Programmed Cell Death of Endothelial Cells in Myocardial Infarction and Its Potential Therapeutic Strategy

Wu, Mingyue; Huang, Zixia; Zeng, Lijin; Wang, Chunfei; Wang, Deming

doi:10.1155/2022/6558060

Cited by 5 publications

(5 citation statements)

References 134 publications

(133 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The occurrence of myocardial infarction serves as the principal manifestation of coronary artery disease, representing a severe and critical condition ( 38 ). Immediate surgical intervention is typically necessary for the management of acute myocardial infarction ( 39 ).…”

Section: Discussionmentioning

confidence: 99%

Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk in patients with periodontitis: a population-based study

Wang,

Ni,

Xiao

et al. 2023

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

BackgroundThe association between periodontitis and cardiovascular disease is increasingly recognized. In this research, a prediction model utilizing machine learning (ML) was created and verified to evaluate the likelihood of coronary heart disease in individuals affected by periodontitis.MethodsWe conducted a comprehensive analysis of data obtained from the National Health and Nutrition Examination Survey (NHANES) database, encompassing the period between 2009 and 2014.This dataset comprised detailed information on a total of 3,245 individuals who had received a confirmed diagnosis of periodontitis. Subsequently, the dataset was randomly partitioned into a training set and a validation set at a ratio of 6:4. As part of this study, we conducted weighted logistic regression analyses, both univariate and multivariate, to identify risk factors that are independent predictors for coronary heart disease in individuals who have periodontitis. Five different machine learning algorithms, namely Logistic Regression (LR), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Classification and Regression Tree (CART), were utilized to develop the model on the training set. The evaluation of the prediction models’ performance was conducted on both the training set and validation set, utilizing metrics including AUC (Area under the receiver operating characteristic curve), Brier score, calibration plot, and decision curve analysis (DCA). Additionally, a graphical representation called a nomogram was created using logistic regression to visually depict the predictive model.ResultsThe factors that were found to independently contribute to the risk, as determined by both univariate and multivariate logistic regression analyses, encompassed age, race, presence of myocardial infarction, chest pain status, utilization of lipid-lowering medications, levels of serum uric acid and serum creatinine. Among the five evaluated machine learning models, the KNN model exhibited exceptional accuracy, achieving an AUC value of 0.977. The calibration plot and brier score illustrated the model's ability to accurately estimate probabilities. Furthermore, the model's clinical applicability was confirmed by DCA.ConclusionOur research showcases the effectiveness of machine learning algorithms in forecasting the likelihood of coronary heart disease in individuals with periodontitis, thereby aiding healthcare professionals in tailoring treatment plans and making well-informed clinical decisions.

show abstract

Section: Discussionmentioning

confidence: 99%

Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk in patients with periodontitis: a population-based study

Wang,

Ni,

Xiao

et al. 2023

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

show abstract

“…Patients with acute myocardial infarction (AMI) had elevated levels of ferroptosis-related genes, such as CXCL2, FTH1, TLR4, JUN, EPAS1, DDIT3, and ATF3, suggesting that ferroptosis plays a significant role in the regulation of ECs metabolism in AMI patients. 41 The ferroptosis inhibitor ferrostatin-1 (Fer-1) upregulated lactate dehydrogenase (LDH), SCL7A11, and GPX4 expression in ApoE 2/2 mice fed with high fat and attenuated AS lesions. Fer-1 also reduced vascular endothelial growth factor A (VEGFA), intercellular cell adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1), and elevated eNOS, alleviating oxidized low-densitylipoprotein (Ox-LDL)-induced endothelial dysfunction in the mouse aortic endothelial cells (MAECs) model, 42 in which VEGFA promotes the expression of b-fibroblast growth factor (b-FGF) and indirectly causes endothelial proliferation.…”

Section: Endothelial Dysfunctionmentioning

confidence: 99%

Section: The Mechanism Of Ferroptosis Involved In the Main Pathologic...mentioning

confidence: 99%

Ferroptosis Involved in Cardiovascular Diseases: Mechanism Exploration of Ferroptosis' Role in Common Pathological Changes

