Expanded Population of Activated Antigen-Engaged Cells within the Naive B Cell Compartment of Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
Polyclonal B cell activation is a well-described feature of systemic lupus erythematosus (SLE), but the immune mechanisms leading to this activation are unclear. To gain insight into these processes, we extensively characterized the activated peripheral blood B cell populations in SLE. PBMC from lupus patients and healthy controls were stained with various combinations of conjugated Ab to identify distinct peripheral B cell subsets, and activation was assessed by measurement of forward scatter and CD80 or CD86 expression using flow cytometry. SLE patients had altered proportions of several B cell subsets, many of which demonstrated increased activation as assessed by forward scatter. This activation occurred at an early developmental stage, as B cells in the transitional (T2) stage were already significantly larger than those seen in controls. Increased proportions of CD80- or CD86-expressing cells were also seen in multiple B cell subsets, with the most striking differences observed in the naive CD27-CD23+ population. Within the CD23+ subset, increased costimulatory molecule expression was most pronounced in an IgD+IgMlow population, suggesting that activation follows Ag engagement. Although controls also had IgD+IgMlowCD23+ cells, they were reduced in number and not activated. Thus, there is an altered response to Ig receptor engagement with self-Ags in lupus.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".