Tuning of CD40–CD154 Interactions in Human B-Lymphocyte Activation: A Broad Array of In Vitro Models for a Complex In Vivo Situation
Bibliographic record
Abstract
Naive and memory B-lymphocyte populations can be activated through the binding of CD154 to CD40, a receptor that is constitutively expressed on the surface of these cells. Models based on the in vitro stimulation of human B lymphocytes through CD40 have greatly contributed to our understanding of the human immune response in healthy individuals and patients suffering from immune disorders. The nature of the engineered CD40 ligands is as diverse as the in vitro models used in studies of CD40-activated B lymphocytes. Monoclonal anti-CD40 antibodies, recombinant CD154 proteins, soluble CD154(+) membranes as well as CD154(+) cell lines have turned out to be very useful tools, and are still in use today. As for any receptor-ligand interaction, parameters such as duration and strength of contact, timing, affinity, and receptor density are major determinants of CD40 binding by CD154 or anti-CD40. Furthermore, variation in the intensity of CD40 stimulation has been shown to influence proliferation, differentiation and immunoglobulin secretion of human hybridomas, B-cell lines, tonsil and blood B lymphocytes. The objective of this review is to present an overview of the great diversity of CD40 agonists used in in vitro models of B-lymphocyte activation, with a particular emphasis on variations in the resulting strength of CD40 signaling generated by these models. A better understanding of these models could open up new avenues for the rational use of human B lymphocytes as antigen-presenting cells in cellular therapies.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".