• Tissue Engineering and Regenerative Medicine
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• 제15권6호
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• pp.735-750
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• 2018

BACKGROUND: The major challenge of tissue engineering is to develop constructions with suitable properties which would mimic the natural extracellular matrix to induce the proliferation and differentiation of cells. Poly(${\varepsilon}$-caprolactone)-poly(ethylene glycol)-poly(${\varepsilon}$-caprolactone) (PCL-PEG-PCL, PCEC), chitosan (CS), nano-silica ($n-SiO_2$) and nano-hydroxyapatite (n-HA) are biomaterials successfully applied for the preparation of 3D structures appropriate for tissue engineering. METHODS: We evaluated the effect of n-HA and $n-SiO_2$ incorporated PCEC-CS nanofibers on physical properties and osteogenic differentiation of human dental pulp stem cells (hDPSCs). Fourier transform infrared spectroscopy, field emission scanning electron microscope, transmission electron microscope, thermogravimetric analysis, contact angle and mechanical test were applied to evaluate the physicochemical properties of nanofibers. Cell adhesion and proliferation of hDPSCs and their osteoblastic differentiation on nanofibers were assessed using MTT assay, DAPI staining, alizarin red S staining, and QRT-PCR assay. RESULTS: All the samples demonstrated bead-less morphologies with an average diameter in the range of 190-260 nm. The mechanical test studies showed that scaffolds incorporated with n-HA had a higher tensile strength than ones incorporated with $n-SiO_2$. While the hydrophilicity of $n-SiO_2$ incorporated PCEC-CS nanofibers was higher than that of samples enriched with n-HA. Cell adhesion and proliferation studies showed that n-HA incorporated nanofibers were slightly superior to $n-SiO_2$ incorporated ones. Alizarin red S staining and QRT-PCR analysis confirmed the osteogenic differentiation of hDPSCs on PCEC-CS nanofibers incorporated with n-HA and $n-SiO_2$. CONCLUSION: Compared to other groups, PCEC-CS nanofibers incorporated with 15 wt% n-HA were able to support more cell adhesion and differentiation, thus are better candidates for bone tissue engineering applications.

• Advances in biomechanics and applications
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• 제1권2호
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• pp.95-109
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• 2014

Peri-acetabular bone ingrowth plays a crucial role in long-term stability of press-fit acetabular cups. A poor bone ingrowth often results in increased cup migration, leading to aseptic loosening of the implant. The rate of peri-prosthetic bone formation is also affected by the polar gap that may be introduced during implantation. Applying a mechano-regulatory tissue differentiation algorithm on a two-dimensional plane strain microscale model, representing implant-bone interface, the objectives of the study are to gain an insight into the process of peri-prosthetic tissue differentiation and to investigate its relationship with implant-bone relative displacement and size of the polar gap. Implant-bone relative displacement was found to have a considerable influence on bone healing and peri-acetabular bone ingrowth. An increase in implant-bone relative displacement from $20{\mu}m$ to $100{\mu}m$ resulted in an increase in fibrous tissue formation from 22% to 60% and reduction in bone formation from 70% to 38% within the polar gap. The increase in fibrous tissue formation and subsequent decrease in bone formation leads to weakening of the implant-bone interface strength. In comparison, the effect of polar gap on bone healing and peri-acetabular bone ingrowth was less pronounced. Polar gap up to 5 mm was found to be progressively filled with bone under favourable implant-bone relative displacements of $20{\mu}m$ along tangential and $20{\mu}m$ along normal directions. However, the average Young's modulus of the newly formed tissue layer reduced from 2200 MPa to 1200 MPa with an increase in polar gap from 0.5 mm to 5 mm, suggesting the formation of a low strength tissue for increased polar gap. Based on this study, it may be concluded that a polar gap less than 0.5 mm seems favourable for an increase in strength of the implant-bone interface.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.915-918
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• 2005

