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5th Global Conference on Myositis (GCOM)

2024· article· en· W4392645102 on OpenAlexfundno aff
Victoria P. Werth, Erin M. Wilfong, Andrew L. Mammen, Chester V. Oddis, Didem Saygın, Vladimir M. Liarski, Siamak Moghadam‐Kia, Rohit Aggarwal, Dana P. Ascherman

Bibliographic record

VenueClinical and Experimental Rheumatology · 2024
Typearticle
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of Environmental Health SciencesNational Institute of Allergy and Infectious DiseasesEuropean Regional Development FundMedical Research CouncilCumming School of Medicine, University of CalgaryPfizerInstituto de Investigación Marqués de ValdecillaUniversity of California, Los AngelesSchool of Medicine, Stanford UniversityCentre hospitalier universitaire Sainte-JustineUniversität Duisburg-EssenUniversidad de CantabriaUniversidad de ChileNational Institutes of HealthUniversity of Oklahoma Health Sciences CenterUniversidade de LisboaUniversità degli Studi di PaviaNational Health and Medical Research CouncilJewish General HospitalUniversity of PittsburghTel Aviv UniversityHumanitas UniversityUniversité de StrasbourgFundação de Amparo à Pesquisa do Estado de São PauloCentre Hospitalier Universitaire de QuébecUniversità di CataniaUniversity of OklahomaMuscular Dystrophy CanadaKeio UniversityUniversity of Occupational and Environmental HealthUniversité de SherbrookeMurdoch UniversityMcGill University Health CentreMcGill UniversityArthritis AustraliaKarolinska InstitutetU.S. Department of EnergyUniversitat Autònoma de BarcelonaGilead SciencesPerelman School of Medicine, University of PennsylvaniaFaculty of Medicine and Health, University of SydneyUniversity of Texas Southwestern Medical CenterUniversity of BathSanjay Gandhi Postgraduate Institute of Medical SciencesJohns Hopkins UniversityBristol-Myers SquibbUniversité LavalOklahoma Medical Research FoundationInstituto de Salud Carlos IIIUniversité de MontréalAssociation of Canadian Universities for Research in AstronomyHumanitas Research HospitalU.S. Department of Veterans Affairs
KeywordsMedicineMyositisPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Background.Anti-synthetase syndrome (ASyS) is an autoimmune condition, characterized by the presence of autoantibodies directed against an aminoacyl-tRNA synthetase (anti-ARS).Patients present clinical symptoms such as myositis, interstitial lung disease, Raynaud's phenomenon, and arthritis.Anti-Jo-1, anti-PL-7 and anti-PL-12 are the most frequent anti-ARS.However, their role in ASyS pathogenesis remains incompletely understood.Therefore, robust animal models are essential to gain a detailed insight into the underlying pathophysiology.Aiming to characterize these pathophysiological features, we established and studied a mouse model for Jo-1, PL-7 and PL-12 associated ASyS.Methods.ASyS was induced in NOD.Idd3/5 mice by injection of 200 µg Jo-1, PL-7 or PL-12 recombinant protein emulsified in Complete Freund's Adjuvant (CFA) in combination with OX86.Controls received CFA and Phosphate Buffered Saline only.Muscle strength was assessed by rotarod tests and the effects on the peripheral immune system were investigated by flow cytometry in spleen and lymph nodes.Morphological characteristics of skeletal muscle and lung tissue of immunized mice and the tissue infiltrating immune cells were validated using histology and immunohistochemistry. Results.Immunization of mice led to clinical symptoms including muscle weakness and demonstrated variations in the immune cell response between the ARS subtypes.Histological analysis of skeletal muscle tissues showed infiltration by immune cells in the epimysium, spreading into the adjacent perifascicular area with progressing disease.Analysis of lung specimens by immunohistological staining demonstrated peribronchial accentuated accumulation of lymphocyte aggregates. Conclusion.We present a mouse model, which recapitulates features of the human phenotype of ASyS, to study the molecular pathogenesis and provide new insights into the pathomechanisms.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.047
GPT teacher head0.402
Teacher spread0.356 · 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 designTheoretical or conceptual
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

Citations0
Published2024
Admission routes1
Has abstractyes

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