GENE THERAPY STRATEGIES FOR TREATMENT OF MUCO-OBSTRUCTIVE LUNG DISEASES

Knowledge of genetic origins and associations of muco-obstructive lung diseases has made inhaled gene therapy an attractive alternative to the current standards of care that are limited to managing disease symptoms. However, despite over two decades of intensive research and development, gene therapy has yet to help patients with cystic fibrosis (CF) or any other muco-obstructive lung diseases. The slow progress is due in part to poor understanding of the biological barriers to inhaled gene therapy. In this dissertation, I first introduce the pathobiology of representative muco-obstructive lung diseases and examine pitfalls of clinically investigated gene vectors of the past and of current options. I then review key components for successful execution of inhaled gene therapy, including gene delivery systems, physiological barriers and strategies to overcome them, and advances in preclinical models with which the most promising systems may be vetted for clinical trials. Secondly, I demonstrate that adeno-associated vectors (AAV), which are more commonly used gene vectors for clinical settings, differ in their ability to diffuse through the CF sputum barrier and mediate various levels of transduction depending on the surface chemistry. Specifically, I compared three AAV vectors in their ability to i) diffuse in CF sputum, ii) provide transgene expression in ALI culture of primary human CF bronchial epithelial cells, and iii) provide transgene expression in a mouse model of muco-obstructive lung diseases. Thirdly, I present the application of a synthetic biodegradable gene vector in ex vivo, in vitro, and in vivo models relevant to muco-obstructive diseases. This gene vector is composed of engineered poly(β-amino ester) polymers and nucleic acid that encodes reporter or therapeutically relevant genes. I found that this gene vector, compared to conventional gene vector, is able to i) efficiently diffuse through CF sputum, ii) safely mediate higher magnitude of and widespread transgene expression in healthy and muco-obstructive lung mouse model, and iii) apically transfer reporter gene to mucus-covered air-liquid interface (ALI) culture of primary human CF bronchial epithelial cells harvested from CF patient lungs with F508del homozygous mutation, the most common form of mutation in the CF patient population

The Impact Of Booth Recommendation System On Exhibition Attendees\u27 Unplanned Visit Behavior: An Extrinsic-Intrinsic Dichotomy Perspective

Our study on unplanned behaviour theory have examined the effect of booth recommendation system (BRS) on exhibition arise from either an extrinsic or intrinsic motivation. Previous studies, however, ignored the importance of the unplanned behavioural effectiveness through BRS that bonds extrinsic and intrinsic motivation together to deliver unexpected outcomes in exhibition. In this paper, we propose a model of the impact of BRS where perception of usefulness and threat to freedom of choice mediates the effect of both extrinsic and intrinsic motivation on unplanned booth visit behavior. We collected data from 101 visitors of exhibition and analyzed it using the Partial Lease Square (PLS) method. Our findings, interestingly, show that only intrinsic motivations (escape, attraction) significantly influence both perceived usefulness of BRS and threat to freedom of choice, however extrinsic motivation (information) does not significantly influences. Perceived usefulness of BRS mediates directly the effect of escape and attraction on unplanned booth visit behavior. The results and implications are further discussed

Smart Tourism of the Korea: A Case Study

The utilization of Information Technology (IT) is spreading in tourism industry with explosive growth of Internet, Social Network Service (SNS) through smart phone applications. Especially, since intensive information has high value on tourism area, IT is becoming a crucial factor in the tourism industry. The smart tourism is explained as an holistic approach that provide tour information, service related to travel, such as destination, food, transportation, reservation, travel guide, conveniently to tourists through IT devices. In our research, we focus on the Korea Tourism Organization’s (KTO’s) smart tourism case. This research concentrates on the necessity and effectiveness of smart tourism which delivers travel information in real-time base. Also, our study overview how KTO’s IT operation manages each channel, website, SNS, applications and finally suggests the smart tourism’s future direction for the successful realization

Layered Division Multiplexing with Distributed Multiple-Input Single-Output Schemes

