CD40-activated B cells from patients with systemic lupus erythematosus can be modulated by therapeutic immunoglobulins in vitro
Bibliographic record
Abstract
INTRODUCTION: Aberrant signaling within and between B and T cells, considered to be central in systemic lupus erythematosus (SLE), could depend on enhanced CD40-CD154 activation. As a result, autoreactive B cells, normally anergic, differentiate and secrete antibodies attacking several normal tissues. Thus restorating B cell homeostasis might help control this disease. In this study, two facets of SLE B cells were investigated, namely their in vitro response to CD40-CD154 and the effect of treatment with human immunoglobulins for intravenous use (IVIg). MATERIALS AND METHODS: Blood samples from SLE patients and healthy volunteers were obtained and used to isolate B cells, which were activated through CD40 in the presence or absence of IVIg. The phenotype, proliferation, and differentiation of the SLE B cells were determined and compared with those of control B cells using flow cytometry and standard ELISA. RESULTS: In this model, CD40-activated SLE B cells, as control B cells, proliferated and differentiated and were characterized by the emergence of CD19(lo)CD38(++)CD138(+)CD27(++) cells. IVIg treatment of the CD40-activated SLE B cells resulted in higher differentiation, characterized by increased secretion rates of IgG and IgM, as reported previously for control B cells. CONCLUSIONS: Taken as a whole, such accelerated differentiation of CD40-activated B cells suggests that IVIg may participate in re-equilibration of the antibody repertoire by replacing pathological antibodies by de novo harmless antibodies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".