Differential responses of human B‐lymphocyte subpopulations to graded levels of CD40–CD154 interaction
Bibliographic record
Abstract
Naïve and memory B-lymphocyte populations are activated by CD154 interaction through cell-surface CD40. This interaction plays an important role in the regulation of the humoral immune response, and increasing evidence indicates that fine variation in CD40 binding influences B lymphocytes, macrophages and dendritic cells in murine models. Here we have investigated whether and how variations in the intensity of the CD40-CD154 interaction could contribute to differential regulation of human B-lymphocyte populations. Proliferation and differentiation of B lymphocytes were monitored in response to graded levels of CD40 stimulation in the presence of interleukin (IL)-2, IL-4 and IL-10. Our results show that the level of CD154 binding to CD40 on B lymphocytes can directly influence the evolution of CD19(+) CD27(-) and CD19(+) CD27(+) cell populations. Furthermore, proliferation, global expansion of CD19(+) cells and emergence of CD38(++) CD138(+) cells, as well as immunoglobulin G (IgG) and IgM secretion, were affected by the level of exposure of B lymphocytes to CD154. These results suggest that the CD40-CD154 interaction is more like a rheostat than an on/off switch, and its variation of intensity may play a role in the regulation of B-lymphocyte activation following the primary and/or secondary humoral immune response.
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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) | 0.001 | 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".