Objective To evaluate the clinical effectiveness of Periocline (2% minocycline hydrochloride ointment) versus Yakang (metronidazole stilus) for treating chronic periodontitis in adults by means of meta-analysis. Methods The following electronic databases as PubMed, The Cochrane Library, EMbase, CNKI, CBM and WanFang Data were searched on computer from inception to July, 2012, and the references of all selected studies were also retrieved to collect the relevant randomized controlled trials (RCTs) on periocline vs. Yakang for chronic periodontitis. According to the inclusion and exclusion criteria, two reviewers independently screened studies, extracted data, and evaluated the methodological quality of the included RCTs. Then meta-analysis was performed using RevMan 5.0 software. Results A total of 7 RCTs involving 737 patients were included. The results of meta-analysis showed that there were no significant differences in probing depth (MD=0.26, 95%CI −0.35 to 0.87, P=0.40), clinical attachment level (MD=−0.10, 95%CI −0.75 to 0.54, P=0.75), sulcus bleeding index (MD=0.12, 95%CI −0.30 to 0.53, P=0.59), and plaque index (MD=0.07, 95%CI −0.09 to 0.22, P=0.41) between the Periocline group and the Yakang group. Conclusion The current evidence shows that based on periodental non-surgical treatment, Periocline is similar to Yakang in improving the symptoms of chronic periodontitis in adults. However, given the low methodological quality and the limited sample size of most included studies, this conclusion still needs to be further proved by conducting more strictly-designed, high-quality and large-scale RCTs. The long-term effectiveness of those 2 treatment modalities also needs to be observed in a longer follow-up duration.
Diffusion tensor imaging technology can provide information on the white matter of the brain, which can be used to explore changes in brain tissue structure, but it lacks the specific description of the microstructure information of brain tissue. The neurite orientation dispersion and density imaging make up for its shortcomings. But in order to accurately estimate the brain microstructure, a large number of diffusion gradients are needed, and the calculation is complex and time-consuming through maximum likelihood fitting. Therefore, this paper proposes a kind of microstructure parameters estimation method based on the proximal gradient network, which further avoids the classic fitting paradigm. The method can accurately estimate the parameters while reducing the number of diffusion gradients, and achieve the purpose of imaging quality better than the neurite orientation dispersion and density imaging model and accelerated microstructure imaging via convex optimization model.