↓ Skip to main content

Biomarkers of inflammation and innate immunity in atrophic nonunion fracture

Overview of attention for article published in Journal of Translational Medicine, September 2016
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
5 X users

Citations

dimensions_citation
17 Dimensions

Readers on

mendeley
66 Mendeley
Title
Biomarkers of inflammation and innate immunity in atrophic nonunion fracture
Published in
Journal of Translational Medicine, September 2016
DOI 10.1186/s12967-016-1019-1
Pubmed ID
Authors

Dominique de Seny, Gaël Cobraiville, Pierre Leprince, Marianne Fillet, Charlotte Collin, Myrielle Mathieu, Jean-Philippe Hauzeur, Valérie Gangji, Michel G. Malaise

Abstract

Nonunion is a failure of healing following a bone fracture. Its physiopathology remains partially unclear and the discovery of new mediators could promote the understanding of bone healing. Thirty-three atrophic nonunion (NU) patients that failed to demonstrate any radiographic improvement for 6 consecutive months were recruited for providing serum samples. Thirty-five healthy volunteers (HV) served as the control group. Proteomics studies were performed using SELDI-TOF-MS and 2D-DIGE approaches, associated or not with Proteominer® preprocessing, to highlight biomarkers specific to atrophic nonunion pathology. Peak intensities were analyzed by two statistical approaches, a nonparametric Mann-Whitney U tests (univariate approach) and a machine-learning algorithm called extra-trees (multivariate approach). Validation of highlighted biomarkers was performed by alternative approaches such as microfluidic LC-MS/MS, nephelometry, western blotting or ELISA assays. From the 35 HV and 33 NU crude serum samples and Proteominer® eluates, 136 spectra were collected by SELDI-TOF-MS using CM10 and IMAC-Cu(2+) ProteinChip arrays, and 665 peaks were integrated for extra-trees multivariate analysis. Accordingly, seven biomarkers and several variants were identified as potential NU biomarkers. Their levels of expression were found to be down- or up-regulated in serum of HV vs NU. These biomarkers are inter-α-trypsin inhibitor H4, hepcidin, S100A8, S100A9, glycated hemoglobin β subunit, PACAP related peptide, complement C3 α-chain. 2D-DIGE experiment allowed to detect 14 biomarkers as being down- or up-regulated in serum of HV vs NU including a cleaved fragment of apolipoprotein A-IV, apolipoprotein E, complement C3 and C6. Several biomarkers such as hepcidin, complement C6, S100A9, apolipoprotein E, complement C3 and C4 were confirmed by an alternative approach as being up-regulated in serum of NU patients compared to HV controls. Two proteomics approaches were used to identify new biomarkers up- or down-regulated in the nonunion pathology, which are involved in bone turn-over, inflammation, innate immunity, glycation and lipid metabolisms. High expression of hepcidin or S100A8/S100A9 by myeloid cells and the presence of advanced glycation end products and complement factors could be the result of a longstanding inflammatory process. Blocking macrophage activation and/or TLR4 receptor could accelerate healing of fractured bone in at-risk patients.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 2%
Unknown 65 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 12%
Researcher 8 12%
Student > Master 8 12%
Other 6 9%
Student > Doctoral Student 5 8%
Other 14 21%
Unknown 17 26%
Readers by discipline Count As %
Medicine and Dentistry 22 33%
Agricultural and Biological Sciences 9 14%
Biochemistry, Genetics and Molecular Biology 5 8%
Nursing and Health Professions 3 5%
Pharmacology, Toxicology and Pharmaceutical Science 2 3%
Other 5 8%
Unknown 20 30%
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 21 October 2017.
All research outputs
#14,860,134
of 22,886,568 outputs
Outputs from Journal of Translational Medicine
#1,978
of 4,004 outputs
Outputs of similar age
#203,578
of 334,695 outputs
Outputs of similar age from Journal of Translational Medicine
#39
of 79 outputs
Altmetric has tracked 22,886,568 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,004 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one is in the 44th percentile – i.e., 44% 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 334,695 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 79 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.