The course of cytokine and chemokine gene expression in clinically suspect arthralgia patients during progression to inflammatory arthritis
Bibliographic record
Abstract
OBJECTIVES: Autoantibody responses increase years before the onset of inflammatory arthritis (IA) and are stable during transitioning from clinically suspect arthralgia (CSA) to IA. Cytokine and chemokine levels also increase years before IA onset. However, the course in the at-risk stage of CSA during progression to disease or non-progression is unknown. To increase the understanding of processes mediating disease development, we studied the course of cytokine, chemokine and related receptors gene expression in CSA patients during progression to IA and in CSA patients who ultimately did not develop IA. METHODS: Whole-blood RNA expression of 37 inflammatory cytokines, chemokines and related receptors was determined by dual-colour reverse transcription multiplex ligation-dependent probe amplification in paired samples of CSA patients at CSA onset and either at IA development or after 24 months without IA development. ACPA-positive and ACPA-negative CSA patients developing IA were compared at CSA onset and during progression to IA. Generalised estimating equations tested changes over time. A false discovery rate approach was applied. RESULTS: None of the cytokine/chemokine genes significantly changed in expression between CSA onset and IA development. In CSA patients without IA development, G-CSF expression decreased (P = 0.001), whereas CCR6 and TNIP1 expression increased (P < 0.001 and P = 0.002, respectively) over a 2 year period. Expression levels in ACPA-positive and ACPA-negative CSA patients who developed IA were similar. CONCLUSION: Whole-blood gene expression of assessed cytokines, chemokines and related receptors did not change significantly from CSA to IA development. This suggests that changes in expression of these molecules may not be related to the final process of developing chronicity and may have occurred preceding CSA onset. Changes in gene expression in CSA patients without IA development may provide clues for processes related to resolution.
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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 | 0.000 | 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".