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Record W2936545636 · doi:10.3389/fimmu.2019.00745

Anti-NT5c1A Autoantibodies as Biomarkers in Inclusion Body Myositis

2019· article· en· W2936545636 on OpenAlexafffund
Adam Amlani, May Y. Choi, Mark A. Tarnopolsky, Lauren Brady, Ann E. Clarke, Ignacio García‐De La Torre, Michael Mähler, Heinrike Schmeling, Claire Barber, Michelle Jung, Marvin J. Fritzler

Bibliographic record

VenueFrontiers in Immunology · 2019
Typearticle
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsMcMaster University Medical CentreUniversity of Calgary
FundersCumming School of Medicine, University of Calgary
KeywordsIIfAutoantibodyMedicineSerologyImmunoassayAntibodyBiomarkerInternal medicineMyositisInclusion body myositisImmunologyChemiluminescent immunoassayMyopathyGastroenterologyPathologyBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: Sporadic Inclusion Body Myositis (sIBM) is an inflammatory myopathy (IIM) without a specific diagnostic biomarker until autoantibodies to cytosolic 5’-nucleotidase 1A (NT5c1A/Mup44) were reported. The objectives of our study were to determine the sensitivity and specificity of anti-NT5c1A for sIBM, demonstrate demographic, clinical and serological predictors for anti-NT5c1A positivity and determine if anti-nuclear antibody (ANA) indirect immunofluorescence (IIF) staining on HEp-2 cells is a reliable screening method for anti-NT5c1A. METHODS: Sera from sIBM patients and controls were stored at -80C until required for analysis. IgG antibodies to NT5c1A were detected by an addressable laser bead immunoassay (ALBIA) using a full-length human recombinant protein. Autoantibodies to other autoimmune myopathy antigens (Jo-1, OJ, TIF1y, PL-12, SAE, EJ, MDA5, PL7, SRP, NXP2, MI-2) were detected by line immunoassay (LIA), chemiluminescence immunoassay (CIA) or enzyme linked immunoassay (ELISA) and ANA detected by IIF on HEp-2 substrate. Demographic, clinical and serological data were obtained by chart review. RESULTS: 43 patients with sIBM, 537 disease control patients with other autoimmune, degenerative and neuromuscular diseases, and 78 healthy controls were included. 48.8% (21/43) of sIBM patients were positive for anti-NT5c1A. The overall sensitivity, specificity, positive predictive value, and negative predictive value of anti-NT5c1A for sIBM was 0.49, 0.92, 0.29, and 0.96, respectively. Compared to sIBM, the frequency of anti-NT5c1A was lower in both the disease control group (8.8%, OR 0.10 [95%CI: 0.05-0.20], p<0.0001) and in the apparently healthy control group (5.1%, OR 0.06 [95%CI: 0.02-0.18], p<0.0001). In the univariable analysis, sIBM patients with more severe muscle weakness were more likely to be anti-NT5c1A positive (OR 4.10 [95% CI: 1.17, 14.33]), although this was not statistically significant (adjusted OR 4.30 [95% CI: 0.89, 20.76], p=0.069) in the multivariable analysis. The ANA of sIBM sera did not demonstrated a consistent IIF pattern associated with anti-NT5c1A. CONCLUSIONS: Anti-NT5c1A has moderate sensitivity and high specificity for sIBM using ALBIA. The presence of anti-NT5c1A antibodies may be associated with muscle weakness. Anti-NT5c1A antibodies were not associated with a specific IIF staining pattern, hence screening using HEp-2 substrate is unlikely to be a useful predictor for presence of these autoantibodies.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.003
GPT teacher head0.228
Teacher spread0.225 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations53
Published2019
Admission routes2
Has abstractyes

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