• 사상체질의학회지
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• 제26권2호
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• pp.165-179
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• 2014

Objectives To evaluate the neuroprotective effects of Hyangsayangwi-tang (HY), a Korean traditional medicinal prescription in a Parkinson's disease mouse model. Methods Four groups(each of 10 mouse per group) were used in this study. The neuroprotective effect of HY was examined in a Parkinson's disease mouse model. C57BL/6 mouse treated with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP, 30mg/kg/day), intraperitoneal (i.p.) for 5 days. Slow behavioral responses and memory disorder is the major clinical symptoms of PD. In order to investigate the effect of HY on recovery of behavioral deficits and memory, we examined the motor function and memory by using Morris water maze and Forced swimming test. Ischemic mouse brain stained with TTC(2,3,5 triphenyl tetrazolium chloride) in the MPTP-induced Parkinson's disease to find out ischemia and tissue damage in mouse. The convenient, simple, and accurate high-performance liquid chromatography (HPLC) method was established for simultaneous determination of neurotransmitters in MPTP-HY group. To measure the amount of dopamine in mice brain, striatum-substantia nigra, was examined by Bradford assay. Immunohistochemistry was examined in the MPTP-induced Parkinson's disease (PD) mouse to evaluate the neuroprotective effects of Hyangsayangwi-tang on hippocampal lesion, ST and SNpc. Results and Conclusions Hyangsayangwi-tang (HY) prevents MPTP-induced loss of serotonin, hippocampus and TH-ir cell.

• 스마트미디어저널
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• 제11권5호
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• pp.26-37
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• 2022

본 논문에서는 시설재배 작물 중 딸기의 초기 병해를 방제하고자 이미지를 자동으로 취득하고, EfficientNet 모델을 활용해 병해를 분석하여 농민에게 병해 여부를 알려주고, 전문가를 통한 병해 진단 서비스를 제안한다. 딸기 생육단계의 이미지를 취득하고, 학습된 EfficientNet 모델을 활용해 병해 진단 분석결과를 농민의 애플리케이션으로 전송 후 전문가의 피드백을 신속하게 받을 수 있다. 데이터 세트로는 실제 시설재배를 운영하는 농민을 섭외하여 시스템을 이용해 이미지를 취득하였고, 핸드폰으로 촬영한 이미지의 초안을 활용하여 데이터가 부족한 문제를 해결했다. 실험 결과 EfficientNet B0부터 B7까지의 정확도는 유사하여 추론 속도가 가장 빠른 B0를 채택했다. 성능향상을 위해 ImageNet으로 사전학습 된 모델을 사용해 Fine-tuning 했고, 100 Epoch부터 급격한 성능향상을 확인했다. 제안하는 서비스는 초기 병해를 빠르게 탐지하여 생산량을 증대시킬 것으로 기대한다.

• Applied Microscopy
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• 제38권2호
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• pp.81-87
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• 2008

알칼리환원수 (Alkaline Reduced Water: ARW)는 아시아를 비롯한 여러 나라에서 음용수로 사용되고 있으며 항산화효과와 장내이상발효 개선효과 등을 중심으로 안전성과 유효성의 검증이 이루어지고 있다. 본 연구에서는 알칼리환원수가 급성 염증성대 장질환 (Inflammatory Bowel Disease: IBD) 동물모델에 미치는 영향을 알아보기 위해, ARW를 2주 동안 급이 시킨 후 4% DDS 로 염증성 대장염을 유발시키고 대장의 길이와 조직병리학적 변화를 관찰하였다. 그 결과 대장의 길이와 육안적, 현미경적 질환점수의 수치가 대조군과 비교하여 유의한 차이를 나타내지 않았다. 이 결과는 DSS로 유도된 급성 염증성장질환 모델에서는 2주 동안의 ARW 급이가 개선효과를 나타내지 않았음을 말해준다. 그러나 ARW가 장내환경을 개선시키고 위장관의 질환을 개선시키는 효과가 있음을 고려해볼 때 급성 IBD 동물모델이 ARW의 효과를 증명 하기에 적합하지 않았거나 MARW의 급이기간이 장내환경을 개선할 만큼 충분히 길지 않았을 가능성을 추측해 볼 수 있다. 알칼리환원수의 장내이상발효 개선효과는 확인되고 있으나 그 기전은 아직까지 구체적으로 확인되어지지 않고 있다. 이번 연구결과에 의하면 인위적으로 유발시킨 급성 염증성 동물모델에서 ARW가 유의한 영향을 미치지 않았지만, 다른 장질환 모델을 이용한 효과 검증과 ARW의 장기 급이에 따른 효과, 그리고 ARW가 장내환경에 미치는 작용기전에 대한 연구가 더 깊이 있게 이루어져야 할 것으로 사료된다.

