Mycophenolate mofetil reduces STAT3 phosphorylation in systemic lupus erythematosus patients
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) is a highly variable autoimmune disease that can involve severe organ-threatening symptoms, such as lupus nephritis. Certain drugs, such as mycophenolate mofetil (MMF), are effective at reducing morbidity associated with nephritis; however, the immune pathways associated with disease suppression are poorly defined. Here, we provide evidence that MMF inhibits phosphorylation of STAT3 and other associated immune pathways. Using mass cytometry and bead-based or ELISA assays, the systemic phenotype of SLE patients not taking (MMF-) or taking (MMF+) MMF were studied. MMF+ SLE patients had significant reductions in total numbers of transitional B cells, plasmablasts, and T cells, specifically CD4+ Th17-type and CD4+ Treg-type cells, compared with MMF- patients. Plasma soluble mediators were decreased in MMF+ patients including chemokines (MIG/CXCL9 and SDF-1α/CXCL12) and growth factors (VEGF-A and PDGF-BB). Soluble mediators and cell subsets grouped by functional properties revealed significant modifications associated with STAT3 and B cell pathways. Further, healthy PBMCs treated with IL-6 revealed a reduction in p-STAT3 following the addition of mycophenolic acid (the active metabolite of MMF). In conclusion, the inhibition of STAT3 phosphorylation by MMF may explain the effectiveness of this treatment in SLE patients, since increased levels of p-STAT3 are associated with disease pathology.
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 | 0.000 |
| 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".