• Journal of Life Science
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• v.32 no.1
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• pp.1-10
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• 2022

Natural products have gained increasing attention due to their advantage of long-term safety and low toxicity for a very long time. Torreya nucifera is widespread in southern Korea and Jeju Island and its seeds are commonly used as edible food. Oriental ingredients have often been reported for their insecticidal, antioxidant and antibacterial properties, but there have not yet been any studies on their antidiabetic effect. In this study, we investigated several biological activities of T. nucifera pericarp (TNP) and seeds (TNS) extracts and proceeded to characterize the antidiabetic compounds of TNS. The initial results suggested that TNS extract at 15 and 10 ㎍/ml concentration has inhibitory effects on α-glucosidase and protein tyrosine phosphatase 1B, that is 14.5 and 4.35 times higher than TNP, respectively. Thus, the stronger antidiabetic TNS was selected for the subsequent experiments to characterize its active compounds. Ultrafiltration was used to determine the apparent molecular weight of the active compounds, showing 300 kDa or more. Finally the mixture was then partially purified using Diaion HP-20 column chromatography by eluting with 50~100% methanol. Therefore we concluded that the active compounds of TNS have potential as therapeutic agents in functional food or supplemental treatment to improve diabetic diseases.

• Clean Technology
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• v.29 no.2
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• pp.126-134
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• 2023

Direct catalytic decomposition is a promising method for controlling the emission of nitrous oxide (N₂O) from the semiconductor and display industries. In this study, a γ-Al₂O₃ catalyst was developed to reduce N₂O emissions by a catalytic decomposition reaction. The γ-Al₂O₃ catalyst was prepared by an extrusion method using boehmite powder, and a N₂O decomposition test was performed using a catalyst reactor that was approximately 25.4 mm (1 in) in diameter packed with approximately 5 mm of catalysts. The N₂O decomposition tests were carried out with approximately 1% N₂O at 550 to 750 ℃, an ambient pressure, and a GHSV=1800-2000 h^-1. To confirm the N₂O decomposition properties and the effect of O₂ and steam on the N₂O decomposition, nitrogen, air, and air and steam were used as atmospheric gases. The catalytic decomposition tests showed that the 1% N₂O had almost completely disappeared at 700 ℃ in an N₂ atmosphere. However, air and steam decreased the conversion rate drastically. The long term stability test carried out under an N₂ atmosphere at 700 ℃ for 350 h showed that the N₂O conversion rate remained very stable, confirming no catalytic activity changes. From the results of the N₂O decomposition tests and long-term stability test, it is expected that the prepared γ-Al₂O₃ catalyst can be used to reduce N₂O emissions from several industries including the semiconductor, display, and nitric acid manufacturing industry.

• Korean Journal of Radiology
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• v.24 no.8
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• pp.807-820
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• 2023

Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. Materials and Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions. CT images in groups 2-7 were converted into the target CT style (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. Results: Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2-7 improved after CT conversion (original vs. converted: 0.63 vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists' scores were significantly higher (P < 0.001) and less variable on converted CT. Conclusion: CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.