Serum irisin level in myocardial infarction patients with or without heart failure
Bibliographic record
Abstract
This study aimed to assess serum irisin level in myocardial infarction (MI) with or without heart failure (HF) and the possible relation between irisin and cardiac markers, tumor necrosis factor-α (TNF-α) and lipid profile. Eighty-six subjects were included (33 patients had MI, 33 patients had MI with HF, and 20 controls). Body mass index (BMI), waist/hip ratio (WHR), systolic and diastolic blood pressure (SBP and DBP), heart rate, and left ventricular ejection fraction (LVEF) were measured. Blood samples were withdrawn on admission for measuring irisin, cardiac markers, TNF-α, total cholesterol (TC), triglycerides (TGs), low-density lipoprotein-cholesterol concentration (LDL-C), and high-density lipoprotein-cholesterol concentration (HDL-C). Patients with MI and HF had reduced serum irisin, LVEF, and HDL-C and higher levels of BMI, WHR, SBP, DBP, troponin-I, creatine kinase-MB (CK-MB), TNF-α, TC, TGs, and LDL-C compared with control. Negative correlations were observed between irisin and BMI, WHR, SBP, DBP, troponin-I, CK-MB, TNF-α, TC, TGs, and LDL-C. However, positive association was noticed between irisin and LVEF and HDL-C. Irisin might be a useful biomarker in diagnosis of MI with or without HF. It could have anti-inflammatory and hypolipidemic effects. Further studies are needed to elucidate the role of irisin as a promising prophylactic or therapeutic agent in cardiovascular diseases.
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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".