CD40 Stimulation of Human Peripheral B Lymphocytes: Distinct Response from Naive and Memory Cells
Bibliographic record
Abstract
During secondary immune response, memory B lymphocytes proliferate and differentiate into Ig-secreting cells. In mice, the binding of CD40 by CD154 clearly enhances the activation and differentiation of memory B lymphocytes. In humans, the role of CD40-CD154 in the stimulation of memory B lymphocytes is not as obvious since in vitro studies reported positive and negative effects on their proliferation and differentiation in Ig-secreting cells. In this study, we examine the response of peripheral memory and naive cells in relation to the duration of CD40-CD154 interaction. We measured the proliferation and differentiation of both subsets stimulated with CD154 and IL-4 for short- (4-5 days) and long-term (>7 days) periods. Following short-term stimulation, memory B lymphocytes did not expand but represented the only subset differentiating into IgG- and IgM-secreting cells. A longer stimulation of this population led to cell death, while promoting naive B lymphocyte proliferation, expansion, and differentiation into IgM- or IgG-secreting cells. This prolonged CD40 stimulation also triggered naive B lymphocytes to switch to IgG and to express CD27 even in absence of somatic hypermutation, suggesting that these latter events could be independent. This study suggests that naive and memory B lymphocytes have distinct requirements to engage an immune response, reflecting their different roles in humoral immunity.
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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) | 0.000 | 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) | 0.004 | 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".