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Extracellular vesicles (EVs) hold significant potential as therapeutic agents and drug carriers. However, current isolation techniques severely limit their clinical application, due to heavy reliance on manual operation, making large-scale isolation of EVs impractical and failing to meet the requirements for clinical translation. Here, we set up the fully automated collection technology and optimum machinery (FACTORY) platform, allowing the efficient collection of high-quality EVs. The platform integrates continuous flow centrifugation and tangential flow filtration (TFF) technologies, achieving a seamless process for the removal of impurities and collection of EVs, thereby ensuring that large scale-manufactured EVs are sterile, mycoplasma free, and low in endotoxins, and exhibit good consistency. We successfully obtained a substantial quantity of EVs utilizing FACTORY, and systematically characterized their EV-specific markers, biological functions, and therapeutic effects. Results indicated that FACTORY significantly promotes the clinical translation of EVs, thereby laying a solid foundation for their application in drug delivery and beyond.
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http://dx.doi.org/10.1016/j.tibtech.2025.06.020 | DOI Listing |
IEEE Comput Graph Appl
September 2025
Autonomous agents powered by Large Language Models are transforming AI, creating an imperative for the visualization area. However, our field's focus on a human in the sensemaking loop raises critical questions about autonomy, delegation, and coordination for such agentic visualization that preserve human agency while amplifying analytical capabilities. This paper addresses these questions by reinterpreting existing visualization systems with semi-automated or fully automatic AI components through an agentic lens.
View Article and Find Full Text PDFDisabil Rehabil Assist Technol
September 2025
International Communication College, Jilin International Studies University, Changchun, Jilin, China.
Background: Conventional automated writing evaluation systems typically provide insufficient support for students with special needs, especially in tonal language acquisition such as Chinese, primarily because of rigid feedback mechanisms and limited customisation.
Objective: This research develops context-aware Hierarchical AI Tutor for Writing Enhancement(CHATWELL), an intelligent tutoring platform that incorporates optimised large language models to deliver instantaneous, customised, and multi-dimensional writing assistance for Chinese language learners, with special consideration for those with cognitive learning barriers.
Methods: CHATWELL employs a hierarchical AI framework with a four-tier feedback mechanism designed to accommodate diverse learning needs.
J Food Sci
September 2025
Faculty of Computing, Federal University of Uberlandia, Uberlândia, Brazil.
The coffee roasting process is a critical factor in determining the final quality of the beverage, influencing its flavour, aroma, and acidity. Traditionally, roast-level classification has relied on manual inspection, which is time-consuming, subjective, and prone to inconsistencies. However, advancements in machine learning (ML) and computer vision, particularly convolutional neural networks (CNNs), have shown great promise in automating and improving the accuracy of this process.
View Article and Find Full Text PDFMol Ecol Resour
September 2025
Centre for Evolutionary Hologenomics (CEH), Globe Institute, University of Copenhagen, Copenhagen, Denmark.
Global efforts to standardise methodologies benefit greatly from open-source procedures that enable the generation of comparable data. Here, we present a modular, high-throughput nucleic acid extraction protocol standardised within the Earth Hologenome Initiative to generate both genomic and microbial metagenomic data from faecal samples of vertebrates. The procedure enables the purification of either RNA and DNA in separate fractions (DREX1) or as total nucleic acids (DREX2).
View Article and Find Full Text PDFCancer Rep (Hoboken)
September 2025
Jian-Zhao Yin Department of Gynecology and Wei-Feng Gao Department of Anesthesiology, Gansu Provincial Hospital, Lanzhou, Gansu, China.
Background: The existing research data cannot fully prove the advantages of single-site Da Vinci robotic surgery (RSS) compared with single-site laparoscopic surgery (LESS) in the treatment of gynecological diseases.
Aims: To evaluate the effectiveness and cost of RSS and LESS in the treatment of gynecological diseases. To provide a theoretical basis for RSS to replace LESS in the treatment of gynecological diseases.