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Rapid detection of bacterial resistance to antibiotics using AFM cantilevers as nanomechanical sensors

Overview of attention for article published in Nature Nanotechnology, June 2013
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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 (98th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

news
7 news outlets
blogs
2 blogs
twitter
11 tweeters
patent
7 patents
facebook
3 Facebook pages

Citations

dimensions_citation
227 Dimensions

Readers on

mendeley
315 Mendeley
citeulike
2 CiteULike
Title
Rapid detection of bacterial resistance to antibiotics using AFM cantilevers as nanomechanical sensors
Published in
Nature Nanotechnology, June 2013
DOI 10.1038/nnano.2013.120
Pubmed ID
Authors

G. Longo, L. Alonso-Sarduy, L. Marques Rio, A. Bizzini, A. Trampuz, J. Notz, G. Dietler, S. Kasas

Abstract

The widespread misuse of drugs has increased the number of multiresistant bacteria, and this means that tools that can rapidly detect and characterize bacterial response to antibiotics are much needed in the management of infections. Various techniques, such as the resazurin-reduction assays, the mycobacterial growth indicator tube or polymerase chain reaction-based methods, have been used to investigate bacterial metabolism and its response to drugs. However, many are relatively expensive or unable to distinguish between living and dead bacteria. Here we show that the fluctuations of highly sensitive atomic force microscope cantilevers can be used to detect low concentrations of bacteria, characterize their metabolism and quantitatively screen (within minutes) their response to antibiotics. We applied this methodology to Escherichia coli and Staphylococcus aureus, showing that live bacteria produced larger cantilever fluctuations than bacteria exposed to antibiotics. Our preliminary experiments suggest that the fluctuation is associated with bacterial metabolism.

Twitter Demographics

The data shown below were collected from the profiles of 11 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 315 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 4 1%
United States 3 <1%
France 3 <1%
United Kingdom 3 <1%
Switzerland 2 <1%
India 2 <1%
Belgium 1 <1%
Canada 1 <1%
Chile 1 <1%
Other 4 1%
Unknown 291 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 99 31%
Researcher 62 20%
Student > Master 40 13%
Student > Bachelor 21 7%
Professor > Associate Professor 15 5%
Other 45 14%
Unknown 33 10%
Readers by discipline Count As %
Engineering 65 21%
Agricultural and Biological Sciences 62 20%
Physics and Astronomy 44 14%
Chemistry 23 7%
Biochemistry, Genetics and Molecular Biology 20 6%
Other 46 15%
Unknown 55 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 83. 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 19 November 2020.
All research outputs
#334,609
of 18,846,561 outputs
Outputs from Nature Nanotechnology
#363
of 3,068 outputs
Outputs of similar age
#2,757
of 168,009 outputs
Outputs of similar age from Nature Nanotechnology
#3
of 60 outputs
Altmetric has tracked 18,846,561 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 3,068 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 34.8. This one has done well, scoring higher than 88% 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 168,009 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 98% of its contemporaries.
We're also able to compare this research output to 60 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.