Background: Plasma markers for stroke could be useful in diagnosis and prognosis and in prediction of response of stroke patients to therapy. PARK7 and nucleoside diphosphate kinase A (NDKA) are increased in human postmortem cerebrospinal fluid (CSF), a model of global brain insult, suggesting that measurement in CSF and, more importantly, in plasma may be useful as a biomarker of stroke. Methods: We used ELISA to measure PARK7 and NDKA in plasma in 3 independent European and North American retrospective studies encompassing a total of 622 stroke patients and 165 control individuals. Results: Increases in both biomarkers were highly significant, with sensitivities of 54%-91% for PARK7 and 70%-90% for NDKA and specificities of 80%-97% for PARK7 and 90%-97% for NDKA. The concentrations of both biomarkers increased within 3 h of stroke onset. Conclusions: PARK7 and NDKA may be useful plasma biomarkers for the early diagnosis of stroke. In addition, this study demonstrated the utility of analysis of postmortem CSF proteins as a first step in the discovery of plasma markers of ischemic brain injury.

No biological marker is currently available for the routine diagnosis of stroke. The aim of this pilot study was to determine whether heart-fatty acid binding protein (H-FABP) could be used as a valid diagnostic biomarker for stroke, as compared with neuron-specific enolase (NSE) and S100B proteins. Using two-dimensional gel electrophoresis separation of cerebrospinal fluid proteins and mass spectrometry techniques, FABP was found elevated in the cerebrospinal fluid of deceased patients, used as a model of massive brain damage. Because H-FABP, a FABP form present in many organs, is also localized in the brain, an enzyme-linked immunosorbant assay was developed to detect H-FABP in stroke versus control plasma samples. However, H-FABP being also a marker of acute myocardial infarction (AMI), troponin-I and creatine kinase-MB levels were assayed at the same time in order to exclude any concomitant heart damage. NSE and S100B levels were assayed simultaneously. These assays were assessed in serial plasma samples from 22 control patients with no AMI or stroke, 20 patients with AMI but no stroke, and 22 patients with an acute stroke but no AMI. Twenty-two out of the 22 control patients and 15 out of the 22 stroke patients were correctly classified, figures much better than those obtained with NSE or S100B, in the same study's population. H-FABP appears to be a valid serum biomarker for the early diagnosis of stroke. Further studies on large cohorts of patients are warranted.

Only few biological markers are currently available for the routine diagnosis of brain damage-related disorders including cerebrovascular, dementia, and other neurodegenerative diseases. In this study, post-mortem cerebrospinal fluid samples were used as a model of massive brain insult to identify new markers potentially relevant for neurodegeneration. The protein pattern of this sample was compared to the one of cerebrospinal fluid from healthy subjects by two-dimensional gel electrophoresis. Using gel imaging, N-terminal microsequencing, mass spectrometry, and immunodetection techniques, we identified 13 differentially expressed proteins. Most of these proteins have been previously reported to be somehow associated with brain destruction or with the molecular mechanisms underlying certain neurodegenerative conditions. These data indicate that the identified proteins indeed represent potential biomarkers of brain damage. We recently showed that H-FABP, a protein highly homologous to E-FABP and A-FABP identified in this study, is a potential marker of Creutzfeldt-Jakob disease and stroke.

Early diagnosis and immediate therapeutic interventions are crucial factors to reduce the damage extent and the risk of death. Currently, the diagnosis of stroke relies on neurological assessment of the patient and neuro-imaging techniques including computed tomography and/or magnetic resonance imaging scan. An early diagnostic marker of stroke, ideally capable to discriminate ischemic from hemorrhagic stroke would considerably improve patient acute management. Using surface-enhanced laser desorption/ionization (SELDI) technology, we aimed at finding new early diagnostic plasmatic markers of stroke. Strong anionic exchange (SAX) SELDI profiles of plasma samples from 21 stroke patients were compared to 21 samples from healthy controls. Seven peaks appeared to be differentially expressed with significant p values (p < 0.05). Proteins were stripped from the SAX chips, separated on a one-dimensional electrophoresis (1-DE) gel and stained using mass spectrometry (MS)-compatible silver staining. Following in-gel tryptic digestion, the peptides were analyzed by MS. Four candidate proteins were identified as apolipoprotein CI (ApoC-I), apolipoprotein CIII (ApoC-III), serum amyloid A (SAA), and antithrombin-III fragment (AT-III fragment). Assessment of ApoC-I and ApoC-III levels in plasma samples using a sandwich enzyme-linked immunosorbent assay (ELISA) allowed to distinguish between hemorrhagic (n = 15) and ischemic (n = 16) stroke (p < 0.001). To the best of our knowledge, ApoC-I and ApoC-III are the first reported plasmatic biomarkers capable to accurately distinguish between ischemic and hemorrhagic stroke in a small number of patients. It requires further investigation in a large cohort of patients.

In this paper we try to identify potential biomarkers for early stroke diagnosis using surfaceenhanced laser desorption/ionization mass spectrometry coupled with analysis tools from machine learning and data mining. Data consist of 42 specimen samples, i.e., mass spectra divided in two big categories, stroke and control specimens. Among the stroke specimens two further categories exist that correspond to ischemic and hemorrhagic stroke; in this paper we limit our data analysis to discriminating between control and stroke specimens. We performed two suites of experiments. In the first one we simply applied a number of different machine learning algorithms; in the second one we have chosen the best performing algorithm as it was determined from the first phase and coupled it with a number of different feature selection methods. The reason for this was 2-fold, first to establish whether feature selection can indeed improve performance, which in our case it did not seem to confirm, but more importantly to acquire a small list of potentially interesting biomarkers. Of the different methods explored the most promising one was support vector machines which gave us high levels of sensitivity and specificity. Finally, by analyzing the models constructed by support vector machines we produced a small set of 13 features that could be used as potential biomarkers, and which exhibited good performance both in terms of sensitivity, specificity and model stability.