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Record W2899287906 · doi:10.1136/lupus-2018-lsm.9

AI-09 T follicular helper (Tfh) cells are increased in asymptomatic anti-nuclear antibody (ANA)<sup>+</sup> individuals and appear to play a role in epitope spreading

2018· article· en· W2899287906 on OpenAlexaff
Joan Wither, Nan‐Hua Chang, Sindhu R. Johnson, Waleed Hafiz, Kieran Manion, Dario Ferri, Ariana Karanxha, Babak Noamani, Dennisse Bonilla, Sina Rusta-Sellehy, Larissa Lisnevskaia, Zahi Touma, Earl D. Silverman, Arthur Bookman, Carolina Landolt-Marticorena, Yuriy Baglaenko

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsHospital for Sick ChildrenLakeridge HealthMount Sinai HospitalUniversity of TorontoUniversity Health Network
FundersLupus Research AllianceNational Center for Advancing Translational SciencesUniversity of FloridaLupus Research InstituteNational Institutes of HealthNational Institute of Allergy and Infectious DiseasesNational Psoriasis Foundation
KeywordsMedicineAsymptomaticAntibodyImmunologyFlow cytometryMemory B cellImmune systemFOXP3B cellInternal medicine

Abstract

fetched live from OpenAlex

Background The diagnosis of Systemic Autoimmune Rheumatic Diseases (SARD), including Systemic Lupus Erythematosus (SLE), relies on the presence of ANAs, many of which can be detected years before clinical manifestations. However, ANAs are also seen in healthy individuals most of whom will not develop SARD. A number of cellular immune changes are seen in SARD, and thus could constitute potential biomarkers/treatment targets for SARD, however it is not known at what point in disease progression these develop. Methods Healthy ANA- controls (n=32) and ANA+ (≥1:160 by immunofluorescence) participants with no (asymptomatic ANA+, n=61), at least one (UCTD, n=35), or meeting SARD classification criteria (n=59) were recruited. Peripheral blood cellular immunological changes were assessed by flow cytometry. Results Consistent with previous reports, SARD patients had increased proportions of activated B cells (CD86+ or CD95+) and in the SLE patient subset there were increased proportions of plasma cells/plasmablasts, as compared to ANA- controls. SARD patients also had reduced proportions of iNKT and IFN-γ producing cells, as well as, increased proportions of memory Tfh (CD4+CXCR5hiPD1hi) and T regulatory (Treg, CD4+FOXP3+HELIOS+) cells, especially in the SLE and Sjogren’s Disease patient subsets. In asymptomatic ANA+ individuals and UCTD patients, similar increases in the proportion of activated B cells, Tfh, and Treg cells, and decreases in the proportion of iNKT and IFN-γ producing cells were seen to those in SARD. In asymptomatic ANA+ individuals and SARD patients, the extent of serologic changes (number of specific ANAs detected by Bioplex® 2200 ANA screening system) positively correlated with activation in the switched memory B cell compartment and the proportion of Tfh cells, with the later being an independent predictor of serologic status in a multivariate analysis. However, significantly elevated levels of Tfh cells could still be seen in asymptomatic ANA+ individuals who lacked specific ANAs. Consistent with a role for Tfh cell in ANA production there was a strong correlation between the proportion of Tfh and plasma cells in asymptomatic ANA+ individuals. In preliminary studies, the majority of Tfh cells in asymptomatic ANA+ and UCTD patients were Tfh2 cells, with a trend to increased proportions of Tfh2 cells and decreased proportions Tfh17 cells as compared to active SLE patients. Conclusions Tfh cells appear to play an important role in the development of a positive ANA and in the epitope spreading that may accompany disease progression, and therefore constitute a promising target for treatment of early disease.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0050.001

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.

Opus teacher head0.004
GPT teacher head0.226
Teacher spread0.221 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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