Clinical Influencing Factors of Acute Myocardial Infarction Based on Improved Machine Learning

Retracted: Clinical Influencing Factors of Acute Myocardial Infarction Based on Improved Machine Learning

[This retracts the article DOI: 10.1155/2021/5569039.].

Uncovering potential diagnostic biomarkers of acute myocardial infarction based on machine learning and analyzing its relationship with immune cells

BACKGROUND

Acute myocardial infarction (AMI) is a common cardiovascular disease. This study aimed to mine biomarkers associated with AMI to aid in clinical diagnosis and management.

METHODS

All mRNA and miRNA data were downloaded from public database. Differentially expressed mRNAs (DEmRNAs) and differentially expressed miRNAs (DEmiRNAs) were identified using the metaMA and limma packages, respectively. Functional analysis of the DEmRNAs was performed. In order to explore the relationship between miRNA and mRNA, we construct miRNA-mRNA negative regulatory network. Potential biomarkers were identified based on machine learning. Subsequently, ROC and immune correlation analysis were performed on the identified key DEmRNA biomarkers.

RESULTS

According to the false discovery rate < 0.05, 92 DEmRNAs and 272 DEmiRNAs were identified. GSEA analysis found that kegg_peroxisome was up-regulated in AMI and kegg_steroid_hormone_biosynthesis was down-regulated in AMI compared to normal controls. 5 key DEmRNA biomarkers were identified based on machine learning, and classification diagnostic models were constructed. The random forests (RF) model has the highest accuracy. This indicates that RF model has high diagnostic value and may contribute to the early diagnosis of AMI. ROC analysis found that the area under curve of 5 key DEmRNA biomarkers were all greater than 0.7. Pearson correlation analysis showed that 5 key DEmRNA biomarkers were correlated with most of the differential infiltrating immune cells.

CONCLUSION

The identification of new molecular biomarkers provides potential research directions for exploring the molecular mechanism of AMI. Furthermore, it is important to explore new diagnostic genetic biomarkers for the diagnosis and treatment of AMI.

A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree

Extreme bradycardia (EB), extreme tachycardia (ET), ventricular tachycardia (VT), and ventricular flutter (VF) are the four types of life-threatening arrhythmias, which are symptoms of cardiovascular diseases. Therefore, in this study, a method of life-threatening arrhythmia recognition is proposed based on pulse rate variability (PRV). First, noise and interference are wiped out from the arterial blood pressure (ABP), and the PRV signal is extracted. Then, 19 features are extracted from the PRV signal, and 15 features with highly important and significant variation were selected by random forest (RF). Finally, the back-propagation neural network (BPNN), extreme learning machine (ELM), and decision tree (DT) are used to build, train, and test classifiers to detect life-threatening arrhythmias. The experimental data are obtained from the MIMIC/Fantasia and the 2015 Physiology Net/CinC Challenge databases. The experimental results show that the DT classifier has the best average performance with accuracy and kappa coefficient (kappa) of 98.76 ± 0.08% and 97.59 ± 0.15%, which are higher than those of the BPNN (accuracy = 94.85 ± 1.33% and kappa = 89.95 ± 2.62%) and ELM (accuracy = 95.05 ± 0.14% and kappa = 90.28 ± 0.28%) classifiers. The proposed method shows better performance in identifying four life-threatening arrhythmias compared to existing methods and has potential to be used for home monitoring of patients with life-threatening arrhythmias.

Levosimendan Postconditioning Attenuates Cardiomyocyte Apoptosis after Myocardial Infarction

BACKGROUND

Levosimendan preconditioning has been shown to attenuate myocardial apoptosis in animal models. However, protective effects of levosimendan postconditioning against myocardial apoptosis following myocardial infarction (MI) have not been evaluated. Therefore, we investigated the effects of levosimendan postconditioning on myocardial apoptosis in MI rat models.

METHODS

In an anoxia/reoxygenation (A/R) model, H9c2 cells were pretreated with or without levosimendan postconditioning after which their apoptosis rates were assessed by flow cytometry, RT-qPCR, and western blot analyses. Then, postconditioning was performed with or without levosimendan in MI rat models. Myocardiocyte apoptosis was evaluated by echocardiography, TTC staining, TUNEL staining, immunohistochemical staining, RT-qPCR, and western blot analysis.

RESULTS

Levosimendan postconditioning inhibited H9c2 cell apoptosis in A/R models by elevating Bcl-2 while suppressing Caspase-3 and Bax at both mRNA and protein levels. Moreover, it improved cardiac functions and reduced the left ventricle infarction area in MI rat models. Compared to the MI control group, cardiomyocyte apoptosis rates in the levosimendan postconditioning group were low. The reduced cardiomyocyte apoptosis rates were associated with downregulation of Bax and Caspase-3 as well as with upregulation of Bcl-2 at mRNA and protein levels.

CONCLUSIONS

Levosimendan postconditioning of MI rat models protected against cardiomyocyte apoptosis, implying that it is a potential strategy for preventing cardiomyocyte apoptosis in the treatment of cardiac dysfunction following MI.