Preferential Accumulation of Activated Th1 Cells Not Only in Rheumatoid Arthritis But Also in Osteoarthritis Joints
Bibliographic record
Abstract
OBJECTIVE: It was previously found that Th1 but not Th17 cells were predominant in the joints of rheumatoid arthritis (RA). To verify whether this is a unique feature of CD4 T cells in RA joints, we performed comparative flow cytometric analysis of CD4 T cells in RA and osteoarthritis (OA) joints. METHODS: Mononuclear cells were isolated from peripheral blood (PB), synovial membrane (SM), and synovial fluid (SF) from a total of 18 RA and 12 OA patients. The expression of surface molecules and cytokine production of CD4 T cells was examined by a flow cytometer. RESULTS: Most CD4 T cells in RA joints expressed memory/activation markers, such as CD45RO, HLA-DR, and CD69. CCR5 was highly expressed on CD4 T cells in SF but not in PB or SM. With regard to Th17-related molecules, CD4 T cells expressing CCR6 were not enriched in either SF or SM. In contrast, CD161-positive cells were abundant in the joint, many of which, however, produced interferon-γ but not interleukin 17A. Virtually all T cells in OA joints, although much less numerous than in RA joints, expressed activation markers. Th1 cells were predominant in both OA and RA joints, while there were a few Th17 cells. The frequency of Th17 cells in the joint tended to be lower in OA than RA. CONCLUSION: There was a quantitative but not qualitative difference in CD4 T cells, including the expression of activation markers and cytokine profiles, between RA and OA joints.
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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 | 0.002 | 0.001 |
| 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) | 0.005 | 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".