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| 1 | Overcoming the obstacles of current photodynamic therapy in tumors using nanoparticles显示文摘Photodynamic therapy(PDT)has been applied in clinical treatment of tumors for a long time.However,insufficient supply of pivotal factors including photosensitizer(PS),light,and oxygen in tumor tissue dramatically reduces the therapeutic efficacy of PDT.Nanoparticles have received an influx of attention as drug carriers,and recent studies have demonstrated their promising potential to overcome the obstacles of PDT in tumor tissue.Physicochemical optimization for passive targeting,ligand modification for active targeting,and stimuli-responsive release achieved efficient delivery of PS to tumor tissue.Various trials using upconversion NPs,two-photon lasers,X-rays,and bioluminescence have provided clues for efficient methods of light delivery to deep tissue.Attempts have been made to overcome unfavorable tumor microenvironments via artificial oxygen generation,Fenton reaction,and combination with other chemical drugs.In this review,we introduce these creative approaches to addressing the hurdles facing PDT in tumors.In particular,the studies that have been validated in animal experiments are preferred in this review over proof-of-concept studies that were only performed in cells. | Donghyun Lee Soonmin Kwon Seok-young Jang Eunyoung Park Yeeun Lee Heebeom Koo | 2022 | Bioactive Materials2022，7，2： | 2 |
| 2 | Nanoparticle Exsolution on Perovskite Oxides:Insights into Mechanism,Characteristics and Novel Strategies显示文摘Supported nanoparticles have attracted considerable attention as a promising catalyst for achieving unique properties in numerous applications,including fuel cells,chemical conversion,and batteries.Nanocatalysts demonstrate high activity by expanding the number of active sites,but they also intensify deactivation issues,such as agglomeration and poisoning,simultaneously.Exsolution for bottomup synthesis of supported nanoparticles has emerged as a breakthrough technique to overcome limitations associated with conventional nanomaterials.Nanoparticles are uniformly exsolved from perovskite oxide supports and socketed into the oxide support by a one-step reduction process.Their uniformity and stability,resulting from the socketed structure,play a crucial role in the development of novel nanocatalysts.Recently,tremendous research efforts have been dedicated to further controlling exsolution particles.To effectively address exsolution at a more precise level,understanding the underlying mechanism is essential.This review presents a comprehensive overview of the exsolution mechanism,with a focus on its driving force,processes,properties,and synergetic strategies,as well as new pathways for optimizing nanocatalysts in diverse applications. | Yo Han Kim Hyeongwon Jeong Bo‑Ram Won Hyejin Jeon Chan‑ho Park Dayoung Park Yeeun Kim Somi Lee Jae‑ha Myung | 2024 | Nano-Micro Letters2024，16，2： | 0 |
| 3 | Explainable Artificial Intelligence-Based Model Drift Detection Applicable to Unsupervised Environments显示文摘Cybersecurity increasingly relies on machine learning(ML)models to respond to and detect attacks.However,the rapidly changing data environment makes model life-cycle management after deployment essential.Real-time detection of drift signals from various threats is fundamental for effectively managing deployed models.However,detecting drift in unsupervised environments can be challenging.This study introduces a novel approach leveraging Shapley additive explanations(SHAP),a widely recognized explainability technique in ML,to address drift detection in unsupervised settings.The proposed method incorporates a range of plots and statistical techniques to enhance drift detection reliability and introduces a drift suspicion metric that considers the explanatory aspects absent in the current approaches.To validate the effectiveness of the proposed approach in a real-world scenario,we applied it to an environment designed to detect domain generation algorithms(DGAs).The dataset was obtained from various types of DGAs provided by NetLab.Based on this dataset composition,we sought to validate the proposed SHAP-based approach through drift scenarios that occur when a previously deployed model detects new data types in an environment that detects real-world DGAs.The results revealed that more than 90%of the drift data exceeded the threshold,demonstrating the high reliability of the approach to detect drift in an unsupervised environment.The proposed method distinguishes itself fromexisting approaches by employing explainable artificial intelligence(XAI)-based detection,which is not limited by model or system environment constraints.In conclusion,this paper proposes a novel approach to detect drift in unsupervised ML settings for cybersecurity.The proposed method employs SHAP-based XAI and a drift suspicion metric to improve drift detection reliability.It is versatile and suitable for various realtime data analysis contexts beyond DGA detection environments.This study significantly contributes to theMLcommunity by addressing the critical issue of managing ML models in real-world cybersecurity settings.Our approach is distinguishable from existing techniques by employing XAI-based detection,which is not limited by model or system environment constraints.As a result,our method can be applied in critical domains that require adaptation to continuous changes,such as cybersecurity.Through extensive validation across diverse settings beyond DGA detection environments,the proposed method will emerge as a versatile drift detection technique suitable for a wide range of real-time data analysis contexts.It is also anticipated to emerge as a new approach to protect essential systems and infrastructures from attacks. | Yongsoo Lee Yeeun Lee Eungyu Lee Taejin Lee | 2023 | Computers, Materials & Continua2023，76，8： | 0 |
| 4 | The SARS-CoV-2 spike protein induces lung cancer migration and invasion in a TLR2-dependent manner显示文摘Dear editor,Severe acute respiratory syndrome coronavirus2(SARS-CoV-2)infection can lead to severe outcomes in patients with cancer[1].It has been reported that patients with lung cancers disproportionately manifest severe COVID-19 with a high rate of hospitalization and death[2].Notably,the SARS-CoV-2 Spike(S)protein can induce hyper-inflammation in both epithelial cells and macrophages through toll-like receptor(TLR)1/TLR2 or TLR2/6-dependent nuclear factor-kappaB(NF-κB)path-way[3].However,molecular and cellular evidence on whether the SARS-CoV-2 virus affects the severity of lung cancer patients through TLR1/2 or TLR2/6 signaling remains unclear. | Mi-Jeong Kim Ji Young Kim Ji Hye Shin Juhee Son Yeeun Kang Soo-Kyung Jeong Duk-Hwan Kim Kyun-Hwan Kim Eunyoung Chun Ki-Young Lee | 2024 | Cancer Communications2024，44，2： | 0 |
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