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Modelling and Detecting Tumour Oxygenation Levels

Overview of attention for article published in PLOS ONE, June 2012
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Article details
Title
Modelling and Detecting Tumour Oxygenation Levels
Published in
PLOS ONE, June 2012
DOI 10.1371/journal.pone.0038597
Pubmed ID
Authors
Abstract

Tumours that are low in oxygen (hypoxic) tend to be more aggressive and respond less well to treatment. Knowing the spatial distribution of oxygen within a tumour could therefore play an important role in treatment planning, enabling treatment to be targeted in such a way that higher doses of radiation are given to the more radioresistant tissue. Mapping the spatial distribution of oxygen in vivo is difficult. Radioactive tracers that are sensitive to different levels of oxygen are under development and in the early stages of clinical use. The concentration of these tracer chemicals can be detected via positron emission tomography resulting in a time dependent concentration profile known as a tissue activity curve (TAC). Pharmaco-kinetic models have then been used to deduce oxygen concentration from TACs. Some such models have included the fact that the spatial distribution of oxygen is often highly inhomogeneous and some have not. We show that the oxygen distribution has little impact on the form of a TAC; it is only the mean oxygen concentration that matters. This has significant consequences both in terms of the computational power needed, and in the amount of information that can be deduced from TACs.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 2 4%
United Kingdom 1 2%
Germany 1 2%
Unknown 41 91%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 14 31%
Researcher 10 22%
Other 2 4%
Student > Master 2 4%
Professor > Associate Professor 2 4%
Other 6 13%
Unknown 9 20%
Readers by discipline
Readers by discipline Count As %
Engineering 9 20%
Physics and Astronomy 8 18%
Medicine and Dentistry 5 11%
Biochemistry, Genetics and Molecular Biology 4 9%
Mathematics 3 7%
Other 8 18%
Unknown 8 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 27 August 2012.
All research outputs
#24,754,523
of 34,361,833 outputs
Outputs from PLOS ONE
#157,355
of 224,520 outputs
Outputs of similar age
#157,016
of 212,839 outputs
Outputs of similar age from PLOS ONE
#3,226
of 4,292 outputs
Altmetric has tracked 34,361,833 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 224,520 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.2. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
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 212,839 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4,292 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.