Tag: Cardiovascular Disease

Research Progress in Finerenone in Cardiovascular Diseases

Announcing a new article publication for Cardiovascular Innovations and Applications journal.   Mineralocorticoid receptor antagonists (MRA) have significant therapeutic effects on heart failure, hypertension, chronic kidney disease and primary aldosteronism. However, steroid MRA can cause hyperkalemia, deterioration of renal insufficiency, menstrual disorder and male breast development, and consequently has found limited clinical applications. In recent years, basic and clinical studies have confirmed that finerenone is a new non-steroidal MRA with high receptor affinity and selectivity, which can decrease adverse effects such as hyperkalemia and exert powerful cardioprotective effects. This article discusses the structure, function, pharmacological mechanism and adverse effects of finerenone, and its cardiovascular protective effects and clinical applications are described in detail, to aid in understanding of the roles of finerenone in treating cardiovascular diseases and to explore future directions.

https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2023.0060

CVIA is available on the ScienceOpen platform and at Cardiovascular Innovations and Applications. Submissions may be made using ScholarOne Manuscripts. There are no author submission or article processing fees. Cardiovascular Innovations and Applications is indexed in the EMBASE, EBSCO, ESCI, OCLC, Primo Central (Ex Libris), Sherpa Romeo, NISC (National Information Services Corporation), DOAJ, Index Copernicus, Research4Life and Ulrich’s web Databases. Follow CVIA on Twitter @CVIA_Journal; or Facebook.

Sun Xue, Dong Yanghong and Gu Jiaxin et al. Research Progress in Finerenone in Cardiovascular Diseases. CVIA. 2023. Vol. 8(1). DOI: 10.15212/CVIA.2023.0060

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Heart Failure with Preserved Ejection Fraction: Important Things to Know About the Stiff Heart

Announcing a new article publication for Cardiovascular Innovations and Applications journal. Heart failure remains a leading cause of morbidity, mortality, and healthcare expenditure, both nationally and worldwide. In the current era of cardiovascular disease, heart failure with preserved ejection fraction (HFpEF) is recognized to be a clinical entity with equal prevalence and similar morbidity and mortality rates to the traditional syndrome of heart failure with reduced ejection fraction (HFrEF), yet with distinct differences. The HFpEF phenotype presents many challenges, beginning with accurate diagnosis, because the differential diagnosis for patients with symptoms of dyspnea in the context of a normal ejection fraction remains very broad. Moreover, although numerous medical and device-based therapies have been identified in the past several decades to improve clinical outcomes in HFrEF, treatment options for HFpEF with similar efficacy are lacking. Familiarity with the current understanding of the underlying pathophysiology of HFpEF can aid in overcoming some of these challenges, although the mechanisms resulting in HFpEF and the proper therapies remain incompletely defined.

https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2023.0058

CVIA is available on the ScienceOpen platform and at Cardiovascular Innovations and Applications. Submissions may be made using ScholarOne Manuscripts. There are no author submission or article processing fees. Cardiovascular Innovations and Applications is indexed in the EMBASE, EBSCO, ESCI, OCLC, Primo Central (Ex Libris), Sherpa Romeo, NISC (National Information Services Corporation), DOAJ, Index Copernicus, Research4Life and Ulrich’s web Databases. Follow CVIA on Twitter @CVIA_Journal; or Facebook.

Juan R. Vilaro. Heart Failure with Preserved Ejection Fraction: Important Things to Know About the Stiff Heart. CVIA. 2023. Vol. 8(1). DOI: 10.15212/CVIA.2023.0058

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Possible Mechanisms of SARS-CoV2-Mediated Myocardial Injury

Announcing a new article publication for Cardiovascular Innovations and Applications journal.    

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has rapidly become a global health emergency. In addition to causing respiratory effects, SARS-CoV-2 can result in cardiac involvement leading to myocardial damage, which is increasingly being explored in the literature. Myocardial injury is an important pathogenic feature of COVID-19. The angiotensin-converting enzyme-2 receptor plays a key role in the pathogenesis of the virus, serving as a “bridge” allowing SARS-CoV-2 to invade the body. However, the exact mechanism underlying how SARS-CoV-2 causes myocardial injury remains unclear. This article summarizes the main possible mechanisms of myocardial injury in patients with COVID-19, including direct myocardial cell injury, microvascular dysfunction, cytokine responses and systemic inflammation, hypoxemia, stress responses, and drug-induced myocardial injury. Understanding of the underlying mechanisms would aid in proper identification and treatment of myocardial injury in patients with COVID-19.

https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2023.0031

CVIA is available on the ScienceOpen platform and at Cardiovascular Innovations and Applications. Submissions may be made using ScholarOne Manuscripts. There are no author submission or article processing fees. Cardiovascular Innovations and Applications is indexed in the EMBASE, EBSCO, ESCI, OCLC, Primo Central (Ex Libris), Sherpa Romeo, NISC (National Information Services Corporation), DOAJ, Index Copernicus, Research4Life and Ulrich’s web Databases. Follow CVIA on Twitter @CVIA_Journal; or Facebook.

Bing Yu, Yalin Wu and Xiaosu Song et al. Possible Mechanisms of SARS-CoV2-Mediated Myocardial Injury. CVIA. 2023. Vol. 8(1). DOI: 10.15212/CVIA.2023.0031

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Artificial Intelligence Solutions for Cardiovascular Disease Detection and Management in Women

Announcing a new article publication for Cardiovascular Innovations and Applications journal. Artificial intelligence (AI) is a method of data analysis that enables machines to learn patterns from datasets and make predictions. With advances in computer chip technology for data processing and the increasing availability of big data, AI can be leveraged to improve cardiovascular care for women – an often understudied and undertreated population. The authors of this article briefly discuss the potential benefits of AI-based solutions in cardiovascular care for women and also highlight inadvertent drawbacks to the use of AI and novel digital technologies in women.

https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2023.0024

CVIA is available on the ScienceOpen platform and at Cardiovascular Innovations and Applications. Submissions may be made using ScholarOne Manuscripts. There are no author submission or article processing fees. Cardiovascular Innovations and Applications is indexed in the EMBASE, EBSCO, ESCI, OCLC, Primo Central (Ex Libris), Sherpa Romeo, NISC (National Information Services Corporation), DOAJ, Index Copernicus, Research4Life and Ulrich’s web Databases. Follow CVIA on Twitter @CVIA_Journal; or Facebook.

Wendy Tatiana Garzon-Siatoya, Andrea Carolina Morales-Lara and Demilade Adedinsewo. Artificial Intelligence Solutions for Cardiovascular Disease Detection and Management in Women: Promise and Perils. CVIA. 2023. Vol. 8(1). DOI: 10.15212/CVIA.2023.0024

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Machine Learning Methods in Real-World Studies of Cardiovascular Disease

Announcing a new article publication for Cardiovascular Innovations and Applications journal.  Cardiovascular disease (CVD) is one of the leading causes of death worldwide, and answers are urgently needed regarding many aspects, particularly risk identification and prognosis prediction. Real-world studies with large numbers of observations provide an important basis for CVD research but are constrained by high dimensionality, and missing or unstructured data. Machine learning (ML) methods, including a variety of supervised and unsupervised algorithms, are useful for data governance, and are effective for high dimensional data analysis and imputation in real-world studies. This article reviews the theory, strengths and limitations, and applications of several commonly used ML methods in the CVD field, to provide a reference for further application.

This article introduces the origin, purpose, theory, advantages and limitations, and applications of multiple commonly used ML algorithms, including hierarchical and k-means clustering, principal component analysis, random forest, support vector machine, and neural networks. An example uses a random forest on the Systolic Blood Pressure Intervention Trial (SPRINT) data to demonstrate the process and main results of ML application in CVD.

ML methods are effective tools for producing real-world evidence to support clinical decisions and meet clinical needs. This review explains the principles of multiple ML methods in plain language, to provide a reference for further application. Future research is warranted to develop accurate ensemble learning methods for wide application in the medical field.

https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2023.0011

CVIA is available on the ScienceOpen platform and at Cardiovascular Innovations and Applications. Submissions may be made using ScholarOne Manuscripts. There are no author submission or article processing fees. Cardiovascular Innovations and Applications is indexed in the EMBASE, EBSCO, ESCI, OCLC, Primo Central (Ex Libris), Sherpa Romeo, NISC (National Information Services Corporation), DOAJ, Index Copernicus, Research4Life and Ulrich’s web Databases. Follow CVIA on Twitter @CVIA_Journal; or Facebook.

Jiawei Zhou, Dongfang You and Jianling Bai et al. Machine Learning Methods in Real-World Studies of Cardiovascular Disease. CVIA. 2023. Vol. 7(1). DOI: 10.15212/CVIA.2023.0011

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