• Microbiology and Biotechnology Letters
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• v.45 no.4
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• pp.358-364
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• 2017

Asymmetric PCR preferentially amplifies one DNA strand for use in DNA hybridization studies. Linear-After-The-Exponential-PCR (LATE-PCR) is an advanced asymmetric PCR method which uses innovatively designed primers at different concentrations. This study aimed to optimise LATE-PCR parameters to produce single-stranded DNA of Candida spp. and Aspergillus spp. for detection via probe hybridisation. The internal transcribed spacer (ITS) region was used to design limiting primer and excess primer for LATE-PCR. Primer annealing and melting temperature, difference of melting temperature between limiting and excess primer and concentration of primers were optimized. In order to confirm the presence of single-stranded DNA, the LATE-PCR product was hybridised with digoxigenin labeled complementary oligonucleotide probe specific for each fungal genus and detected using anti-digoxigenin antibody by dot blotting. Important parameters that determine the production of single-stranded DNA in a LATE-PCR reaction are difference of melting temperature between the limiting and excess primer of at least $5^{\circ}C$ and primer concentration ratio of excess primer to limiting primer at 20:1. LATE-PCR products of Candida albicans, Candida parapsilosis, Candida tropicalis and Aspergillus terreus at up to 1:100 dilution and after 1 h hybridization time, successfully hybridised to respective oligonucleotide probes with no cross reactivity observed between each fungal genus probe and non-target products. For Aspergillus fumigatus, LATE-PCR products were detected at 1:10 dilution and after overnight hybridisation. These results indicate high detection sensitivity for single-stranded DNA produced by LATE-PCR. In conclusion, this advancement of PCR may be utilised to detect fungal pathogens which can aid the diagnosis of invasive fungal disease.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.2
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• pp.513-517
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• 2015

Background: Growing evidence suggests that the members of the ubiquitin-proteasome system (UPS) are important for tumorigenesis. HERC4, one component, is a recently identified ubiqutin ligase. However, the expression level and function role of HERC4 in lung cancer remain unknown. Our objective was to investigate any correlation between HERC4 and development of lung cancer and its clinical significance. Materials and Methods: To determine HERC4 expression in lung cancer, an immunohistochemistry analysis of a tissue microarray containing samples of 10 lung normal tissues, 15 pulmonary neuroendocrine carcinomas, 45 squamous epithelial cancers and 50 adenocarcinomas was conducted. Receiver operating characteristic (ROC) curve analysis was applied to obtain a cut-off point of 52.5%, above which the expression of HERC4 was regarded as "positive". Results: On the basis of ROC curve analysis, positive expression of HERC4 was detected in 0/10 (0.0%) of lung normal tissues, in 4/15 (26.7%) of pulmonary neuroendocrine carcinomas, in 13/45 (28.9%) of squamous epithelial cancers and in 19/50 (38.0%) of adenocarcinomas. It showed that lung tumors expressed more HERC4 protein than adjacent normal tissues (${\chi}^2$=4.675, p=0.031). Furthermore, HERC4 positive expression had positive correlation with pT status (${\chi}^2$=44.894, p=0.000), pN status (${\chi}^2$=43.628, p=0.000), histological grade (${\chi}^2$=7.083, p=0.029) and clinical stage (${\chi}^2$=72.484, p=0.000), but not age (${\chi}^2$=0.910, p=0.340). Conclusions: Our analysis suggested that HERC4 is likely to be a diagnostic biomarker for lung cancer.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.11a
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• pp.281-284
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute). The measurement parameters of Smart UAV propulsion system are gas generator rotational speed, power turbine rotational speed, exhaust gas temperature and torque. But two measurement such as compressor exit pressure and compressor turbine exit temperature were added because they were difficult each component diagnostics using the default measurement parameter. The performance parameters for the estimate of component performance degradation degree are flow capacities and efficiencies for compressor, compressor turbine and power turbine. Database for network learning and test was constructed using a gas turbine performance simulation program. From application results for diagnostics of the PW206C turboshaft engine using the learned networks, it was confirmed that the proposed diagnostics could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Journal of Life Science
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• v.19 no.3
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• pp.417-421
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• 2009

This report describes the normal radiographic and ultrasonographic morphological features of premature minipigs at 4, 8, 12 and 20 weeks of age. Radiographic examination is a simple diagnostic method used to identify the general morphologic state of major organs and their adjacent structures, and to assess the presence of abnormalities. The parameters for evaluation in plain abdominal radiographs are the degree of diffuse serosal margin details, extent of visualization of each organ, their size, shape, and contour, locations of main organs - such as the liver, stomach, spleen, kidneys, urinary bladder - the distribution of the intestines, and assessment of the retroperitoneal space and its contents. Ultrasonographic examinations are used to investigate their internal condition. The parameters for evaluation in abdominal ultrasonography are echogenicity, echotexture, and size and shape of the main organs such as the liver, spleen, kidney, urinary bladder and gastrointestinal tract. Minipigs had similar findings compared to dogs or cats.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.18
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• pp.7971-7975
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• 2014