Advances in Information Technology and in Biomedicine have created new uses for CAD technology with many novel and important biomedical applications. Such applications can be found, for example, in the design and modeling of orthopedics, medical implants, and tissue modeling in which CAD can be used to describe the morphology, heterogeneity, and organizational structure of tissue and anatomy. CAD has also played an important role in computer-aided tissue engineering for biomimetic design, analysis, simulation and freeform fabrication of tissue scaffolds and substitutes. And all the applications require precision geometry of the organs or bones of each patient. But the geometry information currently used is polygon model with none solid geometry and is so rough that it cannot be utilized for accurate analysis, simulation and fabrication. Therefore a case study is performed to deduce a transformation method to build free form surface from a rough polygon data or medical images currently used in the application. This paper describes the transformation procedure in detail and the considerations for accurate organ modeling are discussed.

• ETRI Journal
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• 제39권1호
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• pp.87-96
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• 2017

Ultrasonic surgical devices are routinely used for surgical procedures. The incision and coagulation of tissue generate a temperature of $40^{\circ}C-150^{\circ}C$ and depend on the controllable output power level of the surgical device. Recently, research on the classification of grasped tissues to automatically control the power level was published. However, this research did not consider the specific characteristics of the surgical device, tissue denaturalization, and so on. Therefore, this research proposes a robust algorithm that simulates noise to resemble real situations and classifies tissue using conventional classifier algorithms. In this research, the bioimpedance spectrum for six tissues (liver, large intestine, kidney, lung, muscle, and fat) is measured, and five classifier algorithms are used. A signal-to-noise ratio of additive white Gaussian noise diversifies the testing sets, and as a result, each classifier's performance exhibits a difference. The k-nearest neighbors algorithm shows the highest classification rate of 92.09% (p < 0.01) and a standard deviation of 1.92%, which confirms high reproducibility.

• Asian Pacific Journal of Cancer Prevention
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• 제13권5호
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• pp.1977-1981
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• 2012

Houttuynia cordata Thunb (HCT) is a native herb found in Southeast Asia which features various pharmacological activities against allergy, inflammation, viral and bacterial infection, and cancer. The aims of this study were to determine the cytotoxic effect of 6 fractions obtained from silica gel column chromatography of alcoholic HCT extract on human leukemic Molt-4 cells and demonstrate mechanisms of cell death. Six HCT fractions were cytotoxic to human lymphoblastic leukemic Molt-4 cells in a dose-dependent manner by MTT assay, fraction 4 exerting the greatest effects. Treatment with $IC_{50}$ of HCT fraction 4 significantly induced Molt-4 apoptosis detected by annexinV-FITC/propidium iodide for externalization of phosphatidylserine to the outer layer of cell membrane. The mitochondrial transmembrane potential was reduced in HCT fraction 4-treated Molt-4 cells. Moreover, decreased expression of Bcl-xl and increased levels of Smac/Diablo, Bax and GRP78 proteins were noted on immunoblotting. In conclusion, HCT fraction 4 induces Molt-4 apoptosis cell through an endoplasmic reticulum stress pathway.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제18권2호
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• pp.77-105
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• 2014

Medical imaging techniques have evolved to expand our ability to visualize new contrast information of electrical, optical, and mechanical properties of tissues in the human body using noninvasive measurement methods. In particular, electrical tissue property imaging techniques have received considerable attention for the last few decades since electrical properties of biological tissues and organs change with their physiological functions and pathological states. We can express the electrical tissue properties as the frequency-dependent admittivity, which can be measured in a macroscopic scale by assessing the relation between the time-harmonic electric field and current density. The main issue is to reconstruct spectroscopic admittivity images from 10 Hz to 1 MHz, for example, with reasonably high spatial and temporal resolutions. It requires a solution of a nonlinear inverse problem involving Maxwell's equations. To solve the inverse problem with practical significance, we need deep knowledge on its mathematical formulation of underlying physical phenomena, implementation of image reconstruction algorithms, and practical limitations associated with the measurement sensitivity, specificity, noise, and data acquisition time. This paper discusses a number of issues in electrical tissue property imaging modalities and their future directions.

