• Journal of Veterinary Science
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• v.21 no.2
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• pp.23.1-23.10
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• 2020

The identification of biomarkers that distinguish diseased from healthy individuals is of great interest in human and veterinary fields. In this research area, a metabolomic approach and its related statistical analyses can be useful for biomarker determination and allow non-invasive discrimination of healthy volunteers from breast cancer patients. In this study, we focused on the most common canine neoplasm, mammary gland tumor, and herein, we describe a simple method using ultra-high-performance liquid chromatography to determine the levels of tyrosine and its metabolites (epinephrine, 3,4-dihydroxy-L-phenylalanine, 3,4-dihydroxyphenylacetic acid, and vanillylmandelic acid), tryptophan and its metabolites (5-hydroxyindolacetic acid, indoxyl sulfate, serotonin, and kynurenic acid) in canine mammary cancer urine samples. Our results indicated significantly increased concentrations of three tryptophan metabolites, 5-hydroxyindolacetic acid (p < 0.001), serotonin, indoxyl sulfate (p < 0.01), and kynurenic acid (p < 0.05), and 2 tyrosine metabolites, 3,4-dihydroxy-L-phenylalanine (p < 0.001), and epinephrine (p < 0.05) in urine samples from the mammary gland tumor group compared to concentrations in urine samples from the healthy group. The results indicate that select urinary tyrosine and tryptophan metabolites may be useful as non-invasive diagnostic markers as well as in developing a therapeutic strategy for canine mammary gland tumors.

• Imaging Science in Dentistry
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• v.52 no.3
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• pp.283-288
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• 2022

Purpose: This study was conducted to measure the radiation exposure and image quality of various cone-beam computed tomography (CBCT) machines under common clinical conditions and to analyze the correlation between them. Materials and Methods: Seven CBCT machines used frequently in clinical practice were selected. Because each machine has various sizes of fields of view (FOVs), 1 large FOV and 1 small FOV were selected for each machine. Radiation exposure was measured using a dose-area product (DAP) meter. The quality of the CBCT images was analyzed using 8 image quality parameters obtained using a dental volume tomography phantom. For statistical analysis, regression analysis using a generalized linear model was used. Results: Polymethyl-methacrylate (PMMA) noise and modulation transfer function (MTF) 10% showed statistically significant correlations with DAP values, presenting positive and negative correlations, respectively (P<0.05). Image quality parameters other than PMMA noise and MTF 10% did not demonstrate statistically significant correlations with DAP values. Conclusion: As radiation exposure and image quality are not proportionally related in clinically used equipment, it is necessary to evaluate and monitor radiation exposure and image quality separately.

• International Journal of Industrial Entomology and Biomaterials
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• v.47 no.1
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• pp.1-11
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• 2023

The Indian golden muga silkworm, Antheraea assamensis Helfer is an economically important wild silkworm endemic to Northeastern part of India. In recent years, climate change has posed a threat to muga silk production due to the requirement that larvae be reared outdoors. Since the muga silkworm larvae are exposed to the vagaries of nature, the changing climate has increased the incidence of microbial diseases in the rearing fields. Accurate diagnosis of the disease causing pathogens and its associated epidemiology are prerequisites to manage the diseases in the rearing field. Although conventional microbial culturing methods are widely used to identify pathogenic bacteria, they would not provide meaningful information on a wide variety of silkworm pathogens. The information on use of molecular diagnostic tools in detection of microbial pathogens of wild silk moths is very limited. A wide range of molecular and immunodiagnostic techniques including denaturing gradient gel electrophoresis (DGGE), random amplified polymorphism (RAPD), 16S rRNA/ITSA gene sequencing, multiplex polymerase chain reaction (M-PCR), fluorescence in situ hybridization (FISH), immunofluorescence, and repetitive-element PCR (Rep-PCR), have been used for detecting and characterizing the pathogens of insects with economic significance. Nevertheless, the application of these molecular tools for detecting and typing entomopathogens in surveillance studies of muga silkworm rearing is very limited. Here, we discuss the possible application of these molecular techniques, their advantages and major limitations. These methods show promise in better management of diseases in muga ecosystem.

