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A Real‐Time Cell Death Self‐Reporting Theranostic Agent for Dynamic Optimization of Photodynamic Therapy
https://orcid.org/0000-0002-9977-0237
AbstractThe therapeutic efficiency of photodynamic therapy (PDT) hinges on the drug‐light interval (DLI), yet conventional approaches relying on photosensitizer accumulation often lead to suboptimal irradiation and adverse side effects. Here, a real‐time cell death self‐reporting photodynamic theranostic nanoagent (CDPN) is presented that dynamically monitors extracellular potassium ion ([K⁺]ex) fluctuations as direct indicators of tumor cell death. By exploiting [K⁺]ex dyshomeostasis associated with apoptosis and necrosis, CDPN combines a photosensitizer and a potassium‐sensitive fluorophore within mesoporous silica nanoparticles, encapsulated by a K⁺‐selective membrane for enhanced specificity. In vitro and in vivo studies validate that [K⁺]ex dynamics closely correlate with cell death, enabling precise evaluation of PDT efficacy and data‐driven optimization of the DLI. Using a breast cancer model, CDPN‐guided adjustments identify optimized DLI conditions, achieving significantly improved therapeutic outcomes. This study introduces a new paradigm for PDT, establishing a real‐time, adaptable strategy for guiding treatment parameters and advancing precision oncology.
A Real‐Time Cell Death Self‐Reporting Theranostic Agent for Dynamic Optimization of Photodynamic Therapy
https://orcid.org/0000-0002-9977-0237
AbstractThe therapeutic efficiency of photodynamic therapy (PDT) hinges on the drug‐light interval (DLI), yet conventional approaches relying on photosensitizer accumulation often lead to suboptimal irradiation and adverse side effects. Here, a real‐time cell death self‐reporting photodynamic theranostic nanoagent (CDPN) is presented that dynamically monitors extracellular potassium ion ([K⁺]ex) fluctuations as direct indicators of tumor cell death. By exploiting [K⁺]ex dyshomeostasis associated with apoptosis and necrosis, CDPN combines a photosensitizer and a potassium‐sensitive fluorophore within mesoporous silica nanoparticles, encapsulated by a K⁺‐selective membrane for enhanced specificity. In vitro and in vivo studies validate that [K⁺]ex dynamics closely correlate with cell death, enabling precise evaluation of PDT efficacy and data‐driven optimization of the DLI. Using a breast cancer model, CDPN‐guided adjustments identify optimized DLI conditions, achieving significantly improved therapeutic outcomes. This study introduces a new paradigm for PDT, establishing a real‐time, adaptable strategy for guiding treatment parameters and advancing precision oncology.
A Real‐Time Cell Death Self‐Reporting Theranostic Agent for Dynamic Optimization of Photodynamic Therapy
https://orcid.org/0000-0002-9977-0237
AbstractThe therapeutic efficiency of photodynamic therapy (PDT) hinges on the drug‐light interval (DLI), yet conventional approaches relying on photosensitizer accumulation often lead to suboptimal irradiation and adverse side effects. Here, a real‐time cell death self‐reporting photodynamic theranostic nanoagent (CDPN) is presented that dynamically monitors extracellular potassium ion ([K⁺]ex) fluctuations as direct indicators of tumor cell death. By exploiting [K⁺]ex dyshomeostasis associated with apoptosis and necrosis, CDPN combines a photosensitizer and a potassium‐sensitive fluorophore within mesoporous silica nanoparticles, encapsulated by a K⁺‐selective membrane for enhanced specificity. In vitro and in vivo studies validate that [K⁺]ex dynamics closely correlate with cell death, enabling precise evaluation of PDT efficacy and data‐driven optimization of the DLI. Using a breast cancer model, CDPN‐guided adjustments identify optimized DLI conditions, achieving significantly improved therapeutic outcomes. This study introduces a new paradigm for PDT, establishing a real‐time, adaptable strategy for guiding treatment parameters and advancing precision oncology.
A Real‐Time Cell Death Self‐Reporting Theranostic Agent for Dynamic Optimization of Photodynamic Therapy
Advanced Science
Bian, Wei (Autor:in) / Wang, Qiyue (Autor:in) / He, Cui (Autor:in) / Tao, Pan (Autor:in) / Zheng, Juanjuan (Autor:in) / Zhang, Yulu (Autor:in) / Li, Jing (Autor:in) / Li, Fangyuan (Autor:in) / Jia, Hongyan (Autor:in) / Ling, Daishun (Autor:in)
08.03.2025
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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