Computer aided diagnosis of Coronary Artery Disease, Myocardial Infarction and carotid atherosclerosis using ultrasound images: A review

Ensemble learning of myocardial displacements for myocardial infarction detection in echocardiography

Background

Early detection and localization of myocardial infarction (MI) can reduce the severity of cardiac damage through timely treatment interventions. In recent years, deep learning techniques have shown promise for detecting MI in echocardiographic images. Existing attempts typically formulate this task as classification and rely on a single segmentation model to estimate myocardial segment displacements. However, there has been no examination of how segmentation accuracy affects MI classification performance or the potential benefits of using ensemble learning approaches. Our study investigates this relationship and introduces a robust method that combines features from multiple segmentation models to improve MI classification performance by leveraging ensemble learning.

Materials and Methods

Our method combines myocardial segment displacement features from multiple segmentation models, which are then input into a typical classifier to estimate the risk of MI. We validated the proposed approach on two datasets: the public HMC-QU dataset (109 echocardiograms) for training and validation, and an E-Hospital dataset (60 echocardiograms) from a local clinical site in Vietnam for independent testing. Model performance was evaluated based on accuracy, sensitivity, and specificity.

Results

The proposed approach demonstrated excellent performance in detecting MI. It achieved an F1 score of 0.942, corresponding to an accuracy of 91.4%, a sensitivity of 94.1%, and a specificity of 88.3%. The results showed that the proposed approach outperformed the state-of-the-art feature-based method, which had a precision of 85.2%, a specificity of 70.1%, a sensitivity of 85.9%, an accuracy of 85.5%, and an accuracy of 80.2% on the HMC-QU dataset. On the external validation set, the proposed model still performed well, with an F1 score of 0.8, an accuracy of 76.7%, a sensitivity of 77.8%, and a specificity of 75.0%.

Conclusions

Our study demonstrated the ability to accurately predict MI in echocardiograms by combining information from several segmentation models. Further research is necessary to determine its potential use in clinical settings as a tool to assist cardiologists and technicians with objective assessments and reduce dependence on operator subjectivity. Our research codes are available on GitHub at https://github.com/vinuni-vishc/mi-detection-echo.

Effect of Exercise on Carotid Artery Intima-Media Thickness in Adults: A Systematic Review and Meta-Analysis

BACKGROUND

Carotid intima-media thickness (cIMT) is a validated surrogate marker of atherosclerosis that is independently associated with the risk for cardiovascular disease. Recent studies on the effect of exercise on cIMT have yielded conflicting results.

METHODS

Studies that were available up until October 30, 2021 from the PubMed, Cochrane Library, Embase, and Web of Science databases were included in the analysis. Subgroup analyses were performed to determine the effects of the type, intensity, and duration of exercise on cIMT.

RESULTS

This review included 26 studies with 1370 participants. Compared with control participants, those who engaged in exercise showed a decline in cIMT (weighted mean difference [WMD] -0.02; 95% confidence interval [CI], -0.03 to -0.01; I2 = 90.1%). Participants who engaged in aerobic (WMD -0.02; 95% CI, -0.04 to -0.01; I2 = 52.7%) or resistance (WMD -0.01; 95% CI, -0.02 to -0.00; I2 = 38.5%) exercise showed lower cIMT compared with control participants. An exercise duration of >6 months was associated with a 0.02 mm reduction in cIMT. In participants with low cIMT at baseline (<0.7 mm), exercise alone was not associated with a change in cIMT (WMD -0.01; 95% CI, -0.03 to 0.00; I2 = 93.9%).

CONCLUSIONS

Exercise was associated with reduced cIMT in adults. Aerobic exercise is associated with a greater decline in cIMT than other forms of exercise. Large, multicenter, randomized controlled trials are required to establish optimal exercise protocols for improving the pathological process of atherosclerosis.

Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury

Deep neural networks have become the mainstream approach for analyzing and interpreting histology images. In this study, we established and validated an interpretable DNN model to assess endomyocardial biopsy (EMB) data of patients with myocardial injury. Deep learning models were used to extract features and classify EMB histopathological images of heart failure cases diagnosed with either ischemic cardiomyopathy or idiopathic dilated cardiomyopathy and non-failing cases (organ donors without a history of heart failure). We utilized the gradient-weighted class activation mapping (Grad-CAM) technique to emphasize injured regions, providing an entry point to assess the dominant morphology in the process of a comprehensive evaluation. To visualize clustered regions of interest (ROI), we utilized uniform manifold approximation and projection (UMAP) embedding for dimension reduction. We further implemented a multi-model ensemble mechanism to improve the quantitative metric (area under the receiver operating characteristic curve, AUC) to 0.985 and 0.992 on ROI-level and case-level, respectively, outperforming the achievement of 0.971 ± 0.017 and 0.981 ± 0.020 based on the sub-models. Collectively, this new methodology provides a robust and interpretive framework to explore local histopathological patterns, facilitating the automatic and high-throughput quantification of cardiac EMB analysis.

Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications

Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE^® and Scopus database searches in January 2021. Papers satisfying all search criteria, including a minimum of 50 patients, were further analysed and extracted of relevant data, for a total of 47 publications. Current ML image segmentation, disease risk prediction, and pathology quantitation methods have shown sensitivities and specificities over 70%, compared to expert manual analysis or invasive quantitation. Despite this, inconsistencies in methodology and the reporting of results have prevented inter-model comparison, impeding the identification of approaches with the greatest potential. The clinical potential of this technology has been well demonstrated in Computed Tomography of coronary artery disease, but remains practically limited in other modalities and body regions, particularly due to a lack of routine invasive reference measurements and patient datasets.

Predictive value of carotid artery ultrasonography for the risk of coronary artery disease

PURPOSE

To assess carotid intima-media thickness (IMT), carotid plaques, and cardiovascular risk factors in patients with suspected coronary artery disease (CHD) to determine their association and predictive value for CHD.

METHODS

We performed duplex Doppler ultrasonography of the carotid arteries and coronary angiography or CT in 480 patients with suspected CHD, and investigated their personal and medical histories. Patients were then assigned to the CHD or the control group depending on the presence of coronary lesions. Ultrasonography was performed the morning after admission prior to any treatment, coronary angiography, or CT.

RESULTS

Carotid plaques were mainly distributed in the common carotid artery bifurcation, with a significant difference between the CHD and control groups. Plaque incidence (80%) and IMT were significantly higher (P < .001 and P = .012, respectively) in the CHD (80% and 0.84 ± 0.21 mm) than in the control group (49% and 0.76 ± 0.18 mm). The factors significantly associated with CHD were introduced into a multivariate regression model. Male subject (OR = 1.569, 95%CI 1.004-2.453; P = .048) and plaque burden (OR = 0.457, 95%CI 0.210-0.993; P = .048) were significant predictors for CHD occurrence. The presence of carotid plaques performed significantly better than IMT and the Framingham risk score for predicting CHD lesions (P < .001 for both).

CONCLUSIONS

CHD patients showed higher percentage of clinical (plaques) or subclinical (IMT) carotid artery wall change, and the presence of carotid plaques showed better predictive value than IMT and Framingham risk score for the presence of coronary artery lesions.

Prevalence and risk of subclinical carotid atherosclerosis in the global population with HIV: a systematic review and meta-analysis

The current evidence regarding the prevalence of subclinical carotid atherosclerosis (SCA) for people living with HIV(PLWH) is inconsistent. In this study, we aimed to synthesize data on the prevalence and association of SCA patients with HIV infection. We searched PubMed, EMBASE, Web of Science, Medline, SinoMed, and CNKI from inception to March 2, 2020. The pooled proportion, odds ratio (OR) with 95% confidence intervals (CIs) were calculated. For inclusion, SCA was measured by carotid intima-media thickness (CIMT), with a B-mode ultrasound machine. Twenty-six studies consisting of 6590 participants were identified. The overall prevalence of SCA was 31.6% (95% CI 13.4-53.3; I² = 99%; 4 studies) according to CIMT ≥ 0.78 mm criteria, and 32.3% (19.6-46.4; 97%; 10 studies) according to CIMT ≥ 0.90 mm criteria. SCA prevalence was higher in Europe, over 40 years old and male. What's more, PLWH have a higher likelihood of developing SCA comorbidity than HIV-negative controls (pooled OR 2.66, 95% CI 1.57-4.50, I² = 74%; 9 studies), even after sensitivity analysis (pooled OR 2.58, 1.54-4.31, 73%). This study suggests a high prevalence and risk of SCA in the global population with HIV. As a result, subclinical carotid atherosclerosis deserves more attention from policymakers, HIV health-care providers, researchers, and stakeholders.

