2025 연구성과 (17 / 151)

※ 컨트롤 + 클릭으로 열별 다중 정렬 가능합니다.
Excel 다운로드
WoS SCOPUS Document Type Document Title Abstract Authors Affiliation ResearcherID (WoS) AuthorsID (SCOPUS) Author Email(s) Journal Name JCR Abbreviation ISSN eISSN Volume Issue WoS Edition WoS Category JCR Year IF JCR (%) FWCI ⓘ FWCI Update Date WoS Citation SCOPUS Citation Keywords (WoS) KeywordsPlus (WoS) Keywords (SCOPUS) KeywordsPlus (SCOPUS) Language Publication Stage Publication Year Publication Date DOI JCR Link DOI Link WOS Link SCOPUS Link
○ ○ Article ⓘ Monitoring the formation of infinite-layer transition metal oxides through in situ atomic-resolution electron microscopy ⓘ Infinite-layer transition metal oxides with two-dimensional oxygen coordination exhibit intriguing electronic and magnetic properties due to strong in-plane orbital hybridization. The synthesis of this distinctive structure has primarily relied on kinetically controlled reduction of oxygen-rich phases featuring three-dimensional polyhedral oxygen coordination. Here, using in situ atomic-resolution electron microscopy, we scrutinize the intricate atomic-scale mechanisms of oxygen conduction leading to the transformation of SrFeO2.5 to infinite-layer SrFeO2. The oxygen release is highly anisotropic and governed by the lattice reorientation aligning the fast diffusion channels towards the outlet, which is facilitated by cooperative yet shuffle displacements of iron and oxygen ions. Accompanied with the oxygen release, the three-dimensional to two-dimensional reconfiguration of oxygen is facilitated by the lattice flexibility of FeOx polyhedral layers, adopting multiple discrete transient states following the sequence determined by the least energy-costing pathways. Similar transformation mechanism may operate in cuprate and nickelate superconductors, which are isostructural with SrFeO2. Understanding the structural rearrangements of infinite-layer transition metal oxides at the atomic level remains challenging. Now in situ electron microscopy has been used to monitor the formation of infinite-layer SrFeO2 through an oxygen deintercalation process; lattice flexibility of the FeOx polyhedral layers facilitates the phase transformation. ⓘ Xing, Yaolong; Kim, Inhwan; Kang, Kyeong Tae; Byun, Jinho; Choi, Woo Seok; Lee, Jaekwang; Oh, Sang Ho ⓘ Korea Inst Energy Technol, Dept Energy Engn, Naju, South Korea; Inst Energy Mat & Devices, Korea Inst Energy Technol, Naju, South Korea; Pusan Natl Univ, Dept Phys, Busan, South Korea; Sungkyunkwan Univ, Dept Phys, Suwon, South Korea; Kyungpook Natl Univ, KNU G LAMP Res Ctr, Dept Phys, Daegu 41566, South Korea ⓘ Lee, Joonhyuck/AAD-7668-2019; Choi, Woo Seok/G-8783-2014; Xing, Yaolong/MXJ-9903-2025; Oh, Sang Ho/ISV-3878-2023 ⓘ 57220044509; 57226310158; 56002123900; 57195412299; 14031133800; 55888626200; 57558169000 ⓘ choiws@skku.edu; jaekwangl@pusan.ac.kr; shoh@kentech.ac.kr; ⓘ NATURE CHEMISTRY ⓘ NAT CHEM ⓘ 1755-4330 1755-4349 17 1 SCIE CHEMISTRY, MULTIDISCIPLINARY ⓘ 2024 20.2 4.0 0 2025-05-07 ⓘ 1 0 ⓘ OXYGEN DIFFUSION; IRON; SUPERCONDUCTIVITY; CRYSTAL; POINTS ⓘ ⓘ iron; metal oxide; oxygen; transition element; article; controlled study; diffusion; drug therapy; electron microscopy; hybridization; kinetics; pharmacology; superconductor; synthesis ⓘ English 2025 2025-01 10.1038/s41557-024-01617-7 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ Rebamipide (Mucosta®), a clinically approved drug, alleviates neuroinflammation and dopaminergic neurodegeneration in a Parkinson's disease model ⓘ BackgroundParkinson's disease (PD) is characterized by dopaminergic neuron loss, neuroinflammation, and motor dysfunction. PD is a multifactorial disease, with neuroinflammation driven by NLRP3 inflammasome activation representing an important component of its pathological progression. Therefore, we aimed to evaluate the therapeutic potential of rebamipide (Mucosta (R)), a clinically approved anti-inflammatory agent, in PD by targeting the NLRP3 inflammasome. Specifically, we examined the effects of rebamipide on neuroinflammation, dopaminergic neuron preservation, and motor deficits using BV2 microglia cells and a 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP)-induced mouse model.Main bodyRebamipide alleviated microglial activation and downstream neuroinflammation by suppressing the NLRP3-NEK7 interaction, resulting in dopaminergic neuron protection in the MPTP-induced PD model. Rebamipide downregulated IL-1 beta levels in BV2 microglia cells treated with alpha-synuclein and MPP+. Molecular docking analysis revealed a high binding affinity between rebamipide and the NLRP3-NEK7 interaction interface. Surface plasmon resonance analysis confirmed the direct binding of rebamipide to NLRP3, with notable kinetic affinity, supporting its role as a novel NLRP3 inflammasome inhibitor. Rebamipide significantly downregulated IL-1 beta levels, microglial activation, and dopaminergic neuron loss in the MPTP mouse model by disrupting inflammasome activation. Rebamipide preserved dopamine levels in the striatum and improved motor deficits, including bradykinesia and motor coordination. The neuroprotective effects of rebamipide were neutralized in NLRP3 knockout mice, confirming the dependency of its action on NLRP3.ConclusionConsidering its established clinical use, this study supports repurposing rebamipide for treating PD and other NLRP3 inflammasome-driven neuroinflammatory diseases. ⓘ Lim, Hye-Sun; Park, Jinyoung; Kim, Eunjeong; Lee, Wonhwa; Yun, Hwi-Yeol; Lee, Seung Hoon; Park, Gunhyuk ⓘ Korea Inst Oriental Med, Herbal Med Resources Res Ctr, 111 Geonjae Ro, Naju 58245, Jeollanam Do, South Korea; Sungkyunkwan Univ, Dept Chem, Suwon 16419, South Korea; Kyungpook Natl Univ, KNU Inst Basic Sci, BK21 FOUR KNU Creat BioRes Grp, Dept Biol,KNU G LAMP Res Ctr,Coll Nat Sci, Daegu 41566, South Korea; Sungkyunkwan Univ, SKKU Inst Convergence, Dept MetaBioHlth, Suwon 16419, South Korea; Chungnam Natl Univ, Coll Pharm, Daejeon, South Korea; Chungnam Natl Univ, Convergence Res Ctr, Daejeon, South Korea; Chungnam Natl Univ, Dept BioAI Convergence, Daejeon, South Korea; Chungnam Natl Univ, Dept Biochem, Res Inst Med Sci, Sch Med, 282 Munhwa Ro, Daejeon 35015, South Korea ⓘ Lee, Wonhwa/GLQ-6506-2022 ⓘ 54416010200; 58601377200; 56892981600; 50161632800; 15133707900; 57201966529; 36671844400 ⓘ qp1015@kiom.re.kr; jypark@naver.com; eunjkim@knu.ac.kr; wonhwalee@skku.edu; hyyun@cnu.ac.kr; passion_lsh@cnu.ac.kr; gpark@kiom.re.kr; ⓘ JOURNAL OF NEUROINFLAMMATION ⓘ J NEUROINFLAMM ⓘ 1742-2094 22 1 SCIE IMMUNOLOGY;NEUROSCIENCES ⓘ 2024 