|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Small interfering RNA delivery to the neurons near the amyloid plaques for improved treatment of Alzheimer’s disease显示文摘Gene therapy represents a promising treatment for the Alzheimer’s disease(AD). However,gene delivery specific to brain lesions through systemic administration remains big challenge. In our previous work, we have developed an siRNA nanocomplex able to be specifically delivered to the amyloid plaques through surface modification with both CGN peptide for the blood–brain barrier(BBB)penetration and QSH peptide for β-amyloid binding. But, whether the as-designed nanocomplex could indeed improve the gene accumulation in the impaired neuron cells and ameliorate AD-associated symptoms remains further study. Herein, we prepared the nanocomplexes with an siRNA against β-site amyloid precursor protein-cleaving enzyme 1(BACE1), the rate-limiting enzyme of Aβ production, as the therapeutic siRNA of AD. The nanocomplexes exhibited high distribution in the Aβ deposits-enriched hippocampus, especially in the neurons near the amyloid plaques after intravenous administration. In APP/PS1 transgenic mice, the nanocomplexes down-regulated BACE1 in both mRNA and protein levels,as well as Aβ and amyloid plaques to the level of wild-type mice. Moreover, the nanocomplexes significantly increased the level of synaptophysin and rescued memory loss of the AD transgenic mice without hematological or histological toxicity. Taken together, this work presented direct evidences that the design of precise gene delivery to the AD lesions markedly improves the therapeutic outcome. | Qian Guo Xiaoyao Zheng Peng Yang Xiaoying Pang Kang Qian Pengzhen Wang Shuting Xu Dongyu Sheng Liuchang Wang Jinxu Cao Wei Lu Qizhi Zhang Xinguo Jiang | 2019 | Acta Pharmaceutica Sinica B2019，9，3： | 2 |
| 2 | Modulating autophagic flux via ROS-responsive targeted micelles to restore neuronal proteostasis in Alzheimer’s disease显示文摘Compromised autophagy and defective lysosomal clearance significantly contribute to impaired neuronal proteostasis,which represents a hallmark of Alzheimer’s disease(AD)and other age-related neurodegenerative disorders.Growing evidence has implicated that modulating autophagic flux,instead of inducing autophagosome formation alone,would be more reliable to rescue neuronal proteostasis.Concurrently,selectively enhancing drug concentrations in the leision areas,instead of the whole brain,will maximize therapeutic efficacy while reduing non-selective autophagy induction.Herein,we design a ROS-responsive targeted micelle system(TT-NM/Rapa)to enhance the delivery efficiency of rapamycin to neurons in AD lesions guided by the fusion peptide TPL,and facilitate its intracellular release via ROS-mediated disassembly of micelles,thereby maximizing autophagic flux modulating efficacy of rapamycin in neurons.Consequently,it promotes the efficient clearance of intracellular neurotoxic proteins,β-amyloid and hyperphosphorylated tau proteins,and ameliorates memory defects and neuronal damage in 3×Tg-AD transgenic mice.Our studies demonstrate a promising strategy to restore autophagic flux and improve neuronal proteostasis by rationally-engineered nano-systems for delaying the progression of AD. | Shuting Xu Peng Yang Kang Qian Yixian Li Qian Guo Pengzhen Wang Ran Meng Jing Wu Jinxu Cao Yunlong Cheng Minjun Xu Qizhi Zhang | 2022 | Bioactive Materials2022，7，5： | 1 |
| 3 | Multidimensional autophagy nano-regulator boosts Alzheimer's disease treatment by improving both extra/intraneuronal homeostasis显示文摘Intraneuronal dysproteostasis and extraneuronal microenvironmental abnormalities in Alzheimer’s disease(AD)collectively culminate in neuronal deterioration.In the context of AD,autophagy dysfunction,a multi-link obstacle involving autophagy downregulation and lysosome defects in neurons/microglia is highly implicated in intra/extraneuronal pathological processes.Therefore,multidimensional autophagy regulation strategies co-manipulating“autophagy induction”and“lysosome degradation”in dual targets(neuron and microglia)are more reliable for AD treatment.Accordingly,we designed an RP-1 peptide-modified reactive oxygen species(ROS)-responsive micelles(RT-NM)loading rapamycin or gypenoside XVII.Guided by RP-1 peptide,the ligand of receptor for advanced glycation end products(RAGE),RT-NM efficiently targeted neurons and microglia in AD-affected