• BMB Reports
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• v.52 no.10
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• pp.577-588
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• 2019

DNA methylation at CpG sites is an essential epigenetic mark that regulates gene expression during mammalian development and diseases. Methylome refers to the entire set of methylation modifications present in the whole genome. Over the last several years, an increasing number of reports on brain DNA methylome reported the association between aberrant methylation and the abnormalities in the expression of critical genes known to have critical roles during aging and neurodegenerative diseases. Consequently, the role of methylation in understanding neurodegenerative diseases has been under focus. This review outlines the current knowledge of the human brain DNA methylomes during aging and neurodegenerative diseases. We describe the differentially methylated genes from fetal stage to old age and their biological functions. Additionally, we summarize the key aspects and methylated genes identified from brain methylome studies on neurodegenerative diseases. The brain methylome studies could provide a basis for studying the functional aspects of neurodegenerative diseases.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.343-351
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• 2022

A brain tumor forms when some tissue becomes old or damaged but does not die when it must, preventing new tissue from being born. Manually finding such masses in the brain by analyzing MRI images is challenging and time-consuming for experts. In this study, our main objective is to detect the brain's tumorous part, allowing rapid diagnosis to treat the primary disease instantly. With image processing techniques and deep learning prediction algorithms, our research makes a system capable of finding a tumor in MRI images of a brain automatically and accurately. Our tumor segmentation adopts the U-Net deep learning segmentation on the standard MICCAI BRATS 2018 dataset, which has MRI images with different modalities. The proposed approach was evaluated and achieved Dice Coefficients of 0.9795, 0.9855, 0.9793, and 0.9950 across several test datasets. These results show that the proposed system achieves excellent segmentation of tumors in MRIs using deep learning techniques such as the U-Net algorithm.

• BMB Reports
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• v.55 no.5
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• pp.213-219
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• 2022

The blood-brain barrier (BBB) is an interface between cerebral blood and the brain parenchyma. As a gate keeper, BBB regulates passage of nutrients and exogeneous compounds. Owing to this highly selective barrier, many drugs targeting brain diseases are not likely to pass through the BBB. Thus, a large amount of time and cost have been paid for the development of BBB targeted therapeutics. However, many drugs validated in in vitro models and animal models have failed in clinical trials primarily due to the lack of an appropriate BBB model. Human BBB has a unique cellular architecture. Different physiologies between human and animal BBB hinder the prediction of drug responses. Therefore, a more physiologically relevant alternative BBB model needs to be developed. In this review, we summarize major features of human BBB and current BBB models and describe organ-on-chip models for BBB modeling and their applications in neurological complications.

• Journal of Biomedical Engineering Research
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• v.26 no.3
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• pp.129-132
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• 2005

It is one of the most important issues to determine a target brain image that gives a common coordinate system for a constructing population-based brain atlas. The purpose of this study is to provide a simple and reliable procedure that determines the target brain image among the group based on the inherent structural information of three-dimensional magnetic resonance (MR) images. It uses only 11 lines defined automatically as a feature vector representing structural variations based on the Talairach coordinate system. Average characteristic vector of the group and the difference vectors of each one from the average vector were obtained. Finally, the individual data that had the minimum difference vector was determined as the target. We determined the target brain image by both our algorithm and conventional visual inspection for 20 healthy young volunteers. Eighteen fiducial points were marked independently for each data to evaluate the similarity. Target brain image obtained by our algorithm showed the best result, and the visual inspection determined the second one. We concluded that our method could be used to determine an appropriate target brain image in constructing brain atlases such as disease-specific ones.

• Journal of Gastric Cancer
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• v.21 no.3
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• pp.319-324
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• 2021

The abscopal effect refers to the phenomenon in which local radiotherapy is associated with the regression of metastatic cancer that is distantly located from the irradiated site. Here, we present a case of a patient with advanced gastric cancer and brain metastases who was successfully treated with brain radiotherapy and anti-programmed death-1 (PD-1) therapy-induced abscopal effect. Although anti-PD-1 therapy alone could not prevent disease progression, the metastatic lesions in the brain and also in the abdominal lymph node showed a drastic response after brain radiotherapy and anti-PD-1 therapy. To our knowledge, this is the first reported case of successful treatment of advanced gastric cancer with multiple brain and abdominal lymph node metastases, possibly through anti-PD-1 therapy combined with brain radiotherapy-induced abscopal effect. We suggest that the combination of brain radiotherapy and anti-PD-1 therapy may be considered as a therapeutic option for advanced gastric cancer, especially when there is brain metastasis.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1233-1241
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• 2022

Tumors of the brain are the deadliest, with a life expectancy of only a few years for those with the most advanced forms. Diagnosing a brain tumor is critical to developing a treatment plan to help patients with the disease live longer. A misdiagnosis of brain tumors will lead to incorrect medical treatment, decreasing a patient's chance of survival. Radiologists classify brain tumors via biopsy, which takes a long time. As a result, the doctor will need an automatic classification system to identify brain tumors. Image classification is one application of the deep learning method in computer vision. One of the deep learning's most powerful algorithms is the convolutional neural network (CNN). This paper will introduce a novel deep learning structure and image gradient to classify brain tumors. Meningioma, glioma, and pituitary tumors are the three most popular forms of brain cancer represented in the Figshare dataset, which contains 3,064 T1-weighted brain images from 233 patients. According to the numerical results, our method is more accurate than other approaches.

