物理治疗翻转课堂教学应用效果的系统综述

目的 系统评价翻转课堂教学模式在物理治疗学教学中的应用效果。方法 计算机检索CENTRAL、MEDLINE、EMBASE、CINAHL Plus、Academic Search Premier、Teacher Reference Center、ERIC以及Education Research,纳入翻转课堂教学模式应用于物理治疗学教学的原始研究。检索时限为建库至2021年6月。由2名研究人员独立完成文献筛选、数据提取、质量评价,对翻转课堂教学模式对比传统教学模式在物理治疗学教学中的效果进行综述。结果与结论 共检索文献1 307篇,最终纳入7篇,包括至少770名学生。发表时间集中在2013年至2019年,研究对象为物理治疗学专业学生,主要结局指标为考试成绩。翻转课堂教学模式总体说来可提高学生的笔试成绩,增强高阶思维能力,得到了学生和教师的积极评价。     

PSMA-CAR-CIK转基因细胞体外杀伤三种前列腺癌细胞株效率的研究北大核心

目的:本研究通过检测前列腺特异性膜抗原(prostate specific membrane antigen,PSMA)-肿瘤特异性嵌合抗原受体(chimeric antigen receptor,CAR)-细胞因子诱导的杀伤细胞(cytokine induced killer cell,CIK)转基因细胞杀伤三种前列腺癌细胞株的效率,鉴定其用于后期治疗前列腺癌的可行性。方法:通过已经构建的二代PSMA-CAR慢病毒表达载体转染CIK细胞,构建PSMA-CAR-CIK转基因细胞;通过CCK8法检测CIK细胞及PSMA-CAR-CIK转基因细胞杀伤三种前列腺癌细胞株PC3、LNCaP、DU145的效率及效靶比。结果:转基因PSMA-CAR-CIK细胞构建成功;3 h~3.5 h为检测效应细胞对靶细胞细胞毒活性时与CCK8试剂孵育的最佳时间;两种效应细胞均在效靶比为10∶1、15∶1、20∶1时,杀伤率逐步增高,无统计学意义(P>0.05),但与其它组相比差异显著(P<0.05)。结论:两种效应细胞对于前列腺癌细胞的杀伤,只要效靶比达到10∶1的比例,就能起到一个较好的杀瘤效果,并且随着效应细胞的增加,其抗肿瘤能力逐步加强;转基因PSMA-CAR-CIK细胞的构建为CAR技术治疗前列腺癌的临床应用奠定了基础。     

Effectiveness of a double-carbapenem combinations against carbapenem-resistant Gram-negative bacteria

The emergence of carbapenem-resistant organisms posed considerable threat to global health while only limited treatment options are available and led to efforts to discover a novel way to treat them. To evaluate in vitro synergistic activity of meropenem plus ertapenem, a total of 203 carbapenem-resistant strains, collected from 12 provinces and municipalities in China, were examined with a dual carbapenem combination therapy. The statistical software R was used for analysis. Two hundred and one (201) of carbapenem-resistant strains mainly produced four types of carbapenemase: KPC-2 (n = 142, 69.95%), OXA-232 (n = 7, 3.45%), NDM (n = 38, 18.72%; 36 NDM-1, 1 NDM-4, 1 NDM-5), and IMP (n = 15, 7.39%; 1 IMP-26, 10 IMP-30, 4 IMP-4). Fifty-one out of two hundred and three (51/203 or 25.12%) of the examined strains showed a synergistic effect for the meropenem plus ertapenem combination throughout the checkerboard method, while only three isolates showed potential clinically relevant synergy (3/203, 1.48%). An additive effect was observed in 55/203 (27.09%) of the examined strains. Ninety-seven of the examined isolates (47.78%) showed fractional inhibitory concentration (FIC) greater or equal to 2 (indicating antagonism). The synergistic activity of meropenem plus ertapenem combination suggests this combination can be a possible way to treat the infection caused by the carbapenem-resistant organisms, especially for IMP or NDM producer with a lesser minimum inhibitory concentration (MIC) and the infected individual who was not recommended to use colistin or tigecycline.     

人参内生菌的研究进展

内生菌由细菌、真菌、古菌和原生生物组成，它们生活在植物的活体组织中，具有丰富的次级代谢产物多样性。人参内生菌在人参的生长发育、次级代谢产物的生成和环境适应等方面均有重要的促进作用，对人参的产量和品质有较大影响。随着人们在微生物领域研究的深入，高通量测序技术已经成为研究植物内生菌的重要方法。文章主要从人参内生菌分离与鉴定研究方法、人参内生菌的多样性、人参内生菌及其次级代谢产物的活性、人参内生菌对宿主的影响等4个方面对人参内生菌近年来的研究进展进行讨论，并对其发展方向提出展望，以期为药用植物内生菌研究和品质改良提供新思路、新方法。     

PCI术后病人的体力活动水平及其影响因素分析研究

目的:探讨经皮冠状动脉介入(PCI)术后病人的体力活动水平及其影响因素。方法:选择常州市三级甲等综合医院心内科接受PCI治疗的冠心病病人,收集资料,分析PCI术后冠心病病人体力活动动机、体力活动和自我效能现状;冠心病病人自我效能、体力活动动机与体力活动的相关性,并分析影响PCI术后病人体力活动水平的相关危险因素。结果:共发放调查问卷400份,回收400份,有效率为94.75%(379/400);379例冠心病病人术后每周体力活动为(3570.85±314.19)MET-min,以低、中强度为主;冠心病病人术后自我效能总分、体力活动动机总分与体力活动呈正相关(P<0.05);体质指数(BMI)、PCI次数≥2次、年龄、PCI支架数量≥3枚、自我效能、体力活动动机是影响PCI术后冠心病病人体力活动水平的高危因素(OR>1且P<0.05)。结论:PCI术后病人体力活动水平处于较低的水平,PCI次数、BMI、年龄、PCI支架数量、自我效能、体力活动动机是影响PCI术后冠心病病人体力活动水平的高危因素,临床需针对各高危因素制定相应的干预措施,提高病人体力活动水平。     

Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms

PurposeMachine-learning (ML) approaches have been repeatedly coupled with raw accelerometry to classify physical activity classes, but the features required to optimize their predictive performance are still unknown. Our aim was to identify appropriate combination of feature subsets and prediction algorithms for activity class prediction from hip-based raw acceleration data.MethodsThe hip-based raw acceleration data collected from 27 participants was split into training (70 %) and validation (30 %) subsets. A total of 206 time- (TD) and frequencydomain (FD) features were extracted from 6-second non-overlapping windows of the signal. Feature selection was done using seven filter-based, two wrapper-based, and one embedded algorithm, and classification was performed with artificial neural network (ANN), support vector machine (SVM), and random forest (RF). For every combination between the feature selection method and the classifiers, the most appropriate feature subsets were found and used for model training within the training set. These models were then validated with the left-out validation set.ResultsThe appropriate number of features for the ANN, SVM, and RF ranged from 20 to 45. Overall, the accuracy of all the three classifiers was higher when trained with feature subsets generated using filter-based methods compared with when they were trained with wrapper-based methods (range: 78.1 %–88 % vs. 66 %–83.5 %). TD features that reflect how signals vary around the mean, how they differ with one another, and how much and how often they change were more frequently selected via the feature selection methods.ConclusionsA subset of TD features from raw accelerometry could be sufficient for ML-based activity classification if properly selected from different axes.