• 대한약학회:학술대회논문집
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• 대한약학회 2002년도 Proceedings of the Convention of the Pharmaceutical Society of Korea Vol.2
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• pp.220-222
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• 2002

The concentration effect relationship (pharmacokinetic pharmacodynamic model, PKPD) of drugs used for Parkinson's disease is complex. The benefits and adverse effects of drug treatment have to be considered in terms of short term and long term effects. Acute effects, observed over hours and days, reflect symptomatic benefit while chronic effects, observed over months and years, also reveal influences on the progress of the disease. (omitted)

• International Journal of Industrial Entomology and Biomaterials
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• 제6권1호
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• pp.1-9
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• 2003

The silkworm, Bombyx mori, is prone to infection of various pathogenic organisms. Pebrine, one of the deadliest disease of silkworm caused by highly virulent parasitic microsporidian, Nosema bombycis has been understood since long. Infections of the disease range from chronic to highly virulent and can result in complete loss to the sericulture industry. Several strains and species of microsporidians have since been isolated from the infected silkworms; the disease is becoming increasingly more and more complex. Epizootiology, development of immunodiagnostic kit, use of chemotherapy and thermotherapy techniques has been addressed for identification and control of the disease. A technique of delayed mother moth examination, which plays a decisive role in the detection of the disease and harvestation of stable cocoon crop, has been described. An attempt has been made to review briefly the literature available on various aspects of the pebrine disease in order to develop efficient model(s) for the prevention and control of the disease and to suggest future avenues of investigation in the field of pebrine disease management.

• Journal of information and communication convergence engineering
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• 제20권1호
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• pp.31-40
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• 2022

Coronary heart disease (CHD) is a comorbidity of COVID-19; therefore, routine early diagnosis is crucial. A large number of examination attributes in the context of diagnosing CHD is a distinct obstacle during the pandemic when the number of health service users is significant. The development of a precise machine learning model for diagnosis with a minimum number of examination attributes can allow examinations and healthcare actions to be undertaken quickly. This study proposes a CHD diagnosis model based on feature selection, data balancing, and ensemble-based classification methods. In the feature selection stage, a hybrid SVM-GA combined with fast correlation-based filter (FCBF) is used. The proposed system achieved an accuracy of 94.60% and area under the curve (AUC) of 97.5% when tested on the z-Alizadeh Sani dataset and used only 8 of 54 inspection attributes. In terms of performance, the proposed model can be placed in the very good category.

• 스마트미디어저널
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• 제12권7호
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• pp.52-58
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• 2023

Alzheimer's disease is one type of dementia, the symptoms can be treated by detecting the disease at its early stages. Recently, many computer-aided diagnosis using magnetic resonance image(MRI) have shown a good results in the classification of AD. Taken these MRI images and feed to Free surfer software to extra the features. In consideration, using T1-weighted images and classifying using the convolution neural network (CNN) model are proposed. In this paper, taking the subjects from ADNI of subcortical and cortical features of 190 subjects. Consider the study to reduce the complexity of the model by using the single layer in the Res-Net, VGG, and Alex Net. Multi-class classification is used to classify four different stages, CN, EMCI, LMCI, AD. The following experiment shows for respective classification Res-Net, VGG, and Alex Net with the best accuracy with VGG at 96%, Res-Net, GoogLeNet and Alex Net at 91%, 93% and 89% respectively.

• Journal of applied mathematics & informatics
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• 제41권1호
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• pp.107-121
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• 2023

An epidemic infectious disease model consists of six compartments viz. Susceptible, Exposed, Infected, Quarantine, Recovered, and Virus with nonlinear saturation incidence rate is proposed to know the viral disease dynamics. There exist two biological equilibrium points for the model system. The system's local and global stability is done through Lyapunov's direct method about equilibrium points. The sensitivity analysis has been performed for the basic reproduction number and equilibrium points through the normalized forward sensitivity index. Sensitivity analysis shows that virus growth and quarantine rates are more sensitive parameters. In support of mathematical conclusions, numerical experimentation has been shown.

• Journal of information and communication convergence engineering
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• 제21권2호
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• pp.130-138
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• 2023

The selection of the correct examination variables for diagnosing heart disease provides many benefits, including faster diagnosis and lower cost of examination. The selection of inspection variables can be performed by referring to the data of previous examination results so that future investigations can be carried out by referring to these selected variables. This paper proposes a model for selecting examination variables using an Artificial Bee Swarm Optimization method by considering the variables of accuracy and cost of inspection. The proposed feature selection model was evaluated using the performance parameters of accuracy, area under curve (AUC), number of variables, and inspection cost. The test results show that the proposed model can produce 24 examination variables and provide 95.16% accuracy and 97.61% AUC. These results indicate a significant decrease in the number of inspection variables and inspection costs while maintaining performance in the excellent category.

• ETRI Journal
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• 제46권3호
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• pp.513-525
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• 2024

Deep neural networks trained on labeled medical data face major challenges owing to the economic costs of data acquisition through expensive medical imaging devices, expert labor for data annotation, and large datasets to achieve optimal model performance. The heterogeneity of diseases, such as Alzheimer's disease, further complicates deep learning because the test cases may substantially differ from the training data, possibly increasing the rate of false positives. We propose a reconstruction-based self-supervised anomaly detection model to overcome these challenges. It has a dual-subnetwork encoder that enhances feature encoding augmented by skip connections to the decoder for improving the gradient flow. The novel encoder captures local and global features to improve image reconstruction. In addition, we introduce an entropy-based image conversion method. Extensive evaluations show that the proposed model outperforms benchmark models in anomaly detection and classification using an encoder. The supervised and unsupervised models show improved performances when trained with data preprocessed using the proposed image conversion method.