Identification of Dish-Based Dietary Patterns for Breakfast,Lunch, and Dinner and Their Diet Quality in Japanese Adults

We identified dish-based dietary patterns for breakfast, lunch, and dinner and assessed the diet quality of each pattern. Dietary data were obtained from 392 Japanese adults aged 20–69 years in 2013, using a 4 d dietary record. K-means cluster analysis was conducted based on the amount of each dish group, separately for breakfasts (n = 1462), lunches (n = 1504), and dinners (n = 1500). The diet quality of each dietary pattern was assessed using the Healthy Eating Index 2015 (HEI-2015) and Nutrient-Rich Food Index 9.3 (NRF9.3). The extracted dietary patterns were as follows: ‘bread-based’ and ‘rice-based’ for breakfast; ‘bread’, ‘rice-based’, ‘ramen’, ‘udon/soba’, and ‘sushi/rice bowl dishes’ for lunch; and ‘miscellaneous’, ‘meat dish and beer’, and ‘hot pot dishes’ for dinner. For breakfast, the HEI-2015 and NRF9.3 total scores were higher in the ‘rice-based’ pattern than the ‘bread-based’ pattern. For lunch, the HEI-2015 and NRF9.3 total scores were relatively high in the ‘rice-based’ pattern and low in the ‘ramen’ pattern. For dinner, the HEI-2015 total score was the highest in the ‘meat dish and beer’ pattern, and the NRF9.3 total score was higher in the ‘hot pot dishes’ than the ‘miscellaneous’ pattern. These results suggested that breakfast, lunch, and dinner have distinctive dietary patterns with different diet qualities.     

ITV-Based Robust Optimization for VMAT Planning of Stereotactic Body Radiation Therapy of Lung Cancer

Purpose

Using planning target volume (PTV) to account for setup uncertainties in stereotactic body radiation therapy (SBRT) of lung cancer has been questioned because a significant portion of the PTV contains low-density lung tissue. The purpose of this study is to (1) investigate the feasibility of using robust optimization to account for setup uncertainties in volumetric modulated arc therapy plan for lung SBRT and (2) evaluate the potential normal tissue–sparing benefit of a robust optimized plan compared with a conventional PTV-based optimized plan.

Machine Learning-Based Prediction of In-Hospital Complications in Elderly Patients Using GLIM-, SGA-, and ESPEN 2015-Diagnosed Malnutrition as a Factor

Background: Malnutrition is prevalent in elderly inpatients and is associated with various adverse outcomes during their hospital stay, but the diagnosis of malnutrition still lacks widely applicable criteria. This study aimed to investigate the association of malnutrition diagnosed with the SGA, ESPEN 2015, and GLIM criteria, respectively, with in-hospital complications in elderly patients. Method: Hospitalized patients over 65 years old who had been assessed with the SGA guideline for malnutrition at admission were retrospectively recruited from a large observational cohort study conducted in 34 level-A tertiary hospitals in 18 cities in China from June to September 2014. Malnutrition was then retrospectively diagnosed using the GLIM and ESPEN 2015 criteria, respectively, for comparison with the results of the SGA scale. The risk factors for malnutrition were analyzed using logistic regression, and the value of the three diagnostic criteria in predicting the in-hospital complications was subsequently explored using multivariate regression and the random forest machine learning algorithm. Results: A total of 2526 subjects who met the inclusion and exclusion criteria of the study were selected from the 7122 patients in the dataset, with an average age of 74.63 ± 7.12 years, 59.2% male, and 94.2% married. According to the GLIM, SGA, and ESPEN 2015 criteria, the detection rates of malnutrition were 37.8% (956 subjects), 32.8% (829 subjects), and 17.0% (429 subjects), respectively. The diagnostic consistency between the GLIM and the SGA criteria is better than that between the ESPEN 2015 and the SGA criteria (Kappa statistics, 0.890 vs. 0.590). Logistic regression showed that the risk of developing complications in the GLIM-defined malnutrition patients is 2.414 times higher than that of normal patients, higher than those of the ESPEN 2015 and SGA criteria (1.786 and 1.745 times, respectively). The random forest classifications show that the GLIM criteria have a higher ability to predict complications in these elderly patients than the SGA and ESPEN 2015 criteria with a mean decrease in accuracy of 12.929, 10.251, and 5.819, respectively, and a mean decrease in Gini of 2.055, 1.817, and 1.614, respectively. Conclusion: The prevalence of malnutrition diagnosed with the GLIM criteria is higher than that of the SGA and the ESPEN 2015 criteria. The GLIM criteria are better than the SGA and the ESPEN 2015 criteria for predicting in-hospital complications in elderly patients.     

