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。     

The Transmission of Trauma from Mother to Infant: Radioactive Residues and Counter-Transference in the Case of a Haitian Mother and Her Two-Year-Old Son*

ABSTRACT

Close observation of the interactions between a traumatised mother and her infant son provides information on the modes of transmission of psychic trauma in the mother–infant dyad. Following the presentation of a current literature review on the theme, the subject of “radioactive residue” and counter-transference in the transmission of psychic trauma from mother to infant will be illustrated through a clinical case study that focuses on a Haitian mother and her two-year-old infant son who has been referred to a “transitional care nursery” in urban Paris. The encounter with this mother–infant dyad is analysed through observing the quality of the interactions that take place between the mother and infant in order to determine how a particularly traumatic narrative impacts the mother–infant relationship, in addition to relations with the clinician. Mother and infant respond to one another through the emission and reception of “radioactive residues” as hypothesized by Gampel. This clinical case study shows that there is a need to consider transcultural factors and collective experience and history when analysing traumatic events. Additionally, the case study shows that counter-transference can be an effective clinical tool for gaining access to an infant's experience as the recipient of a traumatic narrative.     

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.     

Self-reported outcomes of a randomized trial comparing three community health worker interventions for diabetes prevention among Cambodian Americans with depression

ObjectiveCambodian Americans have complex, interrelated and persistent medical and mental health problems stemming from genocide and the social determinants of health. We examined changes in multiple domains of self-reported health outcomes from a diabetes prevention trial. Methods: Cambodian Americans with depression and high risk for diabetes (n = 188) were randomized to one of three community health worker interventions: lifestyle vs lifestyle plus medication therapy management vs social services. Assessments were at baseline, 12- and 15-months. Results: The typical participant was 55 years old, female, earned below $20,000 annually, and had 7 years of education. About one-third were taking antidepressant medication and over half had elevated depressive symptoms. Relative to social services, lifestyle and lifestyle plus medication therapy management were both similarly effective at increasing diabetes knowledge, nutrition habits, sleep quality and decreasing pain; 2) lifestyle alone was superior to social services for self-reported health; and, 3) all three groups showed improved anxiety and insomnia. There were no effects on physical activity or physical functioning. Conclusion: Community health worker interventions have multiple benefits beyond delaying diabetes. Practice Implications: Health promotion programs that are designed and delivered appropriately can impact even hard to reach and hard to treat groups.     

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.     

Optimizing access and configuration of trauma centre care in New South Wales

IntroductionGetting the right patient, to the right place, at the right time is dependent on a multitude of modifiable and non-modifiable factors. One potentially modifiable factor is the number and location of trauma centres (TC). Overabundance of TC dilutes volumes and could be associated with worse outcomes. We describe a methodology that evaluates trauma system reconfiguration without reductions in potential access to care. We used the mature trauma system of New South Wales (NSW) as a model given the perceived overabundance of urban major trauma centres (MTC).MethodsWe first evaluated potential access to TC care via ground and air transport through the use of geographic information systems (GIS) network analysis. Potential access was defined as the proportion of the population living within 60-min transport time from a potential scene of injury to a TC by ground or rotary-wing aircraft. Sensitivity analyses were carried out in order to account for potential pre-hospital interventions and/or transport delays; travel times of 15-, 30-, 45-, 60-, and 90-min were also analyzed. We then evaluated if the current configuration of the system (number of urban MTS in the Sydney basin) could be optimized without reductions in potential access to care using two GIS methodologies: location-allocation and individual removal of MTC.Results86% of the NSW population has potential access to a TC within 60 min ground travel time; potential access improves to 99% with rotary-wing transport. The 1% of the population without potential TC access lives in 48% of the land area (>384,000km2). Utilizing two different methodologies we identified that there was no change in potential access by ground transport after removing 1 or 2 MTC in the Sydney basin at the 30-, 45-, and 60-min transport times. However, 0.02% and 0.5% of the population would not have potential access to MTC care at 15 min after removing one and two MTC respectively.DiscussionRedistribution of the number of MTC in the Sydney basin could be achieved without a significant impact on potential access to care. Our approach can be utilized as an initial tool to evaluate a trauma system where overabundance of coverage is present.