10.1 4.0 0 2025-06-11 ⓘ 0 0 Rebamipide; MPTP; NLRP3 inflammasome; Neuroinflammation; Parkinson's disease ⓘ MOUSE MODEL; INFLAMMATION; MICROGLIA ⓘ MPTP; Neuroinflammation; NLRP3 inflammasome; Parkinson’s disease; Rebamipide ⓘ Alanine; Animals; Disease Models, Animal; Dopaminergic Neurons; Male; Mice; Mice, Inbred C57BL; Microglia; Molecular Docking Simulation; Neuroinflammatory Diseases; Neuroprotective Agents; NLR Family, Pyrin Domain-Containing 3 Protein; Parkinsonian Disorders; Quinolones; 1,2,3,6 tetrahydro 1 methyl 4 phenylpyridine; antiinflammatory agent; cryopyrin; cytokine; diaminobenzidine; dopamine; inflammasome; interleukin 18; interleukin 1beta; interleukin 1beta converting enzyme; interleukin 6; lactate dehydrogenase; mcc950; paraformaldehyde; rebamipide; small interfering RNA; tumor necrosis factor; unclassified drug; alanine; cryopyrin; neuroprotective agent; Nlrp3 protein, mouse; quinolone derivative; rebamipide; animal cell; animal experiment; animal model; animal tissue; Article; behavior; binding affinity; bradykinesia; BV-2 cell line; cell culture; controlled study; CRISPR-CAS9 system; crystal structure; cytotoxicity; dopaminergic nerve cell; down regulation; embryo; enzyme linked immunosorbent assay; female; fluorescence; gene expression; gene knockout; genetic transfection; genotyping; high performance liquid chromatography; human; immunofluorescence; immunohistochemistry; KEGG; male; microglia; microinjection; molecular docking; motor coordination; motor dysfunction; mouse; nerve degeneration; nervous system inflammation; neuroprotection; nonhuman; open field test; Parkinson disease; Protein Data Bank; protein expression; protein protein interaction; real time polymerase chain reaction; rotarod test; signal transduction; surface plasmon resonance; animal; C57BL mouse; disease model; drug effect; drug therapy; metabolism; nervous system inflammation; parkinsonism; pathology ⓘ English 2025 2025-05-17 10.1186/s12974-025-03461-z ⓘ 바로가기 바로가기 바로가기 바로가기
○ Article ⓘ A Machine Learning Attack-Resistant PUF-Based Robust and Efficient Mutual Authentication Scheme in Fog-Enabled IoT Environments ⓘ Fog-enabled Internet of Things (IoT) systems have a lot of attention and are being applied in various fields, including smart homes, smart healthcare, smart factories, and smart grids. These fog-enabled IoT systems enhance citizens’ quality of life and provide innovative and high-quality IoT services. However, these systems can be vulnerable to cyber security attacks since an adversary attempts to modify, delete, block, and intercept the exchanged data over an insecure channel. Besides cyber security attacks, IoT can be fragile to physical security attacks because they are deployed in hostile environments. Physical unclonable function (PUF) is a promising solution to address these issues. PUF can protect the security of IoT devices with minimal computation costs against cyber/physical attacks from an adversary. However, with recent advances in artificial intelligence (AI) technology, existing PUFs used in authentication and key agreement (AKA) schemes are susceptible to machine-learning (ML)-based modeling attacks. To address these challenges, we design the ML-based modeling attack-resistant PUF-based robust and efficient AKA scheme in fog-enabled IoT environments. We evaluate the security of the proposed scheme by performing informal and formal security analyses, such as ROR oracle model and AVISPA simulation. We present the implementation to demonstrate the accuracy against ML-based modeling attacks. Moreover, we perform the performance comparison analysis between the proposed scheme and existing schemes based on testbed implementation. Consequently, the proposed scheme provides superior security and efficiency compared to existing schemes and can be suitable for practical fog-enabled IoT systems. © 2014 IEEE. ⓘ Yu, Sungjin; Park, Kisung; Park, Youngho ⓘ Kyungpook National University, School of Electronics and Electrical Engineering, Daegu, 41566, South Korea, Cryptographic Engineering Research Section, Electronics and Telecommunications Research Institute, Daejeon, 34129, South Korea; Gachon University, Department of Computer Engineering (Smart Security), Seongnam, 13120, South Korea; Kyungpook National University, School of Electronics and Electrical Engineering, Daegu, 41566, South Korea ⓘ ⓘ 57203974524; 57194833768; 56962990300 ⓘ kisung@gachon.ac.kr; parkyh@knu.ac.kr; ⓘ IEEE Internet of Things Journal ⓘ IEEE INTERNET THINGS ⓘ 2327-4662 2327-4662 12 12 SCIE ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS;COMPUTER SCIENCE, INFORMATION SYSTEMS ⓘ 2024 8.9 4.1 0 2025-05-07 ⓘ 0 ⓘ ⓘ Authentication; fog computing; Internet of Things (IoT); machine learning (ML); physical unclonable functions (PUFs) ⓘ Adversarial machine learning; Authentication; Fog computing; Smart homes; Attack resistants; Authentication and key agreements; Cyber security; Internet of thing; Key agreement scheme; Learning Based Models; Machine-learning; Mutual authentication; Physical unclonable function; Security attacks; Cyber attacks ⓘ English Final 2025 10.1109/jiot.2025.3544443 ⓘ 바로가기 바로가기 바로가기
○ Review ⓘ Advances and Challenges in Li-Excess Cathode Additives for Next-Generation Li Rechargeable Batteries ⓘ The development of high-energy-density rechargeable batteries requires the integration of Li-excess cathode additives (LECAs) to mitigate irreversible Li+ loss. However, the practical implementation of these additives remains challenging without a clear understanding of their reaction mechanisms. LECAs can be broadly classified into transition-metal-free (TM-free), overlithiated, and hyperlithiated compounds, each exhibiting distinct Li+ release behaviors and electrochemical properties. Despite their potential advantages, the absence of comprehensive mechanistic insights hampers their optimization and commercial application. This review systematically examines the reaction mechanisms of LECAs through advanced in situ and ex situ analytical techniques, providing a detailed understanding of their Li+ compensation processes, structural evolution, and electrochemical performance. Furthermore, we evaluate structural modification strategies aimed at enhancing their stability, redox kinetics, and compatibility with electrolyte systems, focusing on TM and anion cosubstitution, particle size control, and surface engineering. We also identify key technical challenges, such as oxygen evolution, interfacial degradation, and Li+ utilization inefficiencies, highlighting critical obstacles