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Record W1995626787 · doi:10.1186/ar4633

Platelet-derived microparticles serve as an important source of autoantigens and discriminate between levels of disease activity in systemic lupus erythematosus

2014· article· en· W1995626787 on OpenAlexafffund
Éric Boilard, Vincent Bissonnette, Valérie Garceau, Ellie Aghdassi, Nathalie Cloutier, Claudia Beaudoin, Davy Eng, Stacey Morrison, Paul R. Fortin

Bibliographic record

VenueArthritis Research & Therapy · 2014
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversité Laval
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchLupus Research AllianceCentral New York Community FoundationMerck KGaANational Institutes of HealthLupus Foundation of America
KeywordsRheumatologyMedicineImmunologyPlateletDiseaseHematologyInternal medicineSystemic disease

Abstract

fetched live from OpenAlex

Immune complexes (IC) are implicated in the pathogenesis of several autoimmune diseases including systemic lupus erythematosus (SLE). In SLE, submicron extracellular vesicles, called microparticles (MP), are thought to serve as an antigenic surface promoting the deposition of immunoglobulins and the formation of MP-associated immune complexes (mpICs). However, the cellular origin of these mpICs is unknown and whether they correlate with disease activity and particular clinical features remains to establish. The concentrations of mpICs in platelet-poor plasma from 193 women with SLE were determined using high-sensitivity flow cytometry. Considering the recently revealed role of platelets in SLE, we further scrutinized the contribution of platelets to mpICs formation. The platelet and nonplatelet MPs and mpICs were tested for association with lupus disease activity, damage, history of previous arterial disease, and the carotid intima-media thickness and plaque area on ultrasound. To assess whether disease activity and damage are associated with levels of MPs and mpICs, univariate and multivariate negative binomial models were built using the SLE disease activity index 2000 (SLEDAI-2K) and the SLICC/ACR damage index (SDI) as outcome variables. In all models, the predictor variable was the level of MPs or mpICs. When necessary, models were adjusted for covariables such as age, disease duration, menopausal status, hypertension, diabetes, anticoagulant or antiplatelet medication, antimalarial medication, prednisone use, smoking status, and ethnicity. The clinical characteristics of the 193 women studied were: age (mean (SD)) 46.3 (14.7) years; disease duration 18.5 (12.0) years; ethnicity (% Caucasian) 57%; ever-smoker 34%; menopausal in 55%; hypertensive 30%; diabetic 5%; prescribed anticoagulant or antiplatelet medication 25%; prescribed antimalarial medication 74%; prescribed prednisone 44%. Univariate analyses for activity revealed that platelet-derived mpICs, but not mpICs from other cells, were associated with SLEDAI-2K. In the multivariate model, this association remained significant ( P = 0.02 for annexin V platelet mpICs and P = 0.0006 for annexin V platelet mpICs) after adjusting for disease duration, hypertension and currently on prednisone. There was no association between platelet mpICs and SDI. Platelet-derived MPs are a major source of autoantigens serving mpIC formation in SLE. Platelet-derived mpICs are associated with lupus disease activity level on the SLEDAI-2K but not with damage. This is the first report of an association between platelet mpICs and clinical marker of activity in SLE and in any autoimmune disease. Platelet mpICs need to be further considered as a possible biomarker of lupus disease activity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.350
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations2
Published2014
Admission routes2
Has abstractyes

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