Antisense oligonucleotide loaded chitosan nanoparticles were prepared and the release of oligonucleotide from chitosan-TPP/oligonucleotide nanoparticles was investigated. Morphological property, zeta potential and particle size of the prepared chitosan/oligonucleotide nanoparticles were investigated using Field Emission-Scanning Electron Microscope (FE-SEM) and particle size analyzer. The interaction between chitosan and oligonucleotide was confirmed by using capillary zone electrophoresis (CZE), and the released oligonucleotides were determined by spectrophotometric method. Oligonucleotides formed the complexes with chitosan with a unique morphological property. The release of oligonucleotides from nanoparticles was dependent on loading methods and pH conditions. Chitosan/oligomer-TPP nanoparticles, which was prepared by adding TPP after the formation of chitosan/oligonucleotide complex, showed the lowest release percent of oligonucleotides with 41.3% at pH 7.0 among the loading methods. The percent release of oligonucleotide from oligonucleotide loaded chitosan nanoparticle at pH 10 was higher than the one in acidic condition (pH 5.0). The released oligonucleotides from chitosan/oligonucleotide nanoparticles were stable enough for 12 h under the 20% saliva solution. Our results suggest that the sustained release of oligonucleotide from chitosan nanoparticles may be suitable for the local therapeutic application in periodontal diseases.

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A fault section in Korean distribution networks is generally determined as a section between a switch with a fault indicator (FI) and a switch without an FI. However, the existing method cannot be applied to distribution networks with distributed generations (DGs) due to false FIs that are generated by fault currents flowing from the load side of a fault location. To identify the false FIs and make the existing method applicable, this paper proposes a method to determine the fault section by utilizing an artificial neural network (ANN) model for validating FIs, which is difficult to determine using mathematical equations. The proposed ANN model is built by training the relationship between the measured A, B, C, and N phase fault currents acquired by numerous simulations on a sample distribution system, and guarantees 100% FI validations for the test data. The proposed method can accurately distinguish genuine and false Fis by utilizing the ability of the ANN model, thereby enabling the conventional FI-based method to be applied to DG-connected distribution networks without any changes to the equipment and communication infrastructure. To verify the performance of the proposed method, various case studies considering real fault conditions are conducted under a Korean distribution network using MATLAB.

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