• Journal of Biomedical Engineering Research
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• v.29 no.2
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• pp.146-150
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• 2008

In order to study cortical properties in human, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Among many approaches, surface-based method that reconstructs a 3-D model from contour lines on cross-section images is widely used. The conventional method detects match points of contours using the minimum straight distance between any pair of contour points which lie on different contours. Then, it generates a triangle strip. In general, however, it might yield small mismatches between contours in case of brain due to complex anatomical structures. In this paper, therefore, we present an improved method for tilting operation that uses the curvature values calculated from surface information. The usefulness of the proposed method has been verified using brain image.

• Journal of The Korean Association For Science Education
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• v.32 no.3
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• pp.524-531
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• 2012

Categorization is an important human function used to process different stimuli. It is also one of the most important factors affecting measurement of a person's classification ability. Explicit categorization, the representative system by which categorization ability is measured, can verbally describe the categorization rule. The purpose of this study was to develop a prediction model for categorization ability as it relates to the classification process of living organisms using fMRI. Fifty-five participants were divided into two groups: a model generation group, comprised of twenty-seven subjects, and a model verification group, made up of twenty-eight subjects. During prediction model generation, functional connectivity was used to analyze temporal correlations between brain activation regions. A classification ability quotient (CQ) was calculated to identify the verbal categorization ability distribution of each subject. Additionally, the connectivity coefficient (CC) was calculated to quantify the functional connectivity for each subject. Hence, it was possible to generate a prediction model through regression analysis based on participants' CQ and CC values. The resultant categorization ability regression model predictor was statistically significant; however, researchers proceeded to verify its predictive ability power. In order to verify the predictive power of the developed regression model, researchers used the regression model and subjects' CC values to predict CQ values for twenty-eight subjects. Correlation between the predicted CQ values and the observed CQ values was confirmed. Results of this study suggested that explicit categorization ability differs at the brain network level of individuals. Also, the finding suggested that differences in functional connectivity between individuals reflect differences in categorization ability. Last, researchers have provided a new method for predicting an individual's categorization ability by measuring brain activation.

• Journal of Digital Convergence
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• v.17 no.4
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• pp.187-194
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• 2019

The purpose of this study was to develop a long-term future scenario model of the brain implant industry that can be used for the industrial policy and corporate strategy. There are many discussions about the possibility of the brain science industry as a future core industry, but researches on specific industrial development policies and corporate strategy fields are very lacking. This is because this field has not yet accumulated the necessary information to create industrial growth policies or corporate strategies. In order to overcome these limitations, this study developed future scenarios using the system dynamics model for the brain implant industry and proposed a strategy suitable for each scenario. This study can be used as the main data for the policy development of the brain science industry and the strategy development in individual companies. There is a need to study the signal that can identify the future scenario of global market development in the future.

• The Korea Journal of Herbology
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• v.35 no.1
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• pp.1-8
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• 2020

Objectives : This study was conducted to evaluate the effects of Corni Fructus, the dried fruits of Cornus officinalis Sieb., on unilateral common carotid artery occlusion (UCCAO) in mouse model. Methods : The Corni Fructus used in the experiment was extracted with anhydrous methanol, then filtered and freeze-dried. C57BL/6 mice used in the experiments were conducted left UCCAO surgery to set up UCCAO rodent model for mice. The mice were divided into five groups for evaluate the effect of methanol extract of Corni Fructus (COM) on UCCAO induced ischemic brain injury. The expression levels of nitric oxide in cerebrum and serum, body weight change were measured. To determine the effect of UCCAO and COM administration on brain neurons, morphological changes of the cerebrum through a microscope was conducted. And western blot was performed to confirm the underlying mechanism of neuroprotective effect of COM administration. Results : COM administered UCCAO groups (CO50, CO150, and CO500) had no significant effects on nitric oxide production in ipsilateral hemisphere proteins and sera. The CO500, 500 mg/kg COM administration, attenuated UCCAO-induced p38 inflammatory signaling pathway and inflammatory mediators such as iNOS and COX-2. The CO500 group showed resilient morphological changes of hippocampus neuronal cells about brain damage caused by decreased flow of blood. These group also showed decreased inflammation and cellular stress response in neuronal cells. Conclusions : From these results, COM has a neuroprotective property via moderating inflammatory factors and cellular stress inducing factors in brain cells.