"© 2019 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."[EN] Single frequency networks (SFNs) provides an increased spectral efficiency compared to the traditional multiple frequency networks. However, some coverage areas in SFN can be affected by destructive interferences. In order to reduce these situations, distributed multiple-input single-output (MISO) schemes have been adopted in the new digital terrestrial television standards, Alamouti in DVB-T2 and transmit diversity code filter sets in ATSC 3.0. On the other hand, layered division multiplexing (LDM), a non-orthogonal multiple access technology, has been adopted in ATSC 3.0 due to its spectral efficiency increase compared to time or frequency division multiplexing. The LDM signal is formed by a power superposition of two independent signals, which are designed for different reception conditions (mobile and fixed-rooftop). The combination of distributed MISO and LDM techniques has not been evaluated yet. In this paper, the joint transmission of LDM with distributed MISO is analyzed in terms of complexity and the joint performance is evaluated by means of physical layer simulations.This work was supported in part by the ICT Research and Development Program of MSIP/IITP (Development of Transmission Technology for Ultra High Quality UHD) under Grant 2017-0-00081, and in part by the Ministerio de Educacion y Ciencia, Spain, through European FEDER funds under Grant TEC2014-56483-R.Garro, E.; Barjau, C.; Gomez-Barquero, D.; Kim, J.; Park, S.; Hur, N. (2019). Layered Division Multiplexing with Distributed Multiple-Input Single-Output Schemes. IEEE Transactions on Broadcasting. 65(1):30-39. https://doi.org/10.1109/TBC.2018.2823643S303965

Machine Learning-Based Analysis of Adolescent Gambling Factors

Background and aims: Problem gambling among adolescents has recently attracted attention because of easy access to gambling in online environments and its serious effects on adolescent lives. We proposed a machine learning-based analysis method for predicting the degree of problem gambling. Methods: Of the 17,520 respondents in the 2018 National Survey on Youth Gambling Problems dataset (collected by the Korea Center on Gambling Problems), 5,045 students who had gambled in the past 3 months were included in this study. The Gambling Problem Severity Scale was used to provide the binary label information. After the random forest-based feature selection method, we trained four models: random forest (RF), support vector machine (SVM), extra trees (ETs), and ridge regression. Results: The online gambling behavior in the past 3 months, experience of winning money or goods, and gambling of personal relationship were three factors exhibiting the high feature importance. All four models demonstrated an area under the curve (AUC) of >0.7; ET showed the highest AUC (0.755), RF demonstrated the highest accuracy (71.8%), and SVM showed the highest F1 score (0.507) on a testing set. Discussion: The results indicate that machine learning models can convey meaningful information to support predictions regarding the degree of problem gambling. Conclusion: Machine learning models trained using important features showed moderate accuracy in a large-scale Korean adolescent dataset. These findings suggest that the method will help screen adolescents at risk of problem gambling. We believe that expandable machine learning-based approaches will become more powerful as more datasets are collected.11Ysciessciscopu

Military Facility Cost Estimation System Using Case-Based Reasoning in Korea

This manuscript was submitted on January 22, 2010; approved on July 30, 2010; published online on August 3, 2010. Discussion period open until October 1, 2011; separate discussions must be submitted for individual papers.Numerous cost estimations are made repetitively in the initial stages of construction projects in response to ongoing scope changes and often need to be recalculated frequently. In practice, the square foot method, considered an effective method for time-saving, is widely used. However, this method requires a great amount of effort to calculate a unit price and does not consider the uniqueness of each case. Thus, the use of the square foot method could bring about unwanted consequences. For example, in the case of military projects in Korea, significant differences have been reported between estimations made using this method and the actual costs. In an effort to deal with this challenging issue, this research develops a military facility cost estimation (MilFaCE) system, based on case-based reasoning (CBR), using case data from 422 construction projects at 16 military facilities. Based on system validation experiments involving 10 military officers (engineers), the effectiveness of the system in terms of estimation accuracy and user-friendliness is confirmed. Consequently, this research can be a CBR application example of construction cost estimation and a basis for further research into the development of cost estimate systems. DOI: 10.1061/(ASCE)CP.1943-5487.0000082. (C) 2011 American Society of Civil Engineers.This research was supported by grants (R&D06CIT-A03 and 05CIT-01) from the Korea Ministry of Land, Transport, and Marine Affairs and the Ministry of Defense.

