基于网络药理学和分子对接技术探讨黄芪干预腹膜纤维化的机制

目的 运用网络药理学方法及分子对接技术探讨黄芪干预腹膜纤维化的可能机制。方法 利用中药系统药理学数据库及分析平台(TCMSP)检索黄芪的主要化学成分及靶点,并补充文献报道相关药理作用的成分作为潜在活性成分。以"peritoneal fibrosis"为关键词分别在OMIM、Genecards获取目前已知的与腹膜纤维化相关的疾病靶点,后取两者的交集靶点;对交集基因通过STRING数据库与Cytoscape 3.7.2软件构建"药物-成分-靶点-疾病"网络及蛋白互作(PPI)网络并筛选核心网络。基于R软件使用Bioconductor生物信息软件对核心靶点进行GO及KEGG富集分析,最终采用AutoDock软件将主要有效成分与核心靶点进行分子对接,得出其结合能力。结果 筛选出20个黄芪活性成分及文献报道有相关药理作用4个, 457药物作用靶点,与674个腹膜纤维化病靶点取交集,得到86个共同靶点。GO功能富集分析提示黄芪拮抗腹膜纤维化主要参与了蛋白激酶B信号转导的调节、细胞对化学的应激反应、炎症反应的调节等通路; KEGG通路富集分析主要涉及调控肿瘤、磷脂酰肌醇-3-羟激酶-蛋白激酶B(PI3K-Akt)、晚期糖基化终末产物/晚期糖基化终末产物受体(AGE-RAGE)、人类巨细胞病毒感染、HIF-1信号通路等;分子对接结果显示关键靶点与活性成分具有较好的结合能力。结论 黄芪治疗腹膜纤维化的分子机制,可能与抑制炎症及氧化应激反应、调节多种信号通路等相关。     

The National Home-Based Primary Care Learning Network: A Practice-Based Quality Improvement and Research Network

Home-based primary care (HBPC) provides interdisciplinary, comprehensive care at home for homebound older adults and has been largely excluded from the national conversation on care quality and quality improvement. In this Pragmatic Innovations article, we describe the work of the National HBPC Learning Network (LN), which focuses on fostering a continuous learning culture among HBPC practices to improve practice quality, elevate the field of HBPC, and create a community of continuous growth and quality of care accountability. The LN recruits HBPC practices in waves of 9 to 10 practices. It currently comprises 38 HBPC practices that care for 58,000 patients across 25 states (approximately 26% of all patients receiving HBPC in the United States). In a Kickoff meeting, the HBPC practices in each wave are instructed in the basics of quality improvement, develop project aim statements and their first plan-do-study-act cycle, receive an introduction to the LN quality improvement software platform, and review plans for LN engagement. Each month, practices submit updates and receive real-time feedback on their quality improvement work. Monthly virtual workshops are held with all practices that include sharing results of a “1-minute survey” (a monthly 1-to 3-question survey sent to all LN participants on a topic relevant to HBPC practices), a didactic and discussion related to the 1-minute survey topic, and interactive progress updates from LN participants regarding their quality improvement work. Each wave ends with “Moving-up Day,” where practices report on their overall project and reflect on how their practice has changed as a result of the LN. LN practices have addressed and improved performance in multiple HBPC-related quality areas including assessment of functional status and cognitive impairment, falls prevention, advanced care planning, COVID-19 vaccination, and others. We present case studies of 3 LN practices and how LN participation strengthened their practices.     

U-Net神经网络分割锥形束CT影像中下颌磨牙牙体与牙髓腔及其准确性验证

目的: 使用U-net卷积神经网络实现锥形束CT（cone-beam CT, CBCT）影像中下颌磨牙的牙体和牙髓腔的自动分割,采用基于显微CT（Micro-CT）扫描结果构建的三维模型作为金标准,评估分割准确性。方法: 从同济大学附属口腔医院放射科收集20组包含完整单侧下颌磨牙的口腔小视野CBCT数据,预处理后,由牙体牙髓病学专家使用MITK Workbench软件手动标注牙体与牙髓腔,作为U-net神经网络分割算法的训练集。另收集5颗下颌磨牙和相应的小视野CBCT数据,5组数据经相同预处理后作为测试集。随后由完成训练的神经网络和同一专家对测试集数据进行牙体和牙髓腔分割和三维重建。离体牙预处理后行Micro-CT扫描,将三维重建后获得的模型作为金标准。分别比较测试集数据中,专家的手动标注、神经网络分割结果与金标准两两之间的差异。采用Dice相似性系数（Dice similarity coefficient, DSC）、平均对称表面距离（average symmetric surface distance, ASSD）、Hausdorff距离（Hausdorff distance, HD）和形态差异分析对结果进行评估。采用SPSS 20.0软件包对数据进行统计学分析。结果: 神经网络分割结果与金标准相比,其牙体组的DSC为(95.30±1.01)%、ASSD为(0.11±0.02) mm、HD为(1.05±0.31) mm,牙髓腔组的DSC为(81.21±2.27)%、ASSD为(0.15±0.05) mm、HD为(3.29±1.85) mm,结合形态差异分析结果显示,神经网络的分割结果与金标准的牙体与髓室部分基本相似,但在根管部分,能分割出较粗的根管,对于根管下段和侧支根管等较细的根管分割能力有限。结论: 在现有实验条件下,以专家手动标注作为训练样本的U-net神经网络,实现了在CBCT影像上对下颌磨牙牙体与髓室的自动化精准分割。但对根管部分,其分割结果有待进一步提升。     

