Abatacept Reduces Levels of Switched Memory B Cells, Autoantibodies, and Immunoglobulins in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Abatacept (ABA) is a chimeric molecule, able to block the CD28-mediated costimulatory pathway. To evaluate the hypothesis that, through this mechanism of action, ABA may down-modulate the immune responses of B lymphocytes in rheumatoid arthritis (RA), we investigated the serum levels of immunoglobulins (Ig), free light chains (FLC), anticitrullinated protein antibodies (ACPA), and rheumatoid factor (RF), as well as the number of B lymphocytes differentiated into post-switch memory cells in patients treated with ABA. METHODS: The serum levels of Ig, FLC, different ACPA, RF isotypes, and the B cell phenotype were longitudinally evaluated in 30 patients with RA treated with ABA. RESULTS: At baseline, the proportion of total and post-switch memory B cells was lower in RA than in healthy individuals. After 6 months of ABA treatment we observed significant reductions of serum levels of IgG, IgA, and IgM, as well as FLC, with a normalization in many patients who had initially abnormal values. A significant reduction of the titers of IgG- and IgA-ACPA, as well as of IgM-, IgA-, and IgG-RF was also observed. A decrease of autoantibodies below the upper limits of normal values was found in 2 of 26 patients (8%) initially seropositive for IgG-ACPA, 1 of 14 (7%) for IgA-ACPA, 5 of 22 (23%) for IgM-RF, 7 of 22 (30%) for IgA-RF, and 5 of 16 (31%) for IgG-RF. After treatment, the proportion of circulating post-switch memory B cells was also further significantly decreased. CONCLUSION: ABA treatment in patients with RA can reduce signs of polyclonal B cell activation, inducing a trend toward normalization of serum levels of different classes of Ig and of FLC, decreasing titers of ACPA and RF, and percentages of post-switch memory B cells.
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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 | 0.000 | 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".