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A New Skin Tensiometer Device: Computational Analyses To Understand Biodynamic Excisional Skin Tension Lines

Overview of attention for article published in Scientific Reports, July 2016
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Title
A New Skin Tensiometer Device: Computational Analyses To Understand Biodynamic Excisional Skin Tension Lines
Published in
Scientific Reports, July 2016
DOI 10.1038/srep30117
Pubmed ID
Authors

Sharad P. Paul, Justin Matulich, Nick Charlton

Abstract

One of the problems in planning cutaneous surgery is that human skin is anisotropic, or directionally dependent. Indeed, skin tension varies between individuals and at different body sites. Many a surgeon has tried to design different devices to measure skin tension to help plan excisional surgery, or to understand wound healing. However, many of the devices have been beset with problems due to many confounding variables - differences in technical ability, material (sutures) used and variability between different users. We describe the development of a new skin tensiometer that overcomes many historical technical issues. A new skin tension measuring device is presented here. It was designed to be less user-dependent, more reliable and usable on different bodily sites. The design and computational optimizations are discussed. Our skin tensiometer has helped understand the differences between incisional and excisional skin lines. Langer, who pioneered the concept of skin tension lines, created incisional lines that differ from lines caused by forces that need to be overcome when large wounds are closed surgically (excisional tension). The use of this innovative device has led to understanding of skin biomechanics and best excisional skin tension (BEST) lines.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 15%
Researcher 8 15%
Student > Bachelor 7 13%
Professor 3 6%
Student > Master 3 6%
Other 7 13%
Unknown 16 31%
Readers by discipline Count As %
Engineering 15 29%
Medicine and Dentistry 13 25%
Agricultural and Biological Sciences 2 4%
Nursing and Health Professions 1 2%
Veterinary Science and Veterinary Medicine 1 2%
Other 3 6%
Unknown 17 33%
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 25 July 2016.
All research outputs
#21,796,814
of 24,319,828 outputs
Outputs from Scientific Reports
#113,815
of 132,250 outputs
Outputs of similar age
#330,301
of 372,742 outputs
Outputs of similar age from Scientific Reports
#3,052
of 3,630 outputs
Altmetric has tracked 24,319,828 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 132,250 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.6. This one is in the 1st percentile – i.e., 1% 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 372,742 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3,630 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.