WeChat Mini Program
Old Version Features

    • The Evolution of Microfluidic-Based Drug-Loading Techniques for Cells and Their Derivatives.

    Siyu Tong,Jiaqi Niu, Zhitao Wang, Yingao Jiao, Yanfei Fu,Dongxia Li,Xinni Pan,Nengquan Sheng, Li Yan, Peiru Min,Di Chen,Shengsheng Cui,Yanlei Liu,Shujing Lin

    Small (Weinheim an der Bergstrasse, Germany)（2024）

    School of Electronic Information and Electrical Engineering

    Cited 1|Views2
    Abstract
    Conventional drug delivery techniques face challenges related to targeting and adverse reactions. Recent years have witnessed significant advancements in nanoparticle-based drug carriers. Nevertheless, concerns persist regarding their safety and insufficient metabolism. Employing cells and their derivatives, such as cell membranes and extracellular vesicles (EVs), as drug carriers effectively addresses the challenges associated with nanoparticle carriers. However, an essential hurdle remains in efficiently loading drugs into these carriers. With the advancement of microfluidic technology and its advantages in precise manipulation at the micro- and nanoscales, as well as minimal sample loss, it has found extensive application in the loading of drugs using cells and their derivatives, thereby fostering the development of drug-loading techniques. This paper outlines the characteristics and benefits of utilizing cells and their derivatives as drug carriers and provides an overview of current drug-loading techniques, particularly those rooted in microfluidic technology. The significant potential for microfluidic technology in targeted disease therapy through drug delivery systems employing cells and their derivatives, is foreseen.
    More
    Translated text
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined