Comparison of Blood and Synovial Fluid Th17 and Novel Peptidase Inhibitor 16 Treg Cell Subsets in Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Early recognition and treatment of juvenile idiopathic arthritis (JIA) can prevent joint damage and minimize side effects of medication. The balance between proinflammatory and antiinflammatory mechanisms is known to be important in JIA, and we therefore investigated T cell subsets including Th cells, autoaggressive Th17 cells, and regulatory T cells (Treg), including a novel Treg subset in peripheral blood (PB) and synovial fluid (SF) of patients with JIA. METHODS: Fifty children with JIA were enrolled in our study. Frequency, phenotype, and function of T lymphocytes in PB and SF were characterized using flow cytometry. Migration capabilities of PB and SF cells were compared. RESULTS: Synovial T cells showed different phenotype and function compared with PB T cells, with an increased proportion of memory T cells, expression of CCR4, CCR5, CXCR3, interleukin 23R, and an increased ratio of Th17 to Treg. Although Treg were increased in SF compared with the PB, we found a significant decrease in the numbers of peptidase inhibitor 16 (PI16)+ Treg in active joints compared with peripheral blood. Coexpression of CCR4 and CCR6 was reduced on PI16+ Treg in PB and SF of patients with JIA compared with healthy children, however the ability of these cells to migrate toward their ligands was unaffected. CONCLUSION: This is a comprehensive characterization of novel PI16+ Treg and Th17 cells in matched blood and synovial fluid samples of patients with JIA. Despite an increased number of Treg within the inflamed joint, lower numbers of PI16+ Treg but high numbers of Th17 cells might contribute to the inability to control disease.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".