scholarly journals Exosomes, a New Star for Targeted Delivery

2021 ◽  
Vol 9 ◽  
Author(s):  
Huizhi Chen ◽  
Liyan Wang ◽  
Xinling Zeng ◽  
Herbert Schwarz ◽  
Himansu Sekhar Nanda ◽  
...  
Keyword(s):  
Targeted Delivery ◽  
State Of The Art ◽  
Clinical Applications ◽  
Biological Molecules ◽  
Tissue Cell ◽  
Cell Penetration ◽  
Practical Applications ◽  
Cargo Delivery ◽  
Targeted Drug ◽  
Targeting Ability

Exosomes are cell-secreted nanoparticles (generally with a size of 30–150 nm) bearing numerous biological molecules including nucleic acids, proteins and lipids, which are thought to play important roles in intercellular communication. As carriers, exosomes hold promise as advanced platforms for targeted drug/gene delivery, owing to their unique properties, such as innate stability, low immunogenicity and excellent tissue/cell penetration capacity. However, their practical applications can be limited due to insufficient targeting ability or low efficacy in some cases. In order to overcome these existing challenges, various approaches have been applied to engineer cell-derived exosomes for a higher selectivity and effectiveness. This review presents the state-of-the-art designs and applications of advanced exosome-based systems for targeted cargo delivery. By discussing experts’ opinions, we hope this review will inspire the researchers in this field to develop more practical exosomal delivery systems for clinical applications.

scholarly journals Exosomes Derived from Nanocomplex-Loaded Macrophages for Targeted Delivery of Docetaxel and siPLK1 against Castrate-Resistance Prostate Cancer

2020 ◽  
Author(s):  
Jing Tian ◽  
Zongguang Tai ◽  
Wei Zhang ◽  
Xiaoyu Wang ◽  
Zhongjian Chen ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Targeted Delivery ◽  
Drug Carrier ◽  
Drug Loading ◽  
Clinical Applications ◽  
Raw264.7 Macrophages ◽  
New Strategy ◽  
Targeting Ability ◽  
Reduced Toxicity

Abstract Background. As a class of naturally occurring nanoparticles with low immunogenicity and high biocompatibility, exosomes have become a promising drug carrier for cancer therapy. However, their clinical applications remain a challenge due to their unsuitable donors, low scalability, as well as insufficient targeting ability. Here, we describe and validate a new strategy for drug loading into exosomes. We developed a folate-conjugated exosome (Co-Exo-FA) derived from nanocomplex-loaded Raw264.7 macrophages. This Co-Exo-FA containing docetaxel (DTX) and PLK1 siRNA (siPLK1) could be used for targeted therapy of castrate-resistance prostate cancer (CRPC).Results. Our results showed that Co-Exo-FA exhibited high stability, enhanced cancer targeting ability, and led to the suppression of tumor growth with reduced toxicity in vivo. Moreover, the delivery of siPLK1 and DTX using an exosome system effectively silenced the PLK1 gene and exhibited improved anticancer effects.Conclusion. Our results indicated that we managed to overcome major barriers to the efficient utility of exosomes and demonstrated the synergistic efficacy of siPLK1 and DTX in the treatment of CRPC, highlighting their potential value in therapeutic clinical applications.

scholarly journals Exosome-derived noncoding RNAs in gastric cancer: functions and clinical applications

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Xiao-Huan Tang ◽  
Ting Guo ◽  
Xiang-Yu Gao ◽  
Xiao-Long Wu ◽  
Xiao-Fang Xing ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Noncoding Rnas ◽  
Tumour Microenvironment ◽  
Clinical Applications ◽  
Biological Molecules ◽  
Biological Functions ◽  
Membrane Structures ◽  
Chemical Substances ◽  
Expression Levels ◽  
The Stability