to their practical implementation. Finally, we outline future research directions, emphasizing the importance of precise material design, interface stabilization, and scalable fabrication techniques to address these challenges. This review establishes a strategic framework for the rational design and optimization of LECAs, providing insights into their potential for next-generation Li rechargeable batteries. © 2025 American Chemical Society. ⓘ Lee, Wontae; Byeon, Yun Seong; Jeong, Seong Hee; Kim, Jae Uk; Lee, Seongeun; Park, Sangbin; Park, Min-Sik; Yoon, Won-Sub ⓘ Department of Chemistry Education, Kyungpook National University, Daegu, 41566, South Korea; Department of Materials Science and Engineering, Kyung Hee University, Yongin, 17104, South Korea; Department of Materials Science and Engineering, Kyung Hee University, Yongin, 17104, South Korea; Department of Energy Science, SKKU Institute of Energy Science and Technology (SIEST), Sungkyunkwan University, Suwon, 16419, South Korea; Department of Energy Science, SKKU Institute of Energy Science and Technology (SIEST), Sungkyunkwan University, Suwon, 16419, South Korea; Department of Energy Science, SKKU Institute of Energy Science and Technology (SIEST), Sungkyunkwan University, Suwon, 16419, South Korea; Department of Materials Science and Engineering, Kyung Hee University, Yongin, 17104, South Korea; Department of Energy Science, SKKU Institute of Energy Science and Technology (SIEST), Sungkyunkwan University, Suwon, 16419, South Korea ⓘ ⓘ 56962931200; 57409528800; 59457918600; 58577536100; 58577063000; 57559137200; 16679365800; 7103087422 ⓘ mspark@khu.ac.kr; wsyoon@skku.edu; ⓘ ACS Energy Letters ⓘ ACS ENERGY LETT ⓘ 2380-8195 2380-8195 SCIE CHEMISTRY, PHYSICAL;ELECTROCHEMISTRY;ENERGY & FUELS;MATERIALS SCIENCE, MULTIDISCIPLINARY;NANOSCIENCE & NANOTECHNOLOGY ⓘ 2024 18.2 4.1 N/A ⓘ 0 ⓘ ⓘ ⓘ Additives; Advanced Analytics; Cathodes; Electrolytes; Interfaces (materials); Lithium; Lithium batteries; Lithium compounds; Classifieds; Electrochemicals; Higher energy density; Li +; Li rechargeable batteries; Mechanistics; Metal free; Property; Reaction mechanism; Release behaviors; Particle size ⓘ English Article in press 2025 10.1021/acsenergylett.5c01240 ⓘ 바로가기 바로가기 바로가기
○ Article ⓘ AI-Enhanced Resource Allocation for LPWAN-based LoRaWAN:A Hybrid TinyML and Deep Learning Approach ⓘ The integration of Artificial Intelligence (AI) with Low Power Wide Area Networks (LPWAN) offers a promising approach to address resource constraints and dynamic network conditions inherent in these networks. However, deploying complex AI algorithms on resource-limited edge devices presents significant challenges due to their limited computational capabilities. In this study, we propose a hybrid Tiny Machine Learning (TinyML) and Deep Neural Network (DNN)-based solution for optimizing resource allocation in LPWAN-based LoRaWAN networks, targeting both static and mobile applications. Our approach leverages the strengths of a 1-D Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model implemented on the network server, combined with TinyML models deployed on edge devices. The CNN-LSTM model predicts optimal spreading factor and transmission power by analyzing spatial and temporal patterns from real-time data, while the TinyML models enable edge devices to autonomously adjust communication parameters in resource-constrained and disconnected scenarios. This hybrid framework enhances network performance by improving the packet success ratio (PSR), maximizing energy efficiency, and addressing the challenges posed by dynamic IoT environments. © 2014 IEEE. ⓘ Lodhi, Muhammad Ali; Sun, Xiaobing; Mahmood, Khalid; Lodhi, Anum; Park, Youngho; Hussain, Majid ⓘ Yangzhou University, School of Information Engineering, Yangzhou, China; Yangzhou University, School of Information Engineering, Yangzhou, China; National Yunlin University of Science and Technology, Graduate School of Intelligent Data Science, Douliu, 64002, Taiwan; University of Electronic Science and Technology of China, School of computer science and engineering, China; Kyungpook National University, School of Electronics Engineering, Daegu, 41566, South Korea; The University of Faisalabad, Department of Computer Science, Faisalabad, 38850, Pakistan ⓘ ⓘ 57188852028; 24829988300; 57342911900; 59894113900; 56962990300; 59477121800 ⓘ xbsun@yzu.edu.cn; parkyh@knu.ac.kr; ⓘ IEEE Internet of Things Journal ⓘ IEEE INTERNET THINGS ⓘ 2327-4662 2327-4662 SCIE ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS;COMPUTER SCIENCE, INFORMATION SYSTEMS ⓘ 2024 8.9 4.1 0 2025-06-11 ⓘ 0 ⓘ ⓘ Internet of Things; LoRaWAN; Low power networks; Resource allocation; TinyML ⓘ Convolutional neural networks; E-learning; Internet of things; Resource allocation; Convolutional neural network; LoRaWAN; Low Power; Low Power Networks; Machine-learning; Network-based; Resources allocation; Short term memory; Tiny machine learning; Wide-area networks; Deep neural networks ⓘ English Article in press 2025 10.1109/jiot.2025.3568445 ⓘ 바로가기 바로가기 바로가기
○ Article ⓘ Compressive Sensing-Based Demultiplexing of Fast-Time CDM-MIMO PMCW Radar Signals for Self-Code Interference Cancellation ⓘ Phase modulated continuous wave (PMCW) radar is one of promising options for the enhanced sensing capability and the multiple access availability. In this paper, we focus on the advantage of the high-resolution multi-input multi-output (MIMO) radar with fast-time code division multiplexing (CDM). First, we formulate the signal model of the fast-time CDM-MIMO PMCW radar by assigning different code sequences to each transmitter (Tx). We investigate the level of self-code interference induced by simultaneously transmitting Tx signals, as a function of the number of Tx and the type of sequence. For self-code interference cancellation, we propose compressive sensing-based greedy algorithms with significantly reduced computation complexity using the property of the circulant matrix. We show via simulation that the proposed methods can effectively mitigate self-code interference and generate radar images by implementing virtual antenna array with significantly enhanced peak to sidelobe ratio and range/angle estimation accuracy. Since the proposed method can fully leverage the advantages of CDM whose transmit signals share time-frequency resources, it can be utilized for high density Internet of Things networks and increase the fundamental performance of sensing while the reduced computations are suitable for the real-time operation. © 2014 IEEE. ⓘ Park, Jeong-Hoon; Ham, Doyoung; Choi, Jeongsik; Lee, Seongwook; Kim, Seong-Cheol ⓘ Seoul National University, Department of Electrical and Computer Engineering, Institute of New Media and Communications, Seoul, South Korea; Seoul National University, Department of Electrical and Computer Engineering, Institute of New Media and Communications, Seoul, South Korea; Kyungpook National University, School of Electronics Engineering, Daegu, 41566, South Korea; Chung-Ang University, School of Electrical and Electronics Engineering, College of ICT Engineering, Seoul, 06974, South Korea; Seoul National University, Department of Electrical and Computer Engineering, Institute of New Media and Communications, Seoul, South Korea ⓘ ⓘ 57826238600; 57255590800; 58534394200; 57193015189; 22334016200 ⓘ sckim@maxwell.snu.ac.kr; ⓘ IEEE Internet of Things Journal ⓘ IEEE INTERNET THINGS ⓘ 2327-4662 2327-4662 SCIE ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS;COMPUTER SCIENCE, INFORMATION SYSTEMS ⓘ 2024 8.9 4.1 N/A ⓘ 0 ⓘ ⓘ Code Division Multiplexing (CDM); Compressive Sensing (CS); Multi-input multi-output (MIMO); Phase Modulated Continuous Wave (PMCW); Self-code interference ⓘ Cochannel interference; Demultiplexing; Image coding; Image compression; Image enhancement; Image segmentation; Radio interference; Telephone interference; Time division multiplexing; Code division multiplexing; Code interference; Codedivision-multiplexing (CDM); Compressive sensing; Continuous Wave; Multi-input multi-output; Phase modulated; Phase modulated continuous wave; Self-code interference; Phase modulation ⓘ English Article in press 2025 10.1109/jiot.2025.3564614 ⓘ 바로가기 바로가기 바로가기
○ ○ Article ⓘ DRL-Based Physical-Layer Security Optimization in Near-Field MIMO Systems ⓘ The advent of extremely large antenna arrays (ELAAs) is crucial for meeting the performance demands of future sixth-generation (6G) wireless networks. However, ELAA introduces significant near-field communication (NFC) effects, characterized by spherical wavefront propagation, in contrast to the conventional planar waves observed in far-field models (FFMs). As NFC facilitates precise beamfocusing and spatial multiplexing, it inherently increases the risk of eavesdropping, making physical-layer security (PLS) a critical challenge for safeguarding confidential communication. This article investigates a multiple-input-multiple-output (MIMO) system in the near-field regime, utilizing NFC properties to enhance secrecy performance. Unlike FFMs that rely on the angular domain, our approach leverages both angular and distance domains to achieve robust PLS, even when an eavesdropper shares the same angular direction as a legitimate user. We propose a deep reinforcement learning (DRL)-based solution to optimize beamforming, power allocation, and antenna selection. By minimizing antenna use while maximizing secrecy rates, the approach avoids resource wastage and ensures superior security. Numerical simulations demonstrate significant secrecy rate improvements. ⓘ Razaq, Mian Muaz; Peng, Limei ⓘ Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu 41566, South Korea ⓘ Razaq, Muaz/ACN-8991-2022 ⓘ 57221661906; 7201574271 ⓘ mianmuaz97@gmail.com; auroraplm@knu.ac.kr; ⓘ IEEE INTERNET OF THINGS JOURNAL ⓘ IEEE INTERNET THINGS ⓘ 2327-4662 12 12 SCIE ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS;COMPUTER SCIENCE, INFORMATION SYSTEMS ⓘ 2024 8.9 4.1 0 2025-05-07 ⓘ 0 1 Antennas; Security; Antenna arrays; Optimization; Array signal processing; Resource management; Propagation; Focusing; Eavesdropping; Internet of Things; Antenna selection optimization; beam focusing; near field communication; physical-layer security (PLS) ⓘ ⓘ Antenna selection optimization; beam focusing; near field communication; physical-layer security (PLS) ⓘ Beam forming networks; Beamforming; Linear programming; Medium access control; Reinforcement learning; Antenna selection; Antenna selection optimization; Beam focusing; Large antennas; Multiple-Input Multiple- Output systems; Near fields; Near-field communication; Optimisations; Physical layer security; Reinforcement learnings; Deep reinforcement learning ⓘ English 2025 2025-06-15 10.1109/jiot.2025.3552230 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ DRL-Driven Localization With AAV in Near-Field Communications ⓘ In this article, we propose a deep reinforcement learning (DRL)-based multipoint localization scheme (MLS) to efficiently localize Internet of Things (IoT) devices using a single autonomous aerial vehicle (AAV) equipped with a large-scale multiantenna configuration in near-field communication (NFC). By utilizing the spherical wave-based near-field steering vector, the multiantenna array on the AAV captures both the Angle of Arrival (AoA) and received signal strength indicator (RSSI) measurements from IoT devices to estimate their locations relative to the position of the AAV. This approach eliminates the need for multiple hovering points required by a single-antenna AAV (SA-AAV) or the deployment of multiple SA-AAVs. To enhance localization accuracy, key hovering points for the multiantenna AAV (MA-AAV) are strategically selected, with weights assigned based on signal strength to prioritize stronger and more reliable signals. Furthermore, DRL dynamically adjusts the position of the MA-AAV to optimize the tradeoff between localization accuracy and energy consumption. Extensive simulations conducted across rural, urban, and dense urban scenarios demonstrate that the proposed DRL-based MLS significantly improves localization accuracy while reducing the energy consumption of the AAV. ⓘ Khan, Muhammad Fawad; Peng, Limei; Ho, Pin-Han; Chen, Yuguang; Dong, Fangjie ⓘ Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu 41566, South Korea; Univ Elect Sci & Technol China, Shenzhen Inst Adv Study, Shenzhen 518055, Guangdong, Peoples R China; Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada; Shenzhen Unicom, Innovat Business Capabil Ctr, Shenzhen 518000, Peoples R China; Natl Hlth Commiss, Natl Hlth Commiss Ctr Stat & Informat, Beijing 100044, Peoples R China ⓘ ⓘ 56185875800; 7201574271; 7402211578; 59489615100; 59489819900 ⓘ m.fawadkhan@knu.ac.kr; auroraplm@knu.ac.kr; pinhanho71@gmail.com; ⓘ IEEE INTERNET OF THINGS JOURNAL ⓘ IEEE INTERNET THINGS ⓘ 2327-4662 12 13 SCIE ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS;COMPUTER SCIENCE, INFORMATION SYSTEMS ⓘ 2024 8.9 4.1 0 2025-05-07 ⓘ 0 0 Location awareness; Autonomous aerial vehicles; Internet of Things; Antenna arrays; Accuracy; Antennas; Vectors; Noise measurement; Received signal strength indicator; Antenna measurements; Autonomous aerial vehicle (AAV); deep reinforcement learning (DRL); Internet of Things (IoT); localization ⓘ AOA ⓘ Autonomous aerial vehicle (AAV); deep reinforcement learning (DRL); Internet of Things (IoT); localization ⓘ Aircraft communication; Image analysis; Image quality; Image segmentation; Image thinning; Micro air vehicle (MAV); Reinforcement learning; Scales (weighing instruments); Steerable antennas; Time difference of arrival; Aerial vehicle; Deep reinforcement learning; Internet of thing; Localisation; Localization accuracy; Multi-antenna; Near-field communication; Reinforcement learnings; Unmanned aerial vehicle; Deep reinforcement learning ⓘ English 2025 2025-07-01 10.1109/jiot.2025.3550351 ⓘ 바로가기 바로가기 바로가기 바로가기
