Reproducing extracellular matrix adverse remodelling of non-ST myocardial infarction in a large animal model

Prediction of in-hospital mortality risk for patients with acute ST-elevation myocardial infarction after primary PCI based on predictors selected by GRACE score and two feature selection methods

Introduction

Accurate in-hospital mortality prediction following percutaneous coronary intervention (PCI) is crucial for clinical decision-making. Machine Learning (ML) and Data Mining methods have shown promise in improving medical prognosis accuracy.

Methods

We analyzed a dataset of 4,677 patients from the Regional Vascular Center of Primorsky Regional Clinical Hospital No. 1 in Vladivostok, collected between 2015 and 2021. We utilized Extreme Gradient Boosting, Histogram Gradient Boosting, Light Gradient Boosting, and Stochastic Gradient Boosting for mortality risk prediction after primary PCI in patients with acute ST-elevation myocardial infarction. Model selection was performed using Monte Carlo Cross-validation. Feature selection was enhanced through Recursive Feature Elimination (RFE) and Shapley Additive Explanations (SHAP). We further developed hybrid models using Augmented Grey Wolf Optimizer (AGWO), Bald Eagle Search Optimization (BES), Golden Jackal Optimizer (GJO), and Puma Optimizer (PO), integrating features selected by these methods with the traditional GRACE score.

Results

The hybrid models demonstrated superior prediction accuracy. In scenario (1), utilizing GRACE scale features, the Light Gradient Boosting Machine (LGBM) and Extreme Gradient Boosting (XGB) models optimized with BES achieved Recall values of 0.944 and 0.954, respectively. In scenarios (2) and (3), employing SHAP and RFE-selected features, the LGB models attained Recall values of 0.963 and 0.977, while the XGB models achieved 0.978 and 0.99.

Discussion

The study indicates that ML models, particularly the XGB optimized with BES, can outperform the conventional GRACE score in predicting in-hospital mortality. The hybrid models' enhanced accuracy presents a significant step forward in risk assessment for patients post-PCI, offering a potential alternative to existing clinical tools. These findings underscore the potential of ML in optimizing patient care and outcomes in cardiovascular medicine.

Glycosylation of blood cells during the onset and progression of atherosclerosis and myocardial infarction

Protein glycosylation controls cell-cell and cell-extracellular matrix (ECM) communication in immune, vascular, and inflammatory processes, underlining the critical role of this process in the identification of disease biomarkers and the design of novel therapies. Emerging evidence highlights the critical role of blood cell glycosylation in the pathophysiology of atherosclerosis (ATH) and myocardial infarction (MI). Here, we review the role of glycosylation in the interplay between blood cells, particularly erythrocytes, and endothelial cells (ECs), highlighting the involvement of this critical post/cotranslational modification in settings of cardiovascular disease (CVD). Importantly, we focus on emerging preclinical studies and clinical trials based on glycan-targeted drugs to validate their therapeutic potential. These findings may help establish new trends in preventive medicine and delineate novel targeted therapies in CVD.

maresin2 fine-tunes ULK1 O-GlcNAcylation to improve post myocardial infarction remodeling

BACKGROUND

Myocardial infarction (MI) is one of the common causes of hospitalization and death all over the world. Maresin2 (MaR2), a specialized pro-solving mediator of inflammation, has been consolidated to be a novel cytokine fine-tuning inflammatory cascade. However, the precise mechanism is still unknown. Here, we demonstrated that maresin2 relieved myocardial damage via ULK1 O-GlcNAc modification during MI.

METHODS

The myocardial infarction model was established by ligating the left anterior descending artery (LAD). Echocardiography, histopathology, transmission electron microscope, and Western blot were used to evaluate cardiac function and remodeling. Furthermore, primary neonatal rat cardiomyocytes (NRCMs) were cultivated, and immunoprecipitation (IP) assays were performed to explore the specific mechanism.

RESULTS

As suggested, maresin2 treatment protected cardiac function and ameliorated adverse cardiac remodeling. Furthermore, we found that maresin2 facilitated autophagy and inhibited apoptosis under the modulation of O-GlcNAcylation-dependent ULK1 activation. Meanwhile, we discovered that maresin2 treatment ameliorated the inflammation of myocardial cells by inhibiting the interaction of TAK1 and TAB1.

CONCLUSIONS

Maresin2 is likely to promote autophagy while relieving apoptosis and inflammation of myocardial cells, thereby exerting a protective effect on the heart after MI.

From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.