AbstractExosomes are a subpopulation of the tumour microenvironment (TME) that transmit various biological molecules to promote intercellular communication. Exosomes are derived from nearly all types of cells and exist in all body fluids. Noncoding RNAs (ncRNAs) are among the most abundant contents in exosomes, and some ncRNAs with biological functions are specifically packaged into exosomes. Recent studies have revealed that exosome-derived ncRNAs play crucial roles in the tumorigenesis, progression and drug resistance of gastric cancer (GC). In addition, regulating the expression levels of exosomal ncRNAs can promote or suppress GC progression. Moreover, the membrane structures of exosomes protect ncRNAs from degradation by enzymes and other chemical substances, significantly increasing the stability of exosomal ncRNAs. Specific hallmarks within exosomes that can be used for exosome identification, and specific contents can be used to determine their origin. Therefore, exosomal ncRNAs are suitable for use as diagnostic and prognostic biomarkers or therapeutic targets. Regulating the biogenesis of exosomes and the expression levels of exosomal ncRNAs may represent a new way to block or eradicate GC. In this review, we summarized the origins and characteristics of exosomes and analysed the association between exosomal ncRNAs and GC development.

scholarly journals Well-defined single polymer nanoparticles for the antibody-targeted delivery of chemotherapeutic agents

2015 ◽  
Vol 6 (8) ◽  
pp. 1286-1299 ◽  
Author(s):  
D. D. Lane ◽  
D. Y. Chiu ◽  
F. Y. Su ◽  
S. Srinivasan ◽  
H. B. Kern ◽  
...  
Keyword(s):  
Drug Delivery ◽  
Targeted Drug Delivery ◽  
Targeted Delivery ◽  
Raft Polymerization ◽  
Chemotherapeutic Agents ◽  
Polymer Nanoparticles ◽  
Drug Delivery Vehicles ◽  
Molecular Weights ◽  
Delivery Vehicles ◽  
Targeted Drug

Second generation polymeric brushes with molecular weights in excess of 106 Da were synthesize via RAFT polymerization for use as antibody targeted drug delivery vehicles.

scholarly journals Novel clinical applications of state-of-the-art multi-slice computed tomography

2009 ◽  
Vol 25 (S2) ◽  
pp. 241-254 ◽  
Author(s):  
J. D. Schuijf ◽  
V. Delgado ◽  
J. M. van Werkhoven ◽  
F. R. de Graaf ◽  
J. E. van Velzen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
State Of The Art ◽  
Clinical Applications

Macrophage membrane coated nanoparticles: a biomimetic approach for enhanced and targeted delivery

2022 ◽  
Author(s):  
Nafeesa Khatoon ◽  
Zefei Zhang ◽  
Chunhui Zhou ◽  
Maoquan Chu
Keyword(s):  
Drug Delivery ◽  
Targeted Drug Delivery ◽  
Targeted Delivery ◽  
Systemic Toxicity ◽  
Coated Nanoparticles ◽  
Biomimetic Approach ◽  
Targeted Drug ◽  
Macrophage Membrane

The enhanced and targeted drug delivery with low systemic toxicity and subsequent release of drugs is the major concern among researchers and pharmaceutics. Inspite of greater advancement and discoveries in...

Proximal policy optimization with model-based methods

2022 ◽  
pp. 1-12
Author(s):  
Shuailong Li ◽  
Wei Zhang ◽  
Huiwen Zhang ◽  
Xin Zhang ◽  
Yuquan Leng
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Transition Model ◽  
Practical Applications ◽  
Original Algorithm ◽  
Policy Performance ◽  
Model Based ◽  
Model Free ◽  
Future State ◽  
Policy Optimization

Model-free reinforcement learning methods have successfully been applied to practical applications such as decision-making problems in Atari games. However, these methods have inherent shortcomings, such as a high variance and low sample efficiency. To improve the policy performance and sample efficiency of model-free reinforcement learning, we propose proximal policy optimization with model-based methods (PPOMM), a fusion method of both model-based and model-free reinforcement learning. PPOMM not only considers the information of past experience but also the prediction information of the future state. PPOMM adds the information of the next state to the objective function of the proximal policy optimization (PPO) algorithm through a model-based method. This method uses two components to optimize the policy: the error of PPO and the error of model-based reinforcement learning. We use the latter to optimize a latent transition model and predict the information of the next state. For most games, this method outperforms the state-of-the-art PPO algorithm when we evaluate across 49 Atari games in the Arcade Learning Environment (ALE). The experimental results show that PPOMM performs better or the same as the original algorithm in 33 games.