○ Article ⓘ Expanded Hemodialysis with Theranova Dialyzer and Residual Kidney Function in Incident Hemodialysis Patients ⓘ Background:Expanded hemodialysis (HD) using a medium cut-off dialyzer improves the clearance of middle-molecular toxins compared to conventional HD. This study evaluated the effect of expanded HD on preserving residual kidney function in incident HD patients.Methods:Patients who initiated HD were randomized to receive dialysis with either a Theranova 400 (Baxter) or a high-flux dialyzer with a similar surface area over 12 months. The primary outcome was a change in glomerular filtration rate (GFR) over 12 months, as determined by the mean of urea and creatinine clearance. The secondary outcome was a change in 24-hour urine volume, middle molecules, and kidney injury markers.Results:A total of 80 HD patients (mean age [SD]: 63 [12] years; male: 52 [65%]) underwent randomization. Over 12 months, the Theranova group demonstrated a significantly smaller decrease in GFR than the high-flux group (least-squares mean difference of change [95% confidence interval]: -1.4 [-2.4, -0.5] mL/min/1.73 m2). Theranova maintained greater 24-hour urine volume until 9 months, not at 12 months, compared to the high-flux dialyzer. The reduction ratio for / free light chains, TNF-α, and GDF-15 was higher in the Theranova group than in the high-flux group. The increase in the kidney injury marker, IGFBP7, was attenuated in the Theranova group. Hospitalization rate and mortality did not differ between the two groups.Conclusions:This trial suggests that expanded HD using the Theranova dialyzer may slow decline in residual kidney function compared with a high-flux dialyzer in incident HD patients. Copyright © 2025 The Author(s). Published by Wolters Kluwer Health, Inc. ⓘ Lim, Jeong-Hoon; Seo, Yu Jin; Jeon, Yena; Jeon, You Hyun; Jung, Hee-Yeon; Choi, Ji-Young; Park, Sun-Hee; Kim, Chan-Duck; Kang, Seok Hui; Ryu, Jung-Hwa; Kang, Duk-Hee; Cho, Jang-Hee; Kim, Yong-Lim ⓘ Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea; Department of Statistics, Kyungpook National University, Daegu, South Korea; Department of Epidemiology & Biostatistics, University of California at San Francisco, San Francisco, CA, United States; Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea; Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea; Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea; Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea; Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea; Internal Medicine, Yeungnam University Medical Center, Daegu, South Korea; Division of Nephrology, Department of Internal Medicine, Ewha Womans University School of Medicine, Ewha Medical Research Center, Seoul, South Korea; Division of Nephrology, Department of Internal Medicine, Ewha Womans University School of Medicine, Ewha Medical Research Center, Seoul, South Korea; Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea; Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea ⓘ ⓘ 55360244300; 59167627900; 57209909350; 57820096000; 57196396467; 7501393222; 7501831741; 8558530700; 35269232600; 14621981000; 17233695600; 7403536291; 55633533600 ⓘ jh-cho@knu.ac.kr; ylkim@knu.ac.kr; ⓘ Journal of the American Society of Nephrology ⓘ J AM SOC NEPHROL ⓘ 1046-6673 1533-3450 SCIE UROLOGY & NEPHROLOGY ⓘ 2024 9.4 4.1 0 2025-05-07 ⓘ 0 ⓘ ⓘ ⓘ ⓘ English Article in press 2025 10.1681/asn.0000000655 ⓘ 바로가기 바로가기 바로가기
○ ○ Article ⓘ Federated Multiagent Reinforcement Learning for Resource Allocation in NR-V2X Mode 2 ⓘ The Third Generation Partnership Project (3GPP) introduced cellular vehicle-to-everything (C-V2X) for vehicular communications. In the standard, C-V2X Mode 4 is defined for the distributed resource selection. Subsequently, in 3GPP Release 16, NR-V2X is introduced with Mode 1 and Mode 2 for vehicular communications. Likewise C-V2X Mode 4, NR-V2X Mode 2 is used for decentralized resource scheduling. The vehicles select the resources based on their local observations by utilizing the semi-persistent scheduling (SPS). Since, the vehicles select the resources based on the local observation, sensing nature of SPS is challenged by the hidden node problem that lead to resource conflict. To resolve the contention, 3GPP also introduced the physical sidelink feedback channel (PSFCH) to assist the distributive resource scheduling based on the receiver feedback. However, this incurred a signaling overhead. In this work, federated learning is exploited for distributive training via offline method and distributive multiagent-based resource scheduling is performed following the principles of NR-V2X Mode 2. Distributed training favors the model accuracy by accommodating the varying affect of the environment due to the high mobile dynamics. Simulation is conducted by integrating SUMO in conjunction with 3GPP NR-V2X standard. Performance results demonstrate a substantial improvement compared to other deep learning methods, where centralized training and random resource selection procedures are employed. This research marks a significant stride toward efficient and conflict-resilient resource allocation in vehicular communications. ⓘ Saad, Malik Muhammad; Tariq, Muhammad Ashar; Ajmal, Mahnoor; Kim, Dongkyun; Srivastava, Gautam ⓘ Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu 41566, South Korea; Dongseo Univ, Sch Comp Sci, Busan 47011, South Korea; Brandon Univ, Dept Math & Comp Sci, Brandon, MB R7A 6A9, Canada; China Med Univ, Res Ctr Interneural Comp, Taichung 40402, Taiwan; Chitkara Univ, Inst Engn & Technol, Ctr Res Impact & Outcome, Rajpura 140401, India ⓘ Srivastava, Gautam/N-5668-2019 ⓘ 57220715290; 57219865336; 57238144300; 35753648800; 57202588447 ⓘ maliksaad@knu.ac.kr; tariqashar@knu.ac.kr; ajmal.mahnoor@knu.ac.kr; dongkyun@knu.ac.kr; srivastavag@brandonu.ca; ⓘ IEEE INTERNET OF THINGS JOURNAL ⓘ IEEE INTERNET THINGS ⓘ 2327-4662 12 13 SCIE ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS;COMPUTER SCIENCE, INFORMATION SYSTEMS ⓘ 2024 8.9 4.1 0 2025-05-07 ⓘ 0 0 Resource management; Federated learning; Vehicle-to-everything; Training; Sidelink; Reliability; 3GPP; Vehicle dynamics; Computer science; Processor scheduling; Distributive training; federated learning; NR-V2X Mode 2; semi-persistent scheduling (SPS) ⓘ COMMUNICATION ⓘ Distributive Training; Federated learning; NR-V2X Mode 2; Semi-persistent Scheduling ⓘ ⓘ English 2025 2025-07-01 10.1109/jiot.2025.3555195 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ Five-Year Functional Outcomes Among Patients Surviving Aneurysmal Subarachnoid Hemorrhage ⓘ Importance Longitudinal changes in functional levels can provide valuable information about disability. However, longitudinal outcomes in aneurysmal subarachnoid hemorrhage (aSAH) have not been well reported, which could provide insight into appropriate management and information for patients experiencing disability. Objective To investigate the 5-year prognosis and functional outcomes of patients with aSAH. Design, Setting, and Participants This retrospective cohort study used data of patients with aSAH from the Korean Stroke Cohort for Functioning and Rehabilitation study up to 5 years after onset. Data were collected from August 2012 through May 2015 in 9 different hospitals in Korea. Data were analyzed from September 2023 through January 2024. Exposure Patients with aSAH surviving at least 7 days after onset. Main Outcomes and Measures Assessments were performed serially from 7 days to 5 years after onset. Prognosis, measured by the modified Rankin scale (mRS) in terms of positive outcome (mRS score of 0 or 1), and mortality were analyzed. In addition, sequential functional outcomes were assessed using the Functional Independence Measure (FIM) in survivors of aSAH at 5 years after onset. Multiple imputation method was used to handle missing data. Wilcoxon signed-rank test and paired t test were used to analyze differences in functional measurements between each follow-up period. Additionally, a generalized mixed-effects model was used to analyze the longitudinal trajectory of the FIM. Results A total of 338 patients with aSAH (mean [SD] age, 56.3 [13.0] years; 207 female [61.2%]) were included. Among survivors of aSAH at 7 days, the 5-year mortality rate was 8.3% (28 participants). The distribution of mRS significantly improved until 4 years and then plateaued, with 180 (53.3%) and 77 (22.8%) patients reporting an mRS score of 0 and 1, respectively. FIM showed a significant improvement up to 4 years (mean [SD] score, 118.9 [18.7]) and then plateaued. Conclusions and Relevance In this cohort study, the functional outcomes in patients with aSAH continued to improve up to 4 years after onset, with the majority of participants showing favorable outcomes without significant disability, suggesting that proper long-term assessment is needed and appropriate management should be emphasized to maximize potential outcomes of patients with aSAH. ⓘ Lee, Ho Seok; Sohn, Min Kyun; Lee, Jongmin; Kim, Deog Young; Shin, Yong-Il; Oh, Gyung-Jae; Lee, Yang-Soo; Joo, Min Cheol; Lee, So Young; Song, Min-Keun; Han, Junhee; Ahn, Jeonghoon; Lee, Young-Hoon; Kim, Dae Hyun; Kim, Young-Taek; Kim, Yun-Hee; Chang, Won Hyuk ⓘ Sungkyunkwan Univ, Sch Med, Ctr Prevent & Rehabil,Heart Vast Stroke Inst, Dept Phys & Rehabil Med,Samsung Med Ctr, 81 Irwon Ro, Seoul 135710, South Korea; Chungnam Natl Univ, Coll Med, Dept Rehabil Med, Daejeon, South Korea; Konkuk Univ, Sch Med, Dept Rehabil Med, Seoul, South Korea; Yonsei Univ, Coll Med, Dept & Res Inst Rehabil Med, Seoul, South Korea; Pusan Natl Univ, Yangsan Hosp, Dept Rehabil Med, Sch Med, Yangsan, South Korea; Wonkwang Univ, Sch Med, Dept Prevent Med, Iksan, South Korea; Kyungpook Natl Univ Hosp, Sch Med, Dept Rehabil Med, Daegu, South Korea; Wonkwang Univ, Sch Med, Dept Rehabil Med, Iksan, South Korea; Jeju Natl Univ, Jeju Natl Univ Hosp, Dept Anethesiol, Sch Med, Jeju, South Korea; Chonnam Natl Univ, Med Sch, Dept Phys Med & Rehabil, Gwangju, South Korea; Hallym Univ, Dept Stat, Chunchon, South Korea; Ewha Womans Univ, Dept Hlth Convergence, Seoul, South Korea; Chungnam Natl Univ Hosp, Dept Prevent Med, Daejeon, South Korea; Sungkyunkwan Univ, Sch Med, Dept Phys & Rehabil Med, 2066 Seobu Ro, Suwon 16419, Gyeonggi Do, South Korea; Oncon Therapeut, Seoul, South Korea; Sungkyunkwan Univ, Samsung Adv Inst Hlth Sci & Technol, Dept Hlth Sci & Technol, Seoul, South Korea; Sungkyunkwan Univ, Samsung Adv Inst Hlth Sci & Technol, Dept Med Device Management & Res, Seoul, South Korea ⓘ kim, deog young/Q-8498-2019; Kim, Young/AEP-2940-2022 ⓘ 57222648792; 7101840459; 57202882113; 55375583000; 55890990500; 7007056685; 57202952463; 56982599700; 57202327370; 55375416600; 57192890252; 8855402200; 55716155400; 57206099341; 57211411051; 57020121600; 35301717900 ⓘ yunkim@skku.edu; wh.chang@samsung.com; ⓘ JAMA NETWORK OPEN ⓘ JAMA NETW OPEN ⓘ 2574-3805 8 3 SCIE MEDICINE, GENERAL & INTERNAL ⓘ 2024 9.7 4.1 5.81 2025-05-07 ⓘ 1 1 ⓘ STROKE; MORTALITY ⓘ ⓘ Adult; Aged; Female; Humans; Longitudinal Studies; Male; Middle Aged; Prognosis; Recovery of Function; Republic of Korea; Retrospective Studies; Subarachnoid Hemorrhage; Survivors; adult; aged; complication; convalescence; epidemiology; female; human; longitudinal study; male; middle aged; mortality; pathophysiology; prognosis; retrospective study; South Korea; subarachnoid hemorrhage; survivor ⓘ English 2025 2025-03-25 10.1001/jamanetworkopen.2025.1678 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ Generative-Diffusion-Model-Based Deep-Learning Framework for Remaining Useful Life Prediction ⓘ In this letter, we propose a novel and high-performing deep learning framework for remaining useful life (RUL) prediction, called RUL-Diff, by leveraging a generative diffusion model. It is composed of two modules that are connected in tandem: 1) a feature extractor corresponding to the encoder part of our customized U-Net and 2) a RUL predictor constructed by a multilayer perceptron. We further devise an effective two-stage training methodology for the proposed RUL-Diff, in which the feature extractor is initially pretrained for high-quality feature learning, and then, is retrained jointly with the RUL predictor for accurate RUL prediction. Extensive experimental results on NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) datasets