Gao,

Hu,

et al. 2024

Journal of Cardiovascular Pharmacology

View full text Add to dashboard Cite

Regulated cell death is a controlled form of cell death that protects cells by adaptive responses in pathophysiological states. Ferroptosis has been identified as a novel method of controlling cell death in recent years. Several cardiovascular diseases (CVDs) are shown to be profoundly influenced by ferroptosis, and ferroptosis is directly linked to the majority of cardiovascular pathological alterations. Despite this, it is still unclear how ferroptosis affects the pathogenic alterations that take place in CVDs. Based on a review of the mechanisms that regulate ferroptosis, this paper explores the most recent research on the role of ferroptosis in the major pathological changes associated with CVDs, in order to provide new perspectives and strategies for cardiovascular research and clinical treatment.

show abstract

“…ECs line the inner lining of blood vessels and are essential for blood supply homeostasis and cardiac protection. After MI, ECs further inhibit the recovery of cardiac function through several cell death processes that affect coronary artery vessel density in ischemic areas [ 10 ]. On the other hand, ECs have been found to play important roles in maintaining homeostasis by promoting blood vessel formation and building blood vessel barriers [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Single-cell RNA sequencing reveals hub genes of myocardial infarction-associated endothelial cells

Wang,

Dou

2024

BMC Cardiovasc Disord

View full text Add to dashboard Cite

Background Myocardial infarction (MI) is a cardiovascular disease that seriously threatens human health. Dysangiogenesis of endothelial cells (ECs) primarily inhibits recovery from MI, but the specific mechanism remains to be further elucidated. Methods In this study, the single-cell RNA-sequencing data from both MI and Sham mice were analyzed by the Seurat Package (3.2.2). The number of ECs in MI and Sham groups were compared by PCA and tSNE algorithm. FindMarkers function of Seurat was used to analyze the DEGs between the MI and Sham groups. Then, the ECs was further clustered into 8 sub-clusters for trajectory analysis. The BEAM was used to analyze the branch point 3 and cluster the results. In addition, the DEGs in the microarray data set of MI and Sham mice were cross-linked, and the cross-linked genes were used to construct PPI networks. The key genes with the highest degree were identified and analyzed for functional enrichment. Finally, this study cultured human umbilical vein endothelial cells (HUVECs), established hypoxia models, and interfered with hub gene expression in cells. The impact of hub genes on the migration and tube formation of hypoxic-induced HUVECs were verified by Wound healing assays and tubule formation experiments. Results The number and proportion of ECs in the MI group were significantly lower than those in the Sham group. Meantime, 225 DEGs were found in ECs between the MI and Sham groups. Through trajectory analysis, EC4 was found to play an important role in MI. Then, by using BEAM to analyze the branch point 3, and clustering the results, a total of 495 genes were found to be highly expressed in cell Fate2 (mainly EC4). In addition, a total of 194 DEGs were identified in Micro array dataset containing both MI and Sham mice. The hub genes (Timp1 and Fn1) with the highest degree were identified. Inhibiting Timp1 and Fn1 expression promoted the migration and tube formation of HUVECs. Conclusions Our data highlighted the non-linear dynamics of ECs in MI, and provided a foothold for analyzing cardiac homeostasis and pro-angiogenesis in MI.

show abstract

Programmed Cell Death of Endothelial Cells in Myocardial Infarction and Its Potential Therapeutic Strategy

Cited by 5 publications

References 134 publications

Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk in patients with periodontitis: a population-based study

Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk in patients with periodontitis: a population-based study

Ferroptosis Involved in Cardiovascular Diseases: Mechanism Exploration of Ferroptosis' Role in Common Pathological Changes

Single-cell RNA sequencing reveals hub genes of myocardial infarction-associated endothelial cells

Contact Info

Product

Resources

About