• Journal of Ginseng Research
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• v.46 no.3
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• pp.464-472
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• 2022

Background: Gut microbiota influence the central nervous system through gut-brain-axis. They also affect the neurological disorders. Gut microbiota differs in patients with Alzheimer's disease (AD), as a potential factor that leads to progression of AD. Oral intake of Korean Red Ginseng (KRG) improves the cognitive functions. Therefore, it can be proposed that KRG affect the microbiota on the gut-brain-axis to the brain. Methods: Tg2576 were used for the experimental model of AD. They were divided into four groups: wild type (n = 6), AD mice (n = 6), AD mice with 30 mg/kg/day (n = 6) or 100 mg/kg/day (n = 6) of KRG. Following two weeks, changes in gut microbiota were analyzed by Illumina HiSeq4000 platform 16S gene sequencing. Microglial activation were evaluated by quantitative Western blot analyses of Iba-1 protein. Claudin-5, occludin, laminin and CD13 assay were conducted for Blood-brain barrier (BBB) integrity. Amyloid beta (Aβ) accumulation demonstrated through Aβ 42/40 ratio was accessed by ELISA, and cognition were monitored by Novel object location test. Results: KRG improved the cognitive behavior of mice (30 mg/kg/day p < 0.05; 100 mg/kg/day p < 0.01), and decreased Aβ 42/40 ratio (p < 0.01) indicating reduced Aβ accumulation. Increased Iba-1 (p < 0.001) for reduced microglial activation, and upregulation of Claudin-5 (p < 0.05) for decreased BBB permeability were shown. In particular, diversity of gut microbiota was altered (30 mg/kg/day q-value<0.05), showing increased population of Lactobacillus species. (30 mg/kg/day 411%; 100 mg/kg/day 1040%). Conclusions: KRG administration showed the Lactobacillus dominance in the gut microbiota. Improvement of AD pathology by KRG can be medicated through gut-brain axis in mice model of AD.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.1-6
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• 2024

Recently, various techniques are being applied through the development of medical AI, and research has been conducted on the application of super-resolution AI models. In this study, evaluate the results of the application of the super-resolution AI model to brain CT as the basic data for future research. Acquiring CT images of the brain, algorithm for brain and bone windowing setting, and the resolution was downscaled to 5 types resolution image based on the original resolution image, and then upscaled to resolution to create an LR image and used for network input with the original imaging. The SRCNN model was applied to each of these images and analyzed using PSNR, SSIM, Loss. As a result of quantitative index analysis, the results were the best at 256×256, the brain and bone window setting PSNR were the same at 33.72, 35.2, and SSIM at 0.98 respectively, and the loss was 0.0004 and 0.0003, respectively, showing relatively excellent performance in the bone window setting CT image. The possibility of future studies aimed image quality and exposure dose is confirmed, and additional studies that need to be verified are also presented, which can be used as basic data for the above studies.

• International Journal of Stem Cells
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• v.16 no.4
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• pp.385-393
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• 2023

In vertebrates, the entire central nervous system is derived from the neural tube, which is formed through a conserved early developmental morphogenetic process called neurulation. Although the perturbations in neurulation caused by genetic or environmental factors lead to neural tube defects (NTDs), the most common congenital malformation and the precise molecular pathological cascades mediating NTDs are not well understood. Recently, we have developed human spinal cord organoids (hSCOs) that recapitulate some aspects of human neurulation and observed that valproic acid (VPA) could cause neurulation defects in an organoid model. In this study, we identified and verified the significant changes in cell-cell junctional genes/proteins in VPA-treated organoids using transcriptomic and immunostaining analysis. Furthermore, VPA-treated mouse embryos exhibited impaired gene expression and NTD phenotypes, similar to those observed in the hSCO model. Collectively, our data demonstrate that hSCOs provide a valuable biological resource for dissecting the molecular pathways underlying the currently unknown human neurulation process using destructive biological analysis tools.

