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Record W2150906676 · doi:10.3899/jrheum.121315

Immunoregulation in Idiopathic Inflammatory Myopathies: From Dendritic Cells to Immature Regenerating Muscle Cells

2013· letter· en· W2150906676 on OpenAlexvenueno aff
Anne Tournadre, Pierre Miossec

Bibliographic record

VenueThe Journal of Rheumatology · 2013
Typeletter
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsnot available
Fundersnot available
KeywordsMyositisDermatomyositisPolymyositisImmunologyInclusion body myositisAcquired immune systemMedicineImmune systemInnate immune systemPathology

Abstract

fetched live from OpenAlex

Idiopathic inflammatory myopathies (IIM) are a group of chronic muscle disorders of unknown origin that lead to muscle destruction. The original classification of IIM proposed by Bohan and Peter in 1975 including polymyositis (PM) and dermatomyositis (DM) was recently revised with the development of histopathological approaches and the discovery of myositis-specific and myositis-associated antibodies that led to the characterization of new entities. In addition to PM and adult and juvenile DM, these novel classifications distinguish inclusion-body myositis (IBM), immune-mediated necrotizing myopathy, overlap myositis, and cancer-associated myositis1,2. While distinct immunopathogenic mechanisms may occur in each subset of myositis, all IIM share a common inflammatory background associated with clinical, histological, and serological overlap. Several molecular pathways, such as adaptive and innate immune responses, autoimmunity, and nonimmune mechanisms, could influence the pathogenesis of IIM. However, the exact contribution of each one in the development of distinct phenotypes remains unclear. In this issue of The Journal , Gendek-Kubiak and Gendek highlight the contribution of dendritic cells (DC)3. We summarize here recent insights in myositis immunoregulation from DC that are central in the development of adaptive and innate response to nonimmune mechanisms. ### Dendritic cells DC are crucial for the development of adaptive and innate immune responses. DC, subdivided into myeloid DC (mDC) and plasmacytoid DC (pDC), are present in lymphatic and blood systems and peripheral organs. They are equipped with a range of pattern-recognition receptors (PRR) and serve as sentinels of the immune system. Their activation after stimulation of PRR, such as Toll-like receptors (TLR) or C-type lectin receptors (CLR), by microbial components and/or endogenous ligands leads to their maturation and the development of an effector T cell immune response. In muscle biopsies from IIM, both myeloid and plasmacytoid DC have been detected with differences according to IIM subtypes. The DC … Address correspondence to Dr. A. Tournadre, Department of Rheumatology, Gabriel Montpied Hospital, CHU Clermont-Ferrand, 58 rue Montalembert BP69, 63003 Clermont-Ferrand Cedex 1, France. E-mail: atournadre{at}chu-clermontferrand.fr

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.214
Teacher spread0.206 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2013
Admission routes1
Has abstractyes

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