Search

Background: Molecular remodeling in heart failure includes slowing of repolarization, leading to proarrhythmia.Objective: To evaluate the effects of Na(+)/Ca(2+) exchanger (NCX) inhibition on repolarization as a novel antiarrhythmic concept in chronic heart failure (CHF).Methods and Results: CHF was induced by rapid ventricular pacing in rabbits. Left ventricular function was assessed by echocardiography. Monophasic action potentials (MAPs) showed a prolongation of repolarization in CHF after atrioventricular block and stimulation at different cycle lengths. Sotalol (100 μM, n = 13) or veratridine (0.5 μM; n = 15) resulted in a further significant increase in the MAP duration. CHF was associated with an increased dispersion of repolarization, as compared with sotalol-treated (+22 ± 7 ms; P < .05) and veratridine-treated (+20 ± 6 ms; P < .05) sham hearts. In the presence of a low potassium concentration, sotalol and veratridine reproducibly induced early afterdepolarizations (EADs) and polymorphic ventricular tachyarrhythmias (VTs). SEA0400 (1 μM), a pharmacological inhibitor of NCX, significantly shortened the MAP duration (P < .01) and reduced dispersion (P < .05). It suppressed EAD in 6 of 13 sotalol-treated failing hearts and in 9 of 10 veratridine-treated failing hearts, leading to a reduction in VT (60% in sotalol-treated failing hearts and 83% in veratridine-treated failing hearts). Simulations using a mathematical model showed a reduction in the action potential duration and the number of EADs by the NCX block in all subgroups.Conclusions: In an experimental model of CHF, the acute inhibition of NCX (1) reduces the MAP duration, (2) decreases dispersion of repolarization, and (3) suppresses EAD and VT. Our observations indicate for the first time that pharmacological NCX inhibition increases repolarization reserve and protects against VTs in heart failure. [ABSTRACT FROM AUTHOR]

Background: Long QT syndrome (LQTS) is associated with sudden cardiac death resulting from torsades de pointes (TdP), which are triggered by early afterdepolarizations (EADs). The cardiac Na(+)/Ca(2+) exchanger (NCX) has been suggested to work as a trigger for EADs.Objective: The purpose of this study was to test the hypothesis that inhibition of NCX with a newly developed selective NCX inhibitor (SEA0400) reduces TdP.Methods and Results: In 34 Langendorff-perfused rabbit hearts, the I(Kr)-blocker sotalol (100 microM; n = 18) as well as veratridine (0.5 microM; n = 16), an inhibitor of sodium channel inactivation, led to a significant increase in monophasic action potential (MAP) duration thereby mimicking LQTS2 and LQTS3. In bradycardic hearts, recordings of eight MAPs demonstrated an increased dispersion of repolarization (sotalol: 67%; veratridine: 89%; P <.05). After lowering of potassium concentration, sotalol (56%) and veratridine (63%) induced TdP. Perfusion with SEA0400 (1 microM) suppressed EADs in 15 of 16 sotalol hearts and in seven of 13 veratridine hearts. SEA0400 significantly shortened MAP duration and reduced dispersion of repolarization in both groups (P <.05). This reduced TdP incidence in the sotalol group (100%) and in the veratridine group (77%). To investigate the effects of NCX inhibition on the cellular level, we used a computer model of the rabbit ventricular myocyte. I(Na) and I(Kr) were modified to mimic the effects of veratridine and sotalol, respectively. Consistent with our in vitro experiments, reduction of NCX activity accelerated repolarization of the cellular action potential and prevented EADs.Conclusion: In an intact rabbit heart model of LQT2 and LQT3 as well as in a computer model of the rabbit cardiac myocyte, inhibition of NCX is effective in preventing TdP due to a suppression of EADs, a reversion of action potential prolongation, and a reduction of dispersion of repolarization. Our observations suggest a therapeutic benefit of selective NCX inhibition in LQTS. [ABSTRACT FROM AUTHOR]

Identification through image processing has been used in various fields, including product examination and more recently infrastructure inspection, such as identifying cracks in structures. The conventional method, in which a human inspector uses a crack gauge or determines the size through a visual evaluation, results in a subjective evaluation of the extent of concrete life. Currently, an operator inspects the concrete structure by visually examining the concrete surface. However, this method presents numerous problems, including when the inspector has to work in dangerously high places. Therefore, we used machine learning to extract the features of cracks in image processing. We identified that non-arbitrary features, such as color-related features, are also important. Because concrete appears monochromatic, it is difficult for humans to analyze this color-related feature. We compared these newly obtained machine learning features with the previously used arbitrary features and confirmed that the machine learning features were more accurate in terms of detection. We also compared the generation of discriminators based on these features with a fixed threshold for discrimination and the utilization of support vector machine (SVM) and other machine learning methods. In this paper, two issues are discussed: an analysis of the effectiveness of machine learning and SVM-based discriminant generation in detecting cracks, and the classification results based on crack width. Crack width classification using machine learning is useful when sufficient image resolution is not available. The final detection accuracy of the new method was 11.7% better than that of the method using arbitrary features; moreover, the false detection rate was also higher in the proposed method. We further attempted to classify these cracks in accordance with their damage levels. To evaluate the degree of damage, we focused on the difference in the width of the cracks and extracted different features in three classes on the basis of the different crack widths, and were able to classify these clacks. • We used machine learning to extract the features of cracks. We identified that non-arbitrary features, such as color-related features, are also important. Because concrete is apparently monochromatic, it is difficult for humans to analyze this color-related feature. We compared these newly obtained machine learning features with the previously used arbitrary features and confirmed that the machine learning features were more accurate in detection.. • We compared the generation of discriminators based on these features with a fixed threshold for discrimination and the utilization of support vector machine (SVM). The final detection accuracy of the new method was 11.7% better than that of the method using arbitrary features; moreover, the false-positive rate was also higher in the proposed method.. • We attempted to classify these cracks according to their damage levels. To evaluate the degree of damage, we focused on the difference in the width of the cracks and extracted different features in three classes based on the different crack widths. In this paper, the following two issues are discussed: the first is an analysis of the effectiveness of machine learning and SVM-based discriminant generation in detecting cracks, and the second is the classification results based on crack width. Finally, we evaluated the degree of damage by classifying the cracks based on crack width using machine learning.. [ABSTRACT FROM AUTHOR]