浅论中医学对胰腺实体解剖的认知过程

中医学古籍中罕有关于“胰腺”的明确记载，缺乏直观、系统的理论论述。本文试图通过对各个时期具有代表性的医学典籍所记载内容的分析，结合现代解剖学相关理论，梳理中医学对胰腺实体解剖的认识过程。通过分析，笔者认为中医学对胰腺实体解剖的认识具有阶段性：①先秦两汉时期，存在胰腺实体解剖，但并非认为胰腺是脏器；②唐宋时期，胰腺实体解剖更加清晰，在医学上胰腺附属于脾，并非为独立的脏器；③明清时期，胰腺实体解剖明确，部分医家以独立脏器论之，出现“脾”、“胰”之争。中医学理论缺少对胰腺的单独论述，目前学界的主流观点多为“胰属脾”，线性归属以脾笼统代之略显单薄，不利于理论的丰富与发展。笔者认为胰腺藏象应独立于脾单独讨论，现代解剖学对胰腺命名同一，形态结构清楚，位置描述明确，可直接补充进中医学胰腺藏象（藏）理论中，为完善胰腺藏象理论搭建解剖学基础。     

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。     

“T” is for “transgender”: an analysis of children’s picture books featuring transgender protagonists and narrators

ABSTRACT

This project examined 55 picture books featuring transgender, gender expansive or genderqueer protagonists or narrators published between 2008 and 2018. The purpose of the study was to determine how this genre of children’s literature supports and challenges four gender assumptions: the gender binary, gender essentialism, sex/gender congruency and gender stability. Additionally, this critical analysis explored misgendering within this genre and themes of social rejection and acceptance. Protagonists and narrators were permitted a degree of gender nonconformity, however, the majority of picture books missed opportunities for a more complete exploration of gender possibilities.     

基于中医古籍文献的升降散应用溯源及探析

通过对中医古籍中有关升降散论述的文献梳理,全面探讨其内涵。本方是在大黄、僵蚕为雏形的基础上增加蝉蜕、姜黄而成方,几经易名,终在杨璿《伤寒瘟疫条辨》中为后人所熟知。本方广泛用于治疗瘟疫,以丸剂、散剂为主,方便携带,便于服用;重用大黄旨在祛邪、逐秽;应用时视人之体质强弱和量其毒之轻重而判断用药多寡,并辅以米酒、生蜜等以顾护正气。杨璿将其由治疗“热疫”的专方扩展为治疗“表里三焦大热”的通用方剂,扩大了本方治疗疾病范围。     

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.     

基于TCGA数据初步探查肝内胆管癌预后相关基因

目的 分析tCGA数据库中肝内胆管癌（ICC）高通量测序数据，寻找其预后相关基因，构建风险模型，并研究其在ICC组织中表达及作用通路。方法 下载tCGA数据库中33例ICC组织和8例癌旁组织中的RNA-seq表达矩阵数据和患者临床资料信息，利用edgeR软件包进行基因差异表达分析，通过单因素Cox回归分析筛选出预后相关差异基因，对差异基因绘制生存曲线，筛选出具有临床意义的基因，经多因素Cox回归分析并构建风险模型，通过京都基因与基因组百科全书（KEGG）通路富集分析了解预后相关基因的作用通路。结果 通过edgeR分析后得到6 617个差异基因（筛选标准为|log2 Fold Change|＞1，P＜0.05），其中高表达组4 094个，低表达组2 523个。通过功能富集发现，这些基因主要集中在化学物致癌作用、药物代谢-细胞色素P450系统、细胞色素P450对异生物质的代谢影响以及视黄醇代谢通路。经单因素Cox回归、R软件“survival”包生存曲线分析显示，UCN2、CST1、PROS1、SLC35E4、PEMT五个基因对ICC患者预后存在显著性影响。通过多因素Cox回归分析，CST1、PEMT、PROS1构建的风险模型对ICC患者预后具有判断作用。结论 UCN2、CST1、PROS1、SLC35E4、PEMT基因可能成为ICC预后判断指标，为后续临床试验提供数据支持。