• 수면정신생리
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• 제9권1호
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• pp.9-13
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• 2002

Alzheimer's disease (AD) is one of the most common and devastating dementing disorders of old age. Most AD patients showed significant alternation of sleep structure as well as cognitive deficit. Typical findings of sleep architecture in AD patients include lower sleep efficiency, higher stage 1 percentage, and greater frequency of arousals. The slowing of EEG activity is also noted. Abnormalities in REM sleep are of particular interest in AD because the cholinergic system is related to both REM sleep and AD. Several parameters representing REM sleep structure such as REM latency, the amount of REM sleep, and REM density are change in patients with AD. Especially, measurements of EEG slowing during tonic REM sleep can be used as an EEG marker for early detection of possible AD. In addition, a structural defect in the suprachiasmatic nucleus is suggested to cause various chronobiological alternations in AD. Most of alternations related to sleep make sleep disturbances common and disruptive symptoms of AD. In this article, the author reviewed the alternation of sleep structure and circadian rhythm in AD patients.

• 대한의용생체공학회:의공학회지
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• 제31권1호
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• pp.27-32
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• 2010

Noninvasive detection of patients with probable Alzheimer's disease (AD) is of great importance for assisting a medical doctor's decision for early treatment of AD patients. In the present study, we have extracted quantitative electroencephalogram (qEEG) variables, which can be potentially used to diagnose AD, from resting eyes-closed continuous EEGs of 22 AD patients and 27 age-matched normal control (NC) subjects. We have extracted qEEG variables from mean phase coherence (MPC) and EEG coherence, evaluated for all possible combinations of electrode pairs. Preliminary trials to discriminate the two groups with the extracted qEEG variables demonstrated that the use of MPC as a supplementary or alternative measure for the EEG coherence may enhance the accuracy of noninvasive diagnosis of AD.

• Clinical and Experimental Pediatrics
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• 제62권8호
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• pp.292-296
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• 2019

Epidemiologic studies of Kawasaki disease (KD) have shown a new pattern or change of its occurrence suggestive of its pathophysiology or risk factors from the first patient with KD reported in 1961. The incidence of KD in Northeast Asian countries including Japan, South Korea, China, and Taiwan is 10-30 times higher than that in the United States and Europe. Knowing the true epidemiology of KD in each country and the availability of publications of KD epidemiology also could benefit general health care providers and general population. This would enable the early detection and treatment of KD, ultimately reducing the incidence of coronary artery complications and mortality. Therefore, efforts to investigate the true epidemiology of KD should be continued in every country using a questionnaire survey, National Health Insurance system data, or combined methods depending on each country's medical environment to ensure high-quality care of patients with KD.

• Journal of Computing Science and Engineering
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• 제8권2호
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• pp.87-93
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• 2014

Coronary artery disease (CAD) is currently a prevalent disease from which many people suffer. Early detection and treatment could reduce the risk of heart attack. Currently, the golden standard for the diagnosis of CAD is angiography, which is an invasive procedure. In this article, we propose an algorithm that uses data mining techniques, a fuzzy expert system, and the imperialist competitive algorithm (ICA), to make CAD diagnosis by a non-invasive procedure. The ICA is used to adjust the fuzzy membership functions. The proposed method has been evaluated with the Cleveland and Hungarian datasets. The advantage of this method, compared with others, is the interpretability. The accuracy of the proposed method is 94.92% by 11 rules, and the average length of 4. To compare the colonial competitive algorithm with other metaheuristic algorithms, the proposed method has been implemented with the particle swarm optimization (PSO) algorithm. The results indicate that the colonial competition algorithm is more efficient than the PSO algorithm.

