The Contribution of Fortified Ready-to-Eat Cereal to Vitamin and Mineral Intake in the U.S. Population,NHANES 2007–2010

Micronutrients play a pivotal role in achieving and maintaining optimum health across all life stages. Much of the U.S. population fails to meet Estimated Average Requirements (EARs) for key nutrients. This analysis aims to assess the contribution of fortified ready-to-eat cereals (RTEC) to micronutrient intake for U.S. residents aged 2–18, 19–99, and 2–99 years of age according to National Health and Nutrition Examination Survey (NHANES) 2007–2010 data. We used the National Cancer Institute (NCI) method to assess usual intake of 21 micronutrients and the percentage of the population under EARs and above Tolerable Upper Intake Levels (UL). Without fortification of RTECs, the percentage of those aged 2–18 years that were below EARs increased by 155%, 163%, 113%, and 35% for niacin, iron, thiamin, and vitamin A, respectively. For vitamins B6 and zinc, the respective numbers were 118% and 60%. Adults aged 19–99 and 2–99 had lower percentages but similar outcomes. RTECs are associated with improved nutrient adequacy and do not widely affect prevalence above the UL. The data indicate that large proportions of the population fail to achieve micronutrient sufficiency without fortification, and that its use can help Americans reach national nutrient intake goals.     

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釉质发育不全（amelogenesis imperfecta,AI）是一组影响釉质发育的遗传性疾病,由于釉质形成时造釉器的某些功能障碍,导致釉质在厚度、结构和组织上的改变。临床可分为3型：釉质发育不良型（hypoplastic AI,HPAI）,釉质矿化不良型（hypocalcified AI,HCAI）和釉质未成熟型（hypomaturation AI,HMAI）。AI以牙色改变和釉质缺损为主要表现,严重者可伴有颞下颌关节紊乱病（temporomandibular joint disorders,TMD）等。笔者在临床中遇到1例全口乳牙、恒牙严重AI伴TMD病例,现报道如下。     

药学知识库发展与建设

介绍药学知识库内涵，分析建立药学知识库的必要性、意义、发展情况，从数据来源、模型构建、设计要点及应用几方面阐述其构建方法，提出未来发展方向。     

UK reporting radiographers’ perceptions of AI in radiographic image interpretation – Current perspectives and future developments

IntroductionRadiographer reporting is accepted practice in the UK. With a national shortage of radiographers and radiologists, artificial intelligence (AI) support in reporting may help minimise the backlog of unreported images. Modern AI is not well understood by human end-users. This may have ethical implications and impact human trust in these systems, due to over- and under-reliance. This study investigates the perceptions of reporting radiographers about AI, gathers information to explain how they may interact with AI in future and identifies features perceived as necessary for appropriate trust in these systems.MethodsA Qualtrics® survey was designed and piloted by a team of UK AI expert radiographers. This paper reports the third part of the survey, open to reporting radiographers only.Results86 responses were received. Respondents were confident in how an AI reached its decision (n = 53, 62%). Less than a third of respondents would be confident communicating the AI decision to stakeholders. Affirmation from AI would improve confidence (n = 49, 57%) and disagreement would make respondents seek a second opinion (n = 60, 70%). There is a moderate trust level in AI for image interpretation. System performance data and AI visual explanations would increase trust.ConclusionsResponses indicate that AI will have a strong impact on reporting radiographers’ decision making in the future. Respondents are confident in how an AI makes decisions but less confident explaining this to others. Trust levels could be improved with explainable AI solutions.Implications for practiceThis survey clarifies UK reporting radiographers’ perceptions of AI, used for image interpretation, highlighting key issues with AI integration.     

Cystic fibrosis patients have poor sleep quality despite normal sleep latency and efficiency

STUDY OBJECTIVES: Cystic fibrosis (CF) patients may be predisposed to poor sleep quality due to upper and lower airway abnormalities and impaired gas exchange. Previous sleep investigations of CF patients using single-night polysomnography have reported conflicting results. We hypothesized that sampling sleep for a prolonged period in a patient's normal environment may give a more representative assessment of sleep quality than a single-night polysomnogram, and that impaired sleep quality would correlate with pulmonary disease severity and self-assessed sleep quality. DESIGN: Using wrist actigraphy, we measured sleep quality in clinically stable CF patients and age-matched control subjects. In addition, each CF patient and control subject completed the following three questionnaires: the Epworth sleepiness scale; the Pittsburgh sleep quality index (PSQI); and the Medical Outcomes Study 36-item short form. RESULTS: Twenty CF patients and control subjects were enrolled in the study, and were well-matched for age, sex, and body mass index. The mean (+/- SD) FEV(1) for CF patients was 61.0 +/- 20.1% predicted. CF patients and control subjects had similar sleep duration, sleep latency, and sleep efficiency. However, CF patients had higher PSQI scores (6.45 vs 4.55, respectively; p = .04), a higher fragmentation index (FI) [31.72 vs 18.02, respectively; p < 0.001], and less immobile time (88.87 vs 91.89, respectively; p = 0.02). There was a significant correlation of FI with FEV(1) and PSQI scores. CONCLUSIONS: Stable CF patients have disrupted sleep, and sleep disruption may in part be related to the severity of pulmonary disease. In addition, the PSQI may be useful in detecting CF patients with poor sleep quality.     

Non-HDL-C和AI对冠心病患者病情的评估价值

目的：探讨非高密度脂蛋白(Non-HDL-C)和粥样动脉硬化指数(AI)评估冠心病病情的临床价值。方法以冠心病患者为病例组(398例),以健康者为对照组(150例),全自动生化分析仪检测并比较不同组别受试者的一般临床资料、血脂、血糖、Non-HDL-C、AI等指标。结果冠心病组年龄、BMI、血压、甘油三酯、Non-HDL-C、AI等均高于对照组(P<0.05);慢性冠心病组年龄、BMI、收缩压、甘油三酯、TC、LDL-C、HDL-C、空腹血糖、Non-HDL-C、AI等高于不稳定型心绞痛组、急性心肌梗死组(P<0.05);Non-HDL-C与AI具有正相关关系(r2=0.1611,P<0.05);Non-HDL-C、AI均与年龄、HDL-C呈负相关,与LDL-C、TC、甘油三酯呈正相关(P<0.05);Non-HDL-C、AI与冠状动脉病变总分呈正相关(P<0.05)。结论冠心病患者的Non-DHL-C和AI水平明显高于对照组,且Non-DHL-C和AI水平越高,冠心病病变越严重。二者可用于评价冠心病病情严重程度。     

急诊智能化信息系统构建

详细介绍基于医护患一体的急诊智能化信息系统建设目标、具体实现等方面,指出该系统有助于缩短急救患者救治时间,实现诊疗流程精细化管理,为医院质控部门提供有力的数据支撑。