Literature DB >> 30783962

High-Throughput Profiling of Circulating Antibody Signatures for Stroke Diagnosis Using Small Volumes of Whole Blood.

Grant C O'Connell1, Phillip Stafford2, Kyle B Walsh3,4, Opeolu Adeoye3,4, Taura L Barr5.   

Abstract

Accurate stroke recognition during triage can streamline care and afford patients earlier access to life-saving interventions. However, the tools currently available to clinicians for prehospital and early in-hospital identification of stroke are limited. The peripheral immune system is intricately involved in stroke pathology and thus may be targetable for the development of immunodiagnostics. In this preliminary study, we sought to determine whether the circulating antibody pool is altered early in stroke, and whether such alterations could be leveraged for diagnosis. One hundred microliters of peripheral whole blood was sampled from 19 ischemic stroke patients, 17 hemorrhagic stroke patients, and 20 stroke mimics in the acute phase of care. A custom-fabricated high-density peptide array comprising 125,000 unique probes was used to assess the binding characteristics of blood-borne antibodies, and a random forest-based approach was used to select a parsimonious set of probes with an optimal ability to discriminate between groups. The coordinate antibody binding intensities of the top 17 probes identified in our analysis displayed an ability to differentiate the total pool of stroke patients from stroke mimics with 92% sensitivity and 90% specificity, as well as detect hemorrhage with 88% sensitivity and 87% specificity, as determined using a same-set cross-validation. These preliminary findings suggest that stroke-associated alterations in the circulating antibody pool may have clinical utility for diagnosis during triage, and that such a possibility warrants further investigation.

Entities:  

Keywords:  Biomarkers; immunology; machine-learning; molecular diagnostics; proteomics; triage

Mesh:

Substances:

Year:  2019        PMID: 30783962      PMCID: PMC6694452          DOI: 10.1007/s13311-019-00720-9

Source DB:  PubMed          Journal:  Neurotherapeutics        ISSN: 1878-7479            Impact factor:   7.620


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