Dangerous Scenes Recognition During Hoisting Based on Faster Region-Based Convolutional Neural Network

2018 ◽  
Author(s):  
Hongguo Su ◽  
Mingyuan Zhang ◽  
Shengyuan Li ◽  
Xuefeng Zhao
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
State Of The Art ◽  
Spatial Location ◽  
Average Precision ◽  
Practical Applications ◽  
Security Enhancement ◽  
Human Interactions

In the last couple of years, advancements in the deep learning, especially in convolutional neural networks, proved to be a boon for the image classification and recognition tasks. One of the important practical applications of object detection and image classification can be for security enhancement. If dangerous objects or scenes can be identified automatically, then a lot of accidents can be prevented. For this purpose, in this paper we made use of state-of-the-art implementation of Faster Region-based Convolutional Neural Network (Faster R-CNN) based on the monitoring video of hoisting sites to train a model to detect the dangerous object and the worker. By extracting the locations of them, object-human interactions during hoisting, mainly for changes in their spatial location relationship, can be understood whereby estimating whether the scene is safe or dangerous. Experimental results showed that the pre-trained model achieved good performance with a high mean average precision of 97.66% on object detection and the proposed method fulfilled the goal of dangerous scenes recognition perfectly.

scholarly journals Erratum to: State-of-the-art MRI techniques in neuroradiology: principles, pitfalls, and clinical applications

2015 ◽  
Vol 57 (10) ◽  
pp. 1075-1075 ◽  
Author(s):  
Magalie Viallon ◽  
Victor Cuvinciuc ◽  
Benedicte Delattre ◽  
Laura Merlini ◽  
Isabelle Barnaure-Nachbar ◽  
...  
Keyword(s):  
State Of The Art ◽  
Clinical Applications

scholarly journals Electrospray preparation of biocompatible lactide–glycolide copolymer capsules with incorporation of interferon

2019 ◽  
Vol 484 (6) ◽  
pp. 703-708
Author(s):  
I. A. Khlusov ◽  
E. V. Kibler ◽  
V. L. Kudryavtseva ◽  
S. I. Tverdokhlebov ◽  
E. N. Bolbasov ◽  
...  
Keyword(s):  
Cytotoxic Activity ◽  
Tumor Cells ◽  
Targeted Delivery ◽  
Structural Integrity ◽  
Environmental Safety ◽  
Biological Molecules ◽  
Confocal Laser ◽  
Human Interferon ◽  
First Time ◽  
Scanning Electron

The electrospray method was used for the first time to prepare polymeric capsules from bioresorbable dl-lactide and glycolide copolymer loaded with biological molecules from the cell secretome and, in particular, human interferon a-2b (IFN a-2b). The obtained nearly spherical submicron capsules were studied by scanning electron and confocal laser microscopy. The capsules retain the structural integrity and the cytotoxic activity of IFN a-2b towards tumor cells. The electrospray method is distinguished by high adaptability and environmental safety and is suitable for manufacture of a broad range of materials with different composition and morphology promising for the targeted delivery of drugs and biological molecules.

scholarly journals Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification

2020 ◽  
Author(s):  
Zheng Wang ◽  
Zhixiang Wang ◽  
Yinqiang Zheng ◽  
Yang Wu ◽  
Wenjun Zeng ◽  
...  
Keyword(s):  
Video Analysis ◽  
State Of The Art ◽  
Future Research ◽  
Low Resolution ◽  
Research Directions ◽  
Comprehensive Review ◽  
Practical Applications ◽  
Future Research Directions

An efficient and effective person re-identification (ReID) system relieves the users from painful and boring video watching and accelerates the process of video analysis. Recently, with the explosive demands of practical applications, a lot of research efforts have been dedicated to heterogeneous person re-identification (Hetero-ReID). In this paper, we provide a comprehensive review of state-of-the-art Hetero-ReID methods that address the challenge of inter-modality discrepancies. According to the application scenario, we classify the methods into four categories --- low-resolution, infrared, sketch, and text. We begin with an introduction of ReID, and make a comparison between Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and compare existing datasets for performing evaluations, and survey the models that have been widely employed in Hetero-ReID. We also summarize and compare the representative approaches from two perspectives, i.e., the application scenario and the learning pipeline. We conclude by a discussion of some future research directions. Follow-up updates are available at https://github.com/lightChaserX/Awesome-Hetero-reID

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