Rheumatoid Arthritis Clinical Benefits from Abatacept, Cytokine Blockers, and Rituximab Are All Linked to Modulation of Memory B Cell Responses
Bibliographic record
Abstract
Abatacept (ABA) is a biologic agent with great proven efficacy in patients with rheumatoid arthritis (RA)1. At a molecular level, ABA is composed of recombinant domains of cytotoxic T lymphocyte-associated antigen 4 (CTLA-4), fused to the constant region domains of human IgG1, which serves to increase in vivo half-life. This agent binds to CD80/CD86 with a much higher avidity than the CD28 on many T cells, and therefore can act as a physiologic competitive inhibitor that interrupts cell-cell co-stimulatory interactions. In earlier reports, ABA treatment of patients with RA was shown to have striking effects on the modulation of T cell subsets2. The recent report by Scarsi, et al highlights the bidirectional nature of the CD28-CD80/86 interaction and the profound effects that ABA can have on the B cell compartment of the immune system, especially for memory B cells3. With the proven efficacy of the B cell-targeted anti-CD20 agent rituximab (RTX), the central roles of B cells in RA have become well accepted, and B cells also commonly express the co-stimulatory molecules CD80/864. Even at the earliest onset of clinical signs and symptoms, patients with RA display dysregulated immune-cell trafficking and maturation. Compared to healthy subjects, patients with RA also have abnormal levels of circulating memory B cells (identified by CD27 expression), and this may in part reflect their recruitment to the synovial compartment or secondary lymph nodes5. In a small study of 28 patients with RA, Scarsi, et al showed that after 6 months of ABA treatment, patients with clinical responses had significant decreases in levels of switched memory B cells, with persistent decreases in memory B cell subsets also found at 12 months3. ABA therapy also significantly reduced levels of serum total IgG, IgA, and IgM, … Address correspondence to Dr. G. Silverman, Department of Medicine, NYU School of Medicine, 450 E. 29th St., New York, New York 10016, USA. E-mail: Gregg.Silverman{at}nyumc.org
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".