In Vitro Analysis of Fibronectin-Modified Titanium Surfaces

Background

Glow discharge plasma (GDP) procedure is an effective method for grafting various proteins, including albumin, type I collagen, and fibronectin, onto a titanium surface. However, the behavior and impact of titanium (Ti) surface modification is yet to be unraveled.

Purpose

The purpose of this study is to evaluate and analyze the biological properties of fibronectin-grafted Ti surfaces treated by GDP.

Materials and Methods

Grade II Ti discs were initially cleaned and autoclaved to obtain original specimens. Subsequently, the specimens were GDP treated and grafted with fibronectin to form Ar-GDP (Argon GDP treatment only) and GDP-fib (fibronectin coating following GDP treatment) groups. Blood coagulation test and MG-63 cell culture were performed to evaluate the biological effects on the specimen.

Results

There was no significant difference between Ar-GDP and GDP-fib groups in blood compatibility analysis. While in the MTT test, cellular proliferation was benefited from the presence of fibronectin coating. The numbers of cells on Ar-GDP and GDP-fib specimens were greater than those in the original specimens after 24 h of culturing.

Conclusions

GDP treatment combined with fibronectin grafting favored MG-63 cell adhesion, migration, and proliferation on titanium surfaces, which could be attributed to the improved surface properties.     

A Noise-Filtering Method for Link Prediction in Complex Networks

Link prediction plays an important role in both finding missing links in networked systems and complementing our understanding of the evolution of networks. Much attention from the network science community are paid to figure out how to efficiently predict the missing/future links based on the observed topology. Real-world information always contain noise, which is also the case in an observed network. This problem is rarely considered in existing methods. In this paper, we treat the existence of observed links as known information. By filtering out noises in this information, the underlying regularity of the connection information is retrieved and then used to predict missing or future links. Experiments on various empirical networks show that our method performs noticeably better than baseline algorithms.     

杜仲叶片不同发育时期数字基因表达谱分析

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