• Journal of Microbiology and Biotechnology
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• 제31권5호
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• pp.705-709
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• 2021

Porphyromonas gingivalis (P. gingivalis) is a major bacterial pathogen that causes periodontitis, a chronic inflammatory disease of tissues around the teeth. Periodontitis is known to be related to other diseases, such as oral cancer, Alzheimer's disease, and rheumatism. Thus, a precise and sensitive test to detect P. gingivalis is necessary for the early diagnosis of periodontitis. The objective of this study was to optimize a rapid visual detection system for P. gingivalis. First, we performed a visual membrane immunoassay using 3,3',5,5'-tetramethylbenzidine (TMB; blue) and coating and detection antibodies that could bind to the host laboratory strain, ATCC 33277. Antibodies against the P. gingivalis surface adhesion molecules RgpB (arginine proteinase) and Kgp (lysine proteinase) were determined to be the most specific coating and detection antibodies, respectively. Using these two selected antibodies, the streptavidin-horseradish peroxidase (HRP) reaction was performed using a nitrocellulose membrane and visualized with a detection range of 10³-10⁵ bacterial cells/ml following incubation for 15 min. These selected conditions were applied to test other oral bacteria, and the results showed that P. gingivalis could be detected without cross-reactivity to other bacteria, including Streptococcus mutans and Escherichia fergusonii. Furthermore, three clinical strains of P. gingivalis, KCOM 2880, KCOM 2803, and KCOM 3190, were also recognized using this optimized enzyme immunoassay (EIA) system. To conclude, we established optimized conditions for P. gingivalis detection with specificity, accuracy, and sensitivity. These results could be utilized to manufacture economical and rapid detection kits for P. gingivalis.

• 대한유전성대사질환학회지
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• 제14권1호
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• pp.1-9
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• 2014

Many inborn errors of metabolism can be completely cured with early detection and early treatment. This is why neonatal screening on inborn errors of metabolism is implemented worldwide. In 1991, the Ministry of Health & Social affairs adopted a nationwide service program for neonatal screening of phenylketonuria, galactosemia, maple syrup urine disease, homocystinuria, histidinemia and congenital hypothyroidism for newborns delivered from low class pregnant women registered in health centers. Government decreased the test items from six to two, PKU and congenital hypothyroidism to increase test numbers with same budget from 1995. 78 laboratories wanted to participate for neonatal screening test in 1999. Government decided to screen six items of PKU, congenital hypothyroidism, maple syrup urine disease, homocystinuria, galactosemia and congenital adrenal hyperplasia from 2006. In 2014, thirteen laboratories are participating. Inter laboratory quality control was started 6 times a year from 1994. In case a patient with an inherited metabolic disease is diagnosed by screening of government program, special milk is provided at government's expense. According to the government project, from 1997 to 2013, 7,080,569 newborns were screened. 144 PKU, 2.451 congenital hypothyroidism were detected. So incidence of PKU is 1/49,170 and congenital hypothyroidism is 1/2,888. The cost benefit of performing screening procedures coupled with treatment has been estimated to be as high as 1.77 times in PKU, 11.11 times in congenital hypothyroidism than cost without screening. By January 2007, many European countries had expanded of their newborn screening programs by inclusion of Tandem mass spectrometry. We are trying to increase the budget to test all newborns for Tandem mass spectrometry from 2016. We are considering four to five central laboratories which cover all newborns and are equipped with tandem mass spectrometer & enzyme immunoassay for TSH, 17OHP & enzyme colorimetric assay for galactose. And I hope to expand test including Wilson disease screening test and lysosomal storage diseases.

• International Journal of Internet, Broadcasting and Communication
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• 제10권3호
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• pp.88-97
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• 2018

Chronic diseases management can be effectively achieved through early detection, continuous treatment, observation, and self-management, rather than a radar approach where patients are treated only when they visit a medical facility. However, previous studies have not been able to provide integrated chronic disease management services by considering generalized services such as hypertension and diabetes management, and difficult to expand and link to other services using only specific sensors or services. This paper proposes clinical rule flow model based on medical data analysis to provide personalized care for chronic disease management. Also, we implemented that as Rule-based Smart Healthcare System (RSHS). The proposed system executes chronic diseases management rules, manages events and delivers individualized knowledge information by user's request. The proposed system can be expanded into a variety of applications such as diet and exercise service in the future.