Harnessing Machine Intelligence in Automatic Echocardiogram Analysis: Current Status, Limitations, and Future Directions

Echocardiography (echo) is a critical tool in diagnosing various cardiovascular diseases. Despite its diagnostic and prognostic value, interpretation and analysis of echo images are still widely performed manually by echocardiographers. A plethora of algorithms has been proposed to analyze medical ultrasound data using signal processing and machine learning techniques. These algorithms provided opportunities for developing automated echo analysis and interpretation systems. The automated approach can significantly assist in decreasing the variability and burden associated with manual image measurements. In this paper, we review the state-of-the-art automatic methods for analyzing echocardiography data. Particularly, we comprehensively and systematically review existing methods of four major tasks: echo quality assessment, view classification, boundary segmentation, and disease diagnosis. Our review covers three echo imaging modes, which are B-mode, M-mode, and Doppler. We also discuss the challenges and limitations of current methods and outline the most pressing directions for future research. In summary, this review presents the current status of automatic echo analysis and discusses the challenges that need to be addressed to obtain robust systems suitable for efficient use in clinical settings or point-of-care testing.

Diagnostic efficacy of intravascular ultrasound combined with Gd2O3-EPL contrast agent for patients with atherosclerosis

Atherosclerosis is a cardiovascular disease that is pathologically associated with the growth of atherosclerotic plaques and vascular vulnerability. Intravascular ultrasound (IVUS) has been used to evaluate and treat cardiovascular diseases. Accumulating evidence has demonstrated that Gd₂O₃-doped nanoparticles contrast can be applied for the diagnosis of human diseases. In the present study, eplerenone (EPL), a mineralocorticoid receptor antagonist, was first doped with Gd₂O₃ nanoparticles (Gd₂O₃-EPL), following which its diagnostic efficacy for use in IVUS measurements (Gd₂O₃-EPL-IVUS) was evaluated for patients suspected with atherosclerosis. Gd₂O₃-EPL-IVUS presented with higher accuracy and sensitivity compared with IVUS in diagnosing 188 patients with suspected atherosclerosis. Gd₂O₃-EPL-IVUS exhibited stronger signals associated with plaque morphology compared with aloe IVUS for patients with atherosclerosis. In addition, Gd₂O₃-EPL-IVUS application resulted in clearer arterial plaque images compared with IVUS by binding mineralocorticoid receptors. Atherosclerosis was subsequently confirmed in all patients using computerized tomography-coronary angiography. Gd₂O₃-EPL-IVUS showed more accuracy in measuring vessel size, plaque burden and minimal lumen area compared with IVUS analysis alone. In conclusion, these outcomes suggest that Gd₂O₃-EPL-IVUS is a reliable tool for the evaluation of coronary lesions in patients with atherosclerosis.

A Novel Promising Frontier for Human Health: The Beneficial Effects of Nutraceuticals in Cardiovascular Diseases

Cardiovascular diseases (CVDs) such as hypertension, atherosclerosis, myocardial infarction, and diabetes are a significant public health problem worldwide. Although several novel pharmacological treatments to reduce the progression of CVDs have been discovered during the last 20 years, the better way to contain the onset of CVDs remains prevention. In this regard, nutraceuticals seem to own a great potential in maintaining human health, exerting important protective cardiovascular effects. In the last years, there has been increased focus on identifying natural compounds with cardiovascular health-promoting effects and also to characterize the molecular mechanisms involved. Although many review articles have focused on the individual natural compound impact on cardiovascular diseases, the aim of this manuscript was to examine the role of the most studied nutraceuticals, such as resveratrol, cocoa, quercetin, curcumin, brassica, berberine and Spirulina platensis, on different CVDs.

Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991-2020

While coronary angiography is the gold standard diagnostic tool for coronary artery disease (CAD), but it is associated with procedural risk, it is an invasive technique requiring arterial puncture, and it subjects the patient to radiation and iodinated contrast exposure. Artificial intelligence (AI) can provide a pretest probability of disease that can be used to triage patients for angiography. This review comprehensively investigates published papers in the domain of CAD detection using different AI techniques from 1991 to 2020, in order to discern broad trends and geographical differences. Moreover, key decision factors affecting CAD diagnosis are identified for different parts of the world by aggregating the results from different studies. In this study, all datasets that have been used for the studies for CAD detection, their properties, and achieved performances using various AI techniques, are presented, compared, and analyzed. In particular, the effectiveness of machine learning (ML) and deep learning (DL) techniques to diagnose and predict CAD are reviewed. From PubMed, Scopus, Ovid MEDLINE, and Google Scholar search, 500 papers were selected to be investigated. Among these selected papers, 256 papers met our criteria and hence were included in this study. Our findings demonstrate that AI-based techniques have been increasingly applied for the detection of CAD since 2008. AI-based techniques that utilized electrocardiography (ECG), demographic characteristics, symptoms, physical examination findings, and heart rate signals, reported high accuracy for the detection of CAD. In these papers, the authors ranked the features based on their assessed clinical importance with ML techniques. The results demonstrate that the attribution of the relative importance of ML features for CAD diagnosis is different among countries. More recently, DL methods have yielded high CAD detection performance using ECG signals, which drives its burgeoning adoption.

Optical coherence tomography versus intravascular ultrasound in patients with myocardial infarction: a diagnostic performance study of pre-percutaneous coronary interventions

Accurate coronary measurements are important in guiding percutaneous coronary intervention. Intravascular ultrasound is a widely accepted diagnostic modality for coronary measurement before percutaneous coronary intervention. The spatial resolution of optical coherence tomography is 10 times larger than that of intravascular ultrasound. The objective of the study was to compare quantitative and qualitative parameters of frequency domain optical coherence tomography (FDOCT) with those of intravascular ultrasound and coronary angiography in patients with acute myocardial infarction. Diagnostic parameters of coronary angiography, intravascular ultrasound, and FDOCT of 250 patients with coronary artery disease who required admission diagnosis were included in the analyses. Minimum lumen diameter detected by FDOCT was larger than that detected by quantitative coronary angiography (2.11±0.1 vs 1.89±0.09 mm, P<0.0001, q=34.67) but smaller than that detected by intravascular ultrasound (2.11±0.1 vs 2.19±0.11 mm, P<0.0001, q=12.61). Minimum lumen area detected by FDOCT was smaller than that detected by intravascular ultrasound (3.41±0.01 vs 3.69±0.01 mm², P<0.0001). FDOCT detected higher numbers of thrombus, tissue protrusion, dissection, and incomplete stent apposition than those detected by intravascular ultrasound (P<0.0001 for all). More accurate and sensitive results of the coronary lumen can be detected by FDOCT than coronary angiography and intravascular ultrasound (level of evidence: III).

A computer-aided diagnosing system in the evaluation of thyroid nodules-experience in a specialized thyroid center

Background

The evaluation of thyroid nodules with ultrasonography has created a large burden for radiologists. Artificial intelligence technology has been rapidly developed in recent years to reduce the cost of labor and improve the differentiation of thyroid malignancies. This study aimed to investigate the diagnostic performance of a novel computer-aided diagnosing system (CADs: S-detect) for the ultrasound (US) interpretation of thyroid nodule subtypes in a specialized thyroid center.

Methods

Our study prospectively included 180 thyroid nodules that underwent ultrasound interpretation. The CADs and radiologist assessed all nodules. The ultrasonographic features of different subtypes were analyzed, and the diagnostic performances of the CADs and radiologist were compared.

Results

There were seven subtypes of thyroid nodules, among which papillary thyroid cancer (PTC) accounted for 50.6% and follicular thyroid carcinoma (FTC) accounted for 2.2%. Among all thyroid nodules, the CADs presented a higher sensitivity and lower specificity than the radiologist (90.5% vs 81.1%; 41.2% vs 83.5%); the radiologist had a higher accuracy than the CADs (82.2% vs 67.2%) for diagnosing malignant thyroid nodules. The accuracy of the CADs was not as good as that of the radiologist in diagnosing PTCs (70.9% vs 82.1%). The CADs and radiologist presented accuracies of 43.8% and 60.9% in identifying FTCs, respectively.