• Journal of the Korean Society of Radiology
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• v.81 no.6
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• pp.1305-1333
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• 2020

Deep learning has recently achieved remarkable results in the field of medical imaging. However, as a deep learning network becomes deeper to improve its performance, it becomes more difficult to interpret the processes within. This can especially be a critical problem in medical fields where diagnostic decisions are directly related to a patient's survival. In order to solve this, explainable artificial intelligence techniques are being widely studied, and an attention mechanism was developed as part of this approach. In this paper, attention techniques are divided into two types: post hoc attention, which aims to analyze a network that has already been trained, and trainable attention, which further improves network performance. Detailed comparisons of each method, examples of applications in medical imaging, and future perspectives will be covered.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.43-52
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• 2024

Medical imaginings assume a important part in the analysis of tumors and cerebrospinal fluid (CSF) leak. Magnetic resonance imaging (MRI) is an image segmentation technology, which shows an angular sectional perspective of the body which provides convenience to medical specialists to examine the patients. The images generated by MRI are detailed, which enable medical specialists to identify affected areas to help them diagnose disease. MRI imaging is usually a basic part of diagnostic and treatment. In this research, we propose new techniques using the 4D-MRI image segmentation process to detect the brain tumor in the skull. We identify the issues related to the quality of cerebrum disease images or CSF leakage (discover fluid inside the brain). The aim of this research is to construct a framework that can identify cancer-damaged areas to be isolated from non-tumor. We use 4D image light field segmentation, which is followed by MATLAB modeling techniques, and measure the size of brain-damaged cells deep inside CSF. Data is usually collected from the support vector machine (SVM) tool using MATLAB's included K-Nearest Neighbor (KNN) algorithm. We propose a 4D light field tool (LFT) modulation method that can be used for the light editing field application. Depending on the input of the user, an objective evaluation of each ray is evaluated using the KNN to maintain the 4D frequency (redundancy). These light fields' approaches can help increase the efficiency of device segmentation and light field composite pipeline editing, as they minimize boundary artefacts.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.451-459
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• 2024

Recently, the size of the online education service market has been growing, but the B2C platform education service market, where companies take the lead in creating content and consumers consume the content, has become widespread. This makes content creation rigid and may have limitations in creating at various levels. This Study newly establishes the concept and type of customer needs, wants, innovation, and platform business, and presents new start-up success factors in all fields of platform business start-up and a standard diagnostic process for the possibility of platform start-up success. Through the presented process, customers, core activities, and value proposition factors can be derived. Finally, a business model for starting a C2C platform for elementary, middle, and high school education is built centered on the business model canvas.

• Radiation Oncology Journal
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• v.23 no.3
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• pp.169-175
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• 2005

Purpose: Although computed tomography (CT) simulators are commonly used in radiation therapy department, many Institution still use conventional CT for treatments. In this study the setup errors that occur during simulation, CT scan (diagnostic CT scanner), and treatment were evaluated for the twenty one breast cancer patients. Materials and Methods: Errors were determined by calculating the differences in isocenter location, SSD, CLD, and locations of surgical clips implanted during surgery. The anatomic structures on simulation film and DRR image were compared to determine the movement of isocenter between simulation and CT scan. The isocetner point determined from the radio-opaque wires placed on patient's surface during CT scan was moved to new position if there was anatomic mismatch between the two images Results: In 7/21 patients, anatomic structures on DRR Image were different from the simulation Image thus new isocenter points were placed for treatment planning. The standard deviations of the diagnostic CT setup errors relative to the simulator setup in lateral, longitudinal, and anterior-posterior directions were 2.3, 1.6, and 1.6 mm, respectively. The average variation and standard deviation of SSD from AP field were 1.9 mm and 2.3 mm and from tangential fields were 2.8 mm and 3.7 mm. The variation of the CLD for the 21 patients ranged from 0 to 6 mm between simulation and DRR and 0 to 5 mm between simulation and treatment. The group systematic errors analyzed based on clip locations were 1.7 mm in lateral direction, 2.1 mm in AP direction, and 1.7 mm in SI direction. Conclusion: These results represent that there was no significant differences when SSD, CLD, clips' locations and isocenter locations were considered. Therefore, it is concluded that when a diagnostic CT scanner is used to acquire an image, the set-up variation is acceptable compared to using CT simulator for the treatment of breast cancer. However, the patient has to be positioned with care during CT scan in order to reduce the setup error between simulation and CT scan.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.3
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• pp.101-108
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• 2020

Since the performance of deep learning techniques has recently been proven in the field of image processing, there are many attempts to perform classification, analysis, and detection of images using such techniques in various fields. Among them, the expectation of medical image analysis software, which can serve as a medical diagnostic assistant, is increasing. In this study, we are attention to the capsule endoscope image, which has a large data set and takes a long time to judge. The purpose of this paper is to distinguish the gastrointestinal landmarks and to estimate the gastrointestinal transition location that are common to all patients in the judging of capsule endoscopy and take a lot of time. To do this, we designed CNN-based Classifier that can identify gastrointestinal landmarks, and used it to estimate the gastrointestinal transition location by filtering the results. Then, we estimate gastrointestinal transition location about seven of eight patients entered the suspected gastrointestinal transition area. In the case of change from the stomach to the small intestine(pylorus), and change from the small intestine to the large intestine(ileocecal valve), we can check all eight patients were found to be in the suspected gastrointestinal transition area. we can found suspected gastrointestinal transition area in the range of 100 frames, and if the reader plays images at 10 frames per second, the gastrointestinal transition could be found in 10 seconds.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.58 no.2
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• pp.142-148
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• 2013

