Bibliographic record
Abstract
Aim: Psoriatic arthritis (PsA) is an inflammatory arthritis of unknown etiology that develops in approximately 30% of individuals with psoriasis. No objectively measurable biomarker has been identified for PsA, due in part to insufficient knowledge of its etiopathogenesis. This work aims to identify candidate biomarkers of PsA by studying its underlying transcriptomic and epigenomic mechanisms. Methods: Psoriasis (PsC) and PsA patients from a prospective cohort were analyzed. Whole blood, serum, and semen samples were obtained from subsets of patients and unaffected controls for transcriptomic, protein, and epigenomic analyses, respectively. Potential epigenetic mechanisms were also analyzed using self-reported family history data from the entire PsC and PsA cohort to further explore the parent-of-origin effect. Results: Transcriptomic analyses identified several genes involved in innate immunity, particularly toll-like receptor signalling as differentially expressed in whole blood of PsA and PsC patients. Four candidate gene expression biomarkers CXCL10, NOTCH2NL, HAT1, and SETD2 were replicated in an independent cohort of PsC and PsA patients. Soluble CXCL10 was significantly elevated in baseline serum samples of psoriasis patients who later developed PsA compared to patients who did not develop PsA. Excessive paternal transmission was found in PsC and PsA patients, as well as genetic anticipation manifesting as increased disease severity during male transmission. DNA methylation profiling of sperm cells revealed several germ line variations associated with psoriasis and PsA occurring near or within genes involved in inflammatory and immune system processes, including HCG26 within the major histocompatibility complex. Conclusions: Whole blood transcriptomic and serum protein analysis identified the chemokine CXCL10 as a putative predictive biomarker of PsA in PsC patients. Evidence of a parent-of-origin effect, genetic anticipation, and the identification of germ line DNA methylation variations in patients suggest a role for epigenetic mechanisms in psoriatic disease etiopathogenesis, and a potential new avenue of biomarker discovery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".