Comparison of Risk Factor between Lacunar Stroke and Large Artery Atherosclerosis Stroke: A Cross-Sectional Study in China

Background

Stroke is the second most common cause of mortality in China. Although most subtypes of ischemic stroke share similar risk factors, they have different etiologies. Our study aimed to evaluate the different risk factor profiles between the stroke subtypes, lacunar infarcts (LI) and large-artery atherosclerosis (LAA), and clarify the characteristics of current acute ischemic stroke in China.

Methods

In this cross-sectional study, we analyzed the clinical characteristics of 1982 patients with acute ischemic stroke who were admitted to the neurology department at the Peking University First Hospital between 2007 and 2014. Ischemic stroke was further classified into LAA, LI, cardioembolism (CE) and undetermined causes of infarction (UDI) according to TOAST classification. Demographic characteristics, risk factors, as well as the findings of laboratory and imaging tests of 1773 patients with LAA and LI, were analyzed by univariate and multivariate logistic analysis.

Results

Of the 1982 ischemic stroke patients included in this study, 1207 were diagnosed with LAA, 566 with LI, 173 with cardioembolism (CE) and 36 with undetermined causes of infarction (UDI). By comparing the risk factors in multivariate logistic regression analysis, hypertension [odds ratio (OR) = 1.832] and white matter leukoaraiosis (WML) (OR = 1.865) were found to be more strongly correlated with LI than LAA. Low density lipoprotein- cholesterol (LDL-c) (OR = 0.774) were more strongly related to LAA than LI.

Conclusions

This study found that hypertension and WML were more strongly correlated with LI than LAA. LDL-c was more strongly related to LAA than LI.     

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.