Bacterial diseases in soybean are bacterial pustule by Xanthomonas axonopodis pv. glycines, wildfire by Pseudomonas syringae pv. tabaci, bacterial blight by Pseudomonas savastanoi pv. glycines and bacterial brown spot by Pseudomonas syringae pv. syringae in Korea. It is difficult to identify each disease by early symptoms in fields, because the initial symptoms of these diseases are very similar to each other. In this study, we developed multiplex PCR detection method for rapid and accurate diagnosis of bacterial diseases. The glycinecin A of X. axonopodis pv. glycines, the tabtoxin of P. syringae pv. tabaci, the coronatine of P. savastanoi pv. glycines and the syringopeptin of P. syringae pv. syringae have been reported previously. These bacteriocin or phytotoxin producing genes were targeted to design the specific diagnostic primers. The primer pairs for diagnosis of each bacterial diseases were selected without nonspecific reactions. The studies on simultaneous diagnosis method were also conducted with primarily selected 21 primers. As a result, we selected PCR primer sets for multiplex PCR. Sizes of the amplified PCR products using the multiplex PCR primer set consist of 280, 355, 563 and 815 bp, respectively. This multiplex PCR method provides a efficient, sensitive and rapid tool for the diagnosis of the bacterial diseases in soybean.

• Journal of Oriental Neuropsychiatry
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• v.28 no.3
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• pp.263-274
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• 2017

Objectives: To identify the current status of Korean medical practice pattern, diagnosis and treatment of dementia through recognition survey, and to use it as a preliminary data for various dementia research. Methods: Questionnaires were developed through expert meetings. The disease was defined as dementia and mild cognitive impairment, and areas were designated to practice pattern, diagnosis and treatment. From December 18, 2016-January 18, 2017, 221 respondents, including 36 neuropsychiatrists of Korean Medicine and 185 general physicians (including other medical specialists) were included. Results: 1. In both groups, the most commonly used KCD (Korean standard classification of disease and cause of death) were in the order of Unspecified Alzheimer's Dementia (F00.9), Mild Cognitive Impairment (F06.7), and Unspecified Dementia (F03). 2. The most commonly used pattern identification were zang-fu and qi-blood-yin-yang in both groups. 3. Diagnostic evaluation tools were mainly conducted by MMSE, radiologic examination, K-DRS, GDS and CDR in both groups. 4. Both groups reported using acupuncture and herbal medicine mainly. 5. In both groups, the acupuncture method was used extensively in the order of Body, Scalp, and Sa-Am. 6. Neuropsychiatrists used a variety of herbal medicines such as Wonjiseokchangpo-san (Yuanzhushichangpu-san), Yukmijihwang-tang (Liuweidihuang-tang), Palmijihwang-won (Baweidihuang-won), Sunghyangjungki-san (Xingxiang Zhengqi-san) and Ondam-tanggami (Wendan-tangjiawei). General physicians used a variety of herbal medicines such as Ondam-tanggami (Wendan-tangjiawei), Bojungikgi-tang (Buzhongyiqi-tang), Yukmijihwang-tang (Liuweidihuang-tang). 7. Neuropsychiatrists used a variety of Korean herbal preparation products (benefit and non-benefit) such as Ekgan-sangajinpibanha (Yigan-sanjiachenpibanxia), Yukmijihwang-tang (Liuweidihuang-tang), Jodeung-san (houteng-san), Palmijihwang-won (Baweidihuang-won). General physicians used a variety of Korean herbal preparation products such as Bojungikgi-tang (Buzhongyiqi-tang), Banhabaegchulcheonma-tang (banxiabaizhutianma-tang), Yukmijihwang-tang (Liuweidihuang-tang), Ekgan-sangajinpibanha (Yigan-sanjiachenpibanxia), Palmijihwang-won (Baweidihuang-won). Conclusions: By confirming awareness of Korean medical doctors treating dementia in clinical fields and understanding differences between neuropsychiatrists of Korean medicine and general physicians, it can be used to understand guideline users' needs and confirm clinical questions during development of future clinical practice guidelines for dementia.