• 한국멀티미디어학회논문지
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• 제20권3호
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• pp.474-481
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• 2017

In Korea, foot-and-mouth disease has not been reported for several decades, but it began to develop again in 2000. For 2010~2011, when the worst occurred, 3.5 million animals were disposed of resulting in a loss of 2.8 trillion won. In order to prevent the harmful effects of foot-and-mouth disease, vaccination and housing management are being implemented. Despite these measures, foot-and-mouth disease is infected with air through the respiratory tract and accompanies fever after latency. Therefore, it is recognized that measuring and managing the body temperature of livestock at the early stage is the first step of managing this disease. In this paper, we propose a temperature monitoring system that can measure the body temperature by incorporating temperature sensor mounted in ear-tag of cattle and collect body temperature data of each individual cattle through BLE into the control server. The proposed body temperature monitoring system has various advantages such as easy installation without the help of livestock specialists and not damaging the organs of the livestock. So, it is possible to manage the abnormal symptom of cattle in real time and it is believed that the proposed monitoring system will revolutionize the prevention of foot-and-mouth disease.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권8호
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• pp.3312-3327
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• 2020

Plant diseases are a significant yield and quality constraint for farmers around the world due to their severe impact on agricultural productivity. Such losses can have a substantial impact on the economy which causes a reduction in farmer's income and higher prices for consumers. Further, it may also result in a severe shortage of food ensuing violent hunger and starvation, especially, in less-developed countries where access to disease prevention methods is limited. This research presents an investigation of Directional Local Quinary Patterns (DLQP) as a feature descriptor for plants leaf disease detection and Support Vector Machine (SVM) as a classifier. The DLQP as a feature descriptor is specifically the first time being used for disease detection in horticulture. DLQP provides directional edge information attending the reference pixel with its neighboring pixel value by involving computation of their grey-level difference based on quinary value (-2, -1, 0, 1, 2) in 0°, 45°, 90°, and 135° directions of selected window of plant leaf image. To assess the robustness of DLQP as a texture descriptor we used a research-oriented Plant Village dataset of Tomato plant (3,900 leaf images) comprising of 6 diseased classes, Potato plant (1,526 leaf images) and Apple plant (2,600 leaf images) comprising of 3 diseased classes. The accuracies of 95.6%, 96.2% and 97.8% for the above-mentioned crops, respectively, were achieved which are higher in comparison with classification on the same dataset using other standard feature descriptors like Local Binary Pattern (LBP) and Local Ternary Patterns (LTP). Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for plant disease phenotyping.

• Parasites, Hosts and Diseases
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• 제50권2호
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• pp.103-111
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• 2012

Intestinal giant-cystic disease (IGCD) of the Israel carp (Cyprinus carpio nudus) has been recognized as one of the most serious diseases afflicting inland farmed fish in the Republic of Korea, and Thelohanellus kitauei has been identified as the causative agent of the disease. Until now, studies concerning IGCD caused by T. kitauei in the Israel carp have been limited to morphological and histopathological examinations. However, these types of diagnostic examinations are relatively time-consuming, and the infection frequently cannot be detected in its early stages. In this study, we cloned the full-length 18S rRNA gene of T. kitauei isolated from diseased Israel carps, and carried out molecular identification by comparing the sequence with those of other myxosporeans. Moreover, conventional PCR and real-time quantitative PCR (qPCR) using oligonucleotide primers for the amplification of 18S rRNA gene fragment were established for further use as methods for rapid diagnosis of IGCD. Our results demonstrated that both the conventional PCR and real-time quantitative PCR systems applied herein are effective for rapid detection of T. kitauei spores in fish tissues and environmental water.