• Biomolecules & Therapeutics
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• v.27 no.2
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• pp.168-177
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• 2019

Dysregulation of excitatory neurotransmission has been implicated in the pathogenesis of neuropsychiatric disorders. Pharmacological inhibition of N-methyl-D-aspartate (NMDA) receptors is widely used to model neurobehavioral pathologies and underlying mechanisms. There is ample evidence that overstimulation of NMDA-dependent neurotransmission may induce neurobehavioral abnormalities, such as repetitive behaviors and hypersensitization to nociception and cognitive disruption, pharmacological modeling using NMDA has been limited due to the induction of neurotoxicity and blood brain barrier breakdown, especially in young animals. In this study, we examined the effects of intraperitoneal NMDA-administration on nociceptive and repetitive behaviors in ICR mice. Intraperitoneal injection of NMDA induced repetitive grooming and tail biting/licking behaviors in a dose- and age-dependent manner. Nociceptive and repetitive behaviors were more prominent in juvenile mice than adult mice. We did not observe extensive blood brain barrier breakdown or neuronal cell death after peritoneal injection of NMDA, indicating limited neurotoxic effects despite a significant increase in NMDA concentration in the cerebrospinal fluid. These findings suggest that the observed behavioral changes were not mediated by general NMDA toxicity. In the hot plate test, we found that the latency of paw licking and jumping decreased in the NMDA-exposed mice especially in the 75 mg/kg group, suggesting increased nociceptive sensitivity in NMDA-treated animals. Repetitive behaviors and increased pain sensitivity are often comorbid in psychiatric disorders (e.g., autism spectrum disorder). Therefore, the behavioral characteristics of intraperitoneal NMDA-administered mice described herein may be valuable for studying the mechanisms underlying relevant disorders and screening candidate therapeutic molecules.

• Journal of Preventive Medicine and Public Health
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• v.54 no.1
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• pp.55-62
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• 2021

Objectives: After heart disease, brain stroke (BS) is the second most common cause of death worldwide, underscoring the importance of understanding preventable and treatable risk factors for the outcomes of BS. This study aimed to model the survival of patients with BS in the presence of competing risks. Methods: This longitudinal study was conducted on 332 patients with a definitive diagnosis of BS. Demographic characteristics and risk factors were collected by a validated checklist. Patients' mortality status was investigated by telephone follow-up to identify deaths that may be have been caused by stroke or other factors (heart disease, diabetes, high cholesterol, etc.). Data were analyzed by the Lunn-McNeil approach at alpha=0.1. Results: Older age at diagnosis (59-68 years: adjusted hazard ratio [aHR], 2.19; 90% confidence interval [CI], 1.38 to 3.48; 69-75 years: aHR, 5.04; 90% CI, 3.25 to 7.80; ≥76 years: aHR, 5.30; 90% CI, 3.40 to 8.44), having heart disease (aHR, 1.65; 90% CI, 1.23 to 2.23), oral contraceptive pill use (women only) (aHR, 0.44; 90% CI, 0.24 to 0.78) and ischemic stroke (aHR, 0.52; 90% CI, 0.36 to 0.74) were directly related to death from BS. Older age at diagnosis (59-68 years: aHR, 21.42; 90% CI, 3.52 to 130.39; 75-69 years: aHR, 16.48; 90% CI, 2.75 to 98.69; ≥76 years: aHR, 26.03; 90% CI, 4.06 to 166.93) and rural residence (aHR, 2.30; 90% CI, 1.15 to 4.60) were directly related to death from other causes. Significant risk factors were found for both causes of death. Conclusions: BS-specific and non-BS-specific mortality had different risk factors. These findings could be utilized to prescribe optimal and specific treatment.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.583-588
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• 2024

With the emergence of Brain-Computer Interface (BCI) technology, analyzing mirror neurons has become more feasible. However, evaluating the accuracy of BCI systems that rely on human thoughts poses challenges due to their qualitative nature. To harness the potential of BCI, we propose a new approach to measure accuracy based on the characteristics of mirror neurons in the human brain that are influenced by speech speed, depending on the ultimate goal of movement. In Chapter 2 of this paper, we introduce mirror neurons and provide an explanation of human posture estimation for mirror neurons. In Chapter 3, we present a powerful pose estimation method suitable for real-time dynamic environments using the technique of human posture estimation. Furthermore, we propose a method to analyze the accuracy of BCI using this robotic environment.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.139-155
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• 2024

