Evaluation of Thrombomodulin, Heart-Type Fatty-Acid-Binding Protein, Pentraxin-3 and Galectin-3 Levels in Patients with Myocardial Infarction, with and Without ST Segment Elevation
Bibliographic record
Abstract
Background: Medical history, ECG findings and cardiac markers are used in the diagnosis of myocardial infarction (MI). Biomarkers used especially for the diagnosis of MI include high-sensitivity troponins (hsTns), creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), myoglobin, cardiac myosin-binding protein C and new cardiac biomarkers. This study evaluated the levels of serum thrombomodulin (TM), heart-type fatty-acid-binding protein (H-FABP), pentraxin-3 (PTX-3) and galectin-3 (Gal-3) to determine their utility in distinguishing between ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI). Methods: This study included a total of 180 patients (90 patients with acute STEMI and 90 patients with NSTEMI) who presented to the Gaziosmanpaşa Training and Research Hospital, Cardiovascular Surgery and Emergency Department, with ischemic chest pain lasting longer than 30 min. Ninety healthy volunteers were included as the control group. Results: Serum levels of N-terminal pro-brain natriuretic peptide (NT-proBNP), TM, H-FABP, PTX-3 and Gal-3 were significantly different across the STEMI, NSTEMI and control groups (p < 0.001). Strong positive correlations were observed between NT-proBNP and TM, H-FABP, PTX-3 and Gal-3 in the STEMI group. ROC analysis demonstrated excellent diagnostic accuracy for these biomarkers in distinguishing STEMI from NSTEMI and control groups. Conclusions: Vascular inflammation plays an important role in the pathophysiology of STEMI and NSTEMI. A comprehensive cardiac biomarker panel enhances diagnostic accuracy and risk stratification, particularly when distinguishing between STEMI and NSTEMI. The biomarkers hs-TnI, CK-MB, NT-proBNP, TM, H-FABP, PTX-3 and Gal-3 offer complementary information when used together as a panel. Further research and validation are essential to establish standardized protocols for their widespread use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".