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  1. Article ; Online: A Novel Computational Approach for the Discovery of Drug Delivery System Candidates for COVID-19

    Taeheum Cho / Hyo-Sang Han / Junhyuk Jeong / Eun-Mi Park / Kyu-Sik Shim

    International Journal of Molecular Sciences, Vol 22, Iss 2815, p

    2021  Volume 2815

    Abstract: In order to treat Coronavirus Disease 2019 (COVID-19), we predicted and implemented a drug delivery system (DDS) that can provide stable drug delivery through a computational approach including a clustering algorithm and the Schrödinger software. Six ... ...

    Abstract In order to treat Coronavirus Disease 2019 (COVID-19), we predicted and implemented a drug delivery system (DDS) that can provide stable drug delivery through a computational approach including a clustering algorithm and the Schrödinger software. Six carrier candidates were derived by the proposed method that could find molecules meeting the predefined conditions using the molecular structure and its functional group positional information. Then, just one compound named glycyrrhizin was selected as a candidate for drug delivery through the Schrödinger software. Using glycyrrhizin, nafamostat mesilate (NM), which is known for its efficacy, was converted into micelle nanoparticles (NPs) to improve drug stability and to effectively treat COVID-19. The spherical particle morphology was confirmed by transmission electron microscopy (TEM), and the particle size and stability of 300–400 nm were evaluated by measuring DLSand the zeta potential. The loading of NM was confirmed to be more than 90% efficient using the UV spectrum.
    Keywords COVID-19 ; in silico ; machine learning ; clustering ; unsupervised learning ; drug delivery system ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
    Subject code 006
    Language English
    Publishing date 2021-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: A Novel Computational Approach for the Discovery of Drug Delivery System Candidates for COVID-19.

    Cho, Taeheum / Han, Hyo-Sang / Jeong, Junhyuk / Park, Eun-Mi / Shim, Kyu-Sik

    International journal of molecular sciences

    2021  Volume 22, Issue 6

    Abstract: In order to treat Coronavirus Disease 2019 (COVID-19), we predicted and implemented a drug delivery system (DDS) that can provide stable drug delivery through a computational approach including a clustering algorithm and the Schrödinger software. Six ... ...

    Abstract In order to treat Coronavirus Disease 2019 (COVID-19), we predicted and implemented a drug delivery system (DDS) that can provide stable drug delivery through a computational approach including a clustering algorithm and the Schrödinger software. Six carrier candidates were derived by the proposed method that could find molecules meeting the predefined conditions using the molecular structure and its functional group positional information. Then, just one compound named glycyrrhizin was selected as a candidate for drug delivery through the Schrödinger software. Using glycyrrhizin, nafamostat mesilate (NM), which is known for its efficacy, was converted into micelle nanoparticles (NPs) to improve drug stability and to effectively treat COVID-19. The spherical particle morphology was confirmed by transmission electron microscopy (TEM), and the particle size and stability of 300-400 nm were evaluated by measuring DLSand the zeta potential. The loading of NM was confirmed to be more than 90% efficient using the UV spectrum.
    MeSH term(s) A549 Cells ; Anti-Inflammatory Agents/chemistry ; Anti-Inflammatory Agents/therapeutic use ; Benzamidines/chemistry ; Benzamidines/therapeutic use ; COVID-19/drug therapy ; Cell Survival/drug effects ; Cluster Analysis ; Computational Biology/methods ; Computer Simulation ; Databases, Pharmaceutical ; Drug Carriers/chemistry ; Drug Delivery Systems/methods ; Drug Repositioning ; Drug Stability ; Glycyrrhizic Acid/chemistry ; Glycyrrhizic Acid/therapeutic use ; Guanidines/chemistry ; Guanidines/therapeutic use ; Humans ; Hydrophobic and Hydrophilic Interactions ; Micelles ; Microscopy, Electron, Transmission ; Molecular Structure ; Nanoparticles/chemistry ; Particle Size
    Chemical Substances Anti-Inflammatory Agents ; Benzamidines ; Drug Carriers ; Guanidines ; Micelles ; Glycyrrhizic Acid (6FO62043WK) ; nafamostat (Y25LQ0H97D)
    Language English
    Publishing date 2021-03-10
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2019364-6
    ISSN 1422-0067 ; 1422-0067 ; 1661-6596
    ISSN (online) 1422-0067
    ISSN 1422-0067 ; 1661-6596
    DOI 10.3390/ijms22062815
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Feature Analysis of Smart Shoe Sensors for Classification of Gait Patterns.

