Innate Immune Receptors as Dynamic Modulators of Extrafollicular Autoimmune B Cell Response
Bibliographic record
Abstract
The immune system relies on carefully calibrated cellular machineries to enable distinction between endogenous and foreign molecules, with autoimmunity arising when this balance is disrupted. As potent autoantibody factories, B cells are major drivers of many autoimmune diseases. A significant fraction of patients affected by chronic autoimmune diseases such as systemic lupus erythematosus (SLE) exhibit pathogenic accumulation of B-cell subsets that are believed to be derived from the extrafollicular (EF) differentiation pathway. These B-cell subsets, although variously named and exhibiting intrinsic heterogeneity, are all poised producers of autoantibodies that correlate with patient pathophysiology. In addition, they are often characterized by biomarkers known to drive the innate immune response, including toll-like receptors and complement receptors. Although many innate receptors have well-established functions in myeloid cells and other immune cell types, their B cell-specific functions are still under active investigation and are crucial for understanding the molecular pathways that drive B-cell breaks of tolerance. In this review, we summarize studies on innate immune receptors that serve prominent roles in regulating EF B-cell activation in health and autoimmunity. By discussing independent and collaborative functions of these receptors, we hope to provide new perspectives in autoimmune disease signature research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".