• Journal of Life Science
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• v.27 no.11
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• pp.1276-1289
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• 2017

This study isolated and purified the antimicrobial peptide McSSP-31 from an acidified hemocyte extract of a Mytilus coruscus. The antimicrobial peptide was purified by using a $C_{18}$ reversed-phase high-performance liquid chromatography (HPLC). The peptide was determined to be 3330.549 Da by matrix assisted-laser desorption ionization time-of-flight mass spectrophotometer (MALDI-TOF/MS). The N-terminus of a 14 amino-acid sequence was identified as P-S-P-T-R-R-S-T-S-R-S-K-S-R by Edman degradation method. The acquired sequence showed a 93% similarity with the sperm-specific protein Phi-1, which is from M. californianus. The identified open-reading frame (ORF) of peptide was 306 bp encoding 101 amino acids, which was analyzed by rapid amplification of cDNA ends (RACE), cloning and sequencing analysis. We compared the full sequence with other known proteins that reveal the sperm-specific protein Phi-1 (93.5%) of M. californianus. Synthesized antimicrobial peptide (McSSP-31) showed antibacterial activity against gram-positive bacteria including B. subtilis, S. mutans, S. aureus and gram-negative bacteria including E. coli, K. pneumoniae, P. mirabilis, P. aeruginosa and fungi, C. albicans. Also, synthesized peptide showed strong antibacterial activity against antibiotic-resistant strains, including S. aureus. The cytotoxicity of the peptide was determined by using the HUVEC human cell line. The peptide did not exhibit any significant cytotoxic effects on the normal human cell line, and it had very low hemolytic activity with flounder hemoglobin. The results demonstrated that peptide purified from the hemocyte of a M. coruscus exhibits antibacterial activity against various bacteria and has the potential to be an alternative antibiotic agent.

• Journal of Life Science
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• v.28 no.11
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• pp.1301-1315
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• 2018

We purified an antimicrobial peptide from the acidified hemocyte extract of Mytilus coruscus by $C_{18}$ reversed-phase high-performance liquid chromatography (RP-HPLC). The peptide was 4041.866 Da based on matrix-assisted laser desorption ionization time-of-flight mass spectrophotometer (MALDI-TOF/MS) and the 25 amino acids of the N-terminus sequence were identified. Comparison of this sequence of the purified peptide with the N-terminus sequences of other antimicrobial peptides revealed 100% identity with the mytilin B precursor of Mytilus coruscus. We also identified a 312 bp open-reading frame (ORF) encoding 103 amino acids based on the obtained amino acid residues. The nucleotide sequence of this ORF and the amino acid sequence also revealed 100% identity with the mytilin B precursor of Mytilus coruscus. We synthesized two antimicrobial peptides with an alanine residue in the C-terminus, and designated them mytilin B1 and B2. These two antimicrobial peptides showed antimicrobial activity against gram-positive bacteria, including Bacillus cereus and Streptococcus parauberis (minimal effective concentration, MECs $41.6-89.7{\mu}g/ml$), gram-negative bacteria, including Enterobacter cloacae, Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Providencia stuartii, Pseudomonas aeruginosa, and Vibrio ichthyoenteri (MECs $7.4-39.5{\mu}g/ml$), and the fungus Candida albicans (MECs $26.0-31.8{\mu}g/ml$). This antimicrobial activity was stable under heat and salt conditions. Furthermore, the peptides did not exhibit significant hemolytic activity or cytotoxic effects. These results suggest that mytilin B could be applied as alternative antibiotic agent, and they add to the understanding of the innate immunity of hard-shelled mussels.