《中华人民共和国药典（2015年版）》中中药部分的增、修订情况简介

本文简要介绍《中华人民共和国药典（2015年版）》中中药部分的增、修订情况及技术特点。本版药典不仅中药收载品种数大幅增加,而且进一步加强了多药味（成分）的检测、活性成分的控制和杂质（有害物质）的检查,更注重现代分析技术的应用,包括广泛应用了指纹（特征）图谱技术,对提高中药的安全性及其质量整体控制水平具有重要作用。     

《中国药典》2015年版盐酸头孢他美酯干混悬剂有关物质检查方法的商榷

目的：通过对2个厂家8批盐酸头孢他美酯干混悬剂的检验和进一步研究,对该品种有关物质项检查方法进行优化改进。方法：依据《中国药典》2015年版标准进行检验,延长检测时间并考察2个厂家辅料的干扰情况。结果：盐酸头孢他美酯干混悬剂标准中有关物质检查项,需要增加检测时间至主峰保留时间的5倍,对于辅料峰的扣除必要时应采用辅料对照实验进行。结论：建议修订盐酸头孢他美酯干混悬剂标准的有关物质项目,以利于更好地控制该药品质量。     

中国药典2015年版及其临床用药须知收载治疗湿疹成方制剂的用药特点分析

目的 分析中国药典2015年版及《中华人民共和国药典临床用药须知》（以下简称《临床用药须知》）收载治疗湿疹成方制剂的用药特点,为临床治疗湿疹提供科学合理的用药参考。方法 收集中国药典2015年版及《临床用药须知》2015年版中治疗湿疹的成方制剂,并进行分类、频数统计。结果 中国药典及《临床用药须知》共收录治疗湿疹的外用及口服成方制剂分别为17和20种,其处方组成药物中分别含中药67和94味,外用和口服成方制剂主要为清热祛湿剂。结论 中国药典2015年版及《临床使用药须知》收载治疗湿疹外用及口服成方制剂以清热祛湿为主要治则。外用成方制剂中清热药、开窍药使用较多,口服成方制剂中清热药、补虚药使用较多。     

从药典视角谈构建中国药包材标准体系的建议

中国药典2015年版首次收载了药包材标准,中国药典2020年版又收载了16种药包材检测方法,自此中国药典的中药包材标准体系已初步搭建。本文从药典视角出发,结合中国药包材管理现状,谈下一步中国药包材标准体系发展方向,为中国药典中药包材标准体系的构建提供帮助。     

中国药典2020年版(一部)部分类别药材拉丁名修订建议

目的 按照经典药材分类方法对中国药典2020年版（一部）中相关药材拉丁名提出修订建议。方法 通过中国药典2020年版（一部）部分药用植物特征和药材性状描述,对确切入药部位进行分析确定,提出对其拉丁名按大类命名的建议。结果 共有5大类41种药用被子植物类药材的拉丁名存在与确切入药部位不符或不完全,拉丁词选用不当,1种涉及鲜用药材拉丁名未予体现等问题; 33种带地下部分的全草类及同时以2个入药部位的药材拉丁名命名在教学科研实践过程中常有混淆。结论 部分类别药材的拉丁名命名值得商榷,经过分析研究,提出修订建议,为进一步规范药材拉丁名提供参考。