Bibliographic record
Abstract
Psoriatic arthritis (PsA) is an inflammatory arthritis that commonly occurs with psoriasis and is attributed to genetic, immunologic and environmental factors. It shares skin involvement with psoriasis, articular involvement particularly with spondyloarthritis, bowel involvement with Crohn’s disease and eye involvement with uveitis, suggesting the existence of some common pathways. The Th-17 pathway and the IL-23/IL-17 axis have become prominent players in PsA and have considerably increased our understanding of disease pathogenesis. In this review article, we will focus on the genetic, epigenetic, and pharmacogenetic information with respect to PsA. Prominent genes identified in PsA via GWAS include HLA-A, HLA-B, HLA-C, IL-12B, IL-23R, IL-23A, TNIP1, TRAF3IP2, CSF2/P4HA2, FBXL19, REL, TYK2, NOS2, PTPN22, TNFAIP3, IFNLR1, IFIH1, and NFKBIA. These genetic markers have also illuminated key signaling pathways involved in PsA pathogenesis which can be broadly classified into those involved in epidermal differentiation, innate immunity, antigen presentation and processing, and acquired/adaptive immunity. With respect to PsA pathogenesis, the most consistent and predominant genetic effect is located on chromosome 6p21.3 within the major histocompatibility complex (MHC) region. The most significant association for increased PsA risk was with asparagine or serine residue at amino acid position 97 of HLA-B, where asparagine at position 97 of HLA-B represents the HLA-B*27 allele. Moreover, specific HLA alleles have been associated with disease susceptibility, expression and progression in PsA. The prominent emerging role of the Th-17 signaling pathway in PsA pathogenesis will be highlighted. The lack of identified PsA genetic susceptibility loci is largely attributed to the much smaller number of patients, classification criteria used, and the greater clinical heterogeneity of PsA compared with psoriasis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".