• Journal of Korean Neurosurgical Society
• /
• v.64 no.3
• /
• pp.359-366
• /
• 2021

Neuromesodermal progenitors (NMPs) constitute a bipotent cell population that generates a wide variety of trunk cell and tissue types during embryonic development. Derivatives of NMPs include both mesodermal lineage cells such as muscles and vertebral bones, and neural lineage cells such as neural crests and central nervous system neurons. Such diverse lineage potential combined with a limited capacity for self-renewal, which persists during axial elongation, demonstrates that NMPs are a major source of trunk tissues. This review describes the identification and characterization of NMPs across multiple species. We also discuss key cellular and molecular steps for generating neural and mesodermal cells for building up the elongating trunk tissue.

• Journal of Oriental Neuropsychiatry
• /
• v.20 no.2
• /
• pp.31-46
• /
• 2009

Objectives : This experiment was designed to investigate the effect of Gojineumja(Guzhenyinzi, GJEJ) on damaged neural tissue in cultured glial cells and in the mouse brain tissue. Methods : The effects of the GJEJ on activation of astrocytes and caspase 3-positive cell counts in cultured glial cells administered with ${\beta}$-amyloid peptide were investigated. The effects of the GJEJ on levels of glial fibrillary acidic protein(GFAP)-positive reactive astrocyets and caspase 3-positive cells in the hippocampal subfields in the rats administered with scopolamine were investigated. Results : 1. GJEJ reduced levels of activated astrocytes and caspase 3-positive cell counts in cultured glial cells administered with ${\beta}$-amyloid peptide. 2. GJEJ reduced levels of GFAP-positive reactive astrocyets and caspase 3-positive cells in the hippocampal subfields in the rats administered with scopolamine. Conclusions : The present data. suggest that GJEJ may have a protective function of neuronal and non-neuronal cells in damaged neural tissue caused by AD-like stimulations. Further studies on identification of effective molecular components of GJEJ and their interactions with damaged neural cells would be important for understanding molecular mechanism and may be further applicable for the development of therapeutic strategies.

• Current Optics and Photonics
• /
• v.6 no.1
• /
• pp.92-103
• /
• 2022

The development of midinfrared (mid-IR) quantum cascade lasers (QCLs) has enabled rapid high-contrast measurement of the mid-IR spectra of biological tissues. Several studies have compared the differences between the mid-IR spectra of colon cancer and noncancerous colon tissues. Most mid-IR spectrum classification studies have been proposed as machine-learning-based algorithms, but this results in deviations depending on the initial data and threshold values. We aim to develop a process for classifying colon cancer and noncancerous colon tissues through a deep-learning-based convolutional-neural-network (CNN) model. First, we image the midinfrared spectrum for the CNN model, an image-based deep-learning (DL) algorithm. Then, it is trained with the CNN algorithm and the classification ratio is evaluated using the test data. When the tissue microarray (TMA) and routine pathological slide are tested, the ML-based support-vector-machine (SVM) model produces biased results, whereas we confirm that the CNN model classifies colon cancer and noncancerous colon tissues. These results demonstrate that the CNN model using midinfrared-spectrum images is effective at classifying colon cancer tissue and noncancerous colon tissue, and not only submillimeter-sized TMA but also routine colon cancer tissue samples a few tens of millimeters in size.

• BMB Reports
• /
• v.43 no.1
• /
• pp.29-33
• /
• 2010

It is well known that PR/SET family members participate in transcriptional regulation via chromatin remodeling. PRDM10 might play an essential role in gene expression, but no such evidence has been observed so far. To assess PRDM10 expression at various stages of mouse development, we performed immunohistochemistry using available PRDM10 antibody. Embryos were obtained from three distinct developmental stages. At E8.5, PRDM10 expression was concentrated in the mesodermal and neural crest populations. As embryogenesis proceeded further to E13.5, PRMD10 expression was mainly in mesoderm-derived tissues such as somites and neural crest-derived populations such as the facial skeleton. This expression pattern was consistently maintained to the fetal growth period E16.5 and adult mouse, suggesting that PRDM10 may function in tissue differentiation. Our study revealed that PRDM10 might be a transcriptional regulator for normal tissue differentiation during mouse embryonic development.

• Journal of Multimedia Information System
• /
• v.6 no.2
• /
• pp.81-86
• /
• 2019

Deep learning is emerging as one of the best tool in processing data related to medical imaging. In our research work, we have proposed a deep learning based framework CNN (Convolutional Neural Network) for the classification of dysplastic tissue images. The CNN has classified the given images into 4 different classes namely normal tissue, mild dysplastic tissue, moderate dysplastic tissue and severe dysplastic tissue. The dataset under taken for the study consists of 672 tissue images of epithelial squamous layer of oral cavity captured out of the biopsy samples of 52 patients. After applying the data pre-processing and augmentation on the given dataset, 2688 images were created. Further, these 2688 images were classified into 4 categories with the help of expert Oral Pathologist. The classified data was supplied to the convolutional neural network for training and testing of the proposed framework. It has been observed that training data shows 91.65% accuracy whereas the testing data achieves 89.3% accuracy. The results produced by our proposed framework are also tested and validated by comparing the manual results produced by the medical experts working in this area.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2000.11c
• /
• pp.715-721
• /
• 2000

