Comparison of Ticagrelor with Clopidogrel in Reducing Interleukin-17 and Myeloperoxidase Expression in Thrombus and Improving Postprocedural Coronary Flow in ST-segment Elevation Myocardial Infarction Patients
Bibliographic record
Abstract
PURPOSE: This study aimed to explore the effects of ticagrelor (a P2Y12 receptor inhibitor) on interleukin (IL)-17 and myeloperoxidase (MPO) expression in coronary thrombus as well as on the coronary blood flow in ST-segment elevation myocardial infarction (STEMI) patients following percutaneous coronary intervention (PCI). METHODS: Forty STEMI patients who were admitted to the First Affiliated Hospital of Harbin Medical University between August 1, 2014 and December 30, 2014 were enrolled in this study according to a set inclusion criteria. They were randomized to ticagrelor and clopidogrel groups and treated with 180 mg ticagrelor and 600 mg clopidogrel before PCI, respectively. Intracoronary thrombus aspiration was performed by a physician during PCI. Immunohistochemistry and Western blot analysis were carried out to detect the expression of IL-17 and MPO in the thrombus. Corrected thrombolysis in myocardial infarction frame count (CTFC) was used to evaluate blood flow after PCI. RESULTS: Immunohistochemistry results showed that the average positive staining area percentage of IL-17 and MPO in the clopidogrel group was significantly higher than that in the ticagrelor group. Western blot analysis also showed similar results for IL-17 (clopidogrel 0.71 ± 0.036, ticagrelor 0.50 ± 0.56) and MPO (clopidogrel 0.50 ± 0.040; ticagrelor 0.38 ± 0.06). CTFC was lower in the ticagrelor group than that in the clopidogrel group (P < 0.05). CONCLUSIONS: Ticagrelor is more effective than clopidogrel in reducing inflammation thrombosis and improving postprocedural PCI blood flow in STEMI patients. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".