Immunogenicity and protective efficacy of adenoviral and subunit RSV vaccines based on stabilized prefusion F protein in pre-clinical models

Respiratory Syncytial Virus (RSV) remains a leading cause of severe respiratory disease for which no licensed vaccine is available. We have previously described the derivation of an RSV Fusion protein (F) stabilized in its prefusion conformation (preF) as vaccine immunogen and demonstrated superior immunogenicity in naive mice of preF versus wild type RSV F protein, both as protein and when expressed from an Ad26 vaccine vector. Here we address the question if there are qualitative differences between the two vaccine platforms for induction of protective immunity. In naïve mice, both Ad26.RSV.preF and preF protein induced humoral responses, whereas cellular responses were only elicited by Ad26.RSV.preF. In RSV pre-exposed mice, a single dose of either vaccine induced cellular responses and strong humoral responses. Ad26-induced RSV-specific cellular immune responses were detected systemically and locally in the lungs. Both vaccines showed protective efficacy in the cotton rat model, but Ad26.RSV.preF conferred protection at lower virus neutralizing titers in comparison to RSV preF protein. Factors that may contribute to the protective capacity of Ad26.RSV.preF elicited immunity are the induced IgG2a antibodies that are able to engage Fcγ receptors mediating Antibody Dependent Cellular Cytotoxicity (ADCC), and the induction of systemic and lung resident RSV specific CD8 + T cells. These data demonstrate qualitative improvement of immune responses elicited by an adenoviral vector based vaccine encoding the RSV preF antigen compared to the subunit vaccine in small animal models which may inform RSV vaccine development.     

口语化疾病名称向国际规范疾病术语集的映射研究

目的：研究患者口语化疾病名称到术语疾病名称的映射，解决在线口语化疾病名称难以有效利用的问题。方法：抓取“微医网”上患者口语中的疾病名，将PubMed摘要、百度搜索页摘要与百科内容作为语料，训练词向量，用余弦距离计算相似度，由2名具有临床知识和从业经验的编码人员对结果的准确性进行检验。结果：将最相似词条的参数TopN设置为>= 30的时候效果比较好，映射到大类和细类的准确性分别稳定在60%和70%左右。结论：利用词向量将口语化疾病名称映射到国际疾病术语集，具有较强的通用性和可行性，对于在线问诊、智能分诊甚至自动化编码具有明显的参考借鉴价值。     

腺病毒介导的Hes1基因抑制肝细胞癌细胞的增殖与迁移CSCD

目的探讨Hes1基因对肝细胞癌细胞系Hep1-6细胞增殖、迁移与侵袭的影响。方法通过重组腺病毒载体介导Hes1基因在Hep1-6细胞系中过表达,随后测定细胞克隆形成率、增殖速率及体外迁移与侵袭能力的改变。结果Hep1-6细胞分别感染Ad-Hes1和阴性对照Ad-GFP后,感染Ad-Hes1的细胞系克隆形成数显著少于对照组(P<0.001);感染病毒6天后,CCK-8检测感染Ad-Hes1的细胞系在OD450 nm的吸光度值显著低于对照组(P<0.01);感染Ad-Hes1的细胞系24 h和48 h的划痕愈合率显著低于对照组(P<0.001);感染Ad-Hes1的细胞系在Transwell小室培养48 h后迁移进入Transwell下室的细胞数量显著低于对照组(P<0.001);感染Ad-Hes1的细胞系在铺有Matrigel基质胶的Transwell小室培养48 h后侵袭进入Transwell下室的细胞数量显著低于对照组(P<0.01)。结论Hes1有抑制Hep1-6细胞增殖、迁移与侵袭的作用。     

外泌体作为靶向治疗载体的研究进展

外泌体是来源于细胞内膜的纳米级的囊泡，包含有核酸、蛋白和脂质等生命物质，作为一种细胞间交流的机制，在生命活动中发挥着重要作用。相对于脂质体载体，外泌体更稳定，生物相容性好，免疫原性低，而且可以渗透生物屏障，例如血脑屏障、胎盘屏障等。重点介绍外泌体载体靶向性的基础、外泌体载体装载负荷的方法及外泌体载体目前的应用进展和前景。     

脂质纳米粒-mRNA递送系统及其在嵌合抗原受体T细胞治疗中的应用

尽管嵌合抗原受体（CAR）T细胞治疗在血液系统恶性肿瘤患者中取得了显著的临床疗效，但需要进一步优化。脂质纳米粒（LNP）-信使核糖核酸（mRNA）递送系统作为一种非病毒性基因载体运用于CAR-T细胞治疗研究中，一方面通过LNP将密封蛋白-6 mRNA靶向递送至抗原提呈细胞，从而实现抗原提呈细胞辅助性增强密封蛋白-6靶向的CAR-T细胞的功能，以进一步诱导对实体瘤的清除；另一方面，通过LNP将成纤维细胞激活蛋白（FAP）CARmRNA靶向递送至T细胞，实现体内FAP靶向的CAR-T细胞的制备，以通过阻断心脏纤维化过程达到治疗急性心肌损伤的目的。在CAR-T细胞研究和治疗中，LNP-mRNA递送系统具有不与细胞基因组整合、价格便宜、毒副作用小及可修饰等优点，亦存在蛋白瞬时表达导致调控细胞功能的持久性不足及制备等方面的技术局限性。本文综述了LNP-mRNA递送系统及其在CAR-T细胞治疗中的应用研究。     

Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms

PurposeMachine-learning (ML) approaches have been repeatedly coupled with raw accelerometry to classify physical activity classes, but the features required to optimize their predictive performance are still unknown. Our aim was to identify appropriate combination of feature subsets and prediction algorithms for activity class prediction from hip-based raw acceleration data.MethodsThe hip-based raw acceleration data collected from 27 participants was split into training (70 %) and validation (30 %) subsets. A total of 206 time- (TD) and frequencydomain (FD) features were extracted from 6-second non-overlapping windows of the signal. Feature selection was done using seven filter-based, two wrapper-based, and one embedded algorithm, and classification was performed with artificial neural network (ANN), support vector machine (SVM), and random forest (RF). For every combination between the feature selection method and the classifiers, the most appropriate feature subsets were found and used for model training within the training set. These models were then validated with the left-out validation set.ResultsThe appropriate number of features for the ANN, SVM, and RF ranged from 20 to 45. Overall, the accuracy of all the three classifiers was higher when trained with feature subsets generated using filter-based methods compared with when they were trained with wrapper-based methods (range: 78.1 %–88 % vs. 66 %–83.5 %). TD features that reflect how signals vary around the mean, how they differ with one another, and how much and how often they change were more frequently selected via the feature selection methods.ConclusionsA subset of TD features from raw accelerometry could be sufficient for ML-based activity classification if properly selected from different axes.