회원학회
회원학회
| 번호 | 연구제목 | 연구자 | 연구기간 | 발표실적 |
|---|---|---|---|---|
| 내용 | ||||
| 23 | Analysis on Validity and Academic Competency of Mock Test for Korean Medicine National Licensing Examination Using Item Response Theory | 채한, 조은별, 김선경, 최다혜, 이 | 2023.05.08 ~ | 학회지 |
| The national licensing examination is used to evaluate the medical competency at the time of graduation, however no study has been performed on the validity of traditional Korean medicine license examination yet. The purpose of this study was to develop learning analytics using item response theory (IRT) to examine the validity and academic competency of the mock test of the national licensing exam. Classical test theory and IRT were used to evaluate the validity of test items, and IRT was used for test validity and competency analysis. The correlation between competency score of 12 subjects was analyzed using Pearson’s correlation. The distribution of students’ latent competencies was examined by gender and administrative group using a Kernel density map, Latent Profile Analysis, and χ2. The guessing parameter of 340 items was relatively high, and the information level of 12 subjects were relatively low. Significant correlations (r = 0.49–0.83, p < 0.05) were observed between the competency scores of total and 12 subjects. Two (high and low) latent academic competency groups were identified based on the competency score of 12 subjects. The low academic competency group requiring intensive management has a significantly higher frequency of male students with the experience of academic fail in the seven-year course. This study presented the quantitative learning analytics for the national licensing exam of traditional Korean medicine. The multifaceted item and test validities of the mock license test were provided, and an evidence-based approach to competency-based student management and national licensing exam of traditional Korean medicine was suggested. |
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| 22 | Effects of Essential Oils and Fragrant Compounds on Appetite: A Systematic Review | 칸니, 응우옌, 응웬리, 신흥묵, 양인준 | 2023.04.27 ~ | 학회지 |
| Appetite dysregulation is one of the factors contributing to anorexia, bulimia nervosa, obesity, and diabetes. Essential oils or fragrant compounds have been proven to regulate food intake and energy expenditure; hence, this study aimed to summarize their effects on appetite and the underlying mechanisms. The PubMed and Web of Science databases were searched until July 2022. Only two of the 41 studies were performed clinically, and the remaining 39 used animal models. Oral administration was the most common route, and a dosage range of 100–2000 mg/kg for mice or 2–32 mg/kg for rats was applied, with a duration of 12 days to 4 weeks, followed by inhalation (10−6–10−3 mg/cage or 10−9–10−2 mg/cm3 within 1 h). Approximately 11 essential oil samples and 22 fragrant compounds were found to increase appetite, while 12 essential oils and seven compounds decreased appetite. These fragrant components can exert appetite-regulating effects via leptin resistance, the activity of sympathetic/parasympathetic nerves, or the mRNA expression of neuropeptide Y (NPY)/agouti-related protein (AgRP), cocaine- and amphetamine-regulated transcript (CART)/proopiomelanocortin (POMC) in the hypothalamus. Fragrance memory and cognitive processes may also play roles in appetite regulation. The findings of this study accentuate the potential of essential oils and fragrant compounds to regulate appetite and eating disorders. |
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| 21 | Melittin derived peptide-drug conjugate, M-DM1, inhibits tumor progression and induces effector cell infiltration in melanoma by targeting M2 tumor-associated macrophages | 정찬미, 김정동, 한익환, 김소영, 최일섭, 배현수 외 | 2023.04.14 ~ | 학회지 |
| Background: Melanoma has the highest mortality rate among all the types of skin cancer. In melanoma, M2-like tumor-associated macrophages (TAMs) are associated with the invasiveness of tumor cells and a poor prognosis. Hence, the depletion or reduction of M2-TAMs is a therapeutic strategy for the inhibition of tumor progression. The aim of this study was to evaluate the therapeutic effects of M-DM1, which is a conjugation of melittin (M), as a carrier for M2-like TAMs, and mertansine (DM1), as a payload to induce apoptosis of TAMs, in a mouse model of melanoma. Methods: Melittin and DM1 were conjugated and examined for the characterization of M-DM1 by high-performance liquid chromatography and electrospray ionization mass spectrometry. Synthesized M-DM1 were examined for in vitro cytotoxic effects. For the in vivo study, we engrafted murine B16-F10 into right flank of C57BL/6 female mice and administered an array of treatments (PBS, M, DM1, or M-DM1 (20 nmol/kg)). Subsequently, the tumor growth and survival rates were analyzed, as well as examining the phenotypes of tumor-infiltrating leukocytes and expression profiles. Results: M-DM1 was found to specifically reduce M2-like TAMs in melanoma, which potentially leads to the suppression of tumor growth, migration, and invasion. In addition, we also found that M-DM1 improved the survival rates in a mouse model of melanoma compared to M or DM1 treatment alone. Flow cytometric analysis revealed that M-DM1 enhanced the infiltration of CD8+ cytotoxic T cells and natural killer cells (NK cells) in the tumor microenvironment. Conclusion: Taken together, our findings highlight that M-DM1 is a prospective agent with enhanced anti-tumor effects. |
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| 20 | Machine learning-based prediction of Sasang constitution types using comprehensive clinical information and identification of key features for diagnosis | 박사윤, 박무산, 이원융, 이충열, 김지환, 이시우, 김창업 | 2021.09.01 ~ | 학회지 |
| Background Despite the importance of accurate Sasang type diagnosis, a unique form of Korean medicine, there have been concerns about consistency among diagnoses. We investigate a data-driven integrative diagnostic model by applying machine learning to a multicenter clinical dataset with comprehensive features. Methods Extremely randomized trees (ERT), support vector machines, multinomial logistic regression, and K-nearest neighbor were applied, and performances were evaluated by cross-validation. The feature importance of the classifier was analyzed to understand which information is crucial in diagnosis. Results The ERT classifier showed the highest performance, with an overall f1 score of 0.60 ± 0.060. The feature classes of body measurement, personality, general information, and cold–heat were more decisive than others in classifying Sasang types. Costal angle was the most informative feature. In pairwise classification, we found Sasang type-dependent distinctions that body measurement features played a key role in TE-SE and TE-SY datasets, while personality and cold–heat features showed importance in SE-SY dataset. Conclusion Current study investigated a comprehensive diagnostic model for Sasang type using machine learning and achieved better performance than previous studies. This study helps data-driven decision making in clinics by revealing key features contributing to the Sasang type diagnosis. |
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