Enzymatic antioxidant system and endothelial function in patients with metabolic syndrome.
Bibliographic record
Abstract
BACKGROUND: This study examined the relationship between serum glutathione peroxidase 1 (GPx-1) activity and endothelial dysfunction in the subjects with and without metabolic syndrome (MetS). METHODS: This case-control study was conducted on 76 subjects, 38 were patients with MetS and 38 were without MetS. The demographic, clinical, and laboratory features of the subjects were measured and then compared. The MetS was diagnosed according to the definitions of the National Cholesterol Education Program (NCEP) and International Diabetes Federation (IDF). Serum GPx-1 activity was measured by standard methods. Endothelial dysfunction was assessed with flow-mediated dilation (FMD) technique. RESULTS: In case-control study of 76 subjects, all of MetS risk factors including abdominal obesity, triglyceride (TG), low serum level of high-density lipoprotein cholesterol (HDL-C), hypertension (HTN), and fasting plasma glucose (FPG) were significantly higher than healthy individuals (P < 0.050). FMD was significantly lower than normal subjects (P < 0.050). Serum GP-1 activity was significantly lower in patients with MetS compared to normal subjects (21.7 ± 13.5 vs. 79.0 ± 38.6, respectively) (P = 0.001). The value of GPx-1 was significantly correlated with diastolic blood pressure (DBP) (r = -0.249, P = 0.040), C-reactive protein (CRP) (r = -0.409, P = 0.014), and FMD (r = 0.293, P = 0.050) in patients with MetS. The results of logistic regression showed that a unite increase in CRP (mg/dl), FMD (%), and endothelin-1 (ET-1) (pg/ml) and a unit decrease in GPx significantly increased the odds ratio (OR) of MetS; after adjusting for age and sex the results remained significant except for FMD (P < 0.050). CONCLUSION: Endothelial dysfunction is related to serum GPx-1 activity in patients with MetS. GPX-1 activity is associated with risk of cardiovascular diseases (CVDs) and peripheral vascular diseases (PVDs) in patients with MetS.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".