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Blood Plasma Trophic Growth Factors Predict the Outcome in Patients with Acute Ischemic Stroke

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Bioinformatics and Biomedical Engineering (IWBBIO 2020)

Abstract

Stroke is an acute disorder of CNS being the leading factor of mortality and disability of the population. Dynamic assessment of trophic growth factors expression is a promising tool to predict the outcome of ischemic stroke. We investigated the concentration dynamics of the brain-derived neurotrophic factor (BDNF) and vascular endothelial growth factor (VEGF) in blood plasma of patients with acute ischemic stroke. 56 patients took part in the study. Venous blood was collected from all patients on the first, 7th and 21st day of their hospital stay. BDNF and VEGF plasma concentrations were measured using ELISA. Our study shows, that not single, but serial dynamic measures of BDNF plasma concentrations in the acute period of ischemic stroke have a prognostic significance. Increasing of the BDNF plasma concentration on day 7 in comparison to the concentration on day 1 was significantly associated with a better clinical outcome of acute ischemic stroke. Extremely high VEGF plasma concentrations (more than 260 pg/mL) on days 1 and 7 from the ischemic stroke onset were significantly associated with a worse clinical outcome on day 21 and a less favorable rehabilitation prognosis. Serial measurement of plasma concentrations of trophic growth factors in patients with ischemic stroke presents a rather simple, reliable and minimally invasive method of dynamic assessment of the clinical course of acute ischemic stroke and early outcome prediction.

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Acknowledgements

The work was supported by the State Assignment for Research issued by the Ministry of Public Health of the Russian Federation (2018–2020).

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Correspondence to Valeriia Roslavtceva .

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Roslavtceva, V. et al. (2020). Blood Plasma Trophic Growth Factors Predict the Outcome in Patients with Acute Ischemic Stroke. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_3

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  • DOI: https://doi.org/10.1007/978-3-030-45385-5_3

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