• Biomedical Science Letters
• /
• v.12 no.3
• /
• pp.191-196
• /
• 2006

Cardiovascular disease (CVD) risk factors may be acting several decades before CVD becomes manifest. Data from young subjects may be valuable in further elucidating at this issue. We evaluated the association between baPWV (brachial-ankle pulse wave velocity) and cardiovascular risk factors in apparently healthy young adults. A total of 46 male and 91 female adolescents aged $18{\sim}25 years$ were studied. baPWV increased in a dose-responsive manner as the number of metabolic syndrome components. In both gender groups, baPWV was positively correlated with age. In males, waist, circumference total cholesterol, and LDL-cholesterol were positively correlated with baPWV, and in females, blood pressure (BP) was positively correlated with baPWV. Age, gender, mean BP, and Homeostasis model assessment insulin resistance (HOMA-IR) were found to be independent factors associated with baPWV levels. In conclusion, mean BP, age, gender, and HOMA-IR were associated with baPWV in young adults. This result suggests that multiple cardiovascular risk factors may be associated with an increased risk of arterial stiffness in young adults.

• Macromolecular Research
• /
• v.16 no.4
• /
• pp.345-352
• /
• 2008

Poly(lactic-co-glycolic acid) (PLGA) tubes (5 mm in diameter) were fabricated using an electro spinning method and used as a scaffold for artificial blood vessels through the hybridization of smooth muscle cells (SMCs) and endothelial cells (ECs) differentiated from canine bone marrow under previously reported conditions. The potential clinical applications of these artificial blood vessels were investigated using a canine model. From the results, the tubular-type PLGA scaffolds for artificial blood vessels showed good mechanical strength, and the dual-layered blood vessels showed acceptable hybridization behavior with ECs and SMCs. The artificial blood vessels were implanted and substituted for an artery in an adult dog over a 3-week period. The hybridized blood vessels showed neointimal formation with good patency. However, the control vessel (unhybridized vessel) was occluded during the early stages of implantation. These results suggest a shortcut for the development of small diameter, tubular-type, nanofiber blood vessels using a biodegradable material (PLGA).

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2008.04a
• /
• pp.152-155
• /
• 2008

The blood flow characteristics have been closely related to various cardiovascular diseases, it is very important to predict them accurate enough in an efficient way. Thus, this paper proposes a one-dimensional spectral element model for the blood flow through blood vessels. The spectral element model is formulated by using the variational method. The nonlinear terms in spectral element model are all treated as the pseudo-force and an iterative solution method is applied in the frequency domain.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.12
• /
• pp.267-275
• /
• 2022

This paper proposed a Deep Neural Network Model system utilizing Keras for predicting air pollution risk of the cardiovascular disease through the effect of each component of air on the harmful virus using past air information, with analyzing 18,000 data sets of the Seoul Open Data Plaza. By experiments, the model performed tasks with higher accuracy when using methods of sigmoid, binary_crossentropy, adam, and accuracy through 3 hidden layers with each 8 nodes, resulting in 88.92% accuracy. It is meaningful in that any respiratory disease can utilize the risk prediction system if there are data on the effects of each component of air pollution and fine dust on oil-borne diseases. It can be further developed to provide useful information to companies that produce masks and air purification products.

• Journal of Chest Surgery
• /
• v.56 no.5
• /
• pp.295-303
• /
• 2023

Background: The use of Adriamycin (ADR), also known as doxorubicin, as a chemotherapy agent is limited by its detrimental adverse effects, especially cardiotoxicity. Recent studies have emphasized the crucial role of angiotensin II (Ang-II) in the development of ADR-induced cardiomyopathy. This study aimed to explore the potential cardioprotective effects of losartan in a rat model of ADR-induced cardiomyopathy. Methods: Male Sprague-Dawley rats were randomly divided into 3 groups: a control group (group C), an ADR-treated group (ADR 5 mg/kg/wk for 3 weeks via intraperitoneal injections; group A), and co-treatment of ADR with losartan group (same dose of ADR and losartan; 10 mg/kg/day per oral for 3 weeks; group L). Western blot analysis was conducted to demonstrate changes in brain natriuretic peptide, collagen 1, tumor necrosis factor (TNF)-α, interleukin-6, matrix metalloproteinase (MMP)-2, B-cell leukemia/lymphoma (Bcl)-2, Bcl-2-associated X (Bax), and caspase-3 protein expression levels in left ventricular (LV) tissues from each group. Results: Losartan administration reduced LV hypertrophy, collagen content, and the expression of pro-inflammatory factors TNF-α and MMP-2 in LV tissue. In addition, losartan led to a decrease in the expression of the pro-apoptotic proteins Bax and caspase-3 and an increase in the expression of the anti-apoptotic protein Bcl-2. Moreover, losartan treatment induced a reduction in the apoptotic area compared to group A. Conclusion: In an ADR-induced cardiomyopathy rat model, co-administration of ADR with losartan presented cardioprotective effects by attenuating LV hypertrophy, pro-inflammatory factors, and apoptosis in LV tissue.

• Journal of Life Science
• /
• v.20 no.5
• /
• pp.799-804
• /
• 2010

In humans, CYP2C19, a member of the cytochrome P450 subfamily, metabolizes arachidonic acid to produce epoxyicosanoid acids, which are involved in vascular tone and regulation of blood pressure (BP). Recent findings suggest that CYP2C19 gene polymorphisms might be considered as a novel candidate gene for cardiovascular disease. We thus focused on the Korean population to explore the association of two polymorphisms ($CYP2C19^*2$ and $^*3$) in this gene and essential hypertension (EH). A total of 1,241 participants (537 hypertensive subjects and 704 healthy controls) were recruited from the Yonsei Cardiovascular Genome Center in Korea. The CYP2C19 polymorphisms were genotyped using the $SNaPShot^{TM}$ assay. The allele and genotype frequencies of $CYP2C19^*3$ showed significant difference between hypertensives and normotensives (P=0.019 and P=0.023, respectively). Logistic regression analysis indicated that the $CYP2C19^*3$ A allele carriers were significantly associated with EH (OR, 0.723; 95% CI, 0.538-0.972, P=0.032) under a dominant model. In addition, CYP2C19 G-A haplotype ($2C19^*2\;G-^*3$ A combination) was found to significantly reduce EH risk (OR, 0.714, P=0.015). We believe this provides evidence that $CYP2C19^*3$ polymorphism may contribute to a protective effect in the development of EH.