This study performed a multi-classification task to classify mesial temporal lobe epilepsy with left hippocampal sclerosis patients (left mTLE), mesial temporal lobe epilepsy with right hippocampal sclerosis (right mTLE), and healthy controls (HC) using magnetoencephalography (MEG) data. We applied various artificial neural networks and compared the results. As a result of modeling with convolutional neural networks (CNN), recurrent neural networks (RNN), and graph neural networks (GNN), the average k-fold accuracy was excellent in the order of CNN-based model, GNN-based model, and RNN-based model. The wall time was excellent in the order of RNN-based model, GNN-based model, and CNN-based model. The graph neural network, which shows good figures in accuracy, performance, and time, and has excellent scalability of network data, is the most suitable model for brain research in the future.

• Journal of Food Hygiene and Safety
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• v.34 no.4
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• pp.374-379
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• 2019

This study developed predictive growth models of Salmonella enterica Serovar Typhimurium on lettuce washed with chlorine (100~300 ppm) and ultrasound (US, 37 kHz, 380 W) treatment and stored at different temperatures ($10{\sim}25^{\circ}C$) using a polynomial equation. The primary model of specific growth rate (SGR) and lag time (LT) showed a good fit ($R^2{\geq}0.92$) with a Gompertz equation. A secondary model was obtained using a quadratic polynomial equation. The appropriateness of the secondary SGR and LT model was verified by coefficient of determination ($R^2=0.98{\sim}0.99$ for internal validation, 0.97~0.98 for external validation), mean square error (MSE=-0.0071~0.0057 for internal validation, -0.0118~0.0176 for external validation), bias factor ($B_f=0.9918{\sim}1.0066$ for internal validation, 0.9865~1.0205 for external validation), and accuracy factor ($A_f=0.9935{\sim}1.0082$ for internal validation, 0.9799~1.0137 for external validation). The newly developed models for S. Typhimurium could be incorporated into a tertiary modeling program to predict the growth of S. Typhimurium as a function of combined chlorine and US during the storage. These new models may also be useful to predict potential S. Typhimurium growth on lettuce, which is important for food safety purposes during the overall supply chain of lettuce from farm to table. Finally, the models may offer reliable and useful information of growth kinetics for the quantification microbial risk assessment of S. Typhimurium on washed lettuce.

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1281-1289
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• 2022

Objective: Radiomic modeling using multiple regions of interest in MRI of the brain to diagnose juvenile myoclonic epilepsy (JME) has not yet been investigated. This study aimed to develop and validate radiomics prediction models to distinguish patients with JME from healthy controls (HCs), and to evaluate the feasibility of a radiomics approach using MRI for diagnosing JME. Materials and Methods: A total of 97 JME patients (25.6 ± 8.5 years; female, 45.5%) and 32 HCs (28.9 ± 11.4 years; female, 50.0%) were randomly split (7:3 ratio) into a training (n = 90) and a test set (n = 39) group. Radiomic features were extracted from 22 regions of interest in the brain using the T1-weighted MRI based on clinical evidence. Predictive models were trained using seven modeling methods, including a light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, with radiomics features in the training set. The performance of the models was validated and compared to the test set. The model with the highest area under the receiver operating curve (AUROC) was chosen, and important features in the model were identified. Results: The seven tested radiomics models, including light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, showed AUROC values of 0.817, 0.807, 0.783, 0.779, 0.767, 0.762, and 0.672, respectively. The light gradient boosting machine with the highest AUROC, albeit without statistically significant differences from the other models in pairwise comparisons, had accuracy, precision, recall, and F1 scores of 0.795, 0.818, 0.931, and 0.871, respectively. Radiomic features, including the putamen and ventral diencephalon, were ranked as the most important for suggesting JME. Conclusion: Radiomic models using MRI were able to differentiate JME from HCs.

