非高密度脂蛋白胆固醇水平与冠心病患者冠状动脉病变Gensini评分的相关性研究

目的 探讨非高密度脂蛋白胆固醇水平(non-HDL-C)与冠心病(CHD)患者冠状动脉病变Gensini评分的关系及临床意义。方法 对225例疑诊或既往临床诊断CHD患者予以冠状动脉造影(CAG),将造影阴性的39例作为对照组(HC组),造影阳性的186例患者诊断为CHD,结合临床特点分为心绞痛组(AP组)122例和心肌梗死组(AMI组)64例。采用Gensini评分对冠状动脉病变程度评分,测定患者全套血脂水平,探讨non-HDL-C及相关脂质成分与冠状动脉病变程度Gensini评分的相关性;同时对他汀类药物强化降脂达标,低密度脂蛋白胆固醇(LDL-C)<1.80 mmol/L的AP组患者进行non-HDL-C与冠状动脉病变程度Gensini评分的亚组分析。结果 AMI组non-HDL-C水平高于AP组及HC组,AP组non-HDL-C水平高于HC组,差异均有统计学意义(P<0.05);CHD患者non-HDL-C水平与Gensini评分呈正相关(r=0.562,P<0.05);LDL-C控制达标的AP组患者,高non-HDL-C组(≥2.60 mmol/L)比低non-HDL-C组(<2.60 mmol/L)Gensini评分明显升高,差异均有统计学意义(P<0.05)。结论 non-HDL-C与冠状动脉病变程度密切相关,non-HDL-C在评估LDL-C控制达标患者的冠状动脉病变程度及再发心血管事件风险上有一定价值,可作为LDL-C达标后心血管残余风险新的观察指标。     

丹蛭降糖胶囊通过mTOR/S6K1信号通路对糖尿病肾病大鼠的干预作用研究

目的研究丹蛭降糖胶囊对糖尿病肾病大鼠的肾脏病理改变和足细胞自噬水平的影响,初步探讨其相应的作用机制。方法选取GK大鼠40只,采用醋酸脱氧皮质酮-盐皮下注射联合高脂饲料喂养诱导糖尿病肾病模型。造模成功后随机分为模型组、丹蛭降糖胶囊低剂量组[0.54 g/(kg·d)]、高剂量组[1.08 g/(kg·d)]、缬沙坦组[10 mg/(kg·d)],每组10只。另选取10只同周龄正常Wistar大鼠作为正常组。模型组和正常组给予等容积生理盐水。连续灌胃10周后检测空腹血糖(FBG)、血肌酐(SCr)、尿素氮(BUN)和尿微量蛋白(U-mAlb)。Western Blot检测肾小球p-mTOR、p-S6K1、Beclin-1、LC3和Nephrin蛋白的表达,HE染色和PAS染色后于光镜下观察肾脏病理变化,电镜下观察各组亚细胞形态结构变化。结果与正常组比较,模型组FBG、SCr、BUN、U-mAlb水平升高(P<0.01);与模型组比较,丹蛭降糖胶囊低、高剂量组和缬沙坦组U-mAlb水平降低(P<0.01)。模型组可见肾小球基底膜增厚,系膜基质沉积,足细胞损伤,各用药组均有不同程度改善,丹蛭降糖胶囊高剂量组足细胞内有较多自噬体形成。与正常组比较,模型组p-mTOR、p-S6K1的表达增高,Nephrin、Beclin-1和LC3的表达水平降低(P<0.01);与模型组比较,各用药组p-mTOR、p-S6K1的表达下降,Nephrin、Beclin-1和LC3的表达增高(P<0.01)。丹蛭降糖胶囊高剂量组和缬沙坦组Beclin1、LC3Ⅱ/LC3Ⅰ水平较丹蛭降糖胶囊低剂量组升高(P<0.05)。结论丹蛭降糖胶囊具有减轻糖尿病肾病大鼠U-mAIb、足细胞损伤及相关肾脏病理改变的作用,其机制可能与抑制mTOR/S6K1信号通路进而提高足细胞的自噬活性有关。     

Recent advances in the development of microparticles for pulmonary administration

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Immune responses against hepatitis C virus genotype 3a virus-like particles in mice: A novel VLP prime-adenovirus boost strategy

Chronic hepatitis C virus (HCV) infection represents a major health threat to global population. In India, approximately 15–20% of cases of chronic liver diseases are caused by HCV infection. Although, new drug treatments hold great promise for HCV eradication in infected individuals, the treatments are highly expensive. A vaccine for preventing or treating HCV infection would be of great value, particularly in developing countries. Several preclinical trials of virus-like particle (VLP) based vaccine strategies are in progress throughout the world. Previously, using baculovirus based system, we have reported the production of hepatitis C virus-like particles (HCV-LPs) encoding structural proteins for genotype 3a, which is prevalent in India. In the present study, we have generated HCV-LPs using adenovirus based system and tried different immunization strategies by using combinations of both kinds of HCV-LPs with other genotype 3a-based immunogens. HCV-LPs and peptides based ELISAs were used to evaluate antibody responses generated by these combinations. Cell-mediated immune responses were measured by using T-cell proliferation assay and intracellular cytokine staining. We observed that administration of recombinant adenoviruses expressing HCV structural proteins as final booster enhances both antibody as well as T-cell responses. Additionally, reduction of binding of VLP and JFH1 virus to human hepatocellular carcinoma cells demonstrated the presence of neutralizing antibodies in immunized sera. Taken together, our results suggest that the combined regimen of VLP followed by recombinant adenovirus could more effectively inhibit HCV infection, endorsing the novel vaccine strategy.     

