Bibliographic record
Abstract
Abstract Description Psoriatic arthritis (PsA) is an inflammatory arthritis that affects patients with psoriasis and is characterized by dysregulated immune responses and joint and skin inflammation. TRAF1 is an adaptor protein that promotes inflammatory signaling in lymphocytes and attenuates inflammatory signaling in monocytes. A weighted gene co-expression network analysis showed TRAF1 to be the central hub gene in PsA patients, however, TRAF1 levels have not been measured in PsA patients and its role in disease pathogenesis is unknown. We hypothesized that elevated TRAF1 in T cells is a key driver of PsA pathogenesis, and lowering TRAF1 levels can reduce inflammatory cytokine production and T cell activation, offering a novel therapeutic strategy for PsA disease management. Specifically, we (1) assessed TRAF1 protein expression in peripheral blood mononuclear cell (PBMC) samples by spectral flow cytometry from PsA patients (N = 55) relative to healthy donors (N = 20), and (2) examined whether knocking down TRAF1 by shRNA reduced pro-inflammatory activation and cytokine production in PsA patient PBMCs. Compared to healthy controls, PsA patients had elevated levels of TRAF1. Reduced TRAF1 levels by shRNA knockdown in PBMCs, showed a reduction in inflammatory cytokines such as, IFN?, TNF and IL-17. Our findings indicate a critical role for TRAF1 in PsA pathobiology; therefore, targeting TRAF1 in T cells may represent a promising therapeutic approach to mitigate disease activity Funding Sources Krembil Foundation Arthritis Society Canada Topic Categories Immune Mechanisms of Human Disease (HUM)
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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 | 0.000 | 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) | 0.008 | 0.002 |
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".