• Molecules and Cells
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• v.38 no.9
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• pp.781-788
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• 2015

Mutations of MYO15A are generally known to cause severe to profound hearing loss throughout all frequencies. Here, we found two novel MYO15A mutations, c.3871C>T (p.L1291F) and c.5835T>G (p.Y1945X) in an affected individual carrying congenital profound sensorineural hearing loss (SNHL) through targeted resequencing of 134 known deafness genes. The variant, p.L1291F and p.Y1945X, resided in the myosin motor and IQ2 domains, respectively. The p.L1291F variant was predicted to affect the structure of the actin-binding site from three-dimensional protein modeling, thereby interfering with the correct interaction between actin and myosin. From the literature analysis, mutations in the N-terminal domain were more frequently associated with residual hearing at low frequencies than mutations in the other regions of this gene. Therefore we suggest a hypothetical genotype-phenotype correlation whereby MYO15A mutations that affect domains other than the N-terminal domain, lead to profound SNHL throughout all frequencies and mutations that affect the N-terminal domain, result in residual hearing at low frequencies. This genotype-phenotype correlation suggests that preservation of residual hearing during auditory rehabilitation like cochlear implantation should be intended for those who carry mutations in the N-terminal domain and that individuals with mutations elsewhere in MYO15A require early cochlear implantation to timely initiate speech development.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.16
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• pp.6781-6785
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• 2014

Purpose: This study used receiver operating characteristic curves to analyze Surveillance, Epidemiology and End Results (SEER) medulloblastoma (MB) and primitive neuroectodermal tumor (PNET) outcome data. The aim of this study was to identify and optimize predictive outcome models. Materials and Methods: Patients diagnosed from 1973 to 2009 were selected for analysis of socio-economic, staging and treatment factors available in the SEER database for MB and PNET. For the risk modeling, each factor was fitted by a generalized linear model to predict the outcome (brain cancer specific death, yes/no). The area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. A Monte Carlo algorithm was used to estimate the modeling errors. Results: There were 3,702 patients included in this study. The mean follow up time (S.D.) was 73.7 (86.2) months. Some 40% of the patients were female and the mean (S.D.) age was 16.5 (16.6) years. There were more adult MB/PNET patients listed from SEER data than pediatric and young adult patients. Only 12% of patients were staged. The SEER staging has the highest ROC (S.D.) area of 0.55 (0.05) among the factors tested. We simplified the 3-layered risk levels (local, regional, distant) to a simpler non-metastatic (I and II) versus metastatic (III) model. The ROC area (S.D.) of the 2-tiered model was 0.57 (0.04). Conclusions: ROC analysis optimized the most predictive SEER staging model. The high under staging rate may have prevented patients from selecting definitive radiotherapy after surgery.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.451-456
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• 2019

Recently, various bioinformation technologies have been proposed, and research and development on the collection and analysis of the human body related bioinformation have been continuously conducted to support the human life environment and healthcare. These biomedical research and development processes add to the redundancy and complexity of the R&D elements and put a heavy burden on the follow-up research developers. Therefore, this study utilizes an open bioinformation platform that effectively supports the collection and analysis of bioinformation to improve the redundancy and complexity of bioinformatics R&D based on the bioinformatics platform. In addition, I propose an open interface that supports acquisition, processing, analysis, and application of bio-signals. In particular, we propose a biometric information normalization pattern model through data analysis modeling of brain wave information based on an open interface.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1999.10a
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• pp.387-394
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• 1999

Artificial Neural Network (ANN) is a computational model inspired by the structure and operations of the brain. It is massively parallel system consisting of a large number of highly interconnected and simple processing units. The purpose of this paper is to verify the applicability of ANN to predict experimental results through the use of measured experimental data. Although there have been accumulated data based on hysteretic characteristics of structural element with cyclic loading tests, it is difficult to directly apply them for the analysis of elastic and plastic response. Thus, simple models with mathematical formula such as Bi-Linear Model, Ramberg-Osgood Model, Degrading Tri Model, Takeda Model, Slip type Model, and etc, have been used. To verify the practicality and capability of this study, ANN is adapted to several models with mathematical formula using numerical data To show the efficiency of ANN in nonlinear analysis, it is important to determine the adequate input and output variables of hysteretic models and to minimize an error in ANN process. The application example is Beam-Column joint test using the ANN in modeling of the linear and nonlinear hysteretic behavior of structure.