region.This nanocombination therapy activated the whole autophagy-lysosome pathway by autophagy induction(rapamycin)and lysosome improvement(gypenoside XVII),thus enhancing autophagic degradation of neurotoxic aggregates and inflammasomes,and promoting Aβ phagocytosis.Resultantly,it decreased aberrant protein burden,alleviated neuroinflammation,and eventually ameliorated memory defects in 3×Tg-AD transgenic mice.Our research developed a multidimensional autophagy nano-regulator to boost the efficacy of autophagy-centered AD therapy. | Yixian Li Peng Yang Ran Meng Shuting Xu Lingling Zhou Kang Qian Pengzhen Wang Yunlong Cheng Dongyu Sheng Minjun Xu Tianying Wang Jing Wu Jinxu Cao Qizhi Zhang | 2024 | Acta Pharmaceutica Sinica B2024，14，3： | 0 |
| 4 | Reinforced cathode-garnet interface for high-capacity all-solid-state batteries显示文摘Garnet-type solid-state electrolytes(SSEs)are particularly attractive in the construction of all-solid-state lithium(Li)batteries due to their high ionic conductivity,wide electrochemical window and remarkable(electro)chemical stability.However,the intractable issues of poor cathode/garnet interface and general low cathode loading hinder their practical application.Herein,we demonstrate the construction of a reinforced cathode/garnet interface by spark plasma sintering,via co-sintering Li_(6.5)La_(3)Zr_(1.5)Ta_(0.5)O_(12)(LLZTO)electrolyte powder and LiCoO_(2)/LLZTO composite cathode powder directly into a dense dual-layer with 5 wt%Li_(3)BO_(3)as sintering additive.The bulk composite cathode with LiCoO_(2)/LLZTO cross-linked structure is firmly welded to the LLZTO layer,which optimizes both Li-ion and electron transport.Therefore,the one-step integrated sintering process implements an ultra-low cathode/garnet interfacial resistance of 3.9Ωcm^(2)(100◦C)and a high cathode loading up to 2.02 mAh cm^(−2).Moreover,the Li_(3)BO_(3)reinforced LiCoO_(2)/LLZTO interface also effectively mitigates the strain/stress of LiCoO_(2),which facilitates the achieving of superior cycling stability.The bulk-type Li|LLZTO|LiCoO_(2)-LLZTO full cell with areal capacity of 0.73 mAh cm^(−2)delivers capacity retention of 81.7%after 50 cycles at 100μA cm^(−2).Furthermore,we reveal that non-uniform Li plating/stripping leads to the formation of gaps and finally results in the separation of Li and LLZTO electrolyte during long-term cycling,which becomes the dominant capacity decay mechanism in high-capacity full cells.This work provides insight into the degradation of Li/SSE interface and a strategy to radically improve the electrochemical performance of garnet-based all-solid-state Li batteries. | Chenxi Zheng Shijun Tang Fangmei Wen Jinxue Peng Wu Yang Zhongwei Lv Yongmin Wu Weiping Tang Zhengliang Gong Yong Yang | 2022 | Materials Futures2022，1，4： | 0 |
| 5 | Fast Cross-Platform Binary Code Similarity Detection Framework Based on CFGs Taking Advantage of NLP and Inductive GNN显示文摘Cross-platform binary code similarity detection aims at detecting whether two or more pieces of binary code are similar or not.Existing approaches that combine control flow graphs(CFGs)-based function representation and graph convolutional network(GCN)-based similarity analysis are the best-performing ones.Due to a large amount of convolutional computation and the loss of structural information,the use of convolution networks will inevitably bring problems such as high overhead and sometimes inaccuracy.To address these issues,we propose a fast cross-platform binary code similarity detection framework that takes advantage of natural language processing(NLP)and inductive graph neural network(GNN)for basic blocks embedding and function representation respectively by simulating extracting structural features and temporal features.GNN’s node-centric and small batch is a suitable training way for large CFGs,it can greatly reduce computational overhead.Various NLP basic block embedding models and GNNs are evaluated.Experimental results show that the scheme with long short term memory(LSTM)for basic blocks embedding and inductive learning-based Graph SAGE(GAE)for function representation outperforms the state-of-the-art works.In our framework,we can take only 45%overhead.Improve efficiency significantly with a small performance trade-off. | Jinxue PENG Yong WANG Jingfeng XUE Zhenyan LIU | 2024 | Chinese Journal of Electronics2024，33，1： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员