基于文本挖掘和生物医学数据库的新型冠状病毒肺炎药物发现

目的 基于文本挖掘技术和生物医学数据库对新型冠状病毒肺炎（COVID-19）相关文献进行数据挖掘分析，探究COVID-19及其主要症状发热、咳嗽、呼吸障碍相关基因靶点，筛选潜在有效的化学药和中药。方法 使用GenCLiP 3网站获取COVID-19和其主要症状咳嗽、发热、呼吸障碍共4个关键词的共有靶点，在METASCAPE数据库中对其进行基因本体（GO）和通路富集分析，再利用String数据库和Cytoscape软件构建共有靶点的蛋白质相互作用网络，筛选获得核心基因，运用DGIdb数据库、SymMap数据库针对核心基因进行中西医治疗药物预测。结果 获得COVID-19及其主要症状共有基因靶点28个，其中有IL2、IL1B、CCL2等核心基因16个，使用DGIdb数据库筛选获得与16个关键靶点相互作用的化学药包括沙利度胺、来氟米特、环孢素等28种，中药包括虎杖、黄芪、芦荟等70味。结论 COVID-19及其主要症状的病理机制可能和CD4、KNG1、VEGFA等28个共有基因相关，可能通过介导TNF、IL-17等信号通路参与COVID-19病理过程。潜在有效药物可能通过作用相关靶点通路起到治疗COVID-19的作用。     

国外动物智能军事应用情报研究

目的：阐述动物智能在军事学的应用及意义。方法：以学术论文、新闻、报纸、智库报告等作为信息源，综合研究国外动物智能军事应用实例及内在关联，描述动物智能在未来战场应用场景。结果：动物智能军事应用主要是动物特殊能力的军事应用和动物智能的仿生武器应用。结论：动物部队、仿生作战部队和动物战术是未来战争不可忽视的重要组成部分，动物智能的隐匿、灵活、多变等特点正逐渐被广泛应用于战场。     

老年人生命晚期获知疾病信息意向及影响因素CSCD

目的了解老年人生命晚期获知疾病相关信息意向及影响因素。方法2016年10月至2017年6月,采用生命晚期疾病信息意向问卷,利用方便抽样法对福州市中心城区7所养老机构及15个社区的414例年龄≥60岁的老年人进行横断面调查,采用单因素分析、多元线性回归与有序多分类logistic回归分析老年人对疾病相关信息的需求水平、获知程度意向及其影响因素。结果414例老年人疾病相关信息需求得分为(17.1±4.9)分;48.8%(202/414)希望详尽知晓,30.7%(127/414)希望选择性了解,20.5%(85/414)不想知道任何信息;多元线性回归分析显示,年龄、文化程度、是否接受/见过其他生命维持治疗(LSTs)是影响老年人疾病相关信息需求水平的主要因素(标准化回归系数分别为-0.141、0.116、0.115,均P<0.05);有序多分类logistic分析显示,年龄(以60~69岁为参照,70~79岁:OR=0.544,95%CI:0.310~0.957;80~89岁:OR=0.526,95%CI:0.289~0.956)、文化程度(以小学及以下为参照,大专及以上:OR=2.166,95%CI:1.093~4.290)、主要生活费来源(以其他补贴为参照,家人支持:OR=7.303,95%CI:1.157~46.108;退休金:OR=9.288,95%CI:1.502~57.415;公积金/储蓄:OR=15.676,95%CI:2.122~115.793)、是否接受/见过其他LSTs(以是为参照,OR=1.985,95%CI:1.150~3.425)是影响老年人疾病相关信息获知程度意向的主要因素。结论老年人生命晚期获知疾病相关信息的意向程度较高,年龄、文化程度、主要生活费来源、是否接受/见过其他生命维持治疗等是其主要影响因素。     