demonstrate the superiority and effectiveness of the proposed scheme. ⓘ Ha, Sangjun; Sung, Mingyu; Saeed, Faisal; Yun, Sangseok; Kim, Il-Min; Kang, Jae-Mo ⓘ Kyungpook Natl Univ, Dept Artificial Intelligence, Daegu 41566, South Korea; Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China; Pukyong Natl Univ, Dept Informat & Commun Engn, Busan 48513, South Korea; Queens Univ, Dept Elect & Comp Engn, Kingston, ON K7L 3N6, Canada ⓘ ⓘ 59254675400; 57221328242; 58165089300; 56115729600; 36040390300; 56024930400 ⓘ tkdwns5377@gmail.com; alsrb0351@gmail.com; bscsfaisal821@gmail.com; ssyun@pknu.ac.kr; ilmin.kim@queensu.ca; jmkang@knu.ac.kr; ⓘ IEEE INTERNET OF THINGS JOURNAL ⓘ IEEE INTERNET THINGS ⓘ 2327-4662 12 11 SCIE ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS;COMPUTER SCIENCE, INFORMATION SYSTEMS ⓘ 2024 8.9 4.1 0 2025-05-07 ⓘ 0 0 Feature extraction; Training; Data mining; Representation learning; Network architecture; Internet of Things; Diffusion models; Convolutional neural networks; Time series analysis; Deep learning; Deep learning (DL); generative diffusion model; Internet-of-Things (IoT); remaining useful life (RUL) prediction ⓘ ⓘ Deep learning (DL); generative diffusion model; Internet-of-Things (IoT); remaining useful life (RUL) prediction ⓘ Adversarial machine learning; Contrastive Learning; Deep learning; Deep reinforcement learning; Generative adversarial networks; Deep learning; Diffusion model; Feature extractor; Generative diffusion model; Internet-of-thing; Learning frameworks; Model-based OPC; Multilayers perceptrons; Remaining useful life predictions; Remaining useful lives; Prediction models ⓘ English 2025 2025-06-01 10.1109/jiot.2025.3549038 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ Intracerebellar upregulation of Rheb(S16H) ameliorates motor dysfunction in mice with SCA2 ⓘ Cerebellar ataxia (CA) is characterized by impaired balance and coordination due to the loss of cerebellar neurons caused by various factors, and effective treatments are currently lacking. Recently, we observed reduced expression of signaling molecules in the mammalian target of rapamycin complex 1 (mTORC1) pathway in the cerebellum of mice with spinocerebellar ataxia type 2 (SCA2) compared with wild-type mice. To investigate the effects of mTORC1 upregulation on motor dysfunction in mice with SCA2, we administered an intracerebellar injection of adeno-associated virus serotype 1 carrying a constitutively active form of Ras homolog enriched in brain [Rheb(S16H)], which is an upstream activator of mTORC1. This treatment led to increased Rheb(S16H) expression in calbindin-D28K-positive Purkinje cells and increased levels of neurotrophic factors. Additionally, Rheb(S16H) upregulation reduced abnormal behaviors and protected Purkinje cells in mice with SCA2. Our findings suggest that upregulating Rheb(S16H) in the cerebellum may be a promising therapeutic strategy for hereditary CA. ⓘ Kim, Sehwan; Park, Junwoo; Eo, Hyemi; Lee, Gi Beom; Park, Se Min; Shin, Minsang; Lee, Seung Eun; Nam, Youngpyo; Kim, Sang Ryong ⓘ Kyungpook Natl Univ, Sch Life Sci & Biotechnol, FOUR KNU Creat Biores Grp BK21, Daegu 41566, South Korea; Kyungpook Natl Univ, Brain Sci & Engn Inst, Daegu 41404, South Korea; Kyungpook Natl Univ, Sch Med, Dept Microbiol, Daegu 41944, South Korea; Korea Inst Sci & Technol, Res Anim Resource Ctr, Seoul 02792, South Korea ⓘ ⓘ 57193232250; 59672713600; 59672214100; 59441620700; 59442160200; 7401536650; 56323972600; 55143100300; 56486163800 ⓘ blackpyo2@knu.ac.kr; srk75@knu.ac.kr; ⓘ ACTA PHARMACOLOGICA SINICA ⓘ ACTA PHARMACOL SIN ⓘ 1671-4083 1745-7254 46 7 SCIE CHEMISTRY, MULTIDISCIPLINARY;PHARMACOLOGY & PHARMACY ⓘ 2024 8.4 4.1 0 2025-05-07 ⓘ 0 0 cerebellar ataxia; motor dysfunction; AAV1-Rheb(S16H); mTORC1; neurotrophic factor; neuroprotection ⓘ CILIARY NEUROTROPHIC FACTOR; MTOR-MEDIATED S6K1; DOPAMINERGIC-NEURONS; MAMMALIAN TARGET; ATAXIA; RHEB; PATHWAY; STRESS; BDNF; TRANSDUCTION ⓘ AAV1-Rheb(S16H); cerebellar ataxia; motor dysfunction; mTORC1; neuroprotection; neurotrophic factor ⓘ ⓘ English 2025 2025-07 10.1038/s41401-025-01504-y ⓘ 바로가기 바로가기 바로가기 바로가기
○ Article ⓘ Knowledge-Empowered Distributed Learning Platform in Internet of Unmanned Aerial Agents to Support NR-V2X Communication ⓘ NR-V2X Mode 2 is introduced by the Third Generation Partnership Project (3GPP) to support Vehicle-to-Everything (V2X) communication. In NR-V2X Mode 2, vehicles select resources for the exchange of Cooperative Awareness Messages (CAM) in a decentralized manner based on their local observation using semi-persistent scheduling. Resources are distributed over the two-dimensional frequency and time domain, following the LTE frame structure. Since vehicles select resources based on their local observations and due to spectrum scarcity, this may lead to contention. Hence, selecting a resource is challenging, and as each vehicle strives to select a resource, it becomes a consensus problem. To resolve resource contention, in this paper, we propose a Knowledge-empowered Distributed Multi-Agent Deep Reinforcement Learning (K-MADRL) approach. Based on traffic flow information, Long Short-term Memory (LSTM) is employed to deploy Unmanned Internet of Aerial Agents (UIAAs) to collect vehicle state information. UIAAs gather vehicle state knowledge and train the local deep reinforcement learning model. The locally trained model at the UIAA is shared and aggregated at the gNB for the global model update. The trained policy is then sent to the vehicles over System Synchronization Blocks (SSB) for distributed execution. Moreover, the vehicles select the resource based on the joint action, i.e., by anticipating the actions of the neighboring vehicles. Our scheme is compared with other methods such as DRL (deep reinforcement learning), optimization techniques, the SPS method, and random allocation methods used in the NR-V2X environment. The results of the simulations show that our scheme outperforms the other methods. © 2014 IEEE. ⓘ Saad, Malik Muhammad; Jamshed, Muhammad Ali; Tariq, Muhammad Ashar; Nauman, Ali; Kim, Dongkyun ⓘ Kyungpook National University, School of Computer Science and Engineering, Daegu, South Korea; University of Glasgow, College of Science and Engineering, United Kingdom; Kyungpook National University, School of Computer Science and Engineering, Daegu, South Korea; Yeungnam University, School of Computer Science and Engineering, South Korea; Kyungpook National University, School of Computer Science and Engineering, Daegu, South Korea ⓘ ⓘ 57220715290; 57189063536; 57219865336; 57210321511; 35753648800 ⓘ ⓘ IEEE Internet of Things Journal ⓘ IEEE INTERNET THINGS ⓘ 2327-4662 2327-4662 SCIE ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS;COMPUTER SCIENCE, INFORMATION SYSTEMS ⓘ 2024 8.9 4.1 3.26 2025-05-07 ⓘ 1 ⓘ ⓘ Distributed Resource Allocation; Internet of Unmanned Aerial Agents (IUAAs); Knowledge-empowered Distributive Training; NR-V2X Mode 2 ⓘ Aircraft communication; Magnetic levitation vehicles; Mobile telecommunication systems; Reinforcement learning; Resource allocation; Unmanned aerial vehicles (UAV); Distributed learning; Distributed resource allocation; Internet of unmanned aerial agent; Knowledge-empowered distributive training; Learning platform; Local observations; NR-V2X mode 2; Resources based; Third generation partnership project (3GPP); Vehicle state; Deep reinforcement learning ⓘ English Article in press 2025 10.1109/jiot.2025.3532103 ⓘ 바로가기 바로가기 바로가기
