Resistin and prooxidant-antioxidant balance: Markers to discriminate acute coronary syndrome from stable angina.
Bibliographic record
Abstract
BACKGROUND: Resistin and oxidative stress may play a role in the pathogenesis of coronary heart disease (CHD) including acute coronary syndrome (ACS). The aim of this study was to investigate the role of serum resistin and prooxidant-antioxidant balance (PAB) in ACS occurrence in order to differentiate it from stable angina. Moreover, we aimed to determine the correlation between resistin and PAB in patients with ACS and its difference from patients with stable CHD. METHODS: This cross-sectional, descriptive study was conducted on 50 patients with ACS and 50 patients with stable CHD who underwent coronary angiography (CAG). Serum resistin level was measured using enzyme-linked immunosorbent assay (ELISA). PAB and other variables were analyzed using standard methods. RESULTS: A significant increase in serum resistin and PAB was observed in patients with ACS (2.55 ± 0.13 ng/ml and 123.5 ± 5.58 HK unit, respectively) compared to patients with stable CHD (1.53 ± 0.12 ng/ml and 95.9 ± 2.7 HK unit, respectively) (P < 0.001). In addition, a significant positive correlation was seen between serum resistin and PAB in patients with ACS (r = 0.39; P = 0.005), but this correlation was not found in patients with stable CHD (r = 0.21; P = 0.140). Resistin (r = 0.52; P < 0.001) and PAB (r = 0.55; P < 0.001) were significantly associated with high-sensitivity C-reactive protein (hs-CRP) in patients with ACS, but this association was not found in patients with stable CHD (resistin: r = 0.24; P = 0.090; PAB: r = -0.02: P = 0.910). CONCLUSION: High serum resistin or PAB levels, and their association with the occurrence of ACS, can be used as a robust discriminating factor to differentiate ACS from stable CHD.
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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 | 0.000 |
| 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) | 0.001 | 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".