• East Asian mathematical journal
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• v.38 no.3
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• pp.357-363
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• 2022

Immunological imbalance eventually results in the development of various diseases. A typical example is an imbalance of cytokines with immunomodulatory abilities. In this paper, we propose a two-variable delay model to anti-pro-inflammatory cytokine therapy for autoimmune diseases, which are caused by an imbalance between the pro and anti-inflammatory cytokines. The interaction between pro- and anti-inflammatory cytokines were modeled mathematically to investigate the relevance of cytokines in disease processes. The delay time was estimated to maintain the stability of a biologically important steady state. In particular, the effects of delay with anti-pro-inflammatory cytokines therapy in autoinflammatory diseases were studied.

• The Korean Journal of Food And Nutrition
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• v.23 no.4
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• pp.649-658
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• 2010

This study was performed to develop a nutrition education program model for foreign worker patients. Questionnaire and interview were carried out for collecting quantitative and qualitative information from subjects, respectively. All subjects were foreign worker patients who could speak Korean, composed of 75 Chinese, 4 Mongolians and 1 American, aged from 22 to 73 years old. Among the subjects, 36 subjects had gastrointestinal disease(GD), 16 had coronary heart disease(CHD), 6 had diabetes, 6 had liver disease(LD) and the others had various different diseases. List of recommended and restricted foods for foreign workers to prevent GD and CHD were obtained from interviews with the subjects. A nutrition education program model for foreign worker patients having GD and CHD were developed, and small group education method was recommended. The contents of the program include cause and common symptom and basic nutrition care for the patients, choice of foods and cooking methods, behavioral modification, importance of medication and list of foods recommended and restricted for the patients.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.216-226
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• 2020

In this paper, a stacked convolutional autoencoder model is proposed in order to classify Alzheimer's disease with high accuracy in PET/CT images. The proposed model makes use of the latent space representation - which is also called the bottleneck, of the encoder-decoder architecture: The input image is sent through the pipeline and the encoder part, using stacked convolutional filters, extracts the most useful information. This information is in the bottleneck, which then uses Softmax classification operation to classify between Alzheimer's disease, Mild Cognitive Impairment, and Normal Control. Using the data from Dong-A University, the model performs classification in detecting Alzheimer's disease up to 98.54% accuracy.

• ETRI Journal
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• v.46 no.1
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• pp.96-105
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• 2024

We propose a method to automatically predict Alzheimer's disease from speech data using the ChatGPT large language model. Alzheimer's disease patients often exhibit distinctive characteristics when describing images, such as difficulties in recalling words, grammar errors, repetitive language, and incoherent narratives. For prediction, we initially employ a speech recognition system to transcribe participants' speech into text. We then gather opinions by inputting the transcribed text into ChatGPT as well as a prompt designed to solicit fluency evaluations. Subsequently, we extract embeddings from the speech, text, and opinions by the pretrained models. Finally, we use a classifier consisting of transformer blocks and linear layers to identify participants with this type of dementia. Experiments are conducted using the extensively used ADReSSo dataset. The results yield a maximum accuracy of 87.3% when speech, text, and opinions are used in conjunction. This finding suggests the potential of leveraging evaluation feedback from language models to address challenges in Alzheimer's disease recognition.

• Proceedings of the Korean Society of Veterinary Clinics Conference
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• 2007.10a
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• pp.624-624
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• 2007

• Journal of applied mathematics & informatics
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• v.28 no.5_6
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• pp.1073-1088
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• 2010

In this article we want to formulate a disease transmission model, MSEIR model, for a population with individuals travelling between patches i and i + 1 and we derive an explicit formula for the basic reproductive number, $R_0$, employing the spectral radius of the next generation operator. Also, in this article we show that a system of ordinary differential equations for this model has a unique disease-free equilibrium and it is locally asymptotically stable if $R_0$ < 1 and unstable if $R_0$ > 1.

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.845-852
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• 2010

The outcomes of counts commonly occur in the area of disease mapping for mortality rates or disease rates. A Poisson distribution is usually assumed as a model of disease rates in conjunction with a gamma prior. The small area typically refers to a small geographical area or demographic group for which very little information is available from the sample surveys. Under this situation the model-based estimation is very popular, in which the auxiliary variables from various administrative sources are used. The empirical Bayes estimator under Poissongamma model has been considered with its accuracy measures. An accuracy measure using a bootstrap samples adjust the underestimation incurred by the posterior variance as an estimator of true mean squared error. We explain the suggested method through a practical dataset of hitters in baseball games. We also perform a Monte Carlo study to compare the accuracy measures of mean squared error.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.1-12
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• 2022

The current progression in the Internet of Things (IoT) and Machine Learning (ML) based technologies converted the traditional healthcare system into a smart healthcare system. The incorporation of IoT and ML has changed the way of treating patients and offers lots of opportunities in the healthcare domain. In this view, this research article presents a new IoT and ML-based disease diagnosis model for the diagnosis of different diseases. In the proposed model, vital signs are collected via IoT-based smart medical devices, and the analysis is done by using different data mining techniques for detecting the possibility of risk in people's health status. Recommendations are made based on the results generated by different data mining techniques, for high-risk patients, an emergency alert will be generated to healthcare service providers and family members. Implementation of this model is done on Anaconda Jupyter notebook by using different Python libraries in it. The result states that among all data mining techniques, SVM achieved the highest accuracy of 0.897 on the same dataset for classification of Parkinson's disease.

• CELLMED
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• v.13 no.14
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• pp.14.1-14.9
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• 2023

Administration of Scopolamine can be considered a psychopharmacological model of Alzheimer's disease (AD). We made an animal model of Alzheimer's disease (AD) by administering Scopolamine to Blab/c mice. In this study, we investigated the effects of Resplex Alpha on memory impairment and cognitive function in mice in a mouse animal model of Scopolamine-induced memory impairment. Through Y-mazed and passive avoidance behavioral assays, we observed that Resplex Alpha recovered Scopolamine-induced short-term memory and cognitive functions. The results of our study imply that Resplex Alpha may be beneficial in the prevention of Alzheimer's disease (AD).

• Journal of Korean society of Dental Hygiene
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• v.22 no.2
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• pp.107-115
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• 2022

Objectives: This study aimed to examine the effects of dietary protein intake and quality on periodontal disease in Korean adults. Methods: The data used for analysis were obtained from the 7th Korean National Health and Nutrition Examination Survey (2016-2018). Data were analyzed using chi-square and t-test. Additionally, multiple logistic regression analysis was performed to assess the association between dietary protein intake and quality and periodontal disease. Statistical significance level was set at <0.05. Results: Multiple logistic regression analysis of dietary protein intake and periodontal disease in the model adjusted for socioeconomic factors showed that were significantly related to the Q1 (odds ratio [OR]: 1.18, 95% confidence interval [CI]: 1.01-1.39). However, this correlation was not significant in the model in which all variables were corrected. Moreover, analysis of the dietary protein quality and periodontal disease in model 4, which was adjusted for socioeconomic variables, showed that were significantly related to the low score (OR: 1.13, 95% CI: 1.00-1.27). Conclusions: The results showed a significant association between periodontal disease and poor intake and quality of dietary protein in the Korean adult population.