Development of a high-growth enterovirus 71 vaccine candidate inducing cross-reactive neutralizing antibody responses

Although Enterovirus 71 (EV71) has only one serotype based on serum neutralization tests using hyperimmune animal antisera, three major genogroups (A, B and C) including eleven genotypes (A, B1-B2, and C1-C5) can be well classified based on phylogenetic analysis. Since 1997, large-scale EV71 epidemics occurred cyclically with different genotypes in the Asia-Pacific region. Therefore, development of EV71 vaccines is a national priority in several Asian countries. Currently, five vaccine candidates have been evaluated in clinical trials in China (three C4 candidates), Singapore (one B2 candidate), and Taiwan (one B4 candidate). Overall, the peak viral titers of these 5 vaccine candidates could only reach about 10⁷ TCID₅₀/mL. Moreover, genotypes of these 5 candidates are different from the current predominant genotype B5 in Taiwan and South-Eastern Asia. We adapted a high-growth EV71 genotype B5 (HG-B5) virus after multiple passages and plaque selections in Vero cells and the HG-B5 virus could reach high titers (>10⁸ TCID₅₀/mL) in a microcarrier-based cell culture system. The viral particles were further purified and formulated with alum adjuvant. After two doses of intramuscular immunization in rabbits, the HG-B5 vaccine candidate could induce cross-reactive neutralizing antibodies against the three major EV71 genogroups. In conclusion, a high-growth EV71 virus was successfully adapted in Vero cells and could induce broad spectrum neutralizing antibody titers against three (A, B5, and C4) genotypes in rabbits.     

HSP60和p53在胃癌中的表达及意义

[目的]研究热休克蛋白60(HSP60)和p53在胃癌组织中的表达,并分析与胃癌临床病理特征的关系。[方法]采用免疫组化SABC法检测48例胃癌组织和14例正常胃黏膜中HSP60和p53的蛋白表达。[结果]胃癌组织中HSP 60和p53蛋白表达阳性率分别为81.3%、68.9%;HSP60在胃癌中表达与肿瘤分化程度及增殖能力有关(P0.05);p53蛋白在胃癌中表达与肿瘤大小、部位、浸润深度、淋巴结转移和肿瘤增殖能力有关(P0.05);HSP60与p53的表达无相关性。而HSP60在正常胃黏膜中有弱阳性表达(7.1%),在正常胃黏膜中未见p53表达。[结论]HSP60和p53表达与胃癌生物学行为有关,在胃癌的发生、发展过程中发挥着重要的作用。     

经直肠超声引导下前列腺细针穿刺中液基薄层细胞学及免疫细胞化学诊断前列腺癌的应用价值北大核心

目的:探讨经直肠超声引导下前列腺细针穿刺细胞学中液基薄层细胞学(thinprep liquid based cytology test,TCT)及免疫细胞化学(immunocytochemistry,ICC)诊断前列腺癌的应用价值。方法:收集321例前列腺疾病患者细针穿刺物,TCT及传统涂片(conventional smear,CS)制片,其中153例有术后对照,回顾性比较不同制片方法诊断灵敏度、特异度及前列腺癌确诊率,分析漏诊及误诊原因,并结合ICC及前列腺癌分级,寻求早期诊断前列腺癌的有效方法。结果:TCT灵敏度及特异度高于CS,假阳性率及假阴性率减少,尤其是Gleason评分≤7分时明显。TCT联合ICC后,灵敏度及前列腺癌确诊率更高。结论:TCT和CS均能鉴别前列腺病变的良恶性,但TCT对于Gleason评分≤7分的前列腺癌患者有更高诊断价值,ICC辅助诊断后,其价值更高,是前列腺癌早期诊断的有效方法。     

Multi-Class ASD Classification via Label Distribution Learning with Class-Shared and Class-Specific Decomposition

The behavioral and cognitive deficits in autism spectrum disorder (ASD) patients are associated with abnormal brain function. The resting-state functional magnetic resonance imaging (rs-fMRI) is an effective non-invasive tool for revealing the brain dysfunction for ASD patients. However, most rs-fMRI based ASD diagnosis methods are developed for simple binary classification, instead of classification of multiple sub-types in ASD. Besides, they assume that the class boundary in ASD classification is crisp, whereas the symptoms of ASD sub-types are a continuum from mild to severe impairments in both social communication and restrictive repetitive behaviors/interests, and do not have crisp boundary between each other. To this end, we introduce label distribution learning (LDL) into multi-class ASD classification and propose LDL-CSCS under the LDL framework. Specifically, the label distribution is introduced to describe how individual disease labels correlate with the subject. In the learning crierion of LDL-CSCS, the label distribution is decomposed into the class-shared and class-specific components, in which the class-shared component records the common knowledge across all persons and the class-specific component records the specific information in each ASD sub-type. Low-rank constraint is imposed on the class-shared component whereas the group sparse constraint is imposed on the class-specific component, respectively. An Augmented Lagrange Method (ALM) is developed to find the optimal solution. The experimental results show that the proposed method for ASD diagnosis has superior classification performance, compared with some existing algorithms.