No abstract

Thirty ng/mm2 lumbrokinase, a potent fibrinolytic enzyme, was immobilized in a Korean type total artificial heart (KORTAH) valve by photoreaction; polyallylamine was used as a photoreactive linker. Lumbrokinase-immobilized polyurethane valves were then fitted to the total artificial hearts of 3 healthy 50 kg lambs. In the control lamb, the valves were untreated; in one other, only valves on the right were treated; and in the remaining animal, only those on the left. Implants were in place for up to 3 days, and cardiac output was 5 L/min. To facilitate thrombus formation, low doses of heparin were administered. In the control lamb, thrombi was observed only in the inlet parts of the valves. In the other 2 experiments, thrombi formed in untreated control valves but not in lumbrokinase treated valves. The grade of thrombus formation in untreated valves was 1.06+/-1.37 versus 0+/-0 in the treated part by one-sided Student's t-test (p < 0.1). After implantation, fibrinolytic activity was only observed in treated valves by fibrin plate methods. The proteolytic activity of the treated valves was 3 times higher than that of untreated valves using the azocasein method. These data show that lumbrokinase treated polyurethane valves lead to decreased thrombus formation in vivo and that their biocompatibility is therefore greater than that of untreated valves.

The availability of a reliable heart failure model in large animals is important. We report upon our efforts to develop a chronic heart failure model in seven goats using sequential ligation of the left anterior descending (LAD) coronary artery and its diagonal branch. After anesthesia and left thoracotomy, the LAD artery was ligated, and the diagonal vessel at the same level was ligated one hour later. Cardiac measurements were performed with a thermodilution catheter and by ultrasonography. Two months after the operation, the same measurements were made and animals were sacrificed for postmortem examinations of their hearts. Hemodynamic measurements, except cardiac output, showed no significant changes immediately after the coronary artery ligation. Echocardiographic measurements showed significant changes in the ejection fraction and fractional shortening without changes in left ventricular dimensions. Wall motion analyses demonstrated variable degrees of anteroseptal dyskinesia and akinesia in all animals immediately after coronary artery ligation. Five animals have undergone hemodynamic and ultrasonographic studies 2 months after coronary artery ligation. The results obtained from these animals showed significant increases in central venous pressure, right ventricular pressure, pulmonary artery pressure, and pulmonary artery capillary wedge pressure, and a significant decrease in cardiac output. Increases in left ventricular dimensions and decreases in ejection fraction with fractional shortening in ultrasonographic studies were also observed. Pathologically, well-demarcated thin-walled anteroseptal infarcts, with chamber enlargement, were clearly seen with dilatation of the heart chambers in all specimens. Based on this study, we conclude that goats, like sheep, can provide a reliable model of chronic heart failure by coronary artery ligation and in view of the many advantages offered by goats, we believe that this animal model will be useful for cardiac experimentation.

This paper proposes a semi-supervised autoencoder with an auxiliary task (SAAT) to extract a health feature space for power transformer fault diagnosis using dissolved gas analysis (DGA). The health feature space generated by a semi-supervised autoencoder (SSAE) not only identifies normal and thermal/electrical fault types, but also presents the underlying characteristics of DGA. In the proposed approach, by adding an auxiliary task that detects normal and fault states in the loss function of SSAE, the health feature space additionally enables visualization of health degradation properties. The overall procedure of the new approach includes three key steps: 1) preprocessing DGA data, 2) extracting two health features via SAAT, and 3) visualizing the two health features in two-dimensional space. In this paper, we test the proposed approach using massive unlabeled/labeled Korea Electric Power Corporation (KEPCO) databases and IEC TC 10 databases. To demonstrate the effectiveness of the proposed approach, four comparative studies are conducted with these datasets; the studies examined: 1) the effectiveness of an auxiliary detection task, 2) the effectiveness of the visualization method, 3) conventional fault diagnosis methods, and 4) the state-of-the-art, semi-supervised deep learning algorithms. By examining several evaluation metrics, these comparative studies confirm that the proposed approach outperforms SSAE without the auxiliary task, existing methods, and state-of-the-art deep learning algorithms, in terms of defining health degradation performance. We expect that the proposed SAAT-based health feature space approach will be widely applicable to intuitively monitor the health state of power transformers in the real world.