• Journal of Life Science
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• v.28 no.12
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• pp.1545-1553
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• 2018

The International Society of Rare Sugars (ISRS) defines rare sugars as monosaccharides and their derivatives that rarely occur in nature. Rare sugars have recently received much attention because of their many uses including low-calorie sweeteners, bulking agents, and antioxidants, and their various applications including as immunosuppressants in allogeneic rat liver transplantation, as potential inhibitors of various glycosidases and microbial growth, in ischemia-reperfusion injury repair in the rat liver, and in segmented neutrophil production without detrimental clinical effects. Because they rarely exist in nature, the production of rare sugars has been regarded as one of the most important research areas and, generally, they are produced by chemical synthesis. However, the production of rare sugars by bioconversion using enzymes from microorganisms has been receiving increased attention as an environmentally friendly alternative production method. In particular, D-allulose, D-allose, and D-tagatose are of interest as low-calorie sweeteners in various industries. To date, D-tagatose 3-epimerase, D-psicose 3-epimerase, and D-allulose 3-epimerase have been reported as D-allulose bioconversion enzymes, and L-rhamnose isomerase, Galactose 6-phosphate isomerase, and Ribose 5-phosphate isomerase have been identified as D-allose production enzymes. Elsewhere, D-tagatose has been produced by L-arabinose isomerase from various microorganisms. In this study, we report the production of D-allulose, D-allose, and D-tagatose by microorganism enzymes.

• Journal of Environmental Health Sciences
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• v.47 no.1
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• pp.64-77
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• 2021

Objective: Biological monitoring of trace elements in human blood samples has become an important indicator of the health environment. The purpose of this study was to detect and evaluate multiple metal items in blood samples based on ICP-MS, to perform comparative evaluation with the existing analysis method, and to develop and verify a new method. Methods: 100 μL of whole blood from 80 healthy subjects was used to analyze ten metals (Sb, tAs, Cd, Pb, Mn, Hg, Mo, Ni, Se, Tl) using ICP-MS. Verification of the analysis method included calculation of linearity, accuracy, precision and detection limits. In addition, a comparative test with the conventional graphite furnace atomic absorption spectroscopy (GF-AAS) method was performed. In the case of Pb, Cd, and Hg in whole blood, cross-analysis between Pb, Cd, and Hg analysis methods was performed to confirm the difference between the existing method and the new method (ICP-MS). Results: The coefficient of determination (R2) was 0.999 or higher in seven items and 0.995 or higher in three items. The Pb result showed that Pearson's correlation coefficient was very high at 0.983, and the intraclass correlation coefficient was 0.966. The Cd result showed that Pearson's correlation coefficient was 0.917 between the existing method and the new analysis concentration value. Its intraclass correlation coefficient was 0.960, and there was no significant difference between the two groups. Hg had a low correlation at 0.687, and the intraclass correlation coefficient was 0.761, which was lower than that of Pb and Cd. The intra-day and inter-day accuracy of Pd and Cd were satisfactory, but Hg did not meet the criteria for both accuracy and precision when compared with the conventional analysis method. Conclusion: This study can be meaningful in that it proposes a more efficient and feasible analysis method by verifying a blood heavy metal concentration experiment using multiple simultaneous analyses. All samples were processed and analyzed using the new ICP-MS. It was confirmed that the agreement between the two methods was very high, with the agreement between the current and new methods being 0.769 to 0.998. This study proposes an efficient simultaneous methodology capable of analyzing multiple elements with small samples. In the future, studies of various applications and the reliability of ICP-MS analysis methods are required, and research on the verification of accurate, precise, and continuous analysis methods is required.

• Journal of Life Science
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• v.31 no.5
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• pp.464-474
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• 2021

Inflammation induced by metabolic syndromes, cancers, injuries, and sepsis can alter cellular metabolism by reducing mitochondrial function via oxidative stress, thereby resulting in neuropathy and muscle atrophy. In this study, we investigated whether butyrate, a short chain fatty acid produced by gut microbiota, could prevent mitochondrial dysfunction and muscle atrophy induced by lipopolysaccharide (LPS) in the C2C12 cell line. LPS-activated MAPK signaling pathways increased the levels of the mitochondrial fission signal, p-DRP1 (Ser616), and the muscle atrophy marker, atrogin 1. Interestingly, butyrate significantly inhibited the phosphorylation of JNK and p38 and reduced the atrogin 1 level in LPS-treated C2C12 cells while increasing the phosphorylation of DRP1 (Ser637) and levels of mitofusin2, which are both mitochondrial fusion markers. Next, we investigated the effect of MAPK inhibitors, finding that butyrate had the same effect as JNK inhibition in C2C12 cells. Also, butyrate inhibited the LPS-induced expression of pyruvate dehydrogenase kinase 4 (PDK4), resulting in decreased PDHE1α phosphorylation and lactate production, suggesting that butyrate shifted glucose metabolism from aerobic glycolysis to oxidative phosphorylation. Finally, we found that these effects of butyrate on LPS-induced mitochondrial dysfunction were caused by its antioxidant effects. Thus, our findings demonstrate that butyrate prevents LPS-induced muscle atrophy by improving mitochondrial dynamics and metabolic stress via the inhibition of JNK phosphorylation. Consequently, butyrate could be used to improve LPS-induced mitochondrial dysfunction and myopathy in sepsis.

