Catalase and PPARg2 genotype and risk of systemic lupus erythematosus in Koreans
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".