Distinct Profiles of Human B Cell Effector Cytokines: A Role in Immune Regulation?
Bibliographic record
Abstract
There is growing interest in the fundamental roles that B cells may play in regulating immune responses. Emerging animal studies point to an important contribution of B cell effector cytokines to immune modulation, yet little is known about the factors regulating such cytokine production. We report that the profile of human B cell cytokine production is context dependent, being critically influenced by the balance of signals through the B cell receptor and CD40. B cells appropriately stimulated by sequential B cell receptor and CD40 stimulation proliferate and secrete TNF-alpha, lymphotoxin, and IL-6, which can act not only as autocrine growth and differentiation factors, but also serve to amplify the ongoing immune response. In contrast, CD40 stimulation alone, a mimic of a B cell receiving bystander T cell help in the absence of specific Ag recognition, induces negligible proinflammatory cytokines, but significant production of IL-10 that serves to suppress inappropriate immune responses. We thus describe a novel paradigm of reciprocal regulation of B cell effector cytokines, and ascribe active roles for human B cells in either promoting or suppressing local immune responses through context-dependent cytokine production.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".