注射用盐酸柔红霉素的质量评价

目的 按照国家计划抽验要求，评价国内不同企业生产的注射用盐酸柔红霉素的质量。方法 按国家标准检验与探索性研究相结合，对抽验样品进行检验，对检验结果进行统计分析。结果 共抽取样品17批次，按国家标准检验合格率100.0%。探索性研究对主要杂质的来源与结构进行了研究；建立溶液的澄清度检查方法；对包材相容性及稳定性进行了考察。结论 目前国内注射用盐酸柔红霉素总体质量较好；现行标准有待进一步提高，建议现行标准修订有关物质检查方法，增加特定杂质的控制，增加溶液的澄清度检查；建议企业优化生产工艺，以提高产品质量。     

中药生产过程质量控制关键技术研究进展

中医药发展已上升到国家战略层面,在医药行业贯彻实施"中国制造2025"战略的新形势下,中药生产过程质量控制是中药工业需要加快突破的关键领域之一。对中药生产过程质量控制领域在工艺设计、分析检测、过程建模、制造装备等方面的关键共性问题进行解析,综述了中药生产过程质量控制体系中工艺过程理解、生产过程实时分析方法开发、过程控制策略建立3个方面的研究进展;并结合企业研究实践,介绍了质量源于设计(quality by design,Qb D)、过程分析技术(process analytical technology,PAT)、实验设计(design of experiment,DOE)、多变量统计分析等关键技术在上述3个研究方向中的应用进展,分析了实际工业应用的难点问题并对其应用前景进行展望,旨在为中药企业应用和提升生产过程质量控制技术提供参考。     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

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.