https://scholar.korea.ac.kr/handle/2021.sw.korea/842 Thu, 09 Apr 2026 06:20:42 GMT 2026-04-09T06:20:42Z https://scholar.korea.ac.kr/handle/2021.sw.korea/271429 Title: Elongation factor-G1A identified as a novel effector protein translocated into cells and a key modulator of Pseudomonas aeruginosa physiology and host cellular responses Authors: Lee, Yeji; Jin, Yongxin; Wu, Weihui; Ha, Un-Hwan Abstract: Pseudomonas aeruginosa has two closely related elongation factors, EF-G1A and EF-G1B, which share 90 % sequence similarity. Despite their high sequence homology, the role of EF-G in P. aeruginosa pathogenesis remains not fully understood. In our study, we found that compared to EF-G1B, EF-G1A expression reduced bacterial growth and twitching motility, while increasing swimming motility. Notably, EF-G1A was translocated into host cells in a T6SS-dependent manner. This translocation was significantly reduced, though not completely abolished, in strains with mutations in both the T6SS spike protein VgrG1a and the tube protein Hcp1, suggesting that while EF-G1A translocation is influenced by T6SS, additional components are also involved. Moreover, EF-G1A expression reduced T3SS-mediated morphological alterations, as evidenced by the downregulation of T3SS effectors such as ExoS and ExoT. EF-G1A was also found to suppress activation of the NF-κB signaling pathway, leading to decreased production of inflammatory cytokines including IL-6, IL-8, and TNFα. These findings highlight EF-G1A as a key modulator, affecting both P. aeruginosa physiology and host cellular responses, thereby providing new insights into the complex role of EF-G1A in bacterial pathogenesis. © 2025 Elsevier Ltd Fri, 01 Aug 2025 00:00:00 GMT https://scholar.korea.ac.kr/handle/2021.sw.korea/271429 2025-08-01T00:00:00Z https://scholar.korea.ac.kr/handle/2021.sw.korea/269264 Title: Machine learning-integrated biomimetic electronic noses: Future perspectives Authors: Lee, Taeha; Yu, Jun; Lee, Sang Won; Oh, Seung Hyeon; Kang, Dain; Son, Hyunmok; Hwang, Han-Jeong; You, Jae Hyun; Lee, Gyudo Abstract: The electronic nose (E-nose) is an innovative device that mimics the human sense of smell. E-noses are used for effective detection and discrimination between complex odors. Compared to traditional odor detection methods, E-nose technology employs a sensor array that differentiates and measures airborne smells through a combination of electrical signals generated by the sensor array when detecting odors. In addition, the incorporation of machine learning for data processing has enhanced the sensitivity and selectivity of odor molecular detection. However, certain limitations exist, such as a limited range of detectable odor molecules and low analytical accuracy for similar compounds, which challenge the claim that E-noses can fully mimic human olfaction. In this paper, we provides a general overview of the E-nose structure and its operating principles, as well as a summary of recent research and practical constraints in detecting volatile organic compounds. Moreover, this review paper discusses the future development of biomimetic E-noses in conjunction with other technologies and describes their potential commercial applications, including through E-commerce platforms. The critical review contributes to the E-nose literature by offering insights into how the E-nose device can solve real-world problems and by proposing directions for future advancement. Sun, 01 Jun 2025 00:00:00 GMT https://scholar.korea.ac.kr/handle/2021.sw.korea/269264 2025-06-01T00:00:00Z https://scholar.korea.ac.kr/handle/2021.sw.korea/269282 Title: Transfer learning and data augmentation for glucose concentration prediction from colorimetric biosensor images Authors: Choi, Ga-Young; Kim, Na-Ri; Yu, Da-Young; Lee, Taeha; Lee, Gyudo; Hwang, Han-Jeong Abstract: A deep learning algorithm is introduced to accurately predict glucose concentrations using colorimetric paper sensor (CPS) images. We used an image dataset from CPS treated with five different glucose concentrations as input for deep learning models. Transfer learning was performed by modifying four established deep learning models-ResNet50, ResNet101, GoogLeNet, and VGG-19-to predict glucose concentrations. Additionally, we attempted to alleviate the challenge of requiring the large amount of training data by applying data augmentation techniques. Prediction performance was evaluated using coefficients of determination (R-2), root mean squared error (RMSE), and relative-RMSE (rRMSE). GoogLeNet showed the highest coefficient of determination (R-2 = 0.994) and significantly lower prediction errors across all concentration levels compared with a traditional machine learning approach (two-sample t-test, p < 0.001). When data augmentation was performed using 20% of the entire training dataset, the mean prediction error was comparable to that of the original entire training dataset. We presented a novel approach for glucose concentration prediction using deep learning techniques based on transfer learning and data augmentation with image data. Our method uses images from CPS as input and eliminates the need for separate feature extraction, simplifying the prediction process. Thu, 01 May 2025 00:00:00 GMT https://scholar.korea.ac.kr/handle/2021.sw.korea/269282 2025-05-01T00:00:00Z https://scholar.korea.ac.kr/handle/2021.sw.korea/268558 Title: Perfusable cellulose channels from decellularized leaf scaffolds for modeling vascular amyloidosis Authors: Lee, Taeha; Lee, Kang Hyun; Cheong, Da Yeon; Lee, Sang Won; Park, Insu; Lee, Gyudo Abstract: Amyloid infiltration in blood vessels damages them and spreads amyloid to surrounding tissues. Research on amyloid flow and deposition in capillaries is limited due to the lack of suitable models. In this study, we created a decellularized leaf scaffold (DCLS) mimicking complex capillary structures to study vascular amyloidosis. Fluorescent molecules (e.g., Nile red) confirmed the intact cellulose framework of the DCLS. Additionally, DCLS with colorimetric nanoparticles (e.g., polyaniline nanoparticles) showed reversible color changes with pH variations, indicating preserved pore structure. The DCLS's responsiveness and preserved vein structures demonstrate its similarity to human vasculature. Hen egg-white lysozyme amyloid deposition was observed in various areas of the DCLS after perfusion. An amyloid-degrading agent (e.g., trypsin) was then perfused, showing a reduction of 18.3 % after 90 min and 25.5 % after 180 min. This DCLS model offers a more realistic and physiologically meaningful platform for studying intravascular amyloid accumulation and clearance than existing in vitro vascular models. © 2025 Elsevier B.V. Thu, 01 May 2025 00:00:00 GMT https://scholar.korea.ac.kr/handle/2021.sw.korea/268558 2025-05-01T00:00:00Z