Photodynamic therapy(PDT)as a non-invasive anticancer modality has received increasing attention due to its advantages of noninvasiveness,high temporospatial selectivity,simple and controllable operation,etc.PDT mainl...Photodynamic therapy(PDT)as a non-invasive anticancer modality has received increasing attention due to its advantages of noninvasiveness,high temporospatial selectivity,simple and controllable operation,etc.PDT mainly relies on the generation of toxic reactive oxygen species(ROS)by photosensitizers(PSs)under the light irradiation to cause cancer cell apoptosis and death.However,solid tumors usually exhibit an inherent hypoxic microenvironment,which greatly limits the PDT efficacy of these high oxygen-dependent conventional type II PSs.Therefore,it is of great importance to design and develop efficient type I PSs that are less oxygen-dependent for the treatment of hypoxic tumors.Herein,a new strategy for the preparation of efficient type I PSs by introducing the photoinduced electron transfer(PET)mechanism is reported.DR-NO_(2) is obtained by introducing 4-nitrobenzyl to(Z)-2-(5-(4-(diethylamino)-2-hydroxybenzylidene)-4-oxo-3-phenylthiazolidin-2-ylidene)malononitrile(DR-OH)with aggregation-induced emission(AIE)feature.The AIE feature ensures their high ROS generation efficiency in aggregate,and the PET process leads to fluorescence quenching of DR-NO_(2) to promote triplet state formation,which also promotes intramolecular charge separation and electron transfer that is conducive for type I ROS particularly superoxide radicals generation.In addition,DR-NO_(2) nanoparticles are prepared by nanoprecipitation to possess nanoscaled sizes,high cancer cell uptake,and excellent type I ROS generation ability,which results in an excellent performance in PDT ablation of MCF-7 cancer cells.This PET strategy for the development of type I PSs possesses great potential for PDT applications against hypoxic tumors.展开更多
为了探索低碳背景下,交通需求管理策略的具体设置应用,本文按照需求管理四个层面,探讨碳排放对于需求管理措施的影响,并在此基础上,以出行分布熵模型为基础,建立基于低碳背景下的需求管理多目标双层规划模型;随后,通过一个P&R系统(P...为了探索低碳背景下,交通需求管理策略的具体设置应用,本文按照需求管理四个层面,探讨碳排放对于需求管理措施的影响,并在此基础上,以出行分布熵模型为基础,建立基于低碳背景下的需求管理多目标双层规划模型;随后,通过一个P&R系统(Park and Ride)案例,采用遗传算法,利用MATLAB、TRANSCAD等软件,进行模型的求解以及具体的路网流量分配,分析所建立模型在交通需求管理措施实行时的可靠与准确程度。展开更多
文摘Photodynamic therapy(PDT)as a non-invasive anticancer modality has received increasing attention due to its advantages of noninvasiveness,high temporospatial selectivity,simple and controllable operation,etc.PDT mainly relies on the generation of toxic reactive oxygen species(ROS)by photosensitizers(PSs)under the light irradiation to cause cancer cell apoptosis and death.However,solid tumors usually exhibit an inherent hypoxic microenvironment,which greatly limits the PDT efficacy of these high oxygen-dependent conventional type II PSs.Therefore,it is of great importance to design and develop efficient type I PSs that are less oxygen-dependent for the treatment of hypoxic tumors.Herein,a new strategy for the preparation of efficient type I PSs by introducing the photoinduced electron transfer(PET)mechanism is reported.DR-NO_(2) is obtained by introducing 4-nitrobenzyl to(Z)-2-(5-(4-(diethylamino)-2-hydroxybenzylidene)-4-oxo-3-phenylthiazolidin-2-ylidene)malononitrile(DR-OH)with aggregation-induced emission(AIE)feature.The AIE feature ensures their high ROS generation efficiency in aggregate,and the PET process leads to fluorescence quenching of DR-NO_(2) to promote triplet state formation,which also promotes intramolecular charge separation and electron transfer that is conducive for type I ROS particularly superoxide radicals generation.In addition,DR-NO_(2) nanoparticles are prepared by nanoprecipitation to possess nanoscaled sizes,high cancer cell uptake,and excellent type I ROS generation ability,which results in an excellent performance in PDT ablation of MCF-7 cancer cells.This PET strategy for the development of type I PSs possesses great potential for PDT applications against hypoxic tumors.
文摘为了探索低碳背景下,交通需求管理策略的具体设置应用,本文按照需求管理四个层面,探讨碳排放对于需求管理措施的影响,并在此基础上,以出行分布熵模型为基础,建立基于低碳背景下的需求管理多目标双层规划模型;随后,通过一个P&R系统(Park and Ride)案例,采用遗传算法,利用MATLAB、TRANSCAD等软件,进行模型的求解以及具体的路网流量分配,分析所建立模型在交通需求管理措施实行时的可靠与准确程度。