• Biomolecules & Therapeutics
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• v.30 no.2
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• pp.126-136
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• 2022

Liver fibrosis is part of the wound healing process to help the liver recover from the injuries caused by various liver-damaging insults. However, liver fibrosis often progresses to life-threatening cirrhosis and hepatocellular carcinoma. To overcome the limitations of current in vivo liver fibrosis models for studying the pathophysiology of liver fibrosis and establishing effective treatment strategies, we developed a new mouse model of liver fibrosis using polyhexamethylene guanidine phosphate (PHMG-p), a humidifier sterilizer known to induce lung fibrosis in humans. Male C57/BL6 mice were intraperitoneally injected with PHMG-p (0.03% and 0.1%) twice a week for 5 weeks. Subsequently, liver tissues were examined histologically and RNA-sequencing was performed to evaluate the expression of key genes and pathways affected by PHMG-p. PHMG-p injection resulted in body weight loss of ~15% and worsening of physical condition. Necropsy revealed diffuse fibrotic lesions in the liver with no effect on the lungs. Histology, collagen staining, immunohistochemistry for smooth muscle actin and collagen, and polymerase chain reaction analysis of fibrotic genes revealed that PHMG-p induced liver fibrosis in the peri-central, peri-portal, and capsule regions. RNA-sequencing revealed that PHMG-p affected several pathways associated with human liver fibrosis, especially with upregulation of lumican and IRAK3, and downregulation of GSTp1 and GSTp2, which are closely involved in liver fibrosis pathogenesis. Collectively we demonstrated that the PHMG-p-induced liver fibrosis model can be employed to study human liver fibrosis.

• Dementia and Neurocognitive Disorders
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• v.22 no.2
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• pp.61-68
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• 2023

Background and Purpose: Analyzing brain amyloid positron emission tomography (PET) images to access the occurrence of β-amyloid (Aβ) deposition in Alzheimer's patients requires much time and effort from physicians, while the variation of each interpreter may differ. For these reasons, a machine learning model was developed using a convolutional neural network (CNN) as an objective decision to classify the Aβ positive and Aβ negative status from brain amyloid PET images. Methods: A total of 7,344 PET images of 144 subjects were used in this study. The 18F-florbetaben PET was administered to all participants, and the criteria for differentiating Aβ positive and Aβ negative state was based on brain amyloid plaque load score (BAPL) that depended on the visual assessment of PET images by the physicians. We applied the CNN algorithm trained in batches of 51 PET images per subject directory from 2 classes: Aβ positive and Aβ negative states, based on the BAPL scores. Results: The binary classification of the model average performance matrices was evaluated after 40 epochs of three trials based on test datasets. The model accuracy for classifying Aβ positivity and Aβ negativity was (95.00±0.02) in the test dataset. The sensitivity and specificity were (96.00±0.02) and (94.00±0.02), respectively, with an area under the curve of (87.00±0.03). Conclusions: Based on this study, the designed CNN model has the potential to be used clinically to screen amyloid PET images.

• Journal of Korean Neurosurgical Society
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• v.63 no.1
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• pp.26-33
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• 2020

Glioblastoma (GBM) is a disease without any definite cure. Numerous approaches have been tested in efforts to conquer this brain disease, but patients invariably experience recurrence or develop resistance to treatment. New surgical tools, carefully chosen samples, and experimental methods are enabling discoveries at single-cell resolution. The present article reviews the cell-of-origin of isocitrate dehydrogenase (IDH)-wildtype GBM, beginning with the historical background for focusing on cellular origin and introducing the cancer genesis patterned on firework. The authors also review mutations associated with the senescence process in cells of the subventricular zone (SVZ), and biological validation of somatic mutations in a mouse SVZ model. Understanding GBM would facilitate research on the origin of other cancers and may catalyze the development of new management approaches or treatments against IDH-wildtype GBM.