Evaluation of a novel particle-based multi-analyte technology for the detection of anti-fibrillarin antibodies
Bibliographic record
Abstract
Systemic sclerosis (SSc) is a heterogeneous autoimmune disease associated with several anti-nuclear antibodies (ANA), including those in the classification criteria (anti-centromere, anti-topoisomerase I (Scl-70), anti-RNA Pol III). However, the presence of less common antibodies such as anti-fibrillarin (U3-RNP) that generate a clumpy nucleolar pattern by HEp-2 indirect immunofluorescence assay (IFA, ICAP AC-9) are considered disease specific and are with clinical subsets of SSc, therefore playing a role in diagnosis and prognosis. A specific and sensitive anti-fibrillarin assay would be an important addition to serological diagnosis and evaluation of SSc. The goal of this study was to evaluate a new particle-based multi-analyte technology (PMAT) for the measurement of anti-fibrillarin antibodies. A total of 149 patient samples were collected including 47 samples from France (Lyon and Paris, n = 32) and Italy (Careggi Hospital, Florence, n = 15) selected based on AC-9 HEp-2 IFA staining (> 1:640, clumpy nucleolar pattern) and 102 non-SSc controls (inflammatory bowel disease (IBD) n = 20, Sjögren's syndrome (SjS) n = 20, infectious disease (ID) n = 7, systemic lupus erythematosus (SLE) n = 17, rheumatoid arthritis (RA) n = 17, and healthy individuals (HI) n = 21). All samples were tested on the anti-fibrillarin PMAT assay (research use only, Inova Diagnostics, USA). Additionally, the 47 anti-fibrillarin positive samples were also tested on PMAT assays for detecting other autoantibodies in ANA-associated rheumatic diseases (AARD). Anti-fibrillarin antibody data performed by fluorescence enzyme immunoassay (FEIA, Thermo Fisher, Germany) was available for 34 samples. The anti-fibrillarin PMAT assay was positive in 31/32 (96.9%, France) and 12/15 (80.0%, Italy) of samples preselected based on the AC-9 IIF pattern (difference p = 0.09). Collectively, the PMAT assay showed 91.5% (95% confidence interval (CI): 80.1-96.6%) sensitivity with 100.0% (95% CI: 96.4-100.0%) specificity in non-SSc controls. Strong agreement was found between PMAT and FEIA with 100.0% positive qualitative agreement (34/34) and quantitative agreement (Spearman's rho = 0.89, 95% CI: 0.77.9-0.95%, p < 0.0001). Although most anti-fibrillarin positive samples were mono-specific (69.8%), some expressed additional antibodies (namely Scl-70, centromere, dsDNA, Ro52, Ro60, SS-B, Ribo-P, DFS70, and EJ). In conclusion, this first study on anti-fibrillarin antibodies measured using a novel PMAT assay shows promising results where the new PMAT assay had high level of agreement to FEIA for the detection of anti-fibrillarin antibodies. The availability of novel AFA assays such as PMAT might facilitate the clinical deployment, additional studies, standardization efforts, and potentially consideration of AFA for next generations of the classification criteria.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".