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Nanoparticle-Based and Bioengineered Probes and Sensors to Detect Physiological and Pathological Biomarkers in Neural Cells

Overview of attention for article published in Frontiers in Neuroscience, December 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

news
10 news outlets
blogs
1 blog
twitter
2 X users

Readers on

mendeley
98 Mendeley
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Title
Nanoparticle-Based and Bioengineered Probes and Sensors to Detect Physiological and Pathological Biomarkers in Neural Cells
Published in
Frontiers in Neuroscience, December 2015
DOI 10.3389/fnins.2015.00480
Pubmed ID
Authors

Dusica Maysinger, Jeff Ji, Eliza Hutter, Elis Cooper

Abstract

Nanotechnology, a rapidly evolving field, provides simple and practical tools to investigate the nervous system in health and disease. Among these tools are nanoparticle-based probes and sensors that detect biochemical and physiological properties of neurons and glia, and generate signals proportionate to physical, chemical, and/or electrical changes in these cells. In this context, quantum dots (QDs), carbon-based structures (C-dots, grapheme, and nanodiamonds) and gold nanoparticles are the most commonly used nanostructures. They can detect and measure enzymatic activities of proteases (metalloproteinases, caspases), ions, metabolites, and other biomolecules under physiological or pathological conditions in neural cells. Here, we provide some examples of nanoparticle-based and genetically engineered probes and sensors that are used to reveal changes in protease activities and calcium ion concentrations. Although significant progress in developing these tools has been made for probing neural cells, several challenges remain. We review many common hurdles in sensor development, while highlighting certain advances. In the end, we propose some future directions and ideas for developing practical tools for neural cell investigations, based on the maxim "Measure what is measurable, and make measurable what is not so" (Galileo Galilei).

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 1%
Unknown 97 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 18%
Student > Master 14 14%
Researcher 13 13%
Student > Bachelor 12 12%
Professor 4 4%
Other 14 14%
Unknown 23 23%
Readers by discipline Count As %
Chemistry 16 16%
Biochemistry, Genetics and Molecular Biology 14 14%
Engineering 10 10%
Agricultural and Biological Sciences 7 7%
Neuroscience 5 5%
Other 21 21%
Unknown 25 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 88. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 October 2023.
All research outputs
#491,818
of 25,651,057 outputs
Outputs from Frontiers in Neuroscience
#208
of 11,653 outputs
Outputs of similar age
#8,090
of 395,670 outputs
Outputs of similar age from Frontiers in Neuroscience
#4
of 132 outputs
Altmetric has tracked 25,651,057 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,653 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 395,670 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.