Two new terrein derivatives asperterreinones A-B (1-2), one new octahydrocoumarin derivative (±)-asperterreinin A (6), along with seventeen known compounds, were isolated from Aspergillus terreus F6-3, a marine fungus associated with Johnius belengerii. The structures of 1, 2, and 6 were established on the basis of 1D and 2D NMR, mass spectroscopy, comparative electronic circular dichroism (ECD) spectra analysis, density functional theory calculation of 13C NMR, and DP4+ probability analysis. Among all the isolates, eurylene (7), a constituent of the Malaysian medicinal plant Eurycoma longifolia, was obtained from a microbial source for first time. In the in vitro bioassay, 11 and 13 showed potent inhibitory activity against the Escherichia coli β-glucuronidase (EcGUS) with IC50 values of 27.75 ± 0.73 and 17.73 ± 0.81 μM, respectively. It was the first time that questinol (11) and (±)-aspertertone B (13) were reported as potent EcGUS inhibitors.
Coronary physiologic assessment is performed to measure coronary pressure, flow, and resistance or their surrogates to enable the selection of appropriate management strategy and its optimization for patients with coronary artery disease. The value of physiologic assessment is supported by a large body of evidence that has led to major recommendations in clinical practice guidelines. This expert consensus document aims to convey practical and balanced recommendations and future perspectives for coronary physiologic assessment for physicians and patients in the Asia-Pacific region based on updated information in the field that including both wire- and image-based physiologic assessment. This is Part 1 of the whole consensus document, which describes the general concept of coronary physiology, as well as practical information on the clinical application of physiologic indices and novel image-based physiologic assessment.
Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model's classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models' effectiveness.