Epidemiological studies have shown a positive association between intake of foods rich in antioxidants and lower incidence of cardiovascular disease development. Polyphenols are considered the most abundant and important dietary antioxidants. The aim of this study was to evaluate effects of polyphenol-rich chokeberry juice consumption on 24-h ambulatory monitored blood pressure (BP) level in subjects with no pharmacologically treated high normal BP or grade I hypertension. Twenty-three subjects (12 men and 11 women) aged 33-67 were enrolled and instructed to consume 200 mL of juice daily for 4 weeks. Participants were divided in two groups, based on prevalence of sympathetic or parasympathetic activity. Measurements of biochemical parameters and heart rate variability analysis were also applied. At the end of the intervention period, average 24-h and awake systolic and diastolic BP were significantly decreased (P<.05). This was more pronounced in the group with prevalence of sympathetic activity. Significant reduction in triglyceride level (P<.05) and a reducing effect on total and low-density lipoprotein cholesterol were also found. Obtained results indicate a positive impact of regular chokeberry juice consumption on BP and lipid status in pharmacologically untreated hypertensive subjects.
Artificial neural networks (ANNs) are machine learning technique, inspired by the principles found in biological neurons. This technique has been used for prediction and classification problems in many areas of medical signal processing. The aim of this paper was to identify individuals with high risk of death after acute myocardial infarction using ANN. A training dataset for ANN was 1705 consecutive patients who underwent 24-hour ECG monitoring, short ECG analysis, noninvasive beat-to-beat heart-rate variability, and baroreflex sensitivity that were followed for 3 years. The proposed neural network classifier showed good performance for survival prediction: 88% accuracy, 81% sensitivity, 93% specificity, 0.85 -measure, and area under the curve value of 0.77. These findings support the theory that patients with high sympathetic activity (reduced baroreflex sensitivity) have an increased risk of mortality independent of other risk factors and that artificial neural networks can indicate the individuals with a higher risk.
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