Search

Background: Root canal filling materials that are commonly used include AH-Plus, GuttaFlow, and Resilon/Epiphany. In clinical practice, the apical closure performance of three root canal fillers is still debatable. The goal of this study was to conduct a meta-analysis to evaluate the effect of AH Plus, GuttaFlow, and Resilon/Epiphany on apical sealing in order to provide references for their clinical application.Methods: Between January 1, 2000 and June 1, 2022, the computer database was searched for randomized controlled trials on the effects of AH Plus (AH group), GuttaFlow (GF group), and Resilon/Epiphany (RE Group) root canal filling materials on apical sealing. Two evaluators independently assessed the studies' quality and extracted the data. Review 5.4 software was used to conduct a meta-analysis of the included studies.Results: A total of 10 literatures containing 452 isolated teeth were included. The meta-analysis results revealed a significant difference in apical microleakage between the RE group and the AH group [SMD = 1.45, 95% CI (1.20, 1.69), P＜0.00001] and the GF group [SMD = 1.37, 95% CI (0.80, 1.95), P＜0.00001]. There is no significant difference in apical microleakage between the AH and GF groups [SMD = 0.09, 95% CI (-0.21, 0.40), P = 0.55].Conclusions: Based on existing research findings, Resilon/Epiphany has the best apical sealing effect in the short term (≤ 1 week) when compared to AH plus and GuttaFlow.

ObjectiveMany studies have demonstrated that elevated serum uric acid (SUA) level is linked with metabolic syndrome (MetS). However, whether there is a sex difference in the association between SUA and MetS has not been determined. This study aimed to accurately explore the impact of SUA longitudinal changes on MetS by sex.DesignA prospective cohort study.SettingThe Health Check-up Centre of the Second Hospital affiliated with Dalian Medical University from 2010 to 2016.ParticipantsA health check-up cohort of 577 men and 1698 women aged 20–60 years who did not exhibit MetS or hyperuricaemia at baseline and underwent at least two physical examinations from 2010 to 2016.Primary and secondary outcome measuresWeight, height, blood pressure and blood biochemistry parameters, including SUA, were measured. MetS was defined according to the Joint Interim Statement criteria.MethodsBased on longitudinal data, a linear mixed-effects model was constructed to explore the characteristics of SUA dynamic changes in males and females, and joint modelling of longitudinal and survival data was done to analyse the association between SUA dynamic changes and MetS occurrence.ResultsThe natural logarithm of SUA (LNSUA) in females exhibited a gradually increasing trend, and its annual growth rate in females who developed MetS was greater than that of the non-MetS females. The longitudinal growth of SUA in females was a risk factor for the onset of MetS, and the estimated HR was 13.2580 (95% CI 1.9106 to 91.9957) for each 1-unit rise in LNSUA longitudinally. An association between the longitudinal growth of LNSUA and MetS was not found in males.ConclusionsThe longitudinal increase in SUA in females could increase the risk of MetS, even if the SUA changes within the normal range. The longitudinal increase in SUA in males was not a predictor for MetS.

Adding β nucleating agent (β-NA) is the most effective way to induce β-form in isotactic polypropylene (iPP). However, the problem of agglomeration and poor dispersion result in the low nucleation efficiency of β-NA. We reported herein a novel self-dispersed β nucleating agent of iPP, zinc salt of bicycle [2.2.1] hept-5-ene-2-dodecanamide-3-carboxylate (NBDA30), which exhibited a unique nucleation behavior during the crystallization of iPP. The results showed that the content of β crystal of iPP nucleated with 0.2 wt% NBDA30 can reach 0.82 (kβ value), implying that NBDA30 is an effective β nucleating agent of iPP. Polarized optical microscopy (POM) analyses showed that the NBDA30 gradually melted upon increasing temperature and then the melt induced the crystallization of iPP. The unique nucleation behavior was first reported in this study and the nucleation mechanism of iPP induced by NBDA30 was revealed by POM, fourier transform infrared spectroscopy (FTIR), wide-angle X-ray diffraction (WAXD), transmission electron microscopy (TEM) and scanning electron Microscope (SEM). The results indicated that the NBDA30 was converted into ZnO nanoparticles and the group of ROO¯ chemically adsorbed on the surface of ZnO nanoparticles (NPs). The problem of agglomeration of ZnO nanoparticles was well solved by in situ thermal decomposition of NBDA30 and the compatibility of ZnO and iPP matrix was greatly improved by chemical adsorption of organic carboxylic acid on the surface of ZnO NPs. Therefore, the nucleation efficiency of ZnO NPs was greatly increased. The preparation method of self-dispersed nucleating agent can potentially be extended to other nucleating agent or additives to prevent them from aggregating.

The video text is an important clue to infer semantic information of video. The accuracy of text extraction is crucial to the video text retrieval. Most of previous proposed methods can not completely extract the text characters when the text blocks own complicated background or when the stroke width of the text character is unpredictable. In this paper, we put forward a text extraction method, which can accurately extract video texts with complex background and various text character sizes. Experiments are conducted on TV news videos, which show that the performance of the proposed method is encouraging.

In this paper we present a novel approach to detecting scene text based on the edge and color features. Firstly, because the character edge feature is not sensitive to the luminance changes, we extract the edge features to locate the candidate text region coarsely. Secondly, according to the text row character will keep similar color, we use the K-means clustering to extract color feature and locate the candidate text regions accurately. Finally, we use a trained SVM classifier to distinguish the text region from non-text region in these candidate regions. Experimental results show that our algorithm performs well for detecting scene text with various color, font-size and text alignment.