Method’s corner: Allergist’s guide to network meta-analysis

Network meta-analyses (NMAs) simultaneously estimate the effects of multiple possible treatment options for a given clinical presentation. For allergists to benefit optimally from NMAs, they must understand the process and be able to interpret the results. Through a worked example published in Pediatric Allergy and Immunology, we summarize how to identify credible NMAs and interpret them with a focus on recent innovations in the GRADE approach (Grading of Recommendations Assessment, Development, and Evaluation). NMAs build on traditional systematic reviews and meta-analyses that consider only direct paired comparisons by including indirect evidence, thus allowing the simultaneous assessment of the relative effect of all pairs of competing alternatives. Our framework informs clinicians of how to identify credible NMAs and address the certainty of the evidence. Trustworthy NMAs fill a critical gap in providing key inferences using direct and indirect evidence to inform clinical decision making when faced with more than two competing courses of treatment options. This document will help allergists to identify trustworthy NMAs to enhance patient care.     

Assessment of a novel deep learning-based marker-less motion capture system for gait study

BackgroundMarker-less systems based on digital video cameras and deep learning for gait analysis could have a deep impact in clinical routine. A recently developed system has shown promising results in terms of joint center position but has not been yet evaluated in terms of gait outcomes.Research questionHow does this novel marker-less system compare to a marker-based reference system in terms of clinically relevant gait parameters?MethodsThe deep learning method behind the developed marker-less system was trained on a dedicated dataset consisting of forty-one asymptomatic and pathological subjects each performing ten walking trials. The system could estimate the three-dimensional position of seventeen joint centers or keypoints (e.g., neck, shoulders, hip, knee, and ankles). We evaluated the marker-less system against a marker-based system in terms of differences in joint position (Euclidean distance), detection of gait events (e.g., heel strike and toe-off), spatiotemporal parameters (e.g., step length, time), kinematic parameters (e.g., hip and knee extension-flexion), and inter-trial reliability for kinematic parameters.ResultsThe marker-less system was able to estimate the three-dimensional position of joint centers with a mean difference of 13.1 mm (SD = 10.2 mm). 99% of the estimated gait events were estimated within 10 ms of the corresponding reference values. Estimated spatiotemporal parameters showed zero bias. The mean and standard deviation of the differences of the estimated kinematic parameters varied by parameter (for example, the mean and standard deviation for knee extension flexion angle were −3.0° and 2.7°). Inter-trial reliability of the measured parameters was similar to that of the marker-based references.SignificanceThe developed marker-less system can measure the spatiotemporal parameters within the range of the minimum detectable changes obtained using the marker-based reference system. Moreover, except for hip extension flexion, the system showed promising results in terms of several kinematic parameters.     

国内健康信息研究作者合著网络及研究主题分析

目的分析核心作者、作者合著网络特征,推测国内健康信息研究的发展阶段、主要研究力量及主要研究主题。方法以CNKI期刊库为数据源,以"健康信息"及相关词为主题词,检索2000—2018年的相关文献,基于普莱斯定律及综合指数法确定核心作者,使用数据统计软件Excel及社会网络分析软件Vcinet,利用综合指数以及社会网络分析法计算作者间联系合作紧密度,并离析高产合作子网。结果文献量以较快指数型速率增长。作者合作率较高且信息传输渠道较通畅,2000—2018年国内研究的合著率稳定在80%,但学者的合作关系较为单一,多集中在同一机构或同一师门,阻碍了跨机构学者的交流合作。主要合作团队活跃度较高且核心成员多为研究领域的核心作者,科研产出量较大且被引频次较高。结论国内研究正处于旺盛发展期,但未形成稳定的核心作者群体。学者间因同机构或师门关系产生固定合作,健康信息管理、公众健康行为、健康教育等为核心作者或活跃团队研究的主要主题。     

Role of ambulatory blood pressure monitoring in hypertensive patients having controlled office blood pressure

Background and objectivesAmbulatory blood pressure (BP) monitoring has become useful in the diagnosis and management of hypertensive individuals. In this study we tried to know the role of office and ambulatory BP in treated hypertensive patients.Methods and patientsProspective cohort of 561 treated hypertensive patients were enrolled in the study. Hypertension definitions were according to JNC 8 classification. Office BP and ambulatory BP monitoring was done according to defined protocol.ResultsFrom a subgroup of 158 treated hypertensive patients, 91(16.2%) patients were having white coat hypertension (p value 0.00 by Pearson chi square test). In a subset of 403 patients who were having controlled BP on the day of enrolment as well as on the day of attaching ambulatory BP monitor; 98 (17.4%) patients were having masked uncontrolled hypertension (MUCH). In addition there was very significant percentage of non-dippers and reverse dippers. In our study we found that office BP has a moderate to low specificity and sensitivity and low negative predictive value for overall control in treated hypertensive patients.ConclusionAmbulatory BP monitoring should be included in the management protocol of treated hypertensive patients, for the optimal BP control.     

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.