嘌呤能受体X1与肺腺癌预后及免疫细胞浸润的相关性研究

目的：探索嘌呤能受体X1(purinergic receptor，P2RX1)与肺腺癌(LUAD)患者预后及免疫细胞浸润的相关性。方法：利用生物信息学技术分析非小细胞肺癌中P2RX1的表达及其甲基化与患者预后的关系，对P2RX1共表达基因进行富集分析并筛选核心基因。利用TIMER 2.0数据库、R软件等分析P2RX1与免疫细胞、免疫检查点、免疫基质评分等的相关性。结果：P2RX1在LUAD中表达下调，低表达P2RX1的患者预后较差(P<0.05)，且P2RX1与肿瘤纯度、分期等临床病理因素有关(P<0.05)。P2RX1的表达与肺鳞癌患者预后无明显相关。Cg06475633等P2RX1 CpG位点甲基化与患者预后相关。P2RX1共表达基因主要富集于免疫细胞活化、分化等通路和生物学进程，核心基因主要包括BTK、IKZF1等。P2RX1的表达与B细胞浸润、免疫/基质评分、PD-1、CTLA-4等多个免疫检查点显著相关(P<0.05)。结论：P2RX1有望成为LUAD诊断和免疫治疗的新靶点。     

Computer-generated vegan menus: The importance of food composition database choice

Precise data on food chemical composition is crucial for any quantitative nutrition research and indispensable for evaluation and planning of computer-based menus. Moreover, exact food chemical composition is of the utmost importance in composing specific diets that may be low in certain essential nutrients. Therefore, this research evaluated a weekly vegan menu using three different food composition databases (FCDB): the Croatian, the official Danish Food Composition Database and the United States Department of Agriculture (USDA) – National Nutrient Database for Standard Reference. Significant differences (p < 0.05) were determined in evaluated and optimised offers for the same menu when using different FCDBs. Furthermore, in these menus quantities of some nutrients have not reached official daily recommendations needed for prevention of non-communicable diseases. In this research, new vegan menus that can provide adequate quantity of essential nutrients, regardless of FCDB utilized, were created with linear optimisation (LO). This resulted in high quality daily menu offers. However, depending on the FCDB that was used, optimal menus differed in daily meal combinations, and the effectiveness of the optimisation in the minimisation of differences caused by differences of data in FCDBs was tested. Linear optimisation has been proved to be an effective tool in planning of specific diets, such as vegan diets. Nonetheless, the quality of computer-planned menus depends heavily on the quality of FCDB used.     

Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout,with applications to quality of life measurements from clinical trials

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.     

Applications of geospatial analyses in health research among homeless people: A systematic scoping review of available evidence

BackgroundUnequal housing access resulted in more than 150 million homeless people worldwide, with millions more expected to be added every year due to the ongoing climate-related crises. Homeless population has a counterproductive effect on the social, psychological integration efforts by the community and exposure to other severe health-related issues. Geographic Information Systems (GIS) have long been applied in urban planning and policy, housing and homelessness, and health-related research.MethodsWe used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to systematically review 24 articles collected from multiple databases (n = 10) that focused on health-related issues among homeless people and used geospatial analysis techniques in their research.ResultsOur findings indicated a geographic clustering of case study locations– 26 out of the 31 case study sites are from the USA and Canada. Studies used spatial analysis techniques to identify hotspots, clusters and patterns of patient location and population distribution. Studies also reported relationships among the location of homeless shelters and substance use, discarded needles, different infectious and non-infectious disease clusters.ConclusionMost studies were restricted in analyzing and visualizing the patterns and disease clusters; however, geospatial analyses techniques are useful and offer diverse techniques for a more sophisticated understanding of the spatial characteristics of the health issues among homeless people. Better integration of GIS in health research among the homeless would help formulate sensible policies to counter health inequities among this vulnerable population group.     

Registries,Databases and Repositories for Developing Artificial Intelligence in Cancer Care

Modern artificial intelligence techniques have solved some previously intractable problems and produced impressive results in selected medical domains. One of their drawbacks is that they often need very large amounts of data. Pre-existing datasets in the form of national cancer registries, image/genetic depositories and clinical datasets already exist and have been used for research. In theory, the combination of healthcare Big Data with modern, data-hungry artificial intelligence techniques should offer significant opportunities for artificial intelligence development, but this has not yet happened. Here we discuss some of the structural reasons for this, barriers preventing artificial intelligence from making full use of existing datasets, and make suggestions as to enable progress. To do this, we use the framework of the 6Vs of Big Data and the FAIR criteria for data sharing and availability (Findability, Accessibility, Interoperability, and Reuse). We share our experience in navigating these barriers through The Brain Tumour Data Accelerator, a Brain Tumour Charity-supported initiative to integrate fragmented patient data into an enriched dataset. We conclude with some comments as to the limits of such approaches.