Conclusions

The ultrasound CADs presented a higher sensitivity for identifying malignant thyroid nodules than experienced radiologists. The CADs was not as good as experienced radiologists in a specialized thyroid center in identifying PTCs. Radiologists maintained a higher specificity than the CADs for FTC detection.

A NEW APPROACH FOR CLASSIFICATION OF ATHEROSCLEROSIS OF COMMON CAROTID ARTERY FROM ULTRASOUND IMAGES

Background and objectives: The diagnosis of carotid atherosclerosis is of vital importance, as this cardiovascular disease may cause myocardial infarction. One-third of deaths in the world occur due to myocardial infarction, commonly known as heart attack. Atherosclerosis is deposition of plaque in artery wall. It could be detected from the features of intima-media complex of the artery wall. This study proposes a new classification approach to distinguish between symptomatic and asymptomatic plaques using non-invasive carotid B-mode ultrasound images. These two types of plaques have diverse impacts on human life. In the first condition, slowly plaque formation reaches life-threatening condition and the second condition is acute in nature. Hence treatment protocol is to be decided based on the type of plaque.

Methods: To locate the intima-media-complex region, the images are segmented using snake-based segmentation algorithm. Several features are extracted using fixed size blocks selected from the segmented region using gray-level co-occurrence matrix. Finally classification is performed using support vector machine. Results: The performance shows improvement in accuracy using lesser number of features than previous works. The reduction in feature size is achieved by incorporating segmentation in the pre-processing stage. In the classifier, 10-fold cross-validation protocol is used for training and testing and an accuracy of 100% is obtained.

Conclusion: This proposed technique could work as an adjunct tool in quick decision-making for cardiologists and radiologists.

Accuracy of diffusion-weighted imaging-magnetic resonance in differentiating functional from non-functional pituitary macro-adenoma and classification of tumor consistency

PURPOSE

The purpose of this study was to determine the accuracy of selected first or second-order histogram features in differentiation of functional types of pituitary macro-adenomas.

MATERIALS AND METHODS

Diffusion-weighted imaging magnetic resonance imaging was performed on 32 patients (age mean±standard deviation = 43.09 ± 11.02 years; min = 22 and max = 65 years) with pituitary macro-adenoma (10 with functional and 22 with non-functional tumors). Histograms of apparent diffusion coefficient were generated from regions of interest and selected first or second-order histogram features were extracted. Collagen contents of the surgically resected tumors were examined histochemically using Masson trichromatic staining and graded as containing <1%, 1-3%, and >3% of collagen.

RESULTS

Among selected first or second-order histogram features, uniformity ( p = 0.02), 75th percentile ( p = 0.03), and tumor smoothness ( p = 0.02) were significantly different between functional and non-functional tumors. Tumor smoothness > 5.7 × 10^-9 (area under the curve = 0.75; 0.56-0.89) had 70% (95% confidence interval = 34.8-93.3%) sensitivity and 33.33% (95% confidence interval = 14.6-57.0%) specificity for diagnosis of functional tumors. Uniformity ≤179.271 had a sensitivity of 60% (95% confidence interval = 26.2-87.8%) and specificity of 90.48% (95% confidence interval = 69.6-98.8%) with area under the curve = 0.76; 0.57-0.89. The 75th percentile >0.7 had a sensitivity of 80% (95% confidence interval = 44.4-97.5%) and specificity of 66.67% (95% confidence interval = 43.0-85.4%) for categorizing tumors to functional and non-functional types (area under the curve = 0.74; 0.55-0.88). Using these cut-offs, smoothness and uniformity are suggested as negative predictive indices (non-functional tumors) whereas 75th percentile is more applicable for diagnosis of functional tumors.

CONCLUSION

First or second-order histogram features could be helpful in differentiating functional vs non-functional pituitary macro-adenoma tumors.

Bi-stream CNN Down Syndrome screening model based on genotyping array

BACKGROUND

Human Down syndrome (DS) is usually caused by genomic micro-duplications and dosage imbalances of human chromosome 21. It is associated with many genomic and phenotype abnormalities. Even though human DS occurs about 1 per 1,000 births worldwide, which is a very high rate, researchers haven't found any effective method to cure DS. Currently, the most efficient ways of human DS prevention are screening and early detection.

