B cell-T cell interplay in immune regulation: A focus on follicular regulatory T and regulatory B cell functions
Bibliographic record
Abstract
B cells are the core components of humoral immunity. A mature B cell can serve in multiple capacities, including antibody production, antigen presentation, and regulatory functions. Forkhead box P3 (FoxP3)-expressing regulatory T cells (Tregs) are key players in sustaining immune tolerance and keeping inflammation in check. Mounting evidence suggests complex communications between B cells and Tregs. In this review, we summarize the yin-yang regulatory relationships between B cells and Tregs mainly from the perspectives of T follicular regulatory (Tfr) cells and regulatory B cells (Bregs). We discuss the regulatory effects of Tfr cells on B cell proliferation and the germinal center response. Additionally, we review the indispensable role of B cells in ensuring homeostatic Treg survival and describe the function of Bregs in promoting Treg responses. Finally, we introduce a new subset of Tregs, termed Treg-of-B cells, which are induced by B cells, lake the expression of FoxP3 but still own immunomodulatory effects. In this article, we also enumerate a sequence of research from clinical patients and experimental models to clarify the role of Tfr cells in germinal centers and the role of convention B cells and Bregs to Tregs in the context of different diseases. This review offers an updated overview of immunoregulatory networks and unveils potential targets for therapeutic interventions against cancer, autoimmune diseases and allograft rejection.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".