• Food Science and Industry
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• v.55 no.2
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• pp.154-165
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• 2022

Humans have been breeding the mulberry silkworm for the long period of time to obtain silk fabric and nutrient-rich pupae. Currently, silkworm larvae, pupae, and silk-Fibroin hydrolysates are registered as food raw materials, while silkworm feces and Bombyx batryticatus are registered as Korean traditional medicines. Among sericulture products, individually recognized health functional food ingredients include silk-protein acid-hydrolysates for immunity enhancement, Fibroin-hydrolysates for memory improvement, and freeze-dried 5^th instar and 3^rd-day-silkworm powder for lowering-blood sugar. Recently, HongJam produced by steaming and freeze-drying mature silkworms were reported to have various health-promoting effects such as preventing the onset of Alzheimer's disease and Parkinson's disease, enhancing gastro-intestinal functions, improving skin-whitening and hair growth, and extending healthspan. By consuming silkworm products with various health-promoting effects, it is possible to increase the healthspan of human beings, thereby reducing personal and national medical expenses, resulting in increasing the individual's happiness.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.45-51
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• 2021

With the development of deep learning, semantic segmentation methods are being studied in various fields. There is a problem that segmenation accuracy drops in fields that require accuracy such as medical image analysis. In this paper, we improved PSPNet, which is a deep learning based segmentation method to minimized the loss of features during semantic segmentation. Conventional deep learning based segmentation methods result in lower resolution and loss of object features during feature extraction and compression. Due to these losses, the edge and the internal information of the object are lost, and there is a problem that the accuracy at the time of object segmentation is lowered. To solve these problems, we improved PSPNet, which is a semantic segmentation model. The multi-scale attention proposed to the conventional PSPNet was added to prevent feature loss of objects. The feature purification process was performed by applying the attention method to the conventional PPM module. By suppressing unnecessary feature information, eadg and texture information was improved. The proposed method trained on the Cityscapes dataset and use the segmentation index MIoU for quantitative evaluation. As a result of the experiment, the segmentation accuracy was improved by about 1.5% compared to the conventional PSPNet.

• Journal of Naturopathy
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• v.11 no.2
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• pp.136-142
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• 2022

Background: We are necessarily considering changes in the parasite infection rate and methods in Korea from 1970 to the present from the perspective of natural healing. Purposes: This study was to investigate how the difference in the rate of reduction in infection rate has changed and progressed to the present. Methods: A literature review was conducted. Results: Until the 1970s, Korea had one of the highest parasite infection rates. The Parasitic Disease Prevention Act was enacted to control the infection rate in 1966. From 1969, the nationwide national parasite management project was conducted for all students twice a year to treat all parasitic eggs until 1995. In addition, the government commissioned the Korean Association for Parasite Eradication (KAPE) to conduct a national parasite infection survey eight times, from 1971 to 2012, every two years. As a result, the overall egg positive rate of parasite was 84.3% in 1971 but decreased to 2.6% in 2012. In addition, Ascaris lumbricoides, Paragonimus westermani, Taenia spp., and intestinal protozoa were significantly reduced nationwide. Conclusions: Successful control in Korea is judged to have achieved a successful effect by systematically managing national economic growth, social consensus on parasite eradication, improved professional parasite prevention guidelines, and supply of effective anthelminthics.

• Journal of Naturopathy
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• v.11 no.1
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• pp.1-8
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• 2022

Background: A validation study is needed to make domestic applications of German Kneipp therapy. Purpose: The study aims to test the effect of a Kneipp therapeutic program in a domestic forest environment on the autonomic nervous system. Methods: The program was made considering the 4 key elements ('exercise', 'regulative therapy', 'nutrition', and 'phytotherapy') of the Kneipp therapy. Total 3 sessions (once a week for 5 hours in each session) were performed to 40 domestic adults divided into four groups (10 in each group). HRV was measured on the subjects before and after the intervention, and its 6 characteristic parameters (TP, VLF, LF, HF, LF/HF, CSI: Cumulative Stress Index) were compared for statistical analysis. Results: For 33 subjects excluding 7 who were disturbed by interrupt factors during measurements, significant improvements after the intervention, were observed in TP(8.64%, p < ..001), VLF(6.96%, p < .05), LF(15.86%, p < .001), HF(8.46%, p < .01), LF/HF(5.77%, p < .05) and CSI(-16.06%, p < ..001). Conclusions: The Kneipp therapy performed in the forest environment was shown to activate the autonomic nervous system and in particular the sympathetic and parasympatheric nervous to promote heart activity, and was also shown to most significantly reduce cumulative stress. The present observations would be the first preliminary evidence that the Kneipp therapeutic interventation in a domestic forest environment results in positive responses of the autonomic nervous system including stress relief.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.351-357
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• 2021

