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.     

Risk-based cost-benefit analysis of alternative vaccines against COVID-19 in Brazil: Coronavac vs. Astrazeneca vs. Pfizer

We propose a probabilistic model to quantify the cost-benefit of mass Vaccination Scenarios (VSs) against COVID-19. Through this approach, we conduct a six-month simulation, from August 31st, 2021 to March 3rd, 2022, of nine VSs, i.e., the three primary vaccine brands in Brazil (CoronaVac, AstraZeneca and Pfizer), each with three different vaccination rates (2nd doses per week). Since each vaccine has different individual-level effectiveness, we measure the population-level benefit as the probability of reaching herd immunity (HI). We quantify and categorize the cost-benefit of VSs through risk graphs that show: (i) monetary cost vs. probability of reaching HI; and (ii) number of new deaths vs. probability of reaching HI. Results show that AstraZeneca has the best cost-benefit when prioritizing acquisition costs, while Pfizer is the most cost-beneficial when prioritizing the number of deaths. This work provides helpful information that can aid public health authorities in Brazil to better plan VSs. Furthermore, our approach is not restricted to Brazil, the COVID-19 pandemic, or the mentioned vaccine brands. Indeed, the method is flexible so that this study can be a valuable reference for future cost-benefit analyses in other countries and pandemics, especially in the early stages of vaccination, when data is scarce and uncertainty is high.     

A Perioperative Care Display for Understanding High Acuity Patients

Background  The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types. Objectives  In this project, we used general principles of data visualization and a co-design process to produce a clinical display tailored to a specific cognitive task, chosen from the anesthesia domain, but with clear generalizability to other clinical tasks. To support the work of the anesthesia-in-charge (AIC) our task was, for a given day, to depict the acuity level and complexity of each patient in the collection of those that will be operated on the following day. The AIC uses this information to optimally allocate anesthesia staff and providers across operating rooms. Methods  We used a co-design process to collaborate with participants who work in the AIC role. We conducted two in-depth interviews with AICs and engaged them in subsequent input on iterative design solutions. Results  Through a co-design process, we found (1) the need to carefully match the level of detail in the display to the level required by the clinical task, (2) the impedance caused by irrelevant information on the screen such as icons relevant only to other tasks, and (3) the desire for a specific but optional trajectory of increasingly detailed textual summaries. Conclusion  This study reports a real-world clinical informatics development project that engaged users as co-designers. Our process led to the user-preferred design of a single binary flag to identify the subset of patients needing further investigation, and then a trajectory of increasingly detailed, text-based abstractions for each patient that can be displayed when more information is needed.     

贵州省"5+3"模式订单定向医学毕业生的离职意愿及影响因素研究

背景 我国基层全科医生的离职意愿较高，调查其离职意愿并分析影响因素，可以为减少基层卫生人才流失提供思路。目前，完成"5+3"模式（5年临床医学本科教育+3年住院医师规范化培训）培养的订单定向医学毕业生逐步履约进入基层工作，而针对该部分全科医生离职意向的研究相对较少。 目的 调查贵州省"5+3"模式订单定向医学毕业生回归基层工作后的离职意愿及影响因素，为完善吸引卫生人才留任、建设基层全科医生队伍相关政策提供依据。 方法 以贵州省截至2020年底已完成"5+3"模式培养并履约到基层医疗卫生机构工作的2015—2017级订单定向医学毕业生为研究对象。于2021-01-20至2021-02-10对其开展电子问卷调查，内容包括毕业生的一般情况、职业满意度、离职意愿、服务期满后职业方向。共回收问卷347份，其中有效问卷311份，问卷有效回收率为89.6%。采用单因素分析及多元逐步线性回归分析全科医生离职意愿的影响因素。 结果 贵州省"5+3"订单定向医学毕业生的整体离职意愿得分为（3.98±0.98）分，具有离职倾向者229例（73.6%）。不同性别、单位地理位置、每日工作量者的离职意愿得分比较，差异有统计学意义（P<0.05）。多元逐步线性回归分析显示，单位负责人对待下属的方式、在工作中获得的成就感、对当前收入满意程度、家人对工作的支持程度、当地激励政策执行程度是"5+3"订单定向医学毕业生离职意愿的影响因素（P<0.05）。服务期满后，计划留任原基层医疗卫生机构者12例（3.9%），计划去其他基层医疗卫生机构者21例（6.7%），计划离开基层去上级医院工作者196例（63.0%），计划攻读全日制硕士学位者60例（19.3%）。 结论 贵州省"5+3"模式订单定向医学毕业生的离职意愿较高，预计服务期满后基层全科人才流失较多，需从提高收入、重视全科医生心理需求、优化全科医生培养与使用、发展基层医疗卫生机构、加强全科宣传等方面着手改善。     

基于SSR标记与化学成分构建十堰地区天师栗核心种质库＊

目的 利用筛选出十堰的天师栗中高多态性SSR位点评价天师栗种质资源的遗传多样性，结合有效药用成分含量，构建十堰地区天师栗核心种质库。方法 收集十堰地区114份天师栗种质资源，以七叶树基因组为参考，采用荧光毛细管电泳筛选出高多态性SSR位点，对天师栗种质资源进行遗传多样性分析。利用HPLC测定不同种质干燥娑罗子中七叶皂苷的含量。采用最小距离逐步聚类取样策略（LDSS），根据遗传多样性保留程度初步筛选出核心种质，并对该核心种质与原始种质的遗传多样性参数进行T检验，选择与原种质差异不显著的核心种质为最佳核心种质。结果 筛选出13对高多态性SSR分子标记，遗传多样性评价结果表明十堰地区天师栗种质资源遗传多样性较高，遗传分化较小，存在着较大的基因流，114份种质资源未分为不同的亚群，周家坝和辽叶居群间具有较近的遗传亲缘关系，且周家坝居群娑罗子中的七叶皂苷A及七叶皂苷B含量普遍较高。最终筛选出的核心种质共23份，占总种质资源的20.17%，其中周家坝12份样本、辽叶6份样本、普龄5份样本。结论 将SSR分子标记与主要有效药用成分结合，采用LDSS取样策略构建十堰地区天师栗种质资源核心种质库的方法具有可行性，能够有效的保存与管理天师栗种质资源，也为当地天师栗品种改良、新品种选育研究等提供了研究基础。     

空气细颗粒物暴露水平与健康成人大脑灰质体积和认知功能的关联研究

目的:应用磁共振成像(MRI)技术探究空气细颗粒物PM2.5暴露水平对健康成年人大脑灰质体积(GMV)和认知功能的影响。方法:选取487例健康成人,采集一般人口学信息及详细居住信息,并根据居住信息获取各受试近6个月和8岁～15岁期间的PM2.5暴露水平,对所有研究对象完成高分辨率结构MRI扫描,使用中文版精神分裂症认知功能成套测验-共识版(MCCB)进行认知测查,使用相关和回归分析探索空气PM2.5暴露水平与GMV和认知功能的关联。结果:近6个月PM2.5暴露水平与大脑右侧额下回三角部(brodmann分区[BA] 45)和右侧颞中回(BA 22)GMV存在负性关联(FWE校正,P<0.05,团块>10体素);与MCCB认知评估的信息处理速度-连线测试(r=-0.17,P<0.01)和推理和问题解决-迷宫测验评分(r=-0.22,P<0.01)负相关。8～15岁时的累积空气PM2.5暴露量与左侧梭状回(BA 18)和右侧舌回(BA 17)GMV负相关(FWE校正,P<0.05,团块>10体素),且与信息处理速度-连线测试、推理和问题解决-迷宫测验评分...