HCQ could influence disease progression. However, prospective research with a larger sample size and longer follow-up is needed.
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 | 0.001 |
| 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 | 0.000 |
| 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".