Background: From our previous study, we established that cyclin A1 (CCNA1) promoter methylation is strongly correlated with multistep progression of HPV-associated cervical cancer, suggesting potential use as a diagnostic maker of disease. Objectives: The purpose of the present study was to assess the prevalence of CCNA1 promoter methylation in residual cervical cells isolated from liquid-based cytology that underwent hrHPV DNA screening for cervical cancer, and then to evaluate this marker for diagnostic accuracy using parameters like sensitivity, specificity, predictive values and likelihood ratio. Methods: In this retrospective study, histopathology was used as the gold standard method with specimens separated into the following groups: negative (n=31), low-grade squamous intraepithelial lesions (LSIL, n=34) and high-grade squamous intraepithelial lesions or worse (HSIL+, n=32). The hrHPV was detected by Hybrid Capture 2 (HC2) and CCNA1 promoter methylation was examined by CCNA1 duplex methylation specific PCR. Results: The results showed the frequencies of CCNA1 promoter methylation were 0%, 5.88% and 83.33%, while the percentages of hrHPV were 66.67%, 82.35% and 100% in the negative, LSIL and HSIL+ groups, respectively. Although hrHPV infection showed high frequency in all three groups, it could not differentiate between the different groups and grades of precancerous lesions. In contrast, CCNA1 promoter methylation clearly distinguished between negative/LSIL and HSIL+, with high levels of all statistic parameters. Conclusion: CCNA1 promoter methylation is a potential marker for distinguishing between histologic negative/LSIL and HSIL+using cervical cytology samples.

• Nuclear Engineering and Technology
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• v.28 no.1
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• pp.36-43
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• 1996

In this work, a PC-based thermal performance monitoring system is developed for the nuclear power plants. The system performs real-time thermal performance monitoring and diagnosis during plant operation. Specifically, a prototype for the KORI-2 nuclear power unit is developed and examined in this work. The analysis and the fault identification of the thermal cycle of a nuclear power plant is very difficult because the system structure is highly complex and the components are very much inter-related. In this study, some major diagnostic performance parameters are selected in order to represent the thermal cycle effectively and to reduce the computing time. The Fuzzy ARTMAP, a self-organizing neural network, is used to recognize the characteristic pattern change of the performance parameters in abnormal situation. By examination, this algorithm is shown to be able to detect abnormality and to identify the fault component or the change of system operation condition successfully. For the convenience of operators, a graphical user interface is also constructed in this work.

• Proceedings of the Korea Association of Information Systems Conference
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• 2000.11a
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• pp.1.2-24
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• 2000

The field of neural networks has been a recent surge in activity as a result of progress in developments of efficient training algorithms. For this reason, and coupled with the widespread availability of powerful personal computer hardware for running simulations of networks, there is increasing focus on the potential benefits this field can offer. The neural network may be viewed as an advanced pattern recognition technique and can be applied in many areas such as financial time series forecasting, medical diagnostic expert system and etc.. The intention of this study is to build and implement one simple artificial neural networks hereinafter called ANN. For this purpose, some literature survey was undertaken to understand the structures and algorithms of ANN theoretically. Based on the review of theories about ANN, the system adopted 3-layer back propagation algorithms as its learning algorithm to simulate one case of medical diagnostic model. The adopted ANN algorithm was performed in PC by using turbo PASCAL and many input parameters such as the numbers of layers, the numbers of nodes, the number of cycles for learning, learning rate and momentum term. The system output more or less successful results which nearly agree with goals we assumed. However, the system has some limitations such as the simplicity of the programming structure and the range of parameters it can dealing with. But, this study is useful for understanding general algorithms and applications of ANN system and can be expanded for further refinement for more complex ANN algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.956-962
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• 2022

Diffuse thyroid disease has ambiguous diagnostic criteria and many errors occur according to the subjective diagnosis of skilled practitioners. If image processing technology is applied to ultrasound images, quantitative data is extracted, and applied to a computer auxiliary diagnostic system, more accurate and political diagnosis is possible. In this paper, 19 parameters were extracted by applying the Gray level co-occurrence matrix (GLCM) algorithm to ultrasound images classified as normal, mild, and moderate in patients with thyroid disease. Using these parameters, an artificial neural network (ANN) was applied to analyze diffuse thyroid ultrasound images. The final classification rate using ANN was 96.9%. Using the results of the study, it is expected that errors caused by visual reading in the diagnosis of thyroid diseases can be reduced and used as a secondary means of diagnosing diffuse thyroid diseases.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.17-25
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• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.

• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.732-739
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• 2009

Flow phantom with stenosis was manufactured using an auto-injector to obtain angiostenotic flow information and quality assurance (QA) for ultrasound diagnostic instrumentation. Effectiveness of manufactured flow phantom with stenosis was investigated with power Doppler that was known to have diagnostic efficiency for angiostenosis. The flow phantom with stenosis was manufactured to 70% stenosis with 8 mm and 2.4 mm silicon tube, and silicone tube was covered with gelatin that has acoustic characteristics similar to soft tissue. When the linear transducer was used for measurement, the estimated diameter of normal vessel was measured lower than that of normal value, and the estimated diameter of stenosed vessel was measured higher than that of normal value. The measured parameters were not affected except for the radical conditions such as gain of 60%, PRF of 3000 Hz, use of maximal filter or angle. In addition, when the convex transducer was used for measurement, measurement parameters were affected by gain, PRF, filter, and angle. Therefore it is expected that flow phantom with stenosis manufactured with an auto-injector will be utilized effectively for QA of angiostenotic diagnosis.