Accelerating medical progress through medical engineering

1965 ◽  
Vol 29 (3) ◽  
pp. 159
Author(s):  
V.K. Zworykin
Keyword(s):  
Medical Engineering ◽  
Medical Progress

Biomimetic Extracellular Vesicles Embedded with Black Phosphorus for Molecular Recognition-Guided Biomineralization

2018 ◽  
Author(s):  
Yingqian Wang ◽  
Xiaoxia Hu ◽  
Lingling Zhang ◽  
Chunli Zhu ◽  
Jie Wang ◽  
...  
Keyword(s):  
Molecular Recognition ◽  
Extracellular Vesicles ◽  
Inorganic Phosphate ◽  
Physiological Activity ◽  
Medical Engineering ◽  
Matrix Vesicles ◽  
Black Phosphorus ◽  
Photothermal Effect ◽  
Biological Processes ◽  
Extracellular Environment

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>

The medical engineering program of Forschungszentrum Karlsruhe

2000 ◽  
Vol 9 (3) ◽  
pp. 255-267 ◽  
Author(s):  
H. Fischer ◽  
M. Selig ◽  
J. Vagner ◽  
B. Vogel ◽  
E. Hempel ◽  
...  
Keyword(s):  
Medical Engineering ◽  
Engineering Program

scholarly journals Multi-hop assortativities for network classification

2018 ◽  
Vol 7 (4) ◽  
pp. 603-622 ◽  
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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