This study aims to explore the potential of polyaspartic acid grafted dopamine copolymer (PAsp-g-DA) chelated Fe3+ for magnetic resonance imaging (MRI) visual photothermal therapy. Polyaspartic acid grafted copolymer of covalently grafted dopamine and polyethylene glycol (PAsp-g-DA/PEG) was obtained by the ammonolysis reaction of poly succinimide (PSI), and then chelated with Fe3+ in aqueous solution. The relaxivity in vitro, magnetic resonance imaging enhancement in vivo and photothermal conversion effect at 808 nm were investigated. The results showed that polymeric iron coordination had good near-infrared absorption and photothermal conversion properties, good magnetic resonance enhancement effect, and good longitudinal relaxation efficiency under different magnetic field intensities. In summary, this study provides a new magnetic resonance visual photothermal therapeutic agent and a new research idea for the research in related fields.
Objective To improve surgical results, the experience of surgical management of Ebstein anomaly in 36 cases is reported and reviewed. Methods Surgical procedures included tricuspid valve replacement (6 cases), Danielson’s operation (28 cases), Carpentier’s operation (2 cases), among them, there were 5 cases of ablation of right atrioventricular accessory pathway. Results Four patients died early after operation in the hospital, 3 from right heart failure (1 case of tricuspid valve replacement and 2 cas...
Multilevel models are applicable to both the quantitative data and categorical variables. We used the methods, including the multilevel models, analysis of covariance and CMH chi-square test, to analyse different types of data, to explore the application of multilevel models in the analysis of the multicenter clinical trial center effect. The results showed that the analysis of covariance is more sensitive to find the center effect for quantitative data, while multilevel models are more sensitive to categorical variables. It can be seen that results with different analytical methods for center effect are not the same, and the most appropriate method should be selected in accordance with the characteristics of data, the objective of research, and the applicable conditions of the various methods in practical use.
This article introduces two methods used to calculate effect indicators and their standard errors with non-comparative binary data. Then we give an example, the effect indicators and standard errors are calculated using both methods, and meta-analysis with the outcomes is conducted using RevMan software. At last the calculated results are compared with the results of meta-analysis conducted using Stata software with original data based on cases. The results of meta-analysis performed in RevMan software and Stata software are consistent in calculating non-comparative binary data.