• 구강회복응용과학지
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• 제40권1호
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• pp.1-12
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• 2024

목적: 세 가지 칼슘 실리케이트 계열 실러가 흰쥐 하악골 조직에 미치는 영향을 조직학적으로 평가하고자 하였다. 연구 재료 및 방법: 흰쥐의 하악골에 와동을 형성하고 즉시 또는 2주 후 희생한 군, 와동에 CeraSeal (CS), AH Plus Bioceramic (AHB), One-Fil (OF) 실러를 각각 주입하여 2주 후 희생한 군으로 무작위 배정하였다. 모든 군을 조직 처리하고 현미경 하에서 와동 내외 평균 골 면적 분율(%)을 계산하고 파골세포를 계수하였고, 각 군의 결과를 비교하고 일원분산 분석 및 Tukey's test로 통계 분석하였다. 결과: 와동 형성 2주 후 희생한 대조군과 AHB 실러 주입 2주 후 희생한 군에서 와동 내 골조직 형성과 파골세포의 존재를 확인할 수 있었고, 다른 군들에 비해 유의하게 높은 와동 내 골 면적 분율(%)을 보여주었다. 다른 군들에서는 와동 내 어떠한 염증이나 이물 반응이 나타나지 않고 파골세포 또한 관찰되지 않았다. 결론: 실험에 사용된 칼슘 실리케이트 계열 실러는 흰쥐 하악골에 주입하였을 때 양호한 골조직 반응을 보였고, 특히 AHB에서 더 높은 와동 내 골조직 형성이 관찰되었다.

• Journal of Biosystems Engineering
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• 제38권1호
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• pp.55-63
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• 2013

Purpose: The present study aimed at image processing methods for automatic cell pattern recognition and morphological analysis for tissue engineering applications. The primary aim was to ascertain the novel algorithm of adaptive brightness correction from microscopic images for use as a potential image analysis. Methods: General microscopic image of cells has a minor problem which the central area is brighter than edge-area because of the light source. This may affect serious problems to threshold process for cell-number counting or cell pattern recognition. In order to compensate the problem, we processed to find the central point of brightness and give less weight-value as the distance to centroid. Results: The results presented that microscopic images through the brightness correction were performed clearer than those without brightness compensation. And the classification of mixed cells was performed as well, which is expected to be completed with pattern recognition later. Beside each detection ratio of hBMSCs and HeLa cells was 95% and 92%, respectively. Conclusions: Using this novel algorithm of adaptive brightness correction could control the easier approach to cell pattern recognition and counting cell numbers.

• 한국융합학회논문지
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• 제12권10호
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• pp.365-372
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• 2021

본 연구의 목적은 자연어처리에 의해 생성된 욕창간호진술문의 특성을 파악하고, 욕창 단계판별 예측정확도를 평가하기 위함이다. 욕창관련 간호기록은 서술통계를 이용하여 분석하였고, 워드클라우드 생성기를 활용하여 욕창예방 간호기록에서 단어의 특성을 파악하였다. 딥러닝을 이용하여 욕창단계판별 정확도(accuracy ratio) 를 구하였다. 연구결과, 욕창의 단계에 대한 기록 중 2단계와 심부조직손상의심단계가 각각 23.1% 와 23.0 % 로 가장 많았고, 빈도수가 높은 핵심단어는 홍반, 수포, 가피, 부위, 크기 등으로 나타났다. 예측의 정확도가 높은 단계는 0단계, 심부조직손상의심단계, 2단계 순으로 나타났다. 따라서, 이를 활용하여 임상적 의사결정지지 시스템으로 개발된다면, 임상간호사의 욕창관리역량 향상 전략 개발에 기초가 될 수 있을 것이다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2004년도 추계학술대회 논문집
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• pp.1038-1041
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• 2004

In this study, developed a micro-level experimental setup to measure pore pressure and poroelastic modulus in various strain and strain rate about a stress in micro-structure of bone tissue. It is essential device in the development of the model to analysis the interstitial bone fluid flow of the lacuno-canalicular system to be known that would effect on the bone remodeling. The constitution of the experimental setup is as follows, microscopic image processing system; actuator control unit; load measurement system. A pilot study was used an artificial chemical wood to have similar poroelastic property of bone matrix and conducted to validate the suitability of the measurement system.

