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Record W3132418936 · doi:10.1007/s12026-021-09197-1

Evaluation of a novel particle-based multi-analyte technology for the detection of anti-fibrillarin antibodies

2021· article· en· W3132418936 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueImmunologic Research · 2021
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFibrillarinAutoantibodyAntibodySerologyImmunologyImmunoassayRheumatoid arthritisMedicineImmunofluorescenceBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.328
GPT teacher head0.458
Teacher spread0.130 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it