In this research, rule and neuro net based boundary extraction algorithm was developed. Extracting boundary of the interest, lean tissue, is essential for the quality evaluation of the beef based on color machine vision. Major quality features of the beef are size, marveling state of the lean tissue, color of the fat, and thickness of back fat. To evaluate the beef quality, extracting of loin parts from the sectional image of beef rib is crucial and the first step. Since its boundary is not clear and very difficult to trace, neural network model was developed to isolate loin parts from the entire image input. At the stage of training network, normalized color image data was used. Model reference of boundary was determined by binary feature extraction algorithm using R(red) channel. And 100 sub-images(selected from maximum extended boundary rectangle 11${\times}$11 masks) were used as training data set. Each mask has information on the curvature of boundary. The basic rule in boundary extraction is the adaptation of the known curvature of the boundary. The structured model reference and neural net based boundary extraction algorithm was developed and implemented to the beef image and results were analyzed.

• Carbon letters
• /
• v.14 no.2
• /
• pp.63-75
• /
• 2013

Graphene, a carbon crystal sheet of molecular thickness, shows diverse and exceptional properties ranging from electrical and thermal conductivities, to optical and mechanical qualities. Thus, its potential applications include not only physicochemical materials but also extends to biological uses. Here, we review recent experimental studies about graphene for such bioapplications. As a prerequisite to the search to determine the potential of graphene for bioapplications, the essential qualities of graphene that support biocompatibility, were briefly summarized. Then, direct examples of tissue regeneration and tissue engineering utilizing graphenes, were discussed, including uses for cell scaffolds, cell modulating interfaces, drug delivery, and neural interfaces.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.11
• /
• pp.1998-2003
• /
• 2007

A flexible but implantable polyimide-based neural implant was fabricated for reliable and stable long-term monitoring of neural activities from brain. The developed neural implant provides 3-dimensional (3D) $3{\times}3$ structure, avoids any hand handling, and makes the insertion more efficient and reliable. Any film curvature caused by residual stress was not observed in the electrode. The 3D flexible polyimide electrode penetrated a dense gel whose stiffness is close to live brain tissue, because a ${\sim}1{\mu}m$ thick nickel was electroplated along the edge of the shank in order to improve the stiffness. The recording sites were positioned at both side of the shank to increase the probability of recording neural signals from a target volume of tissue. Impedance remained stable over 72 hours because of extremely low moisture uptake in the polyimide dielectric layers. At electrical recording test in vitro, the fabricated electrode showed excellent recording performance, suggesting that this electrode has the potential for great recording from neuron firing and long-term implant performance.

• Journal of Korean Neurosurgical Society
• /
• v.29 no.10
• /
• pp.1345-1351
• /
• 2000

Objective : Around the sellar area, there are many important structures. But, the optimal radiation dosage for minimal toxicity to surrounding neural tissue has not been firmly established. The purpose of this study is to evaluate the radiosurgical outcome of juxtasellar tumors and to investigate the relationship between radiation dosage and toxicity to neural tissue. Method : Between May 1992 and June 2000, we treated 65 juxtasellar tumors by using the Leksell Gamma Knife. Among them, 52 patients who could be followed more than 1 year were included in this study. The radiosurgical dosage to the optic pathway, cavernous sinus, Meckel's cave, hypothalamus, pituitary gland and stalk, and brain stem was analyzed and correlated with clinical outcome. The mean follow-up period was 33.5 months(range 12.2- 99.0 months). Result : The clinical response rate was 69.2%. The volume response rate was 61.0% and the radiologic control rate was 92.7%. There were 4 complications(7.7%) of 2 trigeminal neuropathy, 1 abducens nerve palsy, and 1 trigeminal and transient abducens nerve palsy. The optic apparatus appeared to tolerate doses greater than 10Gy. The risk of cranial nerve complications in cavernous sinus seemed to be related to doses of more than 16Gy. In 3 of 4 patients who received more than 16Gy to cavernous sinus, the abducens or trigeminal neuropathy occurred. Also, one patient who received more than 15Gy to the Meckel's cave, trigeminal neuropathy developed. The hypothalamus, pituitary gland and stalk, and brain stem were relatively tolerable to radiation. Conclusion : Gamma Knife radiosurgery seems to be an effective method to control the growth of juxtasellar tumors. To avoid injury to surrounding important neural tissue, careful dose planning and further study for radiation toxicity to neural tissue were needed.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 1996.06c
• /
• pp.780-791
• /
• 1996

A hybrid image processing system which automatically distinguished lean tissues in the image of a complex beef cut surface and generated the lean tissue contour has been developed. Because of the in homegeneous distribution and fuzzy pattern of fat and lean tissue on the beef cut, conventional image segmentation and contour generation algorithm suffer from a heavy computing requirement, algorithm complexity and poor robustness. The proposed system utilizes an artificial neural network enhance the robustness of processing. The system is composed of pre-network , network and post-network processing stages. At the pre-network stage, gray level images of beef cuts were segmented and resized to be adequate to the network input. Features such as fat and bone were enhanced and the enhanced input image was converted tot he grid pattern image, whose grid was formed as 4 X4 pixel size. at the network stage, the normalized gray value of each grid image was taken as the network input. Th pre-trained network generated the grid image output of the isolated lean tissue. A training scheme of the network and the separating performance were presented and analyzed. The developed hybrid system showed the feasibility of the human like robust object segmentation and contour generation for the complex , fuzzy and irregular image.