The Skin, a Novel Niche for Recirculating B Cells
Bibliographic record
Abstract
B cells infiltrate the skin in many chronic inflammatory diseases caused by autoimmunity or infection. Despite potential contribution to disease, skin-associated B cells remain poorly characterized. Using an ovine model of granulomatous skin inflammation, we demonstrate that B cells increase in the skin and skin-draining afferent lymph during inflammation. Surprisingly, skin B cells are a heterogeneous population that is distinct from lymph node B cells, with more large lymphocytes as well as B-1-like B cells that coexpress high levels of IgM and CD11b. Skin B cells have increased MHC class II, CD1, and CD80/86 expression compared with lymph node B cells, suggesting that they are well-suited for T cell activation at the site of inflammation. Furthermore, we show that skin accumulation of B cells and Ab-secreting cells during inflammation increases local Ab titers, which could augment host defense and autoimmunity. Although skin B cells express typical skin-homing receptors, such as E-selectin ligand and α-4 and β-1 integrins, they are unresponsive to ligands for chemokine receptors associated with T cell homing into skin. Instead, skin B cells migrate toward the cutaneously expressed CCR6 ligand CCL20. Our data support a model in which B cells use CCR6-CCL20 to recirculate through the skin, fulfilling a novel role in skin immunity and inflammation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".