• The Korean Journal of Pain
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• v.29 no.1
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• pp.3-11
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• 2016

A nerve block is an effective tool for diagnostic and therapeutic methods. If a diagnostic nerve block is successful for pain relief and the subsequent therapeutic nerve block is effective for only a limited duration, the next step that should be considered is a nerve ablation or modulation. The nerve ablation causes iatrogenic neural degeneration aiming only for sensory or sympathetic denervation without motor deficits. Nerve ablation produces the interruption of axonal continuity, degeneration of nerve fibers distal to the lesion (Wallerian degeneration), and the eventual death of axotomized neurons. The nerve ablation methods currently available for resection/removal of innervation are performed by either chemical or thermal ablation. Meanwhile, the nerve modulation method for interruption of innervation is performed using an electromagnetic field of pulsed radiofrequency. According to Sunderland's classification, it is first and foremost suggested that current neural ablations produce third degree peripheral nerve injury (PNI) to the myelin, axon, and endoneurium without any disruption of the fascicular arrangement, perineurium, and epineurium. The merit of Sunderland's third degree PNI is to produce a reversible injury. However, its shortcoming is the recurrence of pain and the necessity of repeated ablative procedures. The molecular mechanisms related to axonal regeneration after injury include cross-talk between axons and glial cells, neurotrophic factors, extracellular matrix molecules, and their receptors. It is essential to establish a safe, long-standing denervation method without any complications in future practices based on the mechanisms of nerve degeneration as well as following regeneration.

• Journal of Periodontal and Implant Science
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• v.48 no.2
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• pp.114-123
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• 2018

Purpose: The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Methods: Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. Results: The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. Conclusions: We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

• Toxicological Research
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• v.12 no.2
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• pp.213-221
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• 1996

In order to develop a suitable secondary renal disease model and diagnostic markers of renal disease in the rat, the change of PIIIP (aminoterminal procollagen III peptide) in serum and hydroxyproline levels in the renal tissue that reflect the synthesis of extracellular matrix (ECM) during development of experimental renal diseases were observed. Two types of experimental primary diseases, diabetes mellitus administrated by streptozotocin (STZ, 75 mg/kg, i.p.) and liver cirrhosis produced by bile duct ligation/scission (BDL/s) operation, were induced. The hydroxyproline level increased according to the high PIIIP and NCl(carboxyterminal procollagen IV peptide) in Western blot analysis as early as 1 week in the STZ treated-rat kidney. Increased renal ECM was observed at 15 weeks in STZ and BDL/s model under the microscopic examination. High PAS positive reaction was found in capillary basement membrane in STZ treated-rats and mesangium in BDL/s operated rats at this time, showing the histological characteristics of diabetic nephropathy and cirrhotic glomerulonephritis in human, respectively. Such secondary renal failure were supported by additional tests including urinalysis and renal function test. The serum PIIIP detected by ELISA was a useful parameter to estimate synthesis rate of renal ECM during development of renal disease without extrarenal fibrosis i.e. liver cirrhosis in rats. This study is proposed that STZ treatment or BDL/s operation may be a suitable experimental animal model for the induction and development of chronic secondary renal diseases. Morover, it was found that hydroxyproline level in renal tissues was a good parameter of the change of renal ECM at the early stage of the diseases without apparent histological changes. Especially, serum PIIIP could be a choice as a diagnostic or prognostic marker during the development of renal diseases in rats.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.277-287
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• 2021

The purpose of this study is to establish a model that can quantitatively diagnose personal color. Representative color systems for personal colors have limitations in that it oversimplify personal color diagnosis types or it is difficult to distinguish objective differences between diagnosis types. To develop a brand new color system that enhances this, a PCCS color system capable of logical color was introduced and reclassified based on the four main properties of color. Twenty diagnostic types, which are more diverse than the existing color system were proposed and a quantitative method was used to evaluate the degree of harmony with a subject to find an optimized type of subject. The experimenter's individual competency and subjective intervention were minimized by devising a matrix in which a type suitable for the subject is derived when the coded evaluation result is substituted. Finally a quantitative diagnosis model of personal color consisting of three stages: property diagnosis, coding, and seasonal diagnosis was constructed. It can be seen that this will give diversity, reliability, and accuracy to the existing diagnostic methods.

• 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.

• Journal of the Korean School Mathematics Society
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• v.13 no.1
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• pp.125-141
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• 2010

The aim of this paper is to develop a web-based Virtual Learning System for discrete mathematics learning using CAS (Computer Algebra System), The system contains a series of contents that are common between secondary und university curriculum in discrete mathematics such as sets, relations, matrices, graphs etc. We designed and developed web-based virtual learning contents contained in the proposed system based on Mathematia, webMathematica and phpMath taking advantages of rapid computation and visualization. The virtual learning system for discrete math provides movie lectures and 'practice mode' authored with phpMath in order to enhance conceptual understanding of each movie lesson. In particular, matrix learning is facilitated with conceptual diagram that provides interactive quizzes. Once the quiz results are submitted, Bayesian inference network diagnoses strong and weak parts of learning nodes for generating diagnostic reports to facilitate personalized learning. As part of formative evaluation, the overall responses were collected for future revision of the system with 10 university students.