• Journal of Magnetics
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• v.20 no.4
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• pp.381-386
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• 2015

The purpose of this study is to improve diagnostic efficiency of clinical study by setting up guidelines for more precise examination with a comparative analysis of signal intensity and image distortion depending on the location of X axial of object when performing magnetic resonance diffusion weighted imaging (MR DWI) examination. We arranged the self-produced phantom with a 45 mm of interval from the core of 44 regent bottles that have a 16 mm of external diameter and 55 mm of height, and were placed in 4 rows and 11 columns in an acrylic box. We also filled up water and margarine to portrait the fat. We used 3T Skyra and 18 Channel Body array coil. We also obtained the coronal image with the direction of RL (right to left) by using scan slice thinkness 3 mm, slice gap: 0mm, field of view (FOV): $450{\times}450mm^2$, repetition time (TR): 5000 ms, echo time (TE): 73/118 ms, Matrix: $126{\times}126$, slice number: 15, scan time: 9 min 45sec, number of excitations (NEX): 3, phase encoding as a diffusion-weighted imaging parameter. In order to scan, we set b-value to $0s/mm^2$, $400s/mm^2$, and $1,400s/mm^2$, and obtained T2 fat saturation image. Then we did a comparative analysis on the differences between image distortion and signal intensity depending on the location of X axial based on iso-center of patient's table. We used "Image J" as a comparative analysis programme, and used SPSS v18.0 as a statistic programme. There was not much difference between image distortion and signal intensity on fat and water from T2 fat saturation image. But, the average value depends on the location of X axial was statistically significant (p < 0.05). From DWI image, when b-value was 0 and 400, there was no significant difference up to $2^{nd}$ columns right to left from the core of patient's table, however, there was a decline in signal intensity and image distortion from the $3^{rd}$ columns and they started to decrease rapidly at the $4^{th}$ columns. When b-value was 1,400, there was not much difference between the $1^{st}$ row right to left from the core of patient's table, however, image distortion started to appear from the $2^{nd}$ columns with no change in signal intensity, the signal was getting decreased from the $3^{rd}$ columns, and both signal intensity and image distortion started to get decreased rapidly. At this moment, the reagent bottles from outside out of 11 reagent bottles were not verified from the image, and only 9 reagent bottles were verified. However, it was not possible to verify anything from the $5^{th}$ columns. But, the average value depends on the location of X axial was statistically significant. On T2 FS image, there was a significant decline in image distortion and signal intensity over 180mm from the core of patient's table. On diffusion-weighted image, there was a significant decline in image distortion and signal intensity over 90 mm, and they became unverifiable over 180 mm. Therefore, we should make an image that has a diagnostic value from examinations that are hard to locate patient's position.

• Journal of the korean academy of Pediatric Dentistry
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• v.26 no.4
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• pp.652-663
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• 1999

Craniosynostosis, the premature fusion of cranial sutures, presumably involves disturbance of the interactions between different tissues within the cranial sutures. Interestingly, point mutaions in the genes encoding for the fibroblast growth factor receptors(FGFRs), especially FGFR2, cause various types of human craniosynostosis syndromes. To elucidate the function of these genes in the early morphogenesis of mouse cranial sutures, we first analyzed by in situ hybridization the expression of FGFR2(BEK) and osteopontin, an early marker of osteogenic differentiation, in the sagittal suture of calvaria during embryonic(E15-E18) and postnatal stage(P1-P3). FGFR2(BEK) was intensely expressed in the osteogenic fronts, whose cells undergo differentiation into osteoprogenitor cells that ultimately lay down the bone matrix. Osteopontin was expressed throughout the parietal bones excluding the osteogenic fronts, the periphery of the parietal bones. To further examine the role of FGF-mediated FGFR signaling in cranial suture, we did in vitro experiments in E15.5 mouse calvarial explants. Interestingly, implantation of FGF2 soaked beads onto both the osteogenic fronts and mid-mesenchyme of sagittal suture after 36 hours organ culture resulted in the increase of the tissue thickness and cell number around FGF2 beads, moreover FGF4-soaked beads implanted onto the osteogenic fronts stimulated suture closure due to an accelerated bone growth, compared to FGF4 beads placed onto mid-mesenchyme of sagittal suture and BSA control beads. In addition FGF2 induced the ectopic expression of osteopontin and Msx1 genes. Taken together, these data indicate that FGF-mediated FGFR signaling has a important role in regulating the cranial bone growth and maintenance of cranial suture, and suggest that FGF-mediated FGFR signaling is involved in regulating the balance between the cell proliferation and differentiation through inducing the expression of osteopontin and Msx1 genes.

• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.