IoT devices, which are used in various ways in distributed environments, are becoming more important in data transmitted and received from IoT devices as fields of use such as medical, environment, transportation, bio, and public places are diversified. In this paper, as a method to ensure the reliability of IoT data, an autoencoder-based IoT-linked processing technique is proposed to classify and process numerous data by various important attributes. The proposed technique uses correlation indices for each IoT data so that IoT data is grouped and processed by blockchain by characteristics for IoT linkage processing based on autoencoder. The proposed technique expands and operates into a blockchain-based n-layer structure applied to the correlation index to ensure the reliability of IoT data. In addition, the proposed technique can not only select IoT data by applying weights to IoT collection data according to the correlation index of IoT data, but also reduce the cost of verifying the integrity of IoT data in real time. The proposed technique maintains the processing cost of IoT data so that IoT data can be expanded to an n-layer structure.

• BMB Reports
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• v.55 no.6
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• pp.275-280
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• 2022

The treatment of atopic dermatitis (AD) is challenging due to its complex etiology. From epidermal disruption to chronic inflammation, various cells and inflammatory pathways contribute to the progression of AD. As with immunosuppressants, general inhibition of inflammatory pathways can be effective, but this approach is not suitable for long-term treatment due to its side effects. This study aimed to identify a plant extract (PE) with anti-inflammatory effects on multiple cell types involved in AD development and provide relevant mechanistic evidence. Degranulation was measured in RBL-2H3 cells to screen 30 PEs native to South Korea. To investigate the anti-inflammatory effects of Parasenecio auriculatus var. matsumurana Nakai extract (PAE) in AD, production of cytokines and nitric oxide, activation status of FcεRI and TLR4 signaling, cell-cell junction, and cell viability were evaluated using qRT-PCR, western blotting, confocal microscopy, Griess system, and an MTT assay in RBL-2H3, HEK293, RAW264.7, and HaCaT cells. For in vivo experiments, a DNCBinduced AD mouse model was constructed, and hematoxylin and eosin, periodic acid-Schiff, toluidine blue, and F4/80-staining were performed. The chemical constituents of PAE were analyzed by HPLC-MS. By measuring the anti-degranulation effects of 30 PEs in RBL-2H3 cells, we found that Paeonia lactiflora Pall., PA, and Rehmannia glutinosa (Gaertn.) Libosch. ex Steud. show an inhibitory activity of more than 50%. Of these, PAE most dramatically and consistently suppressed cytokine expression, including IL-4, IL-9, IL-13, and TNF-α. PAE potently inhibited FcεRI signaling, which mechanistically supports its basophil-stabilizing effects, and PAE downregulated cytokines and NO production in macrophages via perturbation of toll-like receptor signaling. Moreover, PAE suppressed cytokine production in keratinocytes and upregulated the expression of tight junction molecules ZO-1 and occludin. In a DNCB-induced AD mouse model, the topical application of PAE significantly improved atopic index scores, immune cell infiltration, cytokine expression, abnormal activation of signaling molecules in FcεRI and TLR signaling, and damaged skin structure compared with dexamethasone. The anti-inflammatory effect of PAE was mainly due to integerrimine. Our findings suggest that PAE could potently inhibit multi-inflammatory cells involved in AD development, synergistically block the propagation of inflammatory responses, and thus alleviate AD symptoms.

• Journal of Life Science
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• v.33 no.8
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• pp.663-679
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• 2023

This review analyzes research trends related to new drug development using artificial intelligence from 2010 to 2022. This analysis organized the abstracts of 2,421 studies into a corpus, and words with high frequency and high connection centrality were extracted through preprocessing. The analysis revealed a similar word frequency trend between 2010 and 2019 to that between 2020 and 2022. In terms of the research method, many studies using machine learning were conducted from 2010 to 2020, and since 2021, research using deep learning has been increasing. Through these studies, we investigated the trends in research on artificial intelligence utilization by field and the strengths, problems, and challenges of related research. We found that since 2021, the application of artificial intelligence has been expanding, such as research using artificial intelligence for drug rearrangement, using computers to develop anticancer drugs, and applying artificial intelligence to clinical trials. This article briefly presents the prospects of new drug development research using artificial intelligence. If the reliability and safety of bio and medical data are ensured, and the development of the above artificial intelligence technology continues, it is judged that the direction of new drug development using artificial intelligence will proceed to personalized medicine and precision medicine, so we encourage efforts in that field.