• Parasites, Hosts and Diseases
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• v.55 no.2
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• pp.143-148
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• 2017

Toxoplasma gondii infections occur throughout the world, and efforts are needed to develop various vaccine candidates expressing recombinant protein antigens. In this study, influenza matrix protein (M1) virus-like particles (VLPs) consisting of T. gondii rhoptry antigen 4 (ROP4 protein) were generated using baculovirus (rBV) expression system. Recombinant ROP4 protein with influenza M1 were cloned and expressed in rBV. SF9 insect cells were coinfected with recombinant rBVs expressing T. gondii ROP4 and influenza M1. As the results, influenza M1 VLPs showed spherical shapes, and T. gondii ROP4 protein exhibited as spikes on VLP surface under transmission electron microscopy (TEM). The M1 VLPs resemble virions in morphology and size. We found that M1 VLPs reacted with antibody from T. gondii-infected mice by western blot and ELISA. This study demonstrated that T. gondii ROP4 protein can be expressed on the surface of influenza M1 VLPs and the M1 VLPs containing T. gondii ROP4 reacted with T. gondii-infected sera, indicating the possibility that M1 VLPs could be used as a coating antigen for diagnostic and/or vaccine candidate against T. gondii infection.

• Journal of Korean Neurosurgical Society
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• v.61 no.5
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• pp.548-558
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• 2018

Objective : Diagnosing acute cerebral infarction is crucial in determining prognosis of stroke patients. Although many serologic tests for prompt diagnosis are available, the clinical application of serologic tests is currently limited. We investigated whether $S100{\beta}$, matrix metalloproteinase-9 (MMP-9), D-dimer, and heat shock protein 70 (HSP70) can be used as biomarkers for acute cerebral infarction. Methods : Focal cerebral ischemia was induced using the modified intraluminal filament technique. Mice were randomly assigned to 30-minute occlusion (n=10), 60-minute occlusion (n=10), or sham (n=5) groups. Four hours later, neurological deficits were evaluated and blood samples were obtained. Infarction volumes were calculated and plasma $S100{\beta}$, MMP-9, D-dimer, and HSP70 levels were measured using enzyme-linked immunosorbent assay. Results : The average infarction volume was $12.32{\pm}2.31mm^3$ and $46.9{\pm}7.43mm^3$ in the 30- and 60-minute groups, respectively. The mean neurological score in the two ischemic groups was $1.6{\pm}0.55$ and $3.2{\pm}0.70$, respectively. $S100{\beta}$, MMP-9, and HSP70 expressions significantly increased after 4 hours of ischemia (p=0.001). Furthermore, $S100{\beta}$ and MMP-9 expressions correlated with infarction volumes (p<0.001) and neurological deficits (p<0.001). There was no significant difference in D-dimer expression between groups (p=0.843). The area under the receiver operating characteristic curve (AUC) showed high sensitivity and specificity for MMP-9, HSP70 (AUC=1), and $S100{\beta}$ (AUC=0.98). Conclusion : $S100{\beta}$, MMP-9, and HSP70 can complement current diagnostic tools to assess cerebral infarction, suggesting their use as potential biomarkers for acute cerebral infarction.

• Imaging Science in Dentistry
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• v.53 no.1
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• pp.43-51
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• 2023

Purpose: This study aimed to assess texture analysis(TA) of cone-beam computed tomography (CBCT) images as a quantitative tool for the differential diagnosis of odontogenic and non-odontogenic maxillary sinusitis(OS and NOS, respectively). Materials and Methods: CBCT images of 40 patients diagnosed with OS (N=20) and NOS (N=20) were evaluated. The gray level co-occurrence (GLCM) matrix parameters, and gray level run length matrix texture (GLRLM) parameters were extracted using manually placed regions of interest on lesion images. Seven texture parameters were calculated using GLCM and 4 parameters using GLRLM. The Mann-Whitney test was used for comparisons between the groups, and the Levene test was performed to confirm the homogeneity of variance (α=5%). Results: The results showed statistically significant differences(P<0.05) between the OS and NOS patients regarding 3 TA parameters. NOS patients presented higher values for contrast, while OS patients presented higher values for correlation and inverse difference moment. Greater textural homogeneity was observed in the OS patients than in the NOS patients, with statistically significant differences in standard deviations between the groups for correlation, sum of squares, sum of entropy, and entropy. Conclusion: TA enabled quantitative differentiation between OS and NOS on CBCT images by using the parameters of contrast, correlation, and inverse difference moment.