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Volume 14   Issue 2   Year 2019
Platelet Adhesion Quantification to Fluorinated Polyethylene from the Structural Caracteristics of Its Surface

Isaev E.A.1, Pervukhin D.V.1, Kornilov V.V.1, Tarasov P.A.1, Grigoriev A.A.1, Rudyak Y.V.2, Rytikov G.O.2, Nazarov V.G.2

1National Research University «Higher School of Economics», Moscow
2Moscow Polytechnic University, Moscow

Abstract. It is necessary not only to develop information and communication infrastructures and algorithms for distributed and cloud processing of data coming from all kinds of sensors and sensors, but also to design new materials that enable the production of safe, effective and accessible to the general public test systems when creating digital health saving systems as part of the development of modern electronic medical monitoring technologies. An analysis of the market for consumables intended for use in rapid diagnostic devices shows that disposable test strips on a flexible polymer base with high biological resistance to the effects of blood components are most in demand. It has been shown that surface modification of polyethylene by fluorination, sulfonation and plasmification methods provides a significant reduction in platelet adhesion to processed polymer films. It was also suggested that the surface energy of the modified material has a determining effect on its hemocompatibility.This work is devoted to the formation of an analytical model of the surface morphology of fluorinated polyethylene, as well as a quantitative analysis of the structural and functional relationships between the parameters of the morphological model and the resistance of the material to platelet adhesion. The widespread use of the discussed approach to increasing the thromboresistance of polymeric materials will increase the reliability of glycemic analyzes performed by patients on their own using portable express diagnostic systems (glucometers).

Key words: biological sensors, platelet adhesion, fluorinated polyethylene, Fourier decomposition, free surface energy, regression model.

Table of Contents Original Article
Math. Biol. Bioinf.
doi: 10.17537/2019.14.420
published in Russian

Abstract (rus.)
Abstract (eng.)
Full text (rus., pdf)


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