Identification of CD64 as a marker for the destructive potential of synovitis in osteoarthritis
Bibliographic record
Abstract
OBJECTIVES: OA is characterized by cartilage degeneration and persistent pain. The majority of OA patients present with synovitis, which is associated with increased cartilage damage. Activated synovial macrophages are key contributors to joint destruction. Therefore, a marker that reflects the activation of these cells could be a valuable tool to characterize the destructive potential of synovitis and benefit monitoring of OA. Here, we aimed to investigate the use of CD64 (FcγRI) as a marker to characterize the damaging potential of synovitis in OA. METHODS: Synovial biopsies were obtained from end-stage OA patients that underwent joint replacement surgery. CD64 protein expression and localization was evaluated using immunohistochemistry and immunofluorescence and quantified using flow cytometry. qPCR was performed to measure the expression of FCGR1 and OA-related genes in synovial biopsies, and in primary chondrocytes and primary fibroblasts stimulated with OA conditioned medium (OAS-CM). RESULTS: Our data exposed a wide range of CD64 expression in OA synovium and showed positive correlations between FCGR1 and S100A8, S100A9, IL1B, IL6 and MMP1/2/3/9/13 expression. CD64 protein correlated with MMP1, MMP3, MMP9, MMP13 and S100A9. Furthermore, we observed that synovial CD64 protein levels in source tissue for OAS-CM significantly associated with the OAS-CM-induced expression of MMP1, MMP3 and especially ADAMTS4 in cultured fibroblasts, but not chondrocytes. CONCLUSION: Together, these results indicate that synovial CD64 expression is associated with the expression of proteolytic enzymes and inflammatory markers related to structural damage in OA. CD64 therefore holds promise as marker to characterize the damaging potential of synovitis.
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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 | 0.001 | 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 | 0.001 | 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".