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X Demographics
Mendeley readers
Attention Score in Context
| Title |
The Development of Translational Biomarkers as a Tool for Improving the Understanding, Diagnosis and Treatment of Chronic Neuropathic Pain
|
|---|---|
| Published in |
Molecular Neurobiology, March 2017
|
| DOI | 10.1007/s12035-017-0492-8 |
| Pubmed ID | |
| Authors |
David A. Buckley, Elaine M. Jennings, Nikita N. Burke, Michelle Roche, Veronica McInerney, Jonathan D. Wren, David P. Finn, Patrick C. McHugh |
| Abstract |
Chronic neuropathic pain (CNP) is one of the most significant unmet clinical needs in modern medicine. Alongside the lack of effective treatments, there is a great deficit in the availability of objective diagnostic methods to reliably facilitate an accurate diagnosis. We therefore aimed to determine the feasibility of a simple diagnostic test by analysing differentially expressed genes in the blood of patients diagnosed with CNP of the lower back and compared to healthy human controls. Refinement of microarray expression data was performed using correlation analysis with 3900 human 2-colour microarray experiments. Selected genes were analysed in the dorsal horn of Sprague-Dawley rats after L5 spinal nerve ligation (SNL), using qRT-PCR and ddPCR, to determine possible associations with pathophysiological mechanisms underpinning CNP and whether they represent translational biomarkers of CNP. We found that of the 15 potential biomarkers identified, tissue inhibitor of matrix metalloproteinase-1 (TIMP1) gene expression was upregulated in chronic neuropathic lower back pain (CNBP) (p = 0.0049) which positively correlated (R = 0.68, p = ≤0.05) with increased plasma TIMP1 levels in this group (p = 0.0433). Moreover, plasma TIMP1 was also significantly upregulated in CNBP than chronic inflammatory lower back pain (p = 0.0272). In the SNL model, upregulation of the Timp1 gene was also observed (p = 0.0058) alongside a strong trend for the upregulation of melanocortin 1 receptor (p = 0.0847). Our data therefore highlights several genes that warrant further investigation, and of these, TIMP1 shows the greatest potential as an accessible and translational CNP biomarker. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 50% |
| United Kingdom | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
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 | 9 | 17% |
| Researcher | 8 | 15% |
| Student > Bachelor | 7 | 13% |
| Student > Master | 5 | 10% |
| Other | 3 | 6% |
| Other | 6 | 12% |
| Unknown | 14 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 7 | 13% |
| Neuroscience | 5 | 10% |
| Biochemistry, Genetics and Molecular Biology | 4 | 8% |
| Agricultural and Biological Sciences | 4 | 8% |
| Engineering | 4 | 8% |
| Other | 10 | 19% |
| Unknown | 18 | 35% |
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 13 June 2018.
All research outputs
#13,900,608
of 23,041,514 outputs
Outputs from Molecular Neurobiology
#1,756
of 3,490 outputs
Outputs of similar age
#164,431
of 309,067 outputs
Outputs of similar age from Molecular Neurobiology
#43
of 108 outputs
Altmetric has tracked 23,041,514 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,490 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 49th percentile – i.e., 49% 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 309,067 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 108 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.