Exosomes are discrete populations of small (40-200 nm in diameter) membranous vesicles that are released into the extracellular space by most cell types, eventually accumulating in the circulation. As molecular messengers, exosomes exert a broad array of vital physiologic functions by transporting information between different cell types. Because of these functional properties, they may have potential as biomarker sources for prognostic and diagnostic disease. Recent research has found that exosomes have potential to be utilized as drug delivery agents for therapeutic targets. However, basic researches on exosomes and researches on their therapeutic potential both require the existence of effective and rapid methods for their separation from human samples. In the current absence of a standardized method, there are several methods available for the separation of exosomes, but very few studies have previously compared the efficiency and suitability of these different methods. This review summarized and compared the available traditional and novel methods for the extraction of exosomes from human samples and considered their advantages and disadvantages for use in clinical laboratories and point-of-care settings.
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http://dx.doi.org/10.1155/2018/3634563 | DOI Listing |
Sci Rep
December 2024
Shandong University of Science and Technology, College of Transportation, Qingdao, 266590, China.
The optimization of auto parts supply chain logistics plays a decisive role in the development of the automotive industry. To reduce logistics costs and improve transportation efficiency, this paper addresses the joint optimization problem of multi-vehicle pickup and delivery transportation paths under time window constraints, coupled with the three-dimensional loading of goods. The model considers mixed time windows, three-dimensional loading constraints, cyclic pickup and delivery paths, varying vehicle loads and volumes, flow balance, and time window constraints.
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December 2024
Department of Computer Science and Digital Technologies, University of East London, London, UK.
Nursing activity recognition has immense importance in the development of smart healthcare management and is an extremely challenging area of research in human activity recognition. The main reasons are an extreme class-imbalance problem and intra-class variability depending on both the subject and the recipient. In this paper, we apply a unique two-step feature extraction, coupled with an intermediate feature 'Angle' and a new feature called mean min max sum to render the features robust against intra-class variation.
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December 2024
Henan College of Transportation, Zhengzhou, 450000, Henan, China.
Novel Human Activity Recognition (HAR) methodologies, which are built upon learning algorithms and employ ubiquitous sensors, have achieved remarkable precision in the identification of sports activities. Such progress benefits all age groups of humanity, and in the future, AI will be used to address difficult problems in scientific research. A novel approach is introduced in this article to utilize motion sensor data in order to categorize and distinguish various categories of sports activities.
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December 2024
Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
Research has shown various hydrolyzed proteins possessed beneficial physiological functions; however, the mechanism of how hydrolysates influence metabolism is unclear. Therefore, the current study aimed to examine the effects of different sources of protein hydrolysates, being the main dietary protein source in extruded diets, on metabolism in healthy adult dogs. Three complete and balanced extruded canine diets were formulated: control chicken meal diet (CONd), chicken liver and heart hydrolysate diet (CLHd), mechanically separated chicken hydrolysate diet (CHd).
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December 2024
State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, China.
Imaging flow cytometry allows image-activated cell sorting (IACS) with enhanced feature dimensions in cellular morphology, structure, and composition. However, existing IACS frameworks suffer from the challenges of 3D information loss and processing latency dilemma in real-time sorting operation. Herein, we establish a neuromorphic-enabled video-activated cell sorter (NEVACS) framework, designed to achieve high-dimensional spatiotemporal characterization content alongside high-throughput sorting of particles in wide field of view.
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