• Asian-Australasian Journal of Animal Sciences
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• 제24권3호
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• pp.429-438
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• 2011

Mastitis is characterized by physical, chemical and bacteriological changes in the milk and pathological changes in the glandular tissue of the udder and affects the quality and quantity of milk. The bacterial contamination of milk from the affected cows render it unfit for human consumption and provides a mechanism of spread of diseases like tuberculosis, sore-throat, Q-fever, brucellosis, leptospirosis etc. and has zoonotic importance. Somatic cell count (SCC) is a useful predictor of intramammary infection (IMI) that includes leucocytes (75%) i.e. neutrophils, macrophages, lymphocytes, erythrocytes and epithelial cells (25%). Leucocytes increase in response to bacterial infection, tissue injury and stress. Somatic cells are protective for the animal body and fight infectious organisms. An elevated SCC in milk has a negative influence on the quality of raw milk. Subclinical mastitis is always related to low milk production, changes to milk consistency (density), reduced possibility of adequate milk processing, low protein and high risk for milk hygiene since it may even contain pathogenic organisms. This review collects and collates relevant publications on the subject.

• 한국식물학회:학술대회논문집
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• 한국식물학회 1987년도 식물생명공학 심포지움 논문집 Proceedings of Symposia on Plant Biotechnology
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• pp.283-307
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• 1987

Glycinin and $\beta$-conglycinin are the most abundant storage protein in soybean. These proteins are known to be synthesized predominantly during germination and cell expansion phase of seed development for short period, and synthesized not in other tissues. Genes encoding these storage proteins are useful system to study the mechanism of development stage and tissue specific gene expression in eukaryotes, especially plants, at the molecular level. The cDNA and genomic clones coding for glycinin have been isolated and regulation mechanism of the gene expression has been studied. Initially, development and tissue-specific expression of the glycinin gene is regulated at the level of transcription. Post-transcriptional processing is also responsible for delayed accumulation of the mRNA. Translational control of the storage protein gene has not been reported. Post-translational modification is another strategic point to regulate the expression of the gene. It is possible to identify positive and/or negative reguratory clements in vivo by producing transgenic plants agter gene manipulation. Elucidation of activation and repression mechanism of soybean storage protein genes will contribute to the understanding of the other plant and eukaryotic genes at molecular level.

• Current Optics and Photonics
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• 제3권1호
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• pp.1-7
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• 2019

Optical stimulation provides a promising alternative to electrical stimulation to selectively modulate tissue. However, developing noninvasive techniques to directly stimulate excitable tissue without introducing genetic modifications and minimizing cellular stress remains an ongoing challenge. Infrared (IR) light has been used to achieve optical pacing for electrophysiological studies in embryonic quail and mammalian hearts. Here, we demonstrate optical stimulation and pacing of the embryonic chicken heart using a pulsed infrared thulium laser with a wavelength of 1927 nm. By recording stereomicroscope outputs and quantifying heart rates and movements through video processing, we found that heart rate increases instantly following irradiation with a large spot size and high radiant exposure. Targeting the atrium using a smaller spot size and lower radiant exposure achieved pacing, as the heart rate synchronized with the laser to 2 Hz. This study demonstrates the viability of using the 1927 nm thulium laser for cardiac stimulation and optical pacing, expanding the optical parameters and IR lasers that can be used to modulate cardiac dynamics.

• International Journal of Computer Science & Network Security
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• 제22권8호
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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.

• IEIE Transactions on Smart Processing and Computing
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• 제6권3호
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• pp.183-192
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• 2017

k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space-based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN-based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in real-time prediction systems. To compensate for this weakness, this paper proposes correlation coefficient-based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule-based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on real-world datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).

• Applied Microscopy
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• 제46권4호
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• pp.184-187
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• 2016

Neuronal connectivity determines brain function. Therefore, understanding the full map of brain connectivity with functional annotations is one of the most desirable but challenging tasks in science. Current methods to achieve this goal are limited by the resolution of imaging tools and the field of view. Macroscale imaging tools (e.g., magnetic resonance imaging, diffusion tensor images, and positron emission tomography) are suitable for large-volume analysis, and the resolution of these methodologies is being improved by developing hardware and software systems. Microscale tools (e.g., serial electron microscopy and array tomography), on the other hand, are evolving to efficiently stack small volumes to expand the dimension of analysis. The advent of mesoscale tools (e.g., tissue clearing and single plane ilumination microscopy super-resolution imaging) has greatly contributed to filling in the gaps between macroscale and microscale data. To achieve anatomical maps with gene expression and neural connection tags as multimodal information hubs, much work on information analysis and processing is yet required. Once images are obtained, digitized, and cumulated, these large amounts of information should be analyzed with information processing tools. With this in mind, post-imaging processing with the aid of many advanced information processing tools (e.g., artificial intelligence-based image processing) is set to explode in the near future, and with that, anatomic problems will be transformed into informatics problems.