METHODS

In this study, we used deep learning techniques and analyzed a set of Illumina genotyping array data. We built a bi-stream convolutional neural networks model to screen/predict the occurrence of DS. Firstly, we built image input data by converting the intensities of each SNP site into chromosome SNP maps. Next, we proposed a bi-stream convolutional neural network (CNN) architecture with nine layers and two branch models. We further merged two CNN branch models into one model in the fourth convolutional layer, and output the prediction in the last layer.

RESULTS

Our bi-stream CNN model achieved 99.3% average accuracies, and very low false-positive and false-negative rates, which was necessary for further applications in disease prediction and medical practice. We further visualized the feature maps and learned filters from intermediate convolutional layers, which showed the genomic patterns and correlated SNPs variations in human DS genomes. We also compared our methods with other CNN and traditional machine learning models. We further analyzed and discussed the characteristics and strengths of our bi-stream CNN model.

CONCLUSIONS

Our bi-stream model used two branch CNN models to learn the local genome features and regional patterns among adjacent genes and SNP sites from two chromosomes simultaneously. It achieved the best performance in all evaluating metrics when compared with two single-stream CNN models and three traditional machine-learning algorithms. The visualized feature maps also provided opportunities to study the genomic markers and pathway components associated with Human DS, which provided insights for gene therapy and genomic medicine developments.

Deep learning for healthcare applications based on physiological signals: A review

BACKGROUND AND OBJECTIVE

We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017.

METHODS

An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review.

RESULTS

During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input.

CONCLUSIONS

This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis.

NONLINEAR ANALYSIS OF CORONARY ARTERY DISEASE, MYOCARDIAL INFARCTION, AND NORMAL ECG SIGNALS

In this study, we analyze nonlinear feature extraction methods in terms of their ability to support the diagnosis of coronary artery disease (CAD) and myocardial infarction (MI). The nonlinear features were extracted from electrocardiogram (ECG) signals that were measured from CAD patients, MI patients as well as normal controls.

We tested 34 recurrence quantification analysis (RQA) features, 14 bispectrum, and 136 cumulant features. The features were extracted from 10,546 normal, 41,545 CAD, and 40,182 MI heart beats. The feature quality was assessed with Student’s [Formula: see text]-test and the [Formula: see text]-value was used for feature ranking.

We found that nonlinear features can effectively represent the physiological realities of the human heart.

Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions

Cardiovascular diseases are one of the top causes of deaths worldwide. In developing nations and rural areas, difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities. A viable solution to this issue is telemedicine, which involves delivering health care and sharing medical knowledge at a distance. Additionally, mHealth, the utilization of mobile devices for medical care, has also proven to be a feasible choice. The integration of telemedicine, mHealth and computer-aided diagnosis systems with the fields of machine and deep learning has enabled the creation of effective services that are adaptable to a multitude of scenarios. The objective of this review is to provide an overview of heart disease diagnosis and management, especially within the context of rural healthcare, as well as discuss the benefits, issues and solutions of implementing deep learning algorithms to improve the efficacy of relevant medical applications.

The role of miR-214 in cardiovascular diseases

Cardiovascular disease (CVD) is the leading cause of death throughout the world. The increase in new patients every year leads to a demand for the identification of valid and novel prognostic and diagnostic biomarkers for the prevention and treatment of cardiovascular diseases. MicroRNAs (miRNAs) are critical endogenous small noncoding RNAs that negatively modulate gene expression by regulating its translation. miRNAs are implicated in most physiological processes of the heart and in the pathological progression of cardiovascular diseases. miR-214 is a deregulated miRNA in many pathological conditions, and it contributes to the pathogenesis of multiple human disorders, including cancer and cardiovascular diseases. miR-214 has dual functions in different cardiac pathological circumstances. However, it is considered as a promising marker in the prognosis, diagnosis and treatment of cardiovascular diseases. In this review, we discuss the role of miR-214 in various cardiac disease conditions, including ischaemic heart diseases, cardiac hypertrophy, pulmonary arterial hypertension (PAH), angiogenesis following vascular injury and heart failure.