↓ Skip to main content

Computational analysis identifies putative prognostic biomarkers of pathological scarring in skin wounds

Overview of attention for article published in Journal of Translational Medicine, February 2018
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 X users

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
37 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Computational analysis identifies putative prognostic biomarkers of pathological scarring in skin wounds
Published in
Journal of Translational Medicine, February 2018
DOI 10.1186/s12967-018-1406-x
Pubmed ID
Authors

Sridevi Nagaraja, Lin Chen, Luisa A. DiPietro, Jaques Reifman, Alexander Y. Mitrophanov

Abstract

Pathological scarring in wounds is a prevalent clinical outcome with limited prognostic options. The objective of this study was to investigate whether cellular signaling proteins could be used as prognostic biomarkers of pathological scarring in traumatic skin wounds. We used our previously developed and validated computational model of injury-initiated wound healing to simulate the time courses for platelets, 6 cell types, and 21 proteins involved in the inflammatory and proliferative phases of wound healing. Next, we analysed thousands of simulated wound-healing scenarios to identify those that resulted in pathological (i.e., excessive) scarring. Then, we identified candidate proteins that were elevated (or decreased) at the early stages of wound healing in those simulations and could therefore serve as predictive biomarkers of pathological scarring outcomes. Finally, we performed logistic regression analysis and calculated the area under the receiver operating characteristic curve to quantitatively assess the predictive accuracy of the model-identified putative biomarkers. We identified three proteins (interleukin-10, tissue inhibitor of matrix metalloproteinase-1, and fibronectin) whose levels were elevated in pathological scars as early as 2 weeks post-wounding and could predict a pathological scarring outcome occurring 40 days after wounding with 80% accuracy. Our method for predicting putative prognostic wound-outcome biomarkers may serve as an effective means to guide the identification of proteins predictive of pathological scarring.

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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 16%
Student > Master 5 14%
Student > Ph. D. Student 4 11%
Student > Bachelor 3 8%
Other 3 8%
Other 5 14%
Unknown 11 30%
Readers by discipline Count As %
Medicine and Dentistry 14 38%
Biochemistry, Genetics and Molecular Biology 2 5%
Agricultural and Biological Sciences 2 5%
Nursing and Health Professions 1 3%
Chemical Engineering 1 3%
Other 4 11%
Unknown 13 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 22 May 2018.
All research outputs
#15,378,413
of 23,630,563 outputs
Outputs from Journal of Translational Medicine
#2,073
of 4,190 outputs
Outputs of similar age
#202,210
of 332,069 outputs
Outputs of similar age from Journal of Translational Medicine
#47
of 89 outputs
Altmetric has tracked 23,630,563 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,190 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one is in the 43rd percentile – i.e., 43% 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 332,069 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 89 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.