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Background: cardiovascular diseases (CVDs) have become the leading causes of death worldwide. Arterial stiffness and elasticity are important indicators of cardiovascular health. Pulse wave analysis (PWA) is essential for analyzing arterial stiffness and elasticity, which are highly dependent on the tidal peak (P 2). P 2 is one of the four key physiological points, which also include percussion peaks (P 1), diastolic notches (P 3), and diastolic peaks (P 4). P 1, P 3, and P 4 are often local maxima or minima, facilitating their identification via the second derivatives method, a classic localization method for key physiological points. Classic methods such as the second derivative method, Empirical Mode Decomposition (EMD), and Wavelet Transform (WT), have been employed for the extraction and analysis of the P 2. Due to individual variation and arterial stiffness, locating the P 2 using classic methods is particularly challenging. Methods: we propose a hybrid neural network based on Residual Networks (ResNet) and bidirectional Long Short-Term Memory Networks (Bi-LSTM), successfully achieving high-precision localization of the P 2 in radial artery pulse signals. Meanwhile, we compared our method with the second derivative method, EMD, WT, Convolutional Neural Networks (CNN) and the hybrid model with ResNet and LSTM. Results: the results indicate that our proposed model exhibits significantly higher accuracy compared to other algorithms. Overall, MAEs and RMSEs for our proposed method are 62.60% and 58.84% on average less than those for other algorithms. The average R Adj 2 is 29.20% higher. The outcomes of the efficiency evaluation suggest that the hybrid model performs more balancedly without any significant shortcomings, which indicates that the Bi-LSTM structure upgrades the performances of LSTM. Significance: our hybrid model can provide the medical field with improved diagnostic tools and promote the development of clinical practice and research.
Background: cardiovascular diseases (CVDs) have become the leading causes of death worldwide. Arterial stiffness and elasticity are important indicators of cardiovascular health. Pulse wave analysis (PWA) is essential for analyzing arterial stiffness and elasticity, which are highly dependent on the tidal peak (P 2). P 2 is one of the four key physiological points, which also include percussion peaks (P 1), diastolic notches (P 3), and diastolic peaks (P 4). P 1, P 3, and P 4 are often local maxima or minima, facilitating their identification via the second derivatives method, a classic localization method for key physiological points. Classic methods such as the second derivative method, Empirical Mode Decomposition (EMD), and Wavelet Transform (WT), have been employed for the extraction and analysis of the P 2. Due to individual variation and arterial stiffness, locating the P 2 using classic methods is particularly challenging. Methods: we propose a hybrid neural network based on Residual Networks (ResNet) and bidirectional Long Short-Term Memory Networks (Bi-LSTM), successfully achieving high-precision localization of the P 2 in radial artery pulse signals. Meanwhile, we compared our method with the second derivative method, EMD, WT, Convolutional Neural Networks (CNN) and the hybrid model with ResNet and LSTM. Results: the results indicate that our proposed model exhibits significantly higher accuracy compared to other algorithms. Overall, MAEs and RMSEs for our proposed method are 62.60% and 58.84% on average less than those for other algorithms. The average R Adj 2 is 29.20% higher. The outcomes of the efficiency evaluation suggest that the hybrid model performs more balancedly without any significant shortcomings, which indicates that the Bi-LSTM structure upgrades the performances of LSTM. Significance: our hybrid model can provide the medical field with improved diagnostic tools and promote the development of clinical practice and research.
Individuals aged ≥65 years will comprise approximately 20% of the global population by 2030. Cardiovascular disease remains the leading cause of death in the world with age-related endothelial "dysfunction" as a key risk factor. As an organ in and of itself, vascular endothelium courses throughout the mammalian body to coordinate blood flow to all other organs and tissues (e.g., brain, heart, lung, skeletal muscle, gut, kidney, skin) in accord with metabolic demand. In turn, emerging evidence demonstrates that vascular aging and its co-morbidities (e.g., neurodegeneration, diabetes, hypertension, kidney disease, heart failure, and cancer) are "channelopathies" in large part. With an emphasis on distinct functional traits and common arrangements across major organs systems, the present literature review encompasses regulation of vascular ion channels that underlie blood flow control throughout the body. Regulation of myoendothelial coupling and local versus conducted signaling are discussed with new perspectives for aging and the development of chronic diseases. While equipped with an awareness of knowledge gaps in the vascular aging field, a section has been included to encompass general feasibility, role of biological sex, and additional conceptual and experimental considerations (e.g., cell regression and proliferation, gene profile analyses). The ultimate goal is for the reader to see and understand major points of deterioration in vascular function while gaining the ability to think of potential mechanistic and therapeutic strategies to sustain organ perfusion and whole-body health with aging.
Background This study aimed to examine the association between the American Heart Association’s (AHA) newly revised Life’s Essential 8 (LE8) algorithm, designed for assessing cardiovascular health (CVH), and cognitive impairment among older adults in the United States. Methods This study employed a cross-sectional design, utilizing data from the 2011–2014 National Health and Nutrition Examination Survey to explore the relationship between CVH and cognitive impairment in older adults. CVH scores are assessed based on the AHA definition of the LE8, categorized into three tiers: low (0–49), medium (50–79), and high (80–100). Cognitive impairment is evaluated using three distinct scoring systems: the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST). The lowest quartile as the cut-off point; below or equal to the lower quartile was considered as low cognitive population, and above the lower quartile was normal population. To analyze the association, multivariable logistic regression and restricted cubic spline (RCS) models were employed. Results A significant negative correlation exists between the LE8 and cognitive impairment. After adjusting for multiple variables, the odds ratios (OR) for cognitive impairment, as measured by the CERAD, AFT, and DSST, were compared between patients with high and low CVH. The results indicated OR values of 0.60 (95% CI: 0.36–0.98), 0.72 (95% CI: 0.52–0.97), and 0.29 (95% CI: 0.16–0.53) for the CERAD, AFT, and DSST, respectively. Additionally, the RCS curve demonstrated a significant linear relationship between lifestyle factors encapsulated by the LE8 and cognitive impairment. Conclusions The findings indicate higher adherence to LE8 was associated with lower odds of cognitive impairment. Furthermore, maintaining optimal CVH is crucial in preventing cognitive impairment.
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