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Journal of Biomedical Nanotechnology
Article . 2014 . Peer-reviewed
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Nanosensors for Regenerative Medicine

Authors: Yeo, David C.; Wiraja, Christian; Mantalaris, Athanasios (Sakis); Xu, Chenjie;

Nanosensors for Regenerative Medicine

Abstract

Assessing biodistribution, fate, and function of implanted therapeutic cells in preclinical animal experiments is critical to realize safe, effective and efficient treatments for subsequent implementation within the clinic. Currently, tissue histology, the most prevalent analytical technique to meet this need, is limited by end-point analysis, high cost and long preparation time. Moreover, it is disadvantaged by an inability to monitor in real-time, qualitative interpretation and ethical issues arising from animal sacrifice. While genetic engineering techniques allow cells to express molecules with detectable signals (e.g., fluorescence, luminescence, T1 (spin-lattice)/T2 (spin-spin) contrast in magnetic resonance imaging, radionuclide), concerns arise regarding technical complexity, high-cost of genetic manipulation, as well as mutagenic cell dysfunction. Alternatively, cells can be labeled using nanoparticle-sensors-nanosensors that emit signals to identify cell location, status and function in a simple, cost-effective, and non-genetic manner. This review article provides the definition, classification, evolution, and applications of nanosensor technology and focuses on how they can be utilized in regenerative medicine. Several examples of direct applications include: (1) monitoring post-transplantation cell behavior, (2) revealing host response following foreign biomaterial implantation, and (3) optimization of cell bioprocess operating conditions. Incorporating nanosensors is expected to expedite the development of cell-based regenerative medicine therapeutics.

Country
Singapore
Related Organizations
Keywords

Diagnostic Imaging, Tissue Scaffolds, Biosensing Techniques, Regenerative Medicine, Bioreactors, Animals, Humans, Nanoparticles, :Engineering::Nanotechnology [DRNTU]

  • BIP!
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    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    19
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
19
Top 10%
Average
Top 10%
Green