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。     

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。     

Increase in Streptococcus pneumoniae serotype 3 associated parapneumonic pleural effusion/empyema after the introduction of PCV13 in Germany

IntroductionPediatric pneumococcal pneumonia complicated by parapneumonic pleural effusion/empyema (PPE/PE) remains a major concern despite general immunization with pneumococcal conjugate vaccines (PCVs).MethodsIn a nationwide pediatric hospital surveillance study in Germany we identified 584 children <18 years of age with bacteriologically confirmed PPE/PE from October 2010 to June 2018. Streptococcus pneumoniae was identified by culture and/or PCR of blood samples and/or pleural fluid and serotyped.ResultsS. pneumoniae was identified in 256 of 584 (43.8%) children by culture (n = 122) and/or PCR (n = 207). The following pneumococcal serotypes were detected in 114 children: serotype 3 (42.1%), 1 (25.4%), 7F (12.3%), 19A (7.9%), other PCV13 serotypes (4.4%) and non-PCV13 serotypes (7.9%). Between October 2010 and June 2014 serotype 1 (38.1%) and serotype 3 (25.4%) were most prevalent, whereas between July 2014 and June 2018 serotype 3 (62.7%) and non-PCV13 serotypes (15.7%) were dominant. Compared to children with other pneumococcal serotypes, children with serotype 3 associated PPE/PE were younger (median 3.2 years [IQR 2.1–4.3 years] vs. median 5.6 years [IQR 3.8–8.2 years]; p < 0.001) and more frequently admitted to intensive care (43 [89.6%] vs. 48 [73.8%]; p = 0.04). Seventy-six of 114 (66.7%) children with pneumococcal PPE/PE had been vaccinated with pneumococcal vaccines. Thirty-nine of 76 (51.3%) had received a vaccine covering the serotype detected. Thirty of these 39 breakthrough cases were age-appropriately vaccinated with PCV13 and considered vaccine failures, including 26 children with serotype 3, three children with serotype 19A and one child with serotype 1.ConclusionFollowing the introduction of PCV13 in general childhood vaccination we observed a strong emergence of serotype 3 associated PPE/PE in the German pediatric population, including a considerable number of younger children with serotype 3 vaccine breakthrough cases and failures. Future PCVs should not only cover newly emerging serotypes, but also include a more effective component against serotype 3.     

情景模拟联合案例讨论在急诊护理临床带教中的应用体会

目的探究急诊护理临床带教中采用情景模拟、案例讨论联合教学法效果。方法在本院实习的若干名护生中,选取124名护生分按照教学方法分组,对照组62名实施传统急诊护理带教,观察组62名实施案例讨论、情景模拟联合教学法,对比临床带教效果。结果两组实践与理论成绩相比,观察组成绩更高(P<0.05)。思维、急救及应激能力自我评价相比,观察组的总提高率高于对照组(P<0.05)。实施案例分析结合情景模拟教学模式后,问卷调查中观察组无护生不赞同此模式,非常赞同此教学模式的护生占总人数的90%以上。结论实施案例讨论、情景模拟联合教学模式后,护生急诊护理实践与理论水平均提升,护生我评价较高。     

案例教学法在消化内科见习课的应用效果评价

目的探讨案例教学法在消化内科见习课的教学效果和体会。方法选取2017年10月—2017年12月在消化内科进行见习课的2015级39-40班护理本科学生44例(全部为女生),抓阄分成对照组(22人)和试验组(22人),对照组给予传统的带教方法,试验组给予案例教学法教学,比较两组护生在理论、技能、综合能力和对带教满意度方面有无差异。两组学生在性别、年龄、专业前的课程成绩比较,差异无统计学意义(均P>0.05)。结果观察组在理论考核成绩、操作考核成绩、教学满意度等方面均高于对照组。结论案例教学法能提高护理教学的教学效果,增加学生的理论、操作能力,提高护理临床思维能力,提升学生的学习主动性和积极性。     

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.     

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.