• Tuberculosis and Respiratory Diseases
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• v.45 no.4
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• pp.835-845
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• 1998

Background: Silica-induced lung diseases is characterized by the accumulation of inflammatory cells at early stage and fibrosis in pulmonary parenchyma and interstitium at late stage. As a consequence of inflammation, silicosis is accompanied with the expansion of interstitial collagen and the formation of fibrotic nodule. In this process, several kinds of lung cells produce cytokines which can amplify and modulate pulmonary fibrosis. The alveolar macrophage is a potent source of proflammatory cytokines and growth factor. But in the process of silicotic inflammation and fibrosis, there are many changes of the kinetics in cytokine network. And the sources of cytokines in each phase are not well known. Method: 2.5 mg of silica was instillated into the lung of C57BL/6J mice. After intratracheal instillation of silica, the lungs were removed for imunohistochemical stain at 1, 2, 7 day, 2, 4, 8, 12 week, respectively. We investigated the expression of IL-1$\beta$, IL-6, TNF-$\alpha$ and TGF-$\beta$ in lung tissue. Results: 1) The expression of IL-6 increased from 1 day after exposure to 8 weeks in vascular endothelium. Also peribronchial area were stained for IL-6 from 7 days and reached the peak level for 4 weeks. 2) The IL-1 $\beta$ was expressed weakly at the alveolar and peribronchial area through 12 weeks. 3) The TNF-$\alpha$ expressed strongly at alveolar and bronchial epithelia during early stage and maintained for 12 weeks. 4) TGF-$\beta$ was expressed strongly at bronchial epithelia and peribronchial area after 1 week and the strongest at 8 weeks. Conclusion: The results above suggests IL-6, TNF-$\alpha$ appear to be a early inflammatory response in silica induced lung fibrosis and TGF-$\beta$ play a major role in the maintenance and modulation of fibrosis in lung tissue. And the regulation of TNF-$\alpha$ production will be a key role in modultion of silica-induced fibrosis.

• Journal of Life Science
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• v.16 no.1
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• pp.131-135
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• 2006

Oligopeptide is known to be an essential nitrogen nutrient for bacterial growth. Oligopeptide can be transported into cytoplasm by a specific transport system, Opp system. Opp system is composed of five proteins, which are transcribed by an operon. These are responsible for oligopeptide binding protein (OppA), permease (OppB and OppC) and energy generation system (OppD and OppF), respectively. Previously, we isolated the opp operon from Vibrio fluvialis and constructed the oppA mutant by allelic exchange method. In this study, we investigated the growth pattern and biofilm production under the different growth condition. When the cells were cultivated using brain heart infusion(BHI) medium, the wild type was faster than the mutant in growth during the exponential phase. However, it showed that the growth pattern of two strains in M9 medium is very similar. The growth of wild type showed better than that of the mutant grown at pH 8. At pH 7, there was no an obvious difference in growth. After 5 mM $H_2O_2$ was treated to the cells $(OD_{600}=1.2)$, the cell survival was examined. The oppA mutation did not affect in survivability. In the presence of $10{\mu}g/ml$ polymyxin B, the biofilm production of the oppA mutant was higher than that of the wild type.

• Korean Journal of Veterinary Research
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• v.25 no.1
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• pp.7-17
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• 1985