    Sunarya, Unang / Sun Hariyani, Yuli / Cho, Taeheum / Roh, Jongryun / Hyeong, Joonho / Sohn, Illsoo / Kim, Sayup / Park, Cheolsoo

    Sensors (Basel, Switzerland)

    2020  Volume 20, Issue 21

    Abstract: Gait analysis is commonly used to detect foot disorders and abnormalities such as supination, pronation, unstable left foot and unstable right foot. Early detection of these abnormalities could help us to correct the walking posture and avoid getting ... ...

    Abstract Gait analysis is commonly used to detect foot disorders and abnormalities such as supination, pronation, unstable left foot and unstable right foot. Early detection of these abnormalities could help us to correct the walking posture and avoid getting injuries. This paper presents extensive feature analyses on smart shoes sensor data, including pressure sensors, accelerometer and gyroscope signals, to obtain the optimum combination of the sensors for gait classification, which is crucial to implement a power-efficient mobile smart shoes system. In addition, we investigated the optimal length of data segmentation based on the gait cycle parameters, reduction of the feature dimensions and feature selection for the classification of the gait patterns. Benchmark tests among several machine learning algorithms were conducted using random forest, k-nearest neighbor (KNN), logistic regression and support vector machine (SVM) algorithms for the classification task. Our experiments demonstrated the combination of accelerometer and gyroscope sensor features with SVM achieved the best performance with 89.36% accuracy, 89.76% precision and 88.44% recall. This research suggests a new state-of-the-art gait classification approach, specifically on detecting human gait abnormalities.
    MeSH term(s) Accelerometry ; Algorithms ; Gait Analysis ; Humans ; Machine Learning ; Pressure ; Shoes ; Support Vector Machine
    Language English
    Publishing date 2020-11-02
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s20216253
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: The impact of weight changes on nonalcoholic Fatty liver disease in adult men with normal weight.

    Cho, Ji-Young / Chung, Tae-Heum / Lim, Kyoung-Mo / Park, Hee-Jin / Jang, Jung-Mi

    Korean journal of family medicine

    2014  Volume 35, Issue 5, Page(s) 243–250

    Abstract: Background: Although it is known that losing weight has an effect on the treatment of non-alcoholic fatty liver disease, the studies that show how losing weight affects the non-alcoholic fatty liver disease for the normal weight male adults are limited ... ...

    Abstract Background: Although it is known that losing weight has an effect on the treatment of non-alcoholic fatty liver disease, the studies that show how losing weight affects the non-alcoholic fatty liver disease for the normal weight male adults are limited so far. In this study, we set body mass index as criteria and investigated how the weight changes for 4 years makes an impact on the risk of non-alcoholic fatty liver disease for the male adults who have the normal body mass index.
    Methods: From January to December of 2004, among the normal weight male adults who had general check-up at the Health Promotion Center of Ulsan University Hospital, 180 people (average age, 47.4 ± 4.61 years) who were diagnosed with fatty liver through abdominal ultrasonography were included in this study and were observed according to the variety of data and ultrasonography after 4 years (2008). People who had a history of drinking more than 140 g of alcohol per week or who had a past medical history were excluded from the analysis. The weight change of subjects was calculated using the formula 'weight change = weight of 2008 (kg) - weight of 2004 (kg)' and classified into three groups, loss group (≤-3.0 kg), stable group (-2.9 to 2.9 kg), and gain group (≥3.0 kg). The odds for disappearance of non-alcoholic fatty liver disease in those three different groups were compared.
    Results: Among 180 subjects, compared with stable group (67.2%, 121 subjects), loss group (11.7%, 21 subjects) showed 18.37-fold increase in the odds of disappearance of non-alcoholic fatty liver disease (95% confidence interval [CI], 4.34 to 77.80) and gain group (21.1%, 38 subjects) showed 0.28-fold decrease in the odds of disappearance of non-alcoholic fatty liver disease (95% CI, 0.10 to 0.83).
    Conclusion: Even for the normal weight people, losing weight has an effect on the improvement of non-alcoholic fatty liver disease.
    Language English
    Publishing date 2014-09-24
    Publishing country Korea (South)
    Document type Journal Article
    ZDB-ID 2674300-0
    ISSN 2092-6715 ; 2005-6443
    ISSN (online) 2092-6715
    ISSN 2005-6443
    DOI 10.4082/kjfm.2014.35.5.243
    Database MEDical Literature Analysis and Retrieval System OnLINE

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