https://scholar.dgist.ac.kr/handle/20.500.11750/16192 2026-04-04T12:10:41Z https://scholar.dgist.ac.kr/handle/20.500.11750/59191 Title: METHOD AND DEVICE FOR PROVIDING INFORMATION FOR CANCER DIAGNOSIS BY USING EXTRACELLULAR VESICLES Author(s): 김해영; 구교권; 김영규; 김은주; 이윤희; 박수현 Abstract: This method utilizes machine learning on the basis of extracellular vesicles obtained through a liquid biopsy, so as to provide information necessary for efficient cancer diagnosis, and comprises: collecting learning data including surface characteristic images of extracellular vesicles extracted from cancer cells; using the learning data so as to construct an artificial intelligence diagnosis model; and deriving cancer-related disease information through the constructed artificial intelligence diagnosis model on the basis of surface characteristic data of extracellular vesicles to be diagnosed. https://scholar.dgist.ac.kr/handle/20.500.11750/58997 Title: Surface Tension-Guided Drop-and-Spread Inkjet Printing for Additive Fabrication of CNT Field-Effect Transistor Biosensors Author(s): Park, Soohyun; Shin, Minhye; Kim, Eunui; Kang, Hongki; Lee, Yoonhee Abstract: Carbon nanotube field-effect transistors (CNT FETs) are highly regarded in nanoelectronics for their superior electrical properties, yet their broader adoption in nanotechnology is hindered by challenges in scalable fabrication. These challenges are primarily related to controlling nanotube density and achieving consistent alignment at electrode junctions for large-scale production. Here, we present a novel in-place inkjet printing technique to construct CNT FETs, ensuring controlled numbers of connected CNTs. We print a series of pico-liter droplets of CNT ink onto prepatterned electrode arrays over a 4-in. silicon wafer, promoting additive device manufacturing without supplementary lithographic steps. Our technique leverages surface tension-driven flow, guiding droplet spread along electrodes, preventing unwanted CNT networks. This approach enhances manufacturing throughput and device yield and efficiently connects an individualized CNT array to electrodes with less than 10 tubes per device. Moreover, we demonstrate biosensing application by functionalizing devices with DNA aptamers, achieving serotonin detection at thresholds as low as 42 pM. This method establishes cost-efficient and simple micromanufacturing protocols essential for the mass production of CNT FET arrays, particularly beneficial for high-throughput bioassays. 2025-07-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/58695 Title: Deep Learning-Based Classification of NSCLC-Derived Extracellular Vesicles Using AFM Nanomechanical Signatures Author(s): Park, Soohyun; Kim, Youngkyu; Kim, Jung-Hee; Kim, Haeyoung; Kim, Kwang-Youl; Kim, Eunjoo; Koo, Gyogwon; Lee, Yoonhee Abstract: Nonsmall cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality, with liquid biopsy emerging as a promising tool for noninvasive diagnostics. Extracellular vesicles (EVs) serve as molecular messengers of the tumor microenvironment, yet precise characterization methods remain limited. Using atomic force microscopy (AFM), we analyzed EVs from NSCLC subtypes (A549, PC9, PC9/GR) and nontumorigenic bronchial epithelial cells (BEAS-2B), revealing that A549-derived EVs exhibited significantly higher stiffness, likely due to KRAS mutation-associated lipid alterations. EGFR mutant EVs (PC9, PC9/GR) showed overlapping nanomechanical properties, correlating with their shared genetic background. To enhance classification, we implemented a DenseNet-based deep learning model for AFM image analysis, integrating nanomechanical and morphological features. This approach significantly improved diagnostic performance, achieving an AUC of 0.92, and notably, EVs from the A549 (KRAS mutant) cell line were classified with 96% accuracy. This study provides the first demonstration of the nanomechanical classification of NSCLC-derived EVs, highlighting the potential of deep learning-enhanced AFM analysis as a powerful tool for advancing liquid biopsy and precision diagnostics. Addressing sample variability and validating performance across clinical samples will be key to enabling its clinical translation. 2025-06-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/57351 Title: In Silico Analysis of Binding Sites for a Novel ssDNA Aptamer Specific to Verrucarin A and Detection in Dust Extracts Author(s): Park, Junyoung; Lee, Yoonhee; Kim, Eunjoo; Choe, Jong Kwon Abstract: An aptamer is a single-stranded oligonucleotide that serves as a chemical antibody with a high specificity and binding affinity that can recognize a wide range of molecules. Effective modification and truncation of aptamers can enhance their binding affinities to particular targets while also broadening their application for uses, such as biosensors. However, a conventional trial-and-error methodology hinders this process. Herein, we demonstrate an in silico method to elucidate the binding site of single-stranded DNA aptamer specific to verrucarin A, a mycotoxin produced by molds in indoor buildings that causes adverse effects in living organisms. The novel ssDNA aptamer exhibited a binding affinity of 29.5 nM, demonstrating a relatively strong affinity compared to those of previously reported typical aptamers for small molecules. Furthermore, the selected aptamer was highly specific toward verrucarin A among structurally related mycotoxins (i.e., verrucarol and zearalenone). The specific binding site of the aptamer predicted via molecular dynamics and molecular docking simulations was highly consistent with the results observed via truncation, single base mutation, and circular dichroism experiments. The fluorescence assay revealed limits of detection and quantification of 4.1 and 12 nM for the aptamer, respectively. Comparing our developed aptasensor with LC-MS/MS methodology revealed that it could detect verrucarin A levels in phosphate-buffered saline and dust extracts with robust precision and consistency. Our findings provide insight for future studies exploring interaction mechanisms with intended targets and practical sensing applications, such as point-of-care detection of verrucarin A. © 2024 American Chemical Society. 2024-09-30T15:00:00Z