Many investigators have been pursuing various attempts so far to produce hepatitis B surface antigen(HBsAg) vaccines using the techniques such as isolation from plasma of chronic HBsAg carrier, recombinant DNA technique or preparation of synthetic peptides specific for immunogenic determinants. Hepatitis B virus can not grow on any cell lines by the tissue culture technique at the present time. The plasma of chronic HBsAg carrier is expensive and its source is limited. The HBsAg from the recombinant DNA technique gave still very low yield. Another approach, therefore, has been initiated to develop a synthetic hepatitis B virus vaccine. The possible use of several distinct synthetic vaccines in prophylaxis can be facilitated by availability of full synthetic immunogens. Peptides synthesized for potential application as antiviral vaccines have been mostly tested in the form of conjugates with carrier proteins, although the free synthetic peptide can be immunogenic. To understand basic knowledges on the antigenicity and immunogenicity of a synthetic peptide specific for major immunogenic determinant of HBsAg, a nonapeptide, $H_2N^{139}Cys-Thr-Lys-Pro-Thr-Asp-Gly-^{146}Asn-Aba$ COOH, which corresponds to HBsAg amino acid residues 139 to 147, was synthesized by the Merrifield's solid-phase method with a slight modification. The antigenicity and immunogenicity of this specific synthetic peptide were examined comparing with purified plasma-derived natural HBsAg. The results obtained are as follows; 1. The peptide synthesized showed the identical amino acid composition to the theoretical value. The degree of purification and molecular weight were acertained by methods of high performance liquid chromatography and mass spectrometry. 2. Using m-maleimidobenzoyl-N-hydroxysuccinimide ester as a conjugating agent, the synthetic peptide was conjugated to rabbit albumin and ${\gamma}$-globulin, tetanus and diphtheria toxoids, and keyhole limpet hemocyanin. Their conjugation yields were 8.3, 9.5, 15.8, 13.5, and 11.2%, respectively. 3. The natural HBsAg was purified from plasma of chronic HBsAg carrier. By the electron microscopic observation of the purified natural HBsAg preparation, no Dane particles were observed and the preparation showed negative DNA polymerase activity. 4. Antigenicity of the synthetic peptide and the plasma-derived natural HBsAg was determined by competition radioimmunoassay using $^{125}I$-natural HBsAg. Their 50% inhibitions appeared as $90{\mu}g/ml$ and $0.12{\mu}g/ml$ for the synthetic peptide and the natural HBsAg, respectively. This indicates that the former was about 750-fold less antigenic than the latter. 5. Immunogenicity of the synthetic peptide was determined by administering the peptide-carrier conjugates into rabbits with and without Freund's complete adjuvant. Regardless the carrier proteins and adjuvant, positive immune responses to the synthetic peptide were observed. The higher antibody titers, however, were shown in the groups administered with Freund's complete adjuvant. 6. Immunizing dose 50% in mice of the various peptide-carrier conjugates was 5.47, 6.00, 65.16, 31.25 and $13.03{\mu}g/dose$ for rabbit albumin and ${\gamma}$-globulin, tetanus and diphtheria toxoids, and keyhole limpet hemocyanin, respectively, while the natural HBsAg showed $0.65{\mu}g/dose$. 7. It was postulated that homologous proteins prefer to heterologous ones as the carriers.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.48 no.3
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• pp.189-196
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• 2022

Plasma refers to an ionized gas that is often referred to as "the fourth phase of matter", following solid, liquid, and gas. Plasma has traditionally been utilized for industrial applications such as welding and neon signs, but its promise in biomedical fields such as cancer treatment and dermatology has lately been recognized. Indeed, due to its beneficial effects in promoting collagen production, improving skin tone, and eliminating harmful bacteria in the skin, plasma treatment constitutes an important target for dermatological research. In this study, a plasma device for cosmetic manufacturing based on nitrogen, the main component of the atmosphere, was designed and assembled. Moreover, nitric oxide (NO) was selected since is easier to follow and evaluate than other nitrogen plasma active species, and its contents were measured to perform a quantitative and qualitative evaluation of plasma. First, an injection method, using different proximities labeled "sinking" and "non sinking" treatments, was performed to test the most efficient plasma treatment method. As a result, it was observed that the formulation obtained by a non sinking treatment was more effective. Furthermore, toner and ampoule were selected as cosmetics formulations, and the characteristics of the formulation and changes in the injected plasma state were observed. In both formulations, the successful injection of NO plasma was 2 times higher in toner formulation than ampoule formulation, and it gradually decreased with time, having dissipated after a week. It was confirmed that the nitrogen plasma used did not affect the stability of the toner and ampoule formulations at low temperature (4 ℃), room temperature (25 ℃), and high temperature (37 ℃ and 50 ℃) conditions. The results of this study demonstrate the potential of plasma cosmetics and highlight the importance of securing the stability of the injected plasma.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.