Medical Engineering and Bioinformatics

10.2495/meb14 ◽  
2014 ◽  
Keyword(s):  
Medical Engineering

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.

Medical engineering and industry

1972 ◽  
Vol 10 (4) ◽  
pp. 545-548 ◽  
Author(s):  
C. D. Ray
Keyword(s):  
Medical Engineering

Program of medical engineering

1970 ◽  
Vol 3 (2) ◽  
pp. 235
Author(s):  
P.H. Curtiss
Keyword(s):  
Medical Engineering

Which are the Best Information Sources for Identifying Emerging Health Care Technologies?: An International Delphi Survey

1998 ◽  
Vol 14 (4) ◽  
pp. 636-643 ◽  
Author(s):  
Glenn Robert ◽  
John Gabbay ◽  
Andrew Stevens
Keyword(s):  
Health Care ◽  
Health Care Providers ◽  
Delphi Study ◽  
Information Sources ◽  
Medical Engineering ◽  
Private Health Care ◽  
Care Providers ◽  
Medical Journals ◽  
Biotechnology Companies ◽  
Health Care Technologies

AbstractThe purpose of this survey was to assess potential information sources for identifying new health care technologies. A three-round Delphi study was conducted, involving 38 selected experts who suggested and assessed potential sources by applying agreed criteria. Twenty-six potential information sources were considered. Timeliness, time efficiency, and sensitivity were important criteria in determining which were the most important sources. The eight recommended sources were: pharmaceutical journals, pharmaceutical and biotechnology companies, specialist medical journals, key medical journals, medical engineering companies, private health care providers, newsletters and bulletins from other health technology assessment agencies, and groups of expert health professionals. There is a need to use a combination of sources because the most useful sources will vary according to the type of technology under consideration.

Sign in / Sign up

Export Citation Format

Share Document