• Nutrition Research and Practice
• /
• v.18 no.4
• /
• pp.511-522
• /
• 2024

BACKGROUND/OBJECTIVES: The study examined the association between homocysteine and diabetes mellitus in patients with H-type hypertension and assessed the possible effect modifiers. SUBJECTS/METHODS: This cross-sectional study included 1,255 eligible participants in the 'H-type Hypertension Management and Stroke Prevention Strategic International Science and Technology Innovation Cooperation Project' among rural Chinese people with H-type hypertension. A multivariate logistic regression model was used to evaluate the relationship between homocysteine and diabetes mellitus. RESULTS: The mean level of total homocysteine (tHcy) in the diabetes mellitus population was 19.37 μmol/L, which was significantly higher than the non-diabetic patients (18.18 μmol/L). When tHcy was analyzed as a continuous variable, the odds ratio (OR) of diabetes was 1.17 (95% confidence interval [CI], 1.01-1.35; per interquartile range). When tHcy was stratified according to the quintile, the ORs for diabetes were 2.86 (95% CI, 1.22-6.69) in the highest quintile (tHcy ≥ 20.60 μmol/L) compared to the reference group (tHcy < 12.04 μmol/L). When tHcy was grouped by 15 μmol/L and 20 μmol/L, patients with tHcy ≥ 20 μmol/L had a significantly (P = 0.037) higher risk of diabetes (OR, 2.03; 95% CI, 1.04-3.96) than in those with tHcy < 15 μmol/L. Subgroup analysis showed that the tHcy-diabetes association was unaffected by other variables. CONCLUSION: In this study of rural Chinese people with H-type hypertension, the tHcy levels showed a positive association with diabetes mellitus. This independent association is unaffected by other potential risk factors.

• Journal of Cardiovascular Imaging
• /
• v.30 no.3
• /
• pp.185-196
• /
• 2022

BACKGROUND: Two-dimensional (2D) strain provides more predictive power than ejection fraction (EF) in patients with ST-elevation myocardial infarction (STEMI). 3D strain and EF are also expected to have better clinical usefulness and overcome several inherent limitations of 2D strain. We aimed to clarify the prognostic significance of 3D strain analysis in patients with STEMI. METHODS: Patients who underwent successful revascularization for STEMI were retrospectively recruited. In addition to conventional parameters, 3D EF, global longitudinal strain (GLS), global area strain (GAS), as well as 2D GLS were obtained. We constructed a composite outcome consisting of all-cause death or re-hospitalization for acute heart failure or ventricular arrhythmia. RESULTS: Of 632 STEMI patients, 545 patients (86.2%) had a reliable 3D strain analysis. During median follow-up of 49.5 months, 55 (10.1%) patients experienced the adverse outcome. Left ventricle EF, 2D GLS, 3D EF, 3D GLS, and 3D GAS were significantly associated with poor outcomes. (all, p < 0.001) The maximum likelihood-ratio test was performed to evaluate the additional prognostic value of 2D GLS or 3D GLS over the prognostic model consisting of clinical characteristics and EF, and the likelihood ratio was 15.9 for 2D GLS (p < 0.001) and 1.49 for 3D GLS (p = 0.22). CONCLUSIONS: The predictive power of 3D strain was slightly lower than the 2D strain. Although we can obtain 3D strains, volume, and EF simultaneously in same cycle, the clinical implications of 3D strains in STEMI need to be investigated further.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.5
• /
• pp.1431-1445
• /
• 2022

We construct a deep neural network model named ECGResNet. This model can diagnosis diseases based on 12-lead ECG data of eight common cardiovascular diseases with a high accuracy. We chose the 16 Blocks of ResNet50 as the main body of the model and added the Squeeze-and-Excitation module to learn the data information between channels adaptively. We modified the first convolutional layer of ResNet50 which has a convolutional kernel of 7 to a superposition of convolutional kernels of 8 and 16 as our feature extraction method. This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes. The model further improves the accuracy of cardiovascular and cerebrovascular disease classification by using a fully connected layer that integrates factors such as gender and age. The ECGResNet model adds Dropout layers to both the residual block and SE module of ResNet50, further avoiding the phenomenon of model overfitting. The model was eventually trained using a five-fold cross-validation and Flooding training method, with an accuracy of 95% on the test set and an F1-score of 0.841.We design a new deep neural network, innovate a multi-scale feature extraction method, and apply the SE module to extract features of ECG data.

• The Journal of Bigdata
• /
• v.6 no.2
• /
• pp.161-168
• /
• 2021

In this study, we propose a cardiovascular disease prediction model using machine learning. First, a multidimensional analysis of various differences between the two groups is performed and the results are visualized. In particular, we propose a predictive model using cost-sensitive learning that can improve the sensitivity for cases where there is a high class imbalance between the normal and patient groups, such as diseases. In this study, a predictive model is developed using CART and XGBoost, which are representative machine learning technologies, and prediction and performance are compared for cardiovascular disease patient data. According to the study results, CART showed higher accuracy and specificity than XGBoost, and the accuracy was about 70% to 74%.