• 한국산학기술학회논문지
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• 제13권6호
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• pp.2672-2679
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• 2012

본 연구는 급성심근경색증 환자의 사망률 측정을 위한 중증도 보정 모형을 개발하여 의료의 질 평가에 필요한 기초자료를 제공하고자 수행되었다. 이를 위해서 질병관리본부의 2005-2008년 퇴원손상환자 699,701건의 자료를 분석하였다. Charlson Comorbidity Index 보정 방법을 이용한 경우와 새롭게 개발된 중증도 보정 모형의 예측력 및 적합도를 비교하기 위해 로지스틱 회귀분석을 실시하였다. 새롭게 개발된 모형에는 연령, 성, 입원경로, PCI 유무, CABG 유무, 동반질환 12가지 변수가 포함되었다. 분석결과 CCI를 이용한 중증도 보정 모형보다 새롭게 개발된 중증도 보정 사망 모형의 C 통계량 값이 0.796(95%CI=0.771-0.821)으로 더 높아 모형의 예측력이 더 우수한 것으로 나타났다. 본 연구를 통하여 중증도 보정 방법에 따라 사망률, 유병률, 예측력에도 차이가 있음을 확인하였다. 향후에 이모형은 의료의 질 평가에 이용하고, 질환별로 임상적 의미와 특성, 모형의 통계적 적합성 등을 고려한 중증도 보정모형이 계속해서 개발되어야 할 것이다.

• 약학회지
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• 제55권6호
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• pp.510-515
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• 2011

Isoquinoline compounds including berberine enhance L-DOPA-induced cytotoxicity in PC12 cells. In this study, the effects of berberine on L-DOPA therapy in unilateral 6-hydroxydopamine (6-OHDA)-induced rat models of parkinsonism were investigated. Rats were prepared for the models of Parkinson's disease by 6-OHDA-lesioning for 14 days and then treated with L-DOPA (10 mg/kg) with or without berberine (5 and 30 mg/kg, i.p.) for 21 days. Treatment with berberine (5 and 30 mg/kg, i.p.) showed a dopaminergic cell loss in substantia nigra of 6-OHDA-lesioned rats treated with L-DOPA: 30 mg/kg berberine was more intensive neurotoxic. The levels of dopamine were also decreased by berberine (5 and 30 mg/ kg) in striatum-substantia nigra of 6-OHDA-lesioned rats treated with L-DOPA. These results suggest that berberine aggravates cell death of dopaminergic neurons in L-DOPA-treated 6-OHDA-lesioned rat models of Parkinson's disease. Therefore, the long-term L-DOPA therapeutic patients with isoquinoline compounds including berberine may need to be checked for the adverse symptoms.

• 한국응용과학기술학회지
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• 제39권3호
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• pp.442-453
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• 2022

다양한 동물 모델이 인간 질병, 의약품의 효능 및 작용 메커니즘을 연구하는 데 사용되고 있다. Zebrafish(Danio rerio)는 여러 가지 장점이 있어 인간 질병에 대한 중개 연구의 모델로 점점 더 폭넓게 활용되고 있다. 본 논문은 Pubmed, Google Scholar, Scopus에서 2020년 12월까지 최근 10년간 zebrafish 모델, 천연물(한약), in vivo 스크리닝의 키워드를 사용하여 저널에 게재된 논문을 검토하여 필요한 정보를 얻었다. 이 리뷰에서 우리는 천연물(한약) 연구에 대한 다양한 제브라피쉬 질병 모델의 최근 경향에 대해 논의하였다. 특히, 암, 안질환, 혈관 질환, 당뇨병 및 합병증, 피부질환에 중점을 두었고, zebrafish 배아를 사용하여 이들 질병에 대한 의약품의 분자 작용 메커니즘에 관해 언급하였다. Zebrafish는 실험실에서 임상 연구까지의 격차를 줄이는 데 중추적 역할을 할 수 있는 중요한 동물 모델이다. Zebrafish는 의약품이나 화장품 개발, 질병의 병인론을 이해하기 위해 사용되고, 이로 인해 생의학 연구에서 설치류의 사용을 줄이는 데 크게 기여하고 있다.

• Journal of Ginseng Research
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• 제47권3호
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• pp.448-457
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• 2023

Background: Alzheimer's disease (AD) is a common form of dementia, and impaired mitophagy is a hallmark of AD. Mitophagy is mitochondrial-specific autophagy. Ginsenosides from Ginseng involve in autophagy in cancer. Ginsenoside Rg1 (Rg1 hereafter), a single compound of Ginseng, has neuroprotective effects on AD. However, few studies have reported whether Rg1 can ameliorate AD pathology by regulating mitophagy. Methods: Human SH-SY5Y cell and a 5XFAD mouse model were used to investigate the effects of Rg1. Rg1 (1µM) was added to β-amyloid oligomer (AβO)-induced or APPswe-overexpressed cell models for 24 hours. 5XFAD mouse models were intraperitoneally injected with Rg1 (10 mg/kg/d) for 30 days. Expression levels of mitophagy-related markers were analyzed by western blot and immunofluorescent staining. Cognitive function was assessed by Morris water maze. Mitophagic events were observed using transmission electron microscopy, western blot, and immunofluorescent staining from mouse hippocampus. The activation of the PINK1/Parkin pathway was examined using an immunoprecipitation assay. Results: Rg1 could restore mitophagy and ameliorate memory deficits in the AD cellular and/or mouse model through the PINK1-Parkin pathway. Moreover, Rg1 might induce microglial phagocytosis to reduce β-amyloid (Aβ) deposits in the hippocampus of AD mice. Conclusion: Our studies demonstrate the neuroprotective mechanism of ginsenoside Rg1 in AD models. Rg1 induces PINK-Parkin mediated mitophagy and ameliorates memory deficits in 5XFAD mouse models.