Bishonokiol A Induces Multiple Cell Death in Human Breast Cancer MCF-7 Cells

Objective: A dimeric neolignan, bishonokiol A (BHNKA) isolated from Magnolia grandiflora, significantly inhibits the proliferation of human breast cancer cells. However, the exact mechanism of BHNKA induced breast cancer cell death is unknown. In this study, we investigated the pharmacological mechanism underlying BHNKA induced MCF-7 cell death. Methods: Cell viability measurement was performed by the MTT assay. Flow cytometry with PI staining, DAPI staining, and electron microscopy were used to analyze cellular death modes. In addition, western blotting, siRNA transfection, ATP assay, and fluorescence microscopy were used to determine the mechanism of BHNKA induced MCF-7 cell death. Results: BHNKA induced cell death by apoptosis, necroptosis and autophagy at the same concentration and time in MCF-7 cells, and electron microscopy confirmed these results. The mechanism of BHNKA triggered apoptosis and autophagy in MCF-7 cells was primarily due to an increase in the Bax/Bcl-2 ratio and simultaneous up-regulation of LC3-II protein expression, respectively. BHNKA induced necroptosis by activation of the RIP1-RIP3-MLKL necroptosis cascade, up-regulation of cyclophilin D (CypD) protein expression to stimulate ROS generation. We further demonstrated that siRNA-mediated down-regulation of CypD protected against BHNKA induced cell death. Conclusions: These results suggest that BHNKA may be a potential lead compound for development as an anti-breast cancer agent for induction of multiple cell death pathways.     

妊娠早期糖化血红蛋白联合PAPP-A对妊娠期糖尿病的预测意义

目的:探讨妊娠早期血清学指标糖化血红蛋白(glycohemoglobin,HbA1c)联合妊娠相关血浆蛋白A(pregnancy-associated plasma protein A,PAPP-A)对妊娠期糖尿病(gestational diabetes mellitus,GDM)的预测意义。方法:随机选取2018年12月1日-2019年7月30日孕11~13+6周于我院门诊产检的妊娠妇女,进行临床资料采集并记录妊娠早期(11~13+6周)空腹血糖(fasting plasma glucose,FPG)、HbA1c、PAPP-A中位数倍数(multiple of the median,MoM)水平,根据孕24~28周进行的75 g口服葡萄糖耐量试验(oral glucose tolerance test,OGTT)结果将研究对象分为研究组和对照组,统计分析妊娠早期血清学指标预测GDM的最佳截断值并得出最适宜的联合预测方案。结果:多因素Logistic回归分析显示,高水平FPG和HbA1c、低水平PAPP-A、受孕方式采用辅助生殖技术、有家族糖尿病史以及妊娠早期体质量指数(BMI)为超重或肥胖均是GDM发生的独立危险因素。有糖尿病家族史和使用辅助生殖技术受孕发生GDM的风险显著增高(OR分别为7.206和47.512,均P<0.001)。分析不同预测指标的受试者工作特征(receiver operating characteristic,ROC)曲线及曲线下面积(area under the curve,AUC)显示,PAPP-A MoM联合HbA1c及FPG诊断时AUC最大(0.728),其后依次为PAPPA MoM联合HbA1c(0.721)、HbA1c联合FPG(0.717),均大于HbA1c(0.707)和FPG(0.647),而PAPP-A MoM的AUC为0.380,对GDM没有诊断意义。结论:具有高风险因素的孕妇,推荐在妊娠早期联合检测HbA1c与PAPPA MoM,以早期预测GDM。     

Graph Convolutional Network Based on Manifold Similarity Learning

In the area of large-scale graph data representation and semi-supervised learning, deep graph-based convolutional neural networks have been widely applied. However, typical graph convolutional network (GCN) aggregates information of neighbor nodes based on binary neighborhood similarity (adjacency matrix). It treats all neighbor nodes of one node equally, which does not suppress the influence of dissimilar neighbor nodes. In this paper, we investigate GCN based on similarity matrix instead of adjacency matrix of graph nodes. Gaussian heat kernel similarity in Euclidean space is first adopted, which is named EGCN. Then biologically inspired manifold similarity is trained in reproducing kernel Hilbert space (RKHS), based on which a manifold GCN (named MGCN) is proposed for graph data representation and semi-supervised learning with four different kernel types. The proposed method is evaluated with extensive experiments on four benchmark document citation network datasets. The objective function of manifold similarity learning converges very quickly on different datasets using various kernel functions. Compared with state-of-the-art methods, our method is very competitive in terms of graph node recognition accuracy. In particular, the recognition rates of MGCN (Gaussian kernel) and MGCN (Polynomial Kernel) outperform that of typical GCN about 3.8% on Cora dataset, 3.5% on Citeseer dataset, 1.3% on Pubmed dataset and 4% on Cora_ML dataset, respectively. Although the proposed MGCN is relatively simple and easy to implement, it can discover local manifold structure by manifold similarity learning and suppress the influence of dissimilar neighbor nodes, which shows the effectiveness of the proposed MGCN.