Retinal angiomatous proliferation (RAP) is a genetic distinct subgroup of exudative age-related macular degeneration which shows a rapid and severe vision loss and high recurrence rates. The pathophysiological mechanisms of RAP is unclear. Recent histopathologic study and en face optical coherence tomography angiography have furthered our understanding of RAP. Clinical features frequently associated with RAP include bilateral disease, presence of reticular pseudodrusen and pigment epithelial detachments. Indocyanine green angiography is the gold standard diagnostic tool. Recently, more and more accurate optical coherence tomography has improved the acknowledgement of stage and diagnosis of RAP. The treatment efficacy of RAP is highly dependent on the stage. Anti-vascular endothelial growth factor therapy is currently the first line of treatment. Other treatment options including combination of photodynamic therapy with antiangiogenic agent intravitreal injections also achieve a reasonable therapeutic outcome. There remain several important questions such as pathogenesis and treatment regimen, to be answered in future RAP research studies.
Objective To study the proximal diameter changes of retinal blood vessel following branch retinal vein occlusion (BRVO). Methods Color fundus photographs and fundus fluorescein angiography (FFA) photographs of 48 patients with typical unilateral BRVO were analyzed using IMAGEnet software. The diameter of retinal artery (RAD) and vein (RVD) close to optic disc (within one DD from the optic disc) in four quadrants including the affected quadrant were measured with linear measuring tools.Results The proximal diameter of RAD and RVD in corresponding normal quadrants of the BRVO eye had no significant change comparing with the contralateral eye. The proximal diameter of RAD, but not RVD of the affected quadrant such as superotemporal （t=-2.342, P=0.026）or inferotemporal （t=-3.069, P=0.010）quadrant, increased remarkably. Conclusions In corresponding affected quadrant with BRVO, only RAD close to optic disc increases markedly, RVD has no significant change.
ObjectiveTo systematically review the efficacy of antidepressants in the prevention of poststroke depression (PSD). MethodsWe searched The Cochrane Library (Issue 2, 2015), PubMed, MEDLINE, EMbase, CNKI and VIP databases to collect randomized controlled trials (RCTs) about antidepressants in preventing PSD from inception to April 2015. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then, meta-analysis was performed using RevMan 5.3 software. ResultsA total of 26 RCTs involving 2 190 patients were included. The results of meta-analysis showed that:compared with the control group, the antidepressants group could significantly reduce the incidence of PSD (OR=0.24, 95%CI 0.17 to 0.36, P<0.000 01). Subgroup analysis based on types of drugs showed that:the selective serotonin reuptake Inhibitor (SSRI) could significantly reduce the incidence of PSD (OR=0.23, 95%CI 0.15 to 0.37, P<0.000 01). Subgroup analysis based on length of time showed that antidepressants could decrease the incidence of PSD in short term (OR=0.11, 95%CI 0.06 to 0.19, P<0.000 01), middle term (OR=0.31, 95%CI 0.21 to 0.46, P<0.000 01) and long term (OR=0.30, 95%CI 0.19 to 0.49, P<0.000 01). In addition, there was no statistical difference in the incidence of adverse effect between the antidepressants group and the control group (P>0.05). ConclusionAntidepressants is effective in the prevention of PSD, and may not affect patient's life quality. Due to the limited quantity and quality of included studies, more high quality studies are needed to verify the above conclusion.
ObjectiveTo evaluate the efficacy and safety of all kinds of hemocoagulase on operative incisions. MethodsDatabases including Web of Science, MEDLINE, EMbase, EBSCO, PubMed, CNKI, WanFang Data and VIP were electronically searched to collect randomized controlled trials (RCTs) about hemocoagulase on operative incisions from the inception to June 20th, 2015. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then, meta-analysis was performed by RevMan 5.2 software. ResultsA total of 16 RCTs involving 1 867 patients were included. The results of meta-analysis showed that, compared with the control group, the hemostatic time (MD=-37.84, 95%CI -52.72 to -22.96, P<0.000 01), blood loss volume per unit area (MD=-0.09, 95%CI -0.10 to -0.07, P<0.000 01), PT of the first postoperative day (MD=-0.37, 95%CI -0.65 to -0.09, P=0.009) were significantly shorter in the hemocoagulase group. However, no significant differences were found in APTT, TT and FIB between two groups. ConclusionHemocoagulase can reduce hemostatic time and blood loss volume in surgical incisions. Due to the limited quantity and quality of the included studies, the above conclusion needs to be further verified by more high quality studies.
The extraction of neuroimaging features of migraine patients and the design of identification models are of great significance for the auxiliary diagnosis of related diseases. Compared with the commonly used image features, this study directly uses time-series signals to characterize the functional state of the brain in migraine patients and healthy controls, which can effectively utilize the temporal information and reduce the computational effort of classification model training. Firstly, Group Independent Component Analysis and Dictionary Learning were used to segment different brain areas for small-sample groups and then the regional average time-series signals were extracted. Next, the extracted time series were divided equally into multiple subseries to expand the model input sample. Finally, the time series were modeled using a bi-directional long-short term memory network to learn the pre-and-post temporal information within each time series to characterize the periodic brain state changes to improve the diagnostic accuracy of migraine. The results showed that the classification accuracy of migraine patients and healthy controls was 96.94%, the area under the curve was 0.98, and the computation time was relatively shorter. The experiments indicate that the method in this paper has strong applicability, and the combination of time-series feature extraction and bi-directional long-short term memory network model can be better used for the classification and diagnosis of migraine. This work provides a new idea for the lightweight diagnostic model based on small-sample neuroimaging data, and contributes to the exploration of the neural discrimination mechanism of related diseases.