Noninvasive vascular function tests for the future prediction of primary cardiovascular diseases

2020 ◽  
Vol 48 (3) ◽  
pp. 113-118
Author(s):  
Steven G. Chrysant
Molecules ◽  
2021 ◽  
Vol 26 (15) ◽  
pp. 4701
Author(s):  
José A. Lupiáñez ◽  
Eva E. Rufino-Palomares ◽  
Amalia Pérez-Jiménez

Our cells and organs are threatened and, in most cases, constantly subjected to the aggression of numerous situations, both endogenous, characterized by unfavorable genetics, and exogenous, by deficient or inadequate nutrition, and even by a hostile environment; in most cases, they ultimately cause a cascade of degenerative and cardiovascular diseases, cancer, and infections, as well as those related to the metabolic syndrome, all of which eventually generate irreversible damage to the organism and, consequently, a significant deterioration in its survival [...]


2011 ◽  
Vol 17 (9) ◽  
pp. S140
Author(s):  
Masanobu Yoshida ◽  
Hirofumi Tomiyama ◽  
Mari Odaira ◽  
Kazuki Shiina ◽  
Akira Yamashina

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Takayuki Matsumoto ◽  
Rita C. Tostes ◽  
R. Clinton Webb

The endothelium plays a pivotal role in vascular homeostasis, and endothelial dysfunction is a major feature of cardiovascular diseases, such as arterial hypertension, atherosclerosis, and diabetes. Recently, uridine adenosine tetraphosphate (Up4A) has been identified as a novel and potent endothelium-derived contracting factor (EDCF). Up4A structurally contains both purine and pyrimidine moieties, which activate purinergic receptors. There is an accumulating body of evidence to show that Up4A modulates vascular function by actions on endothelial and smooth muscle cells. In this paper, we discuss the effects of Up4A on vascular function and a potential role for Up4A in cardiovascular diseases.


Author(s):  
Bellina AS Mushala ◽  
Iain Scott

Adropin is a nutritionally regulated peptide hormone, secreted primarily by the liver, which modulates metabolic homeostasis in a number of tissues. Growing evidence suggests that adropin is an important regulatory component in a number of cardiovascular pathologies, and may be central to the control of cardiac fuel metabolism and vascular function. In this mini-review, we examine the known facets of adropin biology, discuss open questions in the field, and speculate on the therapeutic potential of targeting adropin-related signaling pathways in cardiovascular diseases.


2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
N Boussoussou ◽  
M Boussoussou ◽  
M Rakovics ◽  
L Entz ◽  
A Nemes

Abstract Background There is substantial evidence that the health threat of global climate change is real and it could be a medical emergency. The impact of climate change on health is mediated through atmospheric parameters which are direct environmental stressors on the human body and have a potential cardiovascular (CV) morbidity and mortality effect. Acute cardiovascular diseases (ACVDs) are already major public health issues and in the future unfavourable atmospheric situations, such as increasingly volatile fronts and their negative effects can further increase this problem. Despite evidence about the importance of different atmospheric parameters on health outcomes, there have been few results for atmospheric front patterns' CV effects. Weather fronts are the most complex atmospheric phenomena therefore these atmospheric parameters might have the greatest influence on ACVDs. Purpose We aimed to explore the effects of atmospheric front patterns on ACVDs. Methods A time series Poisson-regression analysis was used to analyse 6499 ACVD hospital admissions, during a five-year period (2009–2013), in light of front patterns. Covariates were three-day (target day and the two previous days) front sequence patterns comprised of the five major front types (no front, warm front, occluded front, cold front, stationary front). Relative risk (RR) estimates for front effects were adjusted for seasonality. The relationship on all ACVDs combined and separately on patient groups by major CV risk factors (hypertension, hyperlipidaemia, diabetes, previous CV diseases) was examined. Results We found that in general, front patterns containing warm front days had a detrimental effect. A warm front, when followed by two days with no fronts present, increased RR by 46% (CI: 4–89%, p=0,015). Cold fronts however were protective. A no front – cold front – occluded front pattern corresponded to a 28% (CI: 8–49%, p=0,037) decrease in RR, with this pattern being present in 1.1% of all days of the study period. Out of the group specific results an occluded front, following days with no fronts present, showed to have the largest effect on hyperlipidaemic patients, increasing RR by 144% (CI: 51–295%, p<0.001). Conclusions This work provides both independent evidence of front patterns' CV effects and a novel tool to investigate and help the understanding of complex associations between atmospheric fronts and ACVDs. The importance of our findings is growing in the context that extreme atmospheric conditions and changes are likely to become more common in the future as a result of climate change. Medical meteorology may open up a new horizon and become an important field of preventive cardiology in the future. In conclusion, a better understanding of atmospheric front effects is of particular importance in order to help identify possible targets for future prevention strategies.


2019 ◽  
Vol 8 (3) ◽  
pp. 8619-8622

People, due to their complexity and volatile actions, are constantly faced with challenges in understanding the situation in the market share and the forecast for the future. For any financial investment, the stock market is a very important aspect. It is necessary to study while understanding the price fluctuations of the stock market. In this paper, the stock market prediction model using the Recurrent Digital natural Network (RDNN) is described. The model is designed using two important machine learning concepts: the recurrent neural network (RNN), multilayer perceptron (MLP) and reinforcement learning (RL). Deep learning is used to automatically extract important functions of the stock market; reinforcement learning of these functions will be useful for future prediction of the stock market, the system uses historical stock market data to understand the dynamic market behavior when you make decisions in an unknown environment. In this paper, the understanding of the dynamic stock market and the deep learning technology for predicting the price of the future stock market are described.


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