Intelligent Medicine (Medical/Engineering Corporation)

Extracellular vesicles (EVs) are involved in the regulation of cell physiological activity and the reconstruction of extracellular environment. Matrix vesicles (MVs) are a type of EVs, and they participate in the regulation of cell mineralization. Herein, bioinspired MVs embedded with black phosphorus are functionalized with cell-specific aptamer (denoted as Apt-bioinspired MVs) for stimulating biomineralization. The aptamer can direct bioinspired MVs to targeted cells, and the increasing concentration of inorganic phosphate originated from the black phosphorus can facilitate cell biomineralization. The photothermal effect of the Apt-bioinspired MVs also positively affects mineralization. In addition, the Apt-bioinspired MVs display outstanding bone regeneration performance. Considering the excellent behavior of the Apt-bioinspired MVs for promoting biomineralization, our strategy provides a way of designing bionic tools for studying the mechanisms of biological processes and advancing the development of medical engineering.<br>

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The medical engineering program of Forschungszentrum Karlsruhe

Minimally Invasive Therapy & Allied Technologies ◽

10.3109/13645700009169656 ◽

2000 ◽

Vol 9 (3) ◽

pp. 255-267 ◽

Cited By ~ 1

Author(s):

H. Fischer ◽

M. Selig ◽

J. Vagner ◽

B. Vogel ◽

E. Hempel ◽

...

Keyword(s):

Medical Engineering ◽

Engineering Program

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ATLANTIC 13B ALLOY

Alloy Digest ◽

10.31399/asm.ad.cu0307 ◽

1976 ◽

Vol 25 (1) ◽

Keyword(s):

Fracture Toughness ◽

Corrosion Resistance ◽

Physical Properties ◽

High Strength ◽

Heat Treating ◽

Casting Alloy ◽

Silicon Bronze ◽

Thin Walled ◽

Engineering Corporation ◽

Bronze Casting

Abstract ATLANTIC 13B Alloy is a silicon bronze casting alloy offering excellent castability and corrosion resistance, good machinability and medium-high strength. It is an all-purpose alloy that is suitable for gears, valve parts and marine fittings. It is especially useful in stress-corrosion environments for thin-walled parts. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties as well as fracture toughness and fatigue. It also includes information on corrosion resistance as well as casting, heat treating, machining, and joining. Filing Code: Cu-307. Producer or source: Atlantic Casting and Engineering Corporation.

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Multi-hop assortativities for network classification

Journal of Complex Networks ◽

10.1093/comnet/cny034 ◽

2018 ◽

Vol 7 (4) ◽

pp. 603-622 ◽

Cited By ~ 1

Author(s):

Leonardo Gutiérrez-Gómez ◽

Jean-Charles Delvenne

Keyword(s):

Machine Learning ◽

Scientific Collaboration ◽

State Of The Art ◽

Medical Engineering ◽

Research Field ◽

Classification Task ◽

Collaboration Network ◽

Structural Patterns ◽

Art Methods

Abstract Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of ‘fingerprints’ to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.

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Medical engineering and industry

Medical & Biological Engineering ◽

10.1007/bf02474205 ◽

1972 ◽

Vol 10 (4) ◽

pp. 545-548 ◽

Cited By ~ 1

Author(s):

C. D. Ray

Keyword(s):

Medical Engineering

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