• Genomics & Informatics
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• v.3 no.3
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• pp.86-93
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• 2005

Human brain EST data provide important clues for our understanding of the molecular biology associated with the function of the normal brain and the molecular pathophysiology with brain disorders. To systematically and efficiently study the function and disorders of the human brain, 45,773 human brain ESTs were collected from 27 human brain cDNA libraries, which were constructed from normal brains and brain disorders such as brain tumors, Parkinson's disease (PO) and epilepsy. An analysis of 45,773 human brain ESTs using our EST analysis pipeline resulted in 38,396 high-quality ESTs and 35,906 ESTs, which were coalesced into 8,246 unique gene clusters, showing a significant similarity to known genes in the human RefSeq, human mRNAs and UniGene database. In addition, among 8,246 gene clusters, 4,287 genes ($52\%$) were found to contain full-length cONA clones. To facilitate the extraction of useful information in collected these human brain ESTs, we developed a user-friendly interface system, the Korea Brain Unigene Database (KBUD). The KBUD web interface allows access to our human brain data through three major search modes, the BioCarta pathway, keywords and BLAST searches. Each result when viewed in KBUD offers comprehensive information concerning the analyzed human brain ESTs provided by our data as well as data linked to various other publiC databases. The user-friendly developed KBUD, the first world-wide web interface for human brain EST data with ESTs of human brain disorders as well as normal brains, will be a helpful system for developing a better understanding of the underlying mechanisms of the normal brain well as brain disorders. The KBUD system is freely accessible at http://kugi.kribb.re.kr/KU/cgi -bin/brain. pI.

• Journal of Veterinary Clinics
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• v.19 no.2
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• pp.247-249
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• 2002

A 6-month-old female Pekingese was euthanized due to poor progrosis after 1 month history of neurologic signs that include depression, ataxia, urination and defecation difficulty. At necropsy, no significant gross abnormalities were noted Histologically, neuronal vacuolation was noted in the brain, primarily cerebellum and occasionally in the brain stem area. Neuronal necrosis and secondary axonal swelling were also observed. Differential diagnoses were able to rule out other diseases which can induce neuronal vacuolation such as lysosomal storage disease, prion infection, and postvaccinal change.

• Journal of Oriental Neuropsychiatry
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• v.22 no.4
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• pp.169-183
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• 2011

Objectives : This study explores perspectives, reasons, functions of dreams in order to develop a treatment model. Methods : We searched CNKI, KISS, NDSL. Papers published since 2002, which are classified in categories. Results : 1. The Recognitions about the dream of Oriental Medicine is based on "Hwangjenaegyeong Eumsabalmong(黃帝內經 淫邪發夢)". 2. By each era, There are a variety of etiological causes, stages. 3. Dreams are generated by the internal stimuli Physiological activities of the Jangbu(臟腑),Deficiencies of yin and yang(陰陽), Pathological changes in the Jangbu(臟腑), Pathology products, external stimuli Yukeum(六淫), Eumsiksang(飮食傷), and unusual feelings. 4. Spirit(神) comes from brain(腦), not heart(心). So what we call brain spirit(腦神) is right. Dreams are the mental activity related to the brain. 5. Dreams are the reflection of the pathological changes of the human body. And it shows the development, changes and prognosis of disease. Dreams sometimes illustrate the incidence of the disease Conclusions : We organized perspectives, Causes and Functions of dreams mentioned in Oriental Medicine Literatures. Health is closely related to the dream. Therefore, we propose that the dream is worth for the diagnosis and treatment in oriental medicine.

• Journal of Korean Medical Science
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• v.33 no.44
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• pp.279.1-279.16
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• 2018

Magnetic resonance-guided focused ultrasound (MRgFUS) is a new emerging neurosurgical procedure applied in a wide range of clinical fields. It can generate high-intensity energy at the focal zone in deep body areas without requiring incision of soft tissues. Although the effectiveness of the focused ultrasound technique had not been recognized because of the skull being a main barrier in the transmission of acoustic energy, the development of hemispheric distribution of ultrasound transducer phased arrays has solved this issue and enabled the performance of true transcranial procedures. Advanced imaging technologies such as magnetic resonance thermometry could enhance the safety of MRgFUS. The current clinical applications of MRgFUS in neurosurgery involve stereotactic ablative treatments for patients with essential tremor, Parkinson's disease, obsessive-compulsive disorder, major depressive disorder, or neuropathic pain. Other potential treatment candidates being examined in ongoing clinical trials include brain tumors, Alzheimer's disease, and epilepsy, based on MRgFUS abilities of thermal ablation and opening the blood-brain barrier. With the development of ultrasound technology to overcome the limitations, MRgFUS is gradually expanding the therapeutic field for intractable neurological disorders and serving as a trail for a promising future in noninvasive and safe neurosurgical care.