○ ○ Article ⓘ Long-Term Oncologic Outcome of Breast-Conserving Treatment in Patients With Breast Cancer With BRCA Variants ⓘ Importance Patients with sporadic breast cancer have comparable prognoses after undergoing either breast-conserving treatment (BCT) or mastectomy. However, there are limited and inconsistent data on the assessment of oncologic outcomes between BCT and mastectomy in patients with pathogenic variants in BRCA1 or BRCA2. Objective To investigate the outcomes of BCT on recurrence and survival in patients with breast cancer with BRCA1 or BRCA2 pathogenic variants. Design, Setting, and Participants This retrospective multicenter cohort study analyzed patients from 13 institutions in South Korea with primary breast cancer with BRCA1 or BRCA2 pathogenic variants who underwent either BCT or mastectomy from January 2008 through December 2015. The median (IQR) follow-up period was 8.3 (6.4-9.6) years. Data were analyzed from September 2023 to August 2024. Exposure BRCA1 or BRCA2 pathogenic variant and BCT. Main Outcomes and Measures Primary outcomes were logoregional recurrence-free survival, distant recurrence-free survival, and overall survival. Propensity score matching (PSM) using the greedy nearest neighbor method was performed to match covariates to minimize potential selection bias. Results A total of 575 female patients with BRCA1 or BRCA2 pathogenic variants were identified, all of whom were South Korean with a mean (SD) age of 42.0 (9.7) years. Among them, 367 patients (66.2%) received BCT and 186 (33.8%) were treated with mastectomy. BCT was not a factor associated with oncologic outcomes, including locoregional recurrence, compared with mastectomy. After adjusting for clinicopathologic characteristics through 1:1 PSM, there were still no statistically significant differences in oncologic outcomes between the BCT group and the mastectomy group. Multivariate analysis showed that the type of breast surgery was not significantly associated with oncologic outcomes. In subgroup analysis among matched patients based on BRCA1 or BRCA2 status, tumor size, lymph node metastasis, histologic grade, and subtype, BCT was also not a factor associated with risk for recurrence. Conclusions and Relevance The findings from this cohort study of patients with BRCA1 or BRCA2 pathogenic variants suggested that there were no significant differences in oncologic outcomes between patients who underwent BCT and those who underwent mastectomy. Therefore, breast conservation with close surveillance can be considered a viable treatment option for BRCA1 or BRCA2 pathogenic variant carriers. Further studies incorporating prospectively collected data are warranted to validate our findings. ⓘ Lee, Janghee; Ryu, Jai Min; Kim, Hong Kyu; Park, Hyung Seok; Kang, Byeongju; Ahn, Sung Gwe; Chung, Min Sung; Shin, Seon-Hi; Go, Junwon; Kim, Sanghwa; Kim, Eun Young; Kang, Young-Joon; Min, Sun Young; Lee, Moohyun; Shin, Eunju; Shin, Jisoo; Lee, Sae Byul; Cha, Chihwan David ⓘ Ewha Womans Univ, Coll Med, Dept Surg, Mokdong Hosp, Seoul, South Korea; Yonsei Univ, Coll Med, Dept Med, Seoul, South Korea; Sungkyunkwan Univ, Sch Med, Samsung Med Ctr, Div Breast Surg,Dept Surg, Seoul, South Korea; Seoul Natl Univ Hosp, Breast Care Ctr, Dept Surg, Seoul, South Korea; Yonsei Univ, Coll Med, Dept Surg, Seoul, South Korea; Kyungpook Natl Univ, Chilgok Hosp, Sch Med, Dept Surg, Daegu, South Korea; Yonsei Univ, Coll Med, Gangnam Severance Hosp, Dept Surg, Seoul, South Korea; Hanyang Univ, Dept Surg, Coll Med, Med Ctr, Seoul, South Korea; Hanyang Univ, Med Res Collaborating Ctr, Biostat Consulting & Res Lab, Seoul, South Korea; NYU, Dept Radiol, Grossman Sch Med, New York, NY USA; Hallym Univ, Sacred Heart Hosp, Dept Breast & Endocrine Surg, Anyang, South Korea; Sungkyunkwan Univ, Sch Med, Kangbuk Samsung Hosp, Dept Surg, Seoul, South Korea; Catholic Univ Korea, Incheon St Marys Hosp, Coll Med, Dept Surg, Incheon, South Korea; Kyung Hee Univ Hosp, Dept Surg, Seoul, South Korea; Keimyung Univ, Sch Med, Dept Surg, Daegu, South Korea; Univ Ulsan, Coll Med, Dept Surg, Div Breast Surg,Asan Med Ctr, Seoul, South Korea ⓘ Ahn, Sung Gwe/AFD-8122-2022 ⓘ 56768641200; 59910958300; 57199392663; 55713697800; 57279815300; 59693417100; 25635235100; 56390638400; 59902020500; 57190119079; 57223746049; 56709101200; 34971730700; 57208629747; 58451484900; 59902411800; 59676348000; 55937544100 ⓘ newstar153@hanmail.net; chachihwan@gmail.com; ⓘ JAMA NETWORK OPEN ⓘ JAMA NETW OPEN ⓘ 2574-3805 8 5 SCIE MEDICINE, GENERAL & INTERNAL ⓘ 2024 9.7 4.1 0 2025-06-11 ⓘ 0 0 ⓘ MUTATION CARRIERS; BRCA2; RISK; RECURRENCE ⓘ ⓘ Adult; BRCA1 Protein; BRCA2 Protein; Breast Neoplasms; Female; Humans; Mastectomy; Mastectomy, Segmental; Middle Aged; Neoplasm Recurrence, Local; Republic of Korea; Retrospective Studies; Treatment Outcome; BRCA1 protein; BRCA2 protein; adult; Article; breast cancer; breast cancer recurrence; breast surgery; breast-conserving surgery; cancer mortality; cancer patient; cancer size; cancer survival; clinical outcome; cohort analysis; comparative study; controlled study; distant recurrence free survival; female; follow up; histopathology; human; lymph node metastasis; major clinical study; mastectomy; multicenter study; observational study; overall survival; patient safety; recurrence free survival; retrospective study; risk factor; South Korea; South Korean; survival analysis; survival rate; aged; article; cancer grading; drug therapy; middle aged; propensity score; radiotherapy; surgery; tumor volume ⓘ English 2025 2025-05-14 10.1001/jamanetworkopen.2025.9840 ⓘ 바로가기 바로가기 바로가기 바로가기
페이지 이동: