Th17 gene expression in psoriatic arthritis synovial fluid and peripheral blood compared to osteoarthritis and cutaneous psoriasis.
Bibliographic record
Abstract
OBJECTIVES: The IL-23/IL-17 axis is central to the pathogenesis of psoriatic arthritis (PsA). We aimed to identify Th17 signalling genes that are dysregulated in synovial fluid of PsA compared to osteoarthritis (OA) patients and to determine if differences in peripheral blood can distinguish PsA from psoriasis patients and controls. METHODS: Synovial fluid cells (SFCs) from 14 PsA and 9 OA patients were obtained and stored in TRIzol reagent. RNA was isolated by phenol-chloroform extraction and purified with RNeasy miniprep kits. Total RNA was extracted from PAXgene whole blood from 20 PsA, 20 psoriasis without arthritis (PsC) and 11 controls. Quantitative RT-PCR arrays were used to profile expression of 84 genes related to the Th17 regulatory network. Fold change differences were compared by Mann-Whitney U-test with false discovery rate (FDR) correction (FDR<0.05). RESULTS: In PsA compared to OA SFCs, a total of 33 genes were up-regulated and 27 genes were down-regulated. Signalling molecules (such as STAT3, FOXP3) were highly expressed in PsA SFCs, while cytokines (such as IL17F, IL6) were more predominant in OA SFCs after non-supervised hierarchal clustering. Nine genes (MMP3, CCL1, IL17C, CCL20, IL17F, IL3, CXCL5, IL6 and CX3CL1) had concordant expression in SFCs and in peripheral blood cells (PBCs) of PsA compared to PsC and/or controls. CONCLUSIONS: We identified expression differences in Th17 signalling genes in PsA compared to OA SFCs, with an elevation of signalling molecules and attenuation of cytokine expression in PsA. A subset of genes was concordant in PBCs; these may thus be potential biomarkers of PsA.
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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 | 0.001 |
| 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) | 0.003 | 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".