Stroke biomarkers in clinical practice: A critical appraisal

Comparison of plasma PARK7 and NDKA diagnostic value in acute stroke

AIM

In this prospective case-control study we aimed to compare diagnostic value of plasma PARK7 and NDKA in early diagnosis of acute stroke and evaluate the validated diagnostic values of PARK7 and NDKA in an independent patient cohort. We then assessed the quantitative relationship between the release of these markers: stroke severity and time. Blood samples were drawn upon hospital admission and 14 days later. PARK7 and NDKA concentrations were measured using an ELISA.

RESULTS

The expression of PARK7 (area under the curve [AUC] = 0.897) in acute stroke patients was more significant than in controls, relative to the NDKA expression (AUC = 0.462); p < 0.05. Their expressions were not related to the clinical characteristics of both groups; p > 0.05.

CONCLUSION

Even though both markers cannot differentiate stroke etiologies (ischemic or hemorrhagic), plasma PARK7 has better diagnostic value than NDKA for early diagnosis of stroke. 72 plasma samples obtained from acute stroke patients and 78 plasma samples collected from non-stroke patients were analyzed in this study.

C-reactive protein for predicting all-cause mortality in patients with acute ischemic stroke: a meta-analysis

Studies on the association of C-reactive protein (CRP) with all-cause mortality in acute ischemic stroke patients have yielded conflicting results. The objective of this meta-analysis was to evaluate the prognostic value of CRP elevation in predicting all-cause mortality amongst patients with acute ischemic stroke. We searched the original observational studies that evaluated the association of CRP elevation with all-cause mortality in patients with acute ischemic stroke using PubMed and Embase databases until 20 January 2018. Pooled multivariate-adjusted hazard ratio (HR) with 95% confidence intervals (CI) of all-cause mortality was obtained for the highest compared with the lowest CRP level or per unit increment CRP level. A total of 3604 patients with acute ischemic stroke from eight studies were identified. Acute ischemic stroke patients with the highest CRP level were independently associated with an increased risk of all-cause mortality (HR: 2.07; 95% CI: 1.60-2.68) compared with the lowest CRP category. The pooled HR of all-cause mortality was 2.40 (95% CI: 1.10-5.21) for per unit increase in log-transformed CRP. Elevated circulating CRP level is associated with the increased risk of all-cause mortality in acute ischemic stroke patients. This meta-analysis supports the routine use of CRP for the death risk stratification in such patients.

Advances in the diagnosis of exosomal miRNAs in ischemic stroke

Early diagnosis, early treatment, and improved prognosis in patients with ischemic stroke are vital requirements. Current clinical practices for the diagnosis of stroke include computed tomography, magnetic resonance imaging, and other traditional imaging methods to quickly check the location, volume, etc, in the hospital; however, diagnosis of the underlying cause of infarction is not effective with these practices. Owing to the coexistence of various etiologies, accurate and timely diagnosis using routine hematology and biochemical tests remains a challenge. Exosomes are membrane vesicles, approximately 30-150 nm in diameter, which fuse with cell membrane and are released into the extracellular space. As one of the research hotspots in the field of medicine in recent years, exosomes can participate in immune response, antigen presentation, cell migration, tumor invasion, and so on. Owing to the important role played by the miRNAs contained in exosomes, the latter have shown great potential in the diagnosis and treatment of ischemic stroke. This article reviews the progress made regarding the exosomal miRNAs as ischemic stroke biomarkers.

CXCR7+ and CXCR4+ stem cells and neuron specific enolase in acute ischemic stroke patients

Stroke causes an efflux of various groups of progenitor/stem cells from bone marrow to bloodstream and a rise in neuron specific enolase (NSE) serum concentrations. The aim of this study was to identify activity of chosen stem/progenitor cells during first 7 days after stroke through correlations between these cells levels and NSE values. Additional goal was to confirm the role of NSE as a prognostic marker of ischemic stroke. Venous blood was collected repeatedly from 67 acute ischemic stroke patients and 15 control subjects, in order to assess NSE with ELISA, and CD45^-CD34 ⁺ CD271+, CD45^-CD34 + CXCR4+, CD45^-CD34 + CXCR7+ and CD45^-CD34 ⁺ CD133 + stem/progenitor cells by means of flow cytometry. Patients underwent repeated assessment with the National Ischemic Stroke Scale and modified Rankin Scale. Ischemic lesion volumes were assessed twice by MRI-DWI (day 1 and 5 ± 2). NSE correlated negatively with MFI levels of the CD45^-CD34 + CXCR7+ cells, and percentage levels of the CD45^-CD34 ⁺ and CD45^-CD34 + CXCR4+ cells. NSE concentrations were significantly higher in patients compared to control subjects. NSE on day 2 positively correlated with lesion volume on both MRI. NSE on day 2 and 6-7 correlated positively with initial NIHSS scores, and on day 1 with mRS score on day 9. In conclusion, in this study NSE indicated some activity of the CD45^-CD34 + CXCR7+, CD45^-CD34 ⁺ and CD45^-CD34 + CXCR4+ stem/progenitor cells in the first 7 days after ischemic stroke. Additionally, this study supports the thesis that NSE might be a valuable prognostic marker in acute ischemic stroke.

Predicting Motor and Cognitive Improvement Through Machine Learning Algorithm in Human Subject that Underwent a Rehabilitation Treatment in the Early Stage of Stroke

BACKGROUND

The objective of this study was to investigate, in subject with stroke, the exact role as prognostic factor of common inflammatory biomarkers and other markers in predicting motor and/or cognitive improvement after rehabilitation treatment from early stage of stroke.

