期刊
TECHNOLOGY AND HEALTH CARE
卷 29, 期 2, 页码 381-391出版社
IOS PRESS
DOI: 10.3233/THC-202655
关键词
HaCaT cells; SURF; deep learning; binary image
This study utilized a deep learning algorithm to process the fuzzy edges of contrast images, investigating the effects of GSPE on VEGF and PEDF expression in HaCaT cells. The experimental results demonstrated that GSPE down-regulated VEGF expression and up-regulated PEDF expression, indicating its potential role in inhibiting early angiogenesis induced by ultraviolet rays.
BACKGROUND: Grape seed proanthocyanidin extract (GSPE) has a certain resistance to contrast light, which makes the boundary of the imaging image unclear. OBJECTIVE: Because of this, an image processing algorithm is needed to process the contrast image to study the role of GSPE in the process of anti-ultraviolet. METHODS: In this paper, the fuzzy edges of contrast images were processed by deep learning algorithm, and the changes of VEGF and PEDF expression in HaCaT cells before and after UVA irradiation and after GSPE intervention were studied. RESULTS: The experiment results show that after processing, the edge and boundary of the image become clear and separable, which can be used to compare and analyze the test process. The image comparison results show that GSPE can down regulate the expression of VEGF gene and protein, and up regulate the expression of PEDF gene and protein. The synergistic effect of GSPE and GSPE can inhibit angiogenesis. It is confirmed that GSPE has the effect of anti-ultraviolet ray induced early angiogenesis.
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