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Record W2145508126 · doi:10.3899/jrheum.111205

NPSR1 Gene Is Associated with Reduced Risk of Rheumatoid Arthritis

2012· article· en· W2145508126 on OpenAlexvenueno aff
Gema Robledo, Miguel Á. González‐Gay, Benjamín Fernández‐Gutiérrez, José Ramón Lamas, Alejandro Balsa, Dora Pascual‐Salcedo, Santos Castañeda, Ricardo Blanco, Isidoro González‐Álvaro, Enrique Raya, Carmen Gómez‐Vaquero, Mario Delgado, Javier Martı́n

Bibliographic record

VenueThe Journal of Rheumatology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicS100 Proteins and Annexins
Canadian institutionsnot available
FundersInstituto de Salud Carlos III
KeywordsMedicineRheumatoid arthritisArthritisInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Neuropeptide S receptor 1 (NPSR1) is a G protein-coupled receptor involved in immune response and is associated with several inflammatory diseases. We investigated the possible contribution of several polymorphisms in the intronic region of NPSR1 to rheumatoid arthritis (RA). METHODS: Genotyping of 7 single-nucleotide polymorphisms (SNP) was performed in a total of 1232 patients with RA and 983 healthy controls of Spanish white origin by real-time polymerase chain reaction technology, using the TaqMan 5'-allele discrimination assay. RESULTS: One out of the 7 SNP analyzed (rs740347) was associated with RA [p after Bonferroni correction (p(BNF)) = 1.2 × 10(-3), OR 0.73]. An association was also observed with rheumatoid factor-positive and shared epitope-positive RA (p(BNF) = 0.011, OR 0.73; p(BNF) = 0.037, OR 0.75, respectively). CONCLUSION: Our results show that variations in the NPSR1 intronic region are associated with low risk in patients with RA, supporting other evidence that this locus represents a common genetic factor in inflammatory diseases.

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)0.001
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 score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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

Citations10
Published2012
Admission routes1
Has abstractyes

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