http://scholar.dgist.ac.kr:80 The Repository digital repository system captures, stores, indexes, preserves, and distributes digital research material. Fri, 12 Jun 2026 12:44:37 GMT 2026-06-12T12:44:37Z https://scholar.dgist.ac.kr/handle/20.500.11750/60416 Title: Structurally engineered ultrasoft PEDOT:PSS fiber microelectrodes with enhanced electrochemical performance for neural interfaces Author(s): Won, Chihyeong; Cho, Young Uk; Kweon, Siyeon; Cho, Sungjoon; Kwon, Chaebeen; Kim, Hyun Woo; Lee, Ju Young; Park, Sang Hoon; Han, Sorim; Kim, Yang Tae; Jang, Jumyoung; Jekal, Janghwan; Kim, Jae Geun; Jang, Kyung-In; Xu, Sheng; Gao, Wei; Cho, Il-Joo; Yu, Ki Jun; Lee, Taeyoon Abstract: Stable and reliable neural interfacing is essential for the diagnosis and treatment of chronic neurological disorders. Flexible neural probes are particularly important for this purpose, as they minimize tissue damage and inflammatory responses while maintaining stable electrode-tissue coupling; however, achieving both high electrical performance and tissue-like mechanics remains challenging. Here, we present a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) fiber microelectrode (PFME), an all-organic neural probe capable of recording single-neuron activities with potential for long-term interfacing. The PFME is entirely composed of organic components and fabricated without thermal processing. In addition, the posttreatment process enables to selectively remove PSS binder networks while promoting PEDOT chain alignment to optimize mechanical compliance and electrochemical performance. In vivo, the PFME enables stable single-unit recordings from the mouse hippocampus. Histological analysis after 1 week of implantation reveals minimal glial activation comparable to that elicited by a conventional probe. This structurally engineered PFME establishes a pathway to achieve minimally invasive neural interfacing platforms for chronic applications. Thu, 30 Apr 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60416 2026-04-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60415 Title: A robust vision language model for molecular status prediction and radiology report generation in adult-type diffuse gliomas Author(s): Park, Yae Won; Kang, Myeongkyun; Ryu, Huiseung; Han, Kyunghwa; Sim, Yongsik; Park, Ji Eun; Chang, Jong Hee; Kim, Se Hoon; Lee, Seung-Koo; Park, Sang Hyun; Ahn, Sung Soo Abstract: We aimed to establish a robust vision-language model ("Glio-LLaMA-Vision") for molecular status prediction and radiology report generation (RRG) in adult-type diffuse gliomas. Multiparametric MRI data and paired radiology reports from 1001 patients with adult-type diffuse gliomas were included in the institutional training set. A vision-language model, Glio-LLaMA-Vision, was developed from LLaMA 3.1 pre-trained on 2.79 million biomedical image-text pairs from PubMed Central and further fine-tuned from the institutional training set. The performance was validated in 100 patients and 75 patients with paired MRI-radiology reports from an institutional validation set and another tertiary institution (AMC), and in 170 and 477 patients with MRI from TCGA and UCSF datasets, respectively. In terms of IDH mutation status prediction, Glio-LLaMA-Vision showed AUCs ranging from 0.85-0.95 in the internal validation and external datasets. In terms of RRG, the BLEU-1 and ROUGE-L scores were 0.50 and 0.49 in the internal validation, respectively, and 0.32 and 0.36 on the AMC dataset, respectively. Overall, 37.8% of generated reports were considered superior or equal to the original reports, while 91.0% of generated reports were considered clinically acceptable by neuroradiologists. In conclusion, Glio-LLaMA-Vision demonstrates promising performance in molecular status prediction and RRG in adult-type diffuse gliomas, showing potential for clinical assistance. Tue, 31 Mar 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60415 2026-03-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60414 Title: Shared and Divergent Transcriptional Programs of Oligodendrocyte Differentiation Across Vertebrate Species Revealed by scRNA-seq Analysis Author(s): Yun, Tery; Park, Junhee; Baek, Myungin Abstract: A myelination is essential for neural function in the vertebrate central nervous system, yet the molecular details of how the oligodendrocyte differentiation program has evolved remain poorly understood. Here, we performed a cross-species single-cell transcriptomic analysis of oligodendrocyte lineage cells in the spinal cord of five vertebrate species: fugu, mudskipper, chicken, mouse, and human. Pseudotime trajectory analysis revealed a shared oligodendrocyte progenitor cell (OPC) to committed oligodendrocyte precursor (COP) to myelin-forming oligodendrocyte (MOL) differentiation trajectory across all species, and CAME-based cross-species mapping confirmed the homology of OPC and MOL identities, while COP showed reduced mapping in teleosts compared with amniotes. Among stage-specific DEGs, highly shared genes (≥4 species) were organized into four co-expression modules encompassing cell projection organization, myelination, synapse assembly, and ribonucleoprotein biogenesis, with evolutionary core genes (all 5 species) enriched for oligodendrocyte differentiation and Wnt signaling. Strikingly, amniote-exclusive genes were enriched for synaptic vesicle transport, cell projection organization, predominantly at the OPC stage. This asymmetry indicates that amniotes have expanded the oligodendrocyte differentiation program at the progenitor stage, potentially linked to the myelination demands of terrestrial locomotor circuits. Our findings provide insights into how the oligodendrocyte differentiation program has been shaped by both deep evolutionary conservation and lineage-specific adaptation. Thu, 30 Apr 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60414 2026-04-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60413 Title: AI-driven digital holographic microscopy for label-free quantitative cellular analysis: toward low-cost and field-deployable platforms Author(s): Moon, Inkyu; Javidi, Bahram Abstract: Recent progress in artificial intelligence (AI) and digital holographic microscopy (DHM) has enabled quantitative, label-free, and noninvasive cellular imaging with unprecedented precision. This review provides an overview of AI-driven DHM technologies that transform classical holographic phase reconstruction and cellular analysis into real-time, portable biomedical solutions. After outlining the optical and computational fundamentals of DHM and quantitative phase imaging, we describe how deep generative and diffusion models substantially enhance phase retrieval accuracy under noisy or single-shot conditions. We then summarize recent biomedical applications, integrating blood, cancer, and cardiac cell analyses into a unified framework of AI-assisted quantitative phenotyping. Deep and self-supervised learning approaches are shown to enable high-accuracy classification of red blood cells and cancer cells and label-free evaluation of cardiomyocyte contractility and drug response. The combination of AI-based reconstruction, self-supervised learning, and physics-informed modeling demonstrates robust performance even with limited labeled data. Finally, we discuss the system-level transition toward low-cost, edge-AI-enabled DHM platforms capable of real-time phase imaging in point-of-care or field environments. We highlight key challenges in data standardization, interpretability, and multimodal integration. Collectively, this review envisions AI-integrated DHM as a scalable, accessible technology bridging advanced quantitative imaging with practical biomedical diagnostics. (c) 2026 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement Thu, 30 Apr 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60413 2026-04-30T15:00:00Z