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Record W1969809218 · doi:10.1191/0961203305lu2091oa

Catalase and PPARg2 genotype and risk of systemic lupus erythematosus in Koreans

2005· article· en· W1969809218 on OpenAlexaff
Karen Eny, Ahmed El‐Sohemy, M. C. Cornelis, Y-K Sung

Bibliographic record

VenueLupus · 2005
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineGenotypeInternal medicineGenotypingOdds ratioSystemic inflammationPopulationRheumatologyImmunologyGastroenterologyInflammationGeneGeneticsBiology

Abstract

fetched live from OpenAlex

Catalase (CAT) and peroxisome proliferator activated receptor-gamma2 (PPARgamma2) are important regulators of oxidative stress and inflammation, which may contribute to the development of systemic lupus erythematosus (SLE). The objective of this study was to investigate the effects of genetic polymorphisms of CAT and PPARy2 on risk and severity of SLE in a Korean population. DNA was isolated from blood samples collected from 345 patients with SLE and 400 controls. Genotyping for the -262C-->T polymorphism of CAT and the Pro 12Ala polymorphism of PPARgamma2 were performed by PCR-RFLP analysis. The severity of SLE was assessed using the Systemic Lupus International Collaborating Clinics/American College of Rheumatology (SLICC/ACR) damage index (SDI). No association was observed between genotypes for any of the clinical manifestations of SLE. CAT and PPARgamma2 genotypes were not associated with either risk or severity of SLE. For subjects who were carriers of the high activity T allele for CAT and have the Pro/Pro genotype for PPARgamma2, the odds ratio (95% confidence interval) for risk of SLE was 0.45 (0.23-1.08). Our results suggest that genetic polymorphisms of CAT and PPARy2 do not play a significant role in the development of SLE in a Korean population. A possible protective effect of a combined genotype warrants further investigation.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.013
GPT teacher head0.267
Teacher spread0.254 · 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 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

Citations17
Published2005
Admission routes1
Has abstractyes

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