METHODS

In this longitudinal cohort study on stroke patients undergoing inpatient rehabilitation, data from 55 participants were analyzed. Functional and clinical data were collected after admission to the rehabilitation unit. Biochemical and hematological parameters were obtained from peripheral venous blood samples on all individuals who participated in the study within 24hours from the admission at the rehabilitative treatment. Data regarding the health status were collected at the end of rehabilitative treatment. First, a feature selection has been performed to estimate the mutual dependence between input and output variables. More specifically, the so called Mutual Information criterion has been exploited. In the second stage of the analysis, the Support Vector Machines (SVMs), a non-probabilistic binary machine learning algorithm widely used for classification and regression, has been used to predict the output of the rehabilitation process. Performances of the linear SVM regression algorithm have been evaluated considering a different number of input features (ranging from 4 to 14). The performance evaluation of the model proposed has been investigated in terms of correlation, Root Mean Square Error (RMSE) and Mean Absolute Deviation Percentage (MADP).

RESULTS

Results on the test samples show a good correlation between all the predicted and measured outputs (i.e. T1 Barthel Index (BI), T1 Motor Functional Independence Measure (FIM), T1 Cognitive FIM and T1 Total FIM) ranging from 0.75 to 0.81. While the MADP is high (i.e., 83.96%) for T1 BI, the other predicted responses (i.e., T1 Motor FIM, T1 Cognitive FIM, T1 Total FIM) disclose a smaller MADP of 30%. Accordingly, the RMSE ranges from 4.28 for T1 Cognitive FIM to 22.6 for T1 BI.

CONCLUSIONS

In conclusion, the authors developed a new predictive model using SVM regression starting from common inflammatory biomarkers and other ratio markers. The main efforts of our model have been accomplished in regard to the evidence that the type of stroke has not shown itself to be a critical input variable to predict the discharge data, furthermore, among the four selected indicators, Barthel at T1 is the less predictable (MADP > 80%), while it is possible to predict T1 Cognitive FIM with an MADP less than 18%.

Selective Sensing and Imaging of Penicillium italicum Spores and Hyphae Using Carbohydrate-Lectin Interactions

The blue-green mold Penicillium italicum is among the most problematic post-harvest plant infections limiting the integrity of citrus and many other crops during storage and transportation, but there is no sensor for its on-site or field detection. We hereby, for the first time, report the development of novel biomolecular sensor for assessing the presence of P. italicum spores and hyphae using carbohydrate-lectin recognitions. Two approaches were developed: (i) lateral tests using standalone poly(amic) acid (PAA) membranes and glass surfaces and (ii) quantitative tests on 96-well polystyrene plates and paper electrodes. In both cases, the surfaces were functionalized with novel derivatized sugar based ligands while staining was performed with gold nanoparticles. Both approaches provided strong signals for 10⁴ spores/mL of P. italicum isolated from experimentally infected lemons as the lowest-reliable concentration. The 96-well plate-based gave the most sensitive detection with a 4 × 10² spores/mL limit of detection, a linear dynamic range between 2.9 × 10³ and 6.02 × 10⁴ spores/mL ( R² = 0.9939) and standard deviation of less than 5% for five replicate measurements. The selectivity of the ligands was tested against Trichaptum biforme, Glomerulla cingulata ( Colletotrichum gloeosporioides), and Aspergillus nidulans fungi species. The highest selectivity was obtained using the sugar-based gold-nanoparticles toward both the spores and the hyphae of P. italicum. The advanced specificity was provided by the utilized sugar ligands employed in the synthesis of gold nanoparticles and was independent from size and shapes of the AuNPs. Accuracy of the sensor response showed dramatic dependence on the sample preparation. In the case of 5-10 min centrifugation at 600 rpm, the spores can be isolated free from hyphae and conidiophore, for which spiked recovery was up to 95% (std ±4). In contrast, for gravity-based precipitation of hyphae, the spiked recovery was 88% (std 11).

Point-of-Care-Testing in Acute Stroke Management: An Unmet Need Ripe for Technological Harvest

Stroke, the second highest leading cause of death, is caused by an abrupt interruption of blood to the brain. Supply of blood needs to be promptly restored to salvage brain tissues from irreversible neuronal death. Existing assessment of stroke patients is based largely on detailed clinical evaluation that is complemented by neuroimaging methods. However, emerging data point to the potential use of blood-derived biomarkers in aiding clinical decision-making especially in the diagnosis of ischemic stroke, triaging patients for acute reperfusion therapies, and in informing stroke mechanisms and prognosis. The demand for newer techniques to deliver individualized information on-site for incorporation into a time-sensitive work-flow has become greater. In this review, we examine the roles of a portable and easy to use point-of-care-test (POCT) in shortening the time-to-treatment, classifying stroke subtypes and improving patient's outcome. We first examine the conventional stroke management workflow, then highlight situations where a bedside biomarker assessment might aid clinical decision-making. A novel stroke POCT approach is presented, which combines the use of quantitative and multiplex POCT platforms for the detection of specific stroke biomarkers, as well as data-mining tools to drive analytical processes. Further work is needed in the development of POCTs to fulfill an unmet need in acute stroke management.

Stroke and other cerebrovascular diseases

To achieve success in developing more effective treatments for stroke, we need a better understanding in all aspects of stroke including prevention, diagnosis, treatment, and post-stroke recovery and complications. The objective of this special issue is to bring to the readership of Neurochemistry International the latest developments and knowledge in a broad spectrum of areas of stroke research in both review and original research articles. Topics include neuroprotective diets, biomarkers used to aid clinical management, neurodegenerative as well as neuroprotective effects of the immune system, potential therapeutic targets, engineered growth factors that promote endogenous neuroregeneration, mechanisms of cerebral small vessel disease, and post stroke epilepsy.