TICAGRELOR ALLEVIATES SEPSIS-INDUCED MYOCARDIAL INJURY VIA AN ADENOSINE-DEPENDENT PATHWAY IN A MOUSE SEPSIS MODEL
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine whether ticagrelor, a classic anti-platelet drug, has a therapeutic effect on sepsis-induced myocardial injury. METHODS: The C57BL6J mice received oral ticagrelor (10, 25 and 50 mg/kg) for seven days after which cecum ligation and puncture (CLP) were performed. An adenosine-receptor antagonist (CGS15943) was administered two hours before CLP. After 24 h, cardiac function was measured using cardiac echocardiography, then the heart and blood were collected. Hematoxylin and eosin (HE) staining and terminal deoxynucleotidyl transferase dUTP nick end labelling (TUNEL staining) were used to observe pathological changes and cardiomyocyte apoptosis. Plasma concentration of TNF-α, IL-6 and adenosine and myocardial tissue levels of TNF-α and IL-6 were determined. Survival analysis was performed. Western blot was used to determine the expression of a signalling protein in the myocardial tissue. RESULTS: The HE and TUNEL staining showed less inflammatory cell infiltration and less cardiomyocyte apoptosis in the ticagrelor group. Cardiac echocardiography showed preserved heart function in the ticagrelor group. Plasma TNF-α, IL-6 and relative expression of TNF-α and IL-6 in myocardial tissue were significantly lower in the ticagrelor group. Plasma adenosine levels were significantly higher in the ticagrelor group. Adenosine-receptor antagonists significantly blocked the protective effect of ticagrelor. Ticagrelor reduced the mortality of sepsis mice, and this reduction was blocked by the adenosine-receptor antagonist. Western blot showed that ticagrelor activated the phosphorylation of AKT and mTOR. Adenosine-receptor antagonists inhibited the activation of AKT and mTOR. CONCLUSION: The protective effect of ticagrelor was dependent on adenosine-receptor activation, with downstream upregulation of phosphorylation of AKT and mTOR.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".