Increased Interleukin 21 and Follicular Helper T-like Cells and Reduced Interleukin 10+ B cells in Patients with New-onset Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To elucidate the potential role of follicular helper T cells (TFH) and interleukin 10 (IL-10)+ B cells in the development of systemic lupus erythematosus (SLE). METHODS: The numbers of peripheral blood CD27+, CD38+, CD86+, CD95+, IL-10+ B cells, and inducible T cell costimulator (ICOS)+, programmed death-1 (PD-1)+, IL-21+, CXCR5+CD4+ TFH-like cells were examined in 23 patients with new onset SLE and 20 healthy controls (HC). RESULTS: In comparison with HC, significantly reduced numbers of CD19+ and IL-10+ B cells, but increased numbers of CD27(high), CD86+, CD95+ B cells, CXCR5+CD4+, ICOS+, PD-1+, and IL-21+ TFH-like cells were detected, which were accompanied by higher levels of serum IL-21, but lower levels of IL-10 in the patients. Treatment with anti-SLE therapy modulated the imbalance of different subsets of B and TFH-like cells. The levels of serum IL-21 and IL-10 were positively correlated with the numbers of CD4+CXCR5+ TFH-like and CD19+CD5+CD1d+ B cells in the patients, respectively. The numbers of CD27(high) B cells were correlated positively with IL-21+ TFH-like cells, but negatively with IL-10+ B cells. The values of SLE Disease Activity Index, C3, and erythrocyte sedimentation rate were correlated positively with serum IL-21, but negatively with IL-10 in those patients. CONCLUSION: Our data indicate that the imbalance of IL-21+ TFH-like, CD27(high), and IL-10+ B cells may be associated with the pathogenesis of SLE, and levels of serum IL-21 and IL-10 may be valuable for evaluating disease activity in SLE.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".