Objective To observe the effectiveness of probucol for non-proliferative diabetic retinopathy (NPDR) with hyperlipidemia. Methods Fifty-two patients (104 eyes) of NPDR with hyperlipidemia were enrolled in this study. The patients were randomly divided into treatment group and control group, 26 patients (52 eyes) in each group. Both groups received diet and exercise guidance, oral hypoglycemic agents and (or) intensive insulin therapy. After blood sugar and blood pressure were controlled, the treatment group received probucol 0.5 g, two times per day; and the control group received atorvastatin of 10 mg, one time per day. The total course was 12 months. Before and after one, three, six and 12 months, all patients underwent vision, ophthalmoscope, fundus fluorescein angiography, blood and urine tested. Variations of visual acuity, fundus condition, macular edema, triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDLC), high-density lipoprotein cholesterol (HDLC) and 8-0HdG were observed before and after treatment. Results The total effective rate of visual prognosis were 44.23% and 40.38% in the treatment group and the control group, the difference had no statistical significacy (Z=-0.335, P＞0.05). Retinal hemorrhages and microaneurysms alleviated after treatment in both groups.The total efficiency of fundus prognosis was 65.38% in the treatment group and 36.54% in the control group, and the difference was statistically significant (Z=-2.973,P＜0.05). Macular edema was in six and five eyes in the treatment group and the control group respectively, which were lower than before treatment, the difference was statistically significant (chi;2=4.833, 4.300;P＜0.05). Between the two groups, the difference was not statistically significant (chi;2=0.102,P＞0.05). Twelve months after treatment, TG, TC and LDLC were decreased in the treatment group (t=15.653, 7.634, 14.871) and control group (t=13.275, 7.415, 13.632), and the difference was statistically significant (P＜0.05). HDLC showed no significant difference than before in the two groups (t=0.584, 0.275;P＞0.05). TG, TC, LDLC and HDLC showed no difference between the two groups (t=1.857, 0.133, 1.671, 0.875;P＞0.05). 8-0HdG decreased gradually during the one, three, six and 12 months in the treatment group (t=7.352,15.581, 27.324, 28.143) and control group (t=6.877, 8.672, 14.671, 14.855) after treatment, and the difference was statistically significant (P＜0.05). In the first month after treatment, 8-0HdG showed no difference between the two groups (t=0.513,P＞0.05). In the 3, 6, and 12 months after treatment, the 8-0HdG was lower in the treatment group than that in the control group, and the difference was statistically significant (t=3.434, 5.917, 5.226;P＜0.05). Conclusion In the treatment of NPDR with hyperlipidemia, probucol can reduce blood lipid, stable visual function and relieve macular edema.
ObjectiveTo observe the expression of probucol on high glucose-induced specificity protein 1(SP1), kelchlike ECH associated protein1 (Keap1), NF-E2-related factor 2 (Nrf2) and glutamate-cysteine ligase catalytic (GCLC) in the cultured human müller cells and preliminary study the antioxidation of the probucol on müller cells.MethodsPrimary cultured human müller cells were randomly divided into four groups: normoglycaemia group (5.5 mmol/L glucose), normoglycaemia with probucol group (5.5 mmol/L glucose+100 μmol/L probucol), hyperglycemia group (25.0 mmol/L glucose), hyperglycemia with probucol group (25.0 mmol/L glucose + 100 μmol/L probucol). Immunofluorescence staining was used to assess distribution of SP1, Keap1, Nrf2, GCLC in human Müller cells. SP1, Keap1, Nrf2 and GCLC messenger RNA (mRNA) expression was evaluated by quantitative real-time RT-PCR (qRT-PCR). Independent sample t test was used to compare the data between the two groups.ResultsAll müller cells expressed glutamine synthetase (＞95%), which confirmed the cultured cells in vitro were the purification of generations of müller cells. The expressions of SP1, Keap1, Nrf2, and GCLC protein were positive in human müller cells. qRT-PCR indicated that SP1 (t=28.30, P＜0.000), Keap1 (t=5.369, P=0.006), and Nrf2 (t=10.59, P=0.001) mRNA in the hyperglycemia group increased obviously compared with the normoglycaemia group; GCLC (t=4.633, P=0.010) mRNA in the hyperglycemia group decreased significantly compared with the normoglycaemia group. However, SP1 (t=12.60, P=0.000) and Keap1 (t=4.076, P=0.015) in the hyperglycemia with probucol group decreased significantly compared with the hyperglycemia group; Nrf2 (t=12.90, P=0.000) and GCLC (t=15.96, P＜0.000) mRNA in the hyperglycemia with probucol group increased obviously compared with with the hyperglycemia group.ConclusionProbucol plays an antioxidant role by inhibiting the expression of SP1, Keap1 and up-regulating the expression of Nrf2, GCLC in müller cells induced by high glucose.
It is of great clinical significance in the differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) because there are enormous differences between them in terms of therapeutic regimens. In this paper, we propose a system based on sparse representation for automatic classification of PCNSL and GBM. The proposed system distinguishes the two tumors by using of the different texture detail information of the two tumors on T1 contrast magnetic resonance imaging (MRI) images. First, inspired by the process of radiomics, we designed a dictionary learning and sparse representation-based method to extract texture information, and with this approach, the tumors with different volume and shape were transformed into 968 quantitative texture features. Next, aiming at the problem of the redundancy in the extracted features, feature selection based on iterative sparse representation was set up to select some key texture features with high stability and discrimination. Finally, the selected key features are used for differentiation based on sparse representation classification (SRC) method. By using ten-fold cross-validation method, the differentiation based on the proposed approach presents accuracy of 96.36%, sensitivity 96.30%, and specificity 96.43%. Experimental results show that our approach not only effectively distinguish the two tumors but also has strong robustness in practical application since it avoids the process of parameter extraction on advanced MRI images.