• Bulletin of the Korean Chemical Society
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• 제32권11호
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• pp.3967-3972
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• 2011

A whole body physiologically based pharmacokinetic (PBPK) model was applied to investigate absorption, distribution, and physiologic variations on pharmacokinetics of imatinib in human body. Previously published pharmacokinetic data of the drug after intravenous (i.v.) infusion and oral administration were simulated by the PBPK model. Oral dose absorption kinetics were analyzed by adopting a compartmental absorption and transit model in gut section. Tissue/plasma partition coefficients of drug after i.v. infusion were also used for oral administration. Sensitivity analysis of the PBPK model was carried out by taking parameters that were commonly subject to variation in human. Drug concentration in adipose tissue was found to be higher than those in other tissues, suggesting that adipose tissue plays a role as a storage tissue for the drug. Variations of metabolism in liver, body weight, and blood/plasma partition coefficient were found to be important factors affecting the plasma concentration profile of drug in human body.

• 대한의용생체공학회:의공학회지
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• 제24권3호
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• pp.141-149
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• 2003

본 논문에서는 초음파 의용 영상시스템의 B-모드 영상으로 잘 관찰되지 않는 암이나 종양을 진단하기 위하여 인체 연조직의 탄성계수를 측정하여 영상화하는 방법을 제안하였다. 연조직의 단단함을 측정하기 위하여 조직에 정현파 진동을 인가하고 연조직의 기계적인 진동의 크기를 초음파 영상의 스페클 밝기의 시간적 변화를 추정하여 구하였다. 제안한 방법은 B-모드 영상에서 비교적 적은 계산량으로 쉽게 조직의 단단함을 추정할 수 있음을 시뮬레이션과 실험으로 검증하였다

• Journal of information and communication convergence engineering
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• 제20권1호
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• pp.58-64
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• 2022

Breast ultrasonic reading is critical as a primary screening test for the early diagnosis of breast cancer. However, breast ultrasound examinations show significant differences in diagnosis based on the difference in image quality according to the ultrasonic equipment, experience, and proficiency of the examiner. Accordingly, studies are being actively conducted to analyze the texture characteristics of normal breast tissue, positive tumors, and malignant tumors using breast ultrasonography and to use them for computer-assisted diagnosis. In this study, breast ultrasonography was conducted to select 247 ultrasound images of 71 normal breast tissues, 87 fibroadenomas among benign tumors, and 89 malignant tumors. The selected images were calculated using a statistical method with 21 feature parameters extracted using the gray level co-occurrence matrix algorithm, and classified as normal breast tissue, benign tumor, and malignancy. In addition, we proposed five feature parameters that are available for computer-aided diagnosis of breast cancer classification. The average classification rate for normal breast tissue, benign tumors, and malignant tumors, using this feature parameter, was 82.8%.

• Biomolecules & Therapeutics
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• 제30권2호
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• pp.179-183
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• 2022

Traditionally, pathologists microscopically examine tissue sections to detect pathological lesions; the many slides that must be evaluated impose severe work burdens. Also, diagnostic accuracy varies by pathologist training and experience; better diagnostic tools are required. Given the rapid development of computer vision, automated deep learning is now used to classify microscopic images, including medical images. Here, we used a Inception-v3 deep learning model to detect mouse lung metastatic tumors via whole slide imaging (WSI); we cropped the images to 151 by 151 pixels. The images were divided into training (53.8%) and test (46.2%) sets (21,017 and 18,016 images, respectively). When images from lung tissue containing tumor tissues were evaluated, the model accuracy was 98.76%. When images from normal lung tissue were evaluated, the model accuracy ("no tumor") was 99.87%. Thus, the deep learning model distinguished metastatic lesions from normal lung tissue. Our approach will allow the rapid and accurate analysis of various tissues.