Clinical and Lifestyle Determinants of Continuous Glucose Monitoring Metrics in Insulin-Treated Patients with Type 2 Diabetes Mellitus

Background There was limited evidence to evaluate the association between lifestyle habits and continuous glucose monitoring (CGM) metrics. Thus, we aimed to depict the behavioral and metabolic determinants of CGM metrics in insulin-treated patients with type 2 diabetes mellitus (T2DM). Methods This is a prospective observational study. We analyzed data from 122 insulin-treated patients with T2DM. Participants wore Dexcom G6 and Fitbit, and diet information was identified for 10 days. Multivariate-adjusted logistic regression analysis was performed for the simultaneous achievement of CGM-based targets, defined by the percentage of time in terms of hyper, hypoglycemia and glycemic variability (GV). Intake of macronutrients and fiber, step counts, sleep, postprandial C-peptide-to-glucose ratio (PCGR), information about glucose lowering medications and metabolic factors were added to the analyses. Additionally, we evaluated the impact of the distribution of energy and macronutrient during a day, and snack consumption on CGM metrics. Results Logistic regression analysis revealed that female, participants with high PCGR, low glycosylated hemoglobin (HbA1c) and daytime step count had a higher probability of achieving all targets based on CGM (odds ratios [95% confidence intervals] which were 0.24 [0.09 to 0.65], 1.34 [1.03 to 1.25], 0.95 [0.9 to 0.99], and 1.15 [1.03 to 1.29], respectively). And participants who ate snacks showed a shorter period of hyperglycemia and less GV compared to those without. Conclusion We confirmed that residual insulin secretion, daytime step count, HbA1c, and women were the most relevant determinants of adequate glycemic control in insulin-treated patients with T2DM. In addition, individuals with snack consumption were exposed to lower times of hyperglycemia and GV

PEGylated enhanced cell penetrating peptide nanoparticles for lung gene therapy

The lung remains an attractive target for the gene therapy of monogenetic diseases such as cystic fibrosis (CF). Despite over 27 clinical trials, there are still very few gene therapy vectors that have shown any improvement in lung function; highlighting the need to develop formulations with improved gene transfer potency and the desirable physiochemical characteristics for efficacious therapy. Herein, we introduce a novel cell penetrating peptide (CPP)-based non-viral vector that utilises glycosaminoglycan (GAG)-binding enhanced transduction (GET) for highly efficient gene transfer. GET peptides couple directly with DNA through electrostatic interactions to form nanoparticles (NPs). In order to adapt the GET peptide for efficient in vivo delivery, we engineered PEGylated versions of the peptide and employed a strategy to form DNA NPs with different densities of PEG coatings. We were able to identify candidate formulations (PEGylation rates ≥40%) that shielded the positively charged surface of particles, maintained colloidal stability in bronchoalveolar lavage fluid (BALF) and retained gene transfer activity in human bronchial epithelial cell lines and precision cut lung slices (PCLS) in vitro. Using multiple particle tracking (MPT) technology, we demonstrated that PEG-GET complexes were able to navigate the mucus mesh and diffuse rapidly through patient CF sputum samples ex vivo. When tested in mouse lung models in vivo, PEGylated particles demonstrated superior biodistribution, improved safety profiles and efficient gene transfer of a reporter luciferase plasmid compared to non-PEGylated complexes. Furthermore, gene expression was significantly enhanced in comparison to polyethylenimine (PEI), a non-viral gene carrier that has been widely tested in pre-clinical settings. This work describes an innovative approach that combines novel GET peptides for enhanced transfection with a tuneable PEG coating for efficacious lung gene therapy