| Title |
Differential Proteomics for Distinguishing Ischemic Stroke from Controls: a Pilot Study of the SpecTRA Project
|
|---|---|
| Published in |
Translational Stroke Research, January 2018
|
| DOI | 10.1007/s12975-018-0609-z |
| Pubmed ID | |
| Authors |
A. M. Penn, V. Saly, A. Trivedi, M. L. Lesperance, K. Votova, A. M. Jackson, N.S. Croteau, R. F. Balshaw, M. B. Bibok, D. S. Smith, K. K. Lam, J. Morrison, L. Lu, S. B. Coutts, C. H. Borchers |
| Abstract |
A diagnostic blood test for stroke is desirable but will likely require multiple proteins rather than a single "troponin." Validating large protein panels requires large patient numbers. Mass spectrometry (MS) is a cost-effective tool for this task. We compared differences in the abundance of 147 protein markers to distinguish 20 acute cerebrovascular syndrome (ACVS) patients who presented to the Emergency Department of one urban hospital within < 24 h from onset) and from 20 control patients who were enrolled via an outpatient neurology clinic. We targeted proteins from the stroke literature plus cardiovascular markers previously studied in our lab. One hundred forty-one proteins were quantified using MS, 8 were quantified using antibody protein enrichment with MS, and 32 were measured using ELISA, with some proteins measured by multiple techniques. Thirty proteins (4 by ELISA and 26 by the MS techniques) were differentially abundant between mimic and stroke after adjusting for age in robust regression analyses (FDR < 0.20). A logistic regression model using the first two principal components of the proteins significantly improved discrimination between strokes and controls compared to a model based on age alone (p < 0.001, cross-validated AUC 0.93 vs. 0.78). Significant proteins included markers of inflammation (47%), coagulation (40%), atrial fibrillation (7%), neurovascular unit injury (3%), and other (3%). These results suggest the potential value of plasma proteins as biomarkers for ACVS diagnosis and the role of plasma-based MS in this area. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 45 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 18% |
| Researcher | 8 | 18% |
| Student > Master | 5 | 11% |
| Other | 4 | 9% |
| Professor | 3 | 7% |
| Other | 6 | 13% |
| Unknown | 11 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 13% |
| Medicine and Dentistry | 6 | 13% |
| Agricultural and Biological Sciences | 4 | 9% |
| Mathematics | 3 | 7% |
| Neuroscience | 3 | 7% |
| Other | 9 | 20% |
| Unknown | 14 | 31% |
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 28 January 2018.
All research outputs
#16,034,775
of 23,798,792 outputs
Outputs from Translational Stroke Research
#238
of 455 outputs
Outputs of similar age
#274,682
of 444,935 outputs
Outputs of similar age from Translational Stroke Research
#6
of 13 outputs
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