• Molecules and Cells
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• 제47권1호
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• pp.100005.1-100005.15
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• 2024

Amyotrophic lateral sclerosis is a devastating neurodegenerative disease with a complex genetic basis, presenting both in familial and sporadic forms. The hexanucleotide (G₄C₂) repeat expansion in the C9orf72 gene, which triggers distinct pathogenic mechanisms, has been identified as a major contributor to familial and sporadic Amyotrophic lateral sclerosis cases. Animal models have proven pivotal in understanding these mechanisms; however, discrepancies between models due to variable transgene sequence, expression levels, and toxicity profiles complicate the translation of findings. Herein, we provide a systematic comparison of 7 publicly available Drosophila transgenes modeling the G₄C₂ expansion under uniform conditions, evaluating variations in their toxicity profiles. Further, we tested 3 previously characterized disease-modifying drugs in selected lines to uncover discrepancies among the tested strains. Our study not only deepens our understanding of the C9orf72 G₄C₂ mutations but also presents a framework for comparing constructs with minute structural differences. This work may be used to inform experimental designs to better model disease mechanisms and help guide the development of targeted interventions for neurodegenerative diseases, thus bridging the gap between model-based research and therapeutic application.

• 한국동물학회:학술대회논문집
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• 한국동물학회 2001년도 한국생물과학협회 학술발표대회
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• pp.107.2-108
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• 2001

No Abstract, See Full Text

• 한국멀티미디어학회논문지
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• 제23권10호
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• pp.1250-1257
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• 2020

The early detection of diseases is important in agriculture because diseases are major threats of reducing crop yield for farmers. The shape and color of plant leaf are changed differently according to the disease. So we can detect and estimate the disease by inspecting the visual feature in leaf. This study presents a vision-based leaf classification method for detecting the diseases of tomato crop. ResNet-50 model was used to extract the visual feature in leaf and classify the disease of tomato crop, since the model showed the higher accuracy than the other ResNet models with different depths. We propose a new ensemble approach using several DCNN classifiers that have the same structure but have been trained at different ranges in the DCNN layers. Experimental result achieved accuracy of 97.19% for PlantVillage dataset. It validates that the proposed method effectively classify the disease of tomato crop.

• Genomics & Informatics
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• 제19권1호
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• pp.11.1-11.8
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• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

• 대한한방내과학회지
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• 제44권3호
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• pp.418-438
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• 2023

Objective: This study was conducted to review the effects of herbal medicine on respiratory diseases induced by the treatment of particulate matter in in vivo animal models. Methods: Literature searches were performed in seven databases (Pubmed, Embase, Cochrane Library, KISS, KTKP, OASIS, and ScienceON). After the searched studies were screened based on the inclusion/exclusion criteria, the publication date, origin, used animals, induction of particulate matter models, herbal medicine used for intervention, study design, outcome measure, and results of studies were analyzed. Results: Among a total of 972 studies primarily searched, 34 studies were finally included in our study. Of this number, 29 studies induced animal models by using only particulate matter, and 5 studies induced animal models with respiratory diseases, such as asthma and chronic obstructive pulmonary disease, by using particulate matter and other materials. In the selected studies, the treatments of herbal medicine in particulate matter models suppressed oxidative stress and inflammation in lung tissue, bronchoalveolar lavage fluid, and blood as well as lung injury in histological analysis. Conclusion: The results of this study suggest that herbal medicine is effective in treating respiratory diseases induced by particulate matter. These results are also expected to be useful data for designing further studies. However, more systematically designed in vivo studies related to particulate matter are needed.

• Journal of Gastric Cancer
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• 제21권3호
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• pp.268-278
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• 2021

Purpose: While several prognostic models for the stratification of death risk have been developed for patients with advanced gastric cancer receiving first-line chemotherapy, they have seldom been tested in the Chinese population. This study investigated the performance of these models and identified the optimal tools for Chinese patients. Materials and Methods: Patients diagnosed with metastatic or recurrent gastric adenocarcinoma who received first-line chemotherapy were eligible for inclusion in the validation cohort. Their clinical data and survival outcomes were retrieved and documented. Time-dependent receiver operating characteristic (ROC) and calibration curves were used to evaluate the predictive ability of the models. Kaplan-Meier curves were plotted for patients in different risk groups divided by 7 published stratification tools. Log-rank tests with pairwise comparisons were used to compare survival differences. Results: The analysis included a total of 346 patients with metastatic or recurrent disease. The median overall survival time was 11.9 months. The patients were different into different risk groups according to the prognostic stratification models, which showed variability in distinguishing mortality risk in these patients. The model proposed by Kim et al. showed relative higher predicting abilities compared to the other models, with the highest χ² (25.8) value in log-rank tests across subgroups, and areas under the curve values at 6, 12, and 24 months of 0.65 (95% confidence interval [CI]: 0.59-0.72), 0.60 (0.54-0.65), and 0.63 (0.56-0.69), respectively. Conclusions: Among existing prognostic tools, the models constructed by Kim et al., which incorporated performance status score, neutrophil-to-lymphocyte ratio, alkaline phosphatase, albumin, and tumor differentiation, were more effective in stratifying Chinese patients with gastric cancer receiving first-line chemotherapy.