Cognition in breast cancer survivors: A pilot study of interval and continuous exercise

Objectives

The current study investigated the effects of two exercise interventions on cognitive function amongst breast cancer survivors.

基于网络药理学的济脉通片降压机制研究

目的 采用网络药理学阐明济脉通片多成分-多靶点-多途径的作用理念，为进一步研究济脉通片降压药效物质基础和机制提供一定理论参考。方法 通过TCMSP数据库，结合口服利用度（≥ 30%）和类药性分析（≥ 0.18）参数，筛选济脉通片的活性成分；通过Drugbank和TCMSP数据库进行靶点预测分析；通过GENCARD数据库筛选出高血压疾病相关基因；结合DAVID和KEGG数据库进行GO分析和通路分析；使用Cystoscope软件构建"化合物-靶点-作用通路"网络图。结果 经筛选后得到济脉通片的33个化合物，148个潜在靶基因并映射到了223条信号通路，其中31条信号通路与高血压的发生发展密切相关，其中AGE-RAGE signaling pathway in diabetic complications、PI3K-Akt signaling pathway、TNFsignaling pathway、Adrenergic signaling in cardiomyocytes和Focal adhesion为重要的通路枢纽。结论 济脉通片主要通过多成分、多靶点、多通路调节血管内皮功能、炎症反应、钙钠离子转运、糖脂代谢等参与血管舒张、改善炎症、调节机体代谢、调节离子转运等而产生降压作用。     

The relationships among monocyte subsets,miRNAs and inflammatory cytokines in patients with acute myocardial infarction

Background

Acute myocardial infarction (AMI) causes irreversible myocardial damage and release of inflammatory mediators, including cytokines, chemokines and miRNAs. We aimed to investigate changes in the levels of cytokines (IL-6, TNF-α and IL-10), miRNAs profiles (miR-146 and miR-155) and distribution of different monocyte subsets (CD14++CD16-, CD14++CD16+, CD14+CD16++) in the acute and post-healing phases of AMI.

实时剪切波弹性成像在拉米夫定治疗乙肝纤维化过程中的疗效评估作用

目的探讨实时剪切波弹性成像在评估拉米夫定治疗乙肝纤维化临床疗效中的作用。方法选取2017年1月～2018年1月我院收治的80例乙肝纤维化患者,根据随机数字法分为对照组和观察组,每组40例。对照组采用常规治疗,观察组在对照组的基础上给予拉米夫定进行治疗,治疗6个月。比较两组治疗前后肝功能、肝纤维化指标,利用实时剪切波弹性成像评估两组治疗前后肝脏弹性模量值及肝穿刺活检情况,并进行比较。结果两组治疗后AST、ALT、TBIL水平均明显低于治疗前,差异有统计学意义(P0.05);观察组治疗后AST、ALT、TBIL水平均明显低于对照组,差异有统计学意义(P0.05);两组治疗后HA、LN、PⅢPN-P、CL-Ⅳ水平及杨氏弹性模量值均显著低于治疗前,差异有统计学意义(P0.05);观察组治疗后HA、LN、PⅢPN-P、CL-Ⅳ水平及杨氏弹性模量值均显著低于对照组,差异有统计学意义(P0.05);与治疗前相比,两组治疗后肝穿刺活检结果均有好转,尤其是汇管区、肝小叶内炎症及肝纤维化改善明显。结论实时剪切波弹性成像作为肝脏硬度定量检测的无创性技术,可用于拉米夫定治疗乙肝纤维化临床疗效评估的重要方法。     

Outcomes after occupational therapy intervention for traumatic brachial plexus injury: A prospective longitudinal cohort study

Study DesignProspective longitudinal cohort study.IntroductionTraumatic brachial plexus injuries (BPIs) can be devastating and negatively impact daily function and quality of life. Occupational therapists play an important role in rehabilitation; however, studies identifying outcomes are lacking.PurposeThis study aims to describe outcomes including motor recovery, upper limb function, participation, pain, and quality of life for people receiving occupational therapy intervention.MethodsA convenience sample of English-speaking adults (n = 30) with a traumatic BPI, attending the clinic between December 1, 2014, to November 30, 2016, participated. Participants received occupational therapy focusing on sensorimotor retraining and activity-based rehabilitation. Data on active range of motion (goniometry), strength (Medical Research Council (MRC)), upper-limb function (UEFI15, QuickDASH), participation (PSFS), pain (Brief Pain Inventory), and quality of life (EQ-5D-3L) were collected at baseline, 3, 6, 9, and 12 months.ResultsElbow flexion strength showed significant improvement at all time-points, average increase 2.17 (MRC) (95% confidence interval: 1.29-3.04; P < .001) and mean final MRC grading 3.86 (standard error: 0.44). Significant improvements at 12 months were seen in: shoulder abduction strength and range, flexion strength and range, external rotation range; elbow extension strength and flexion range; thumb flexion and extension strength. Upper limb function (QuickDASH) showed significant improvement (mean change = 18.85; 95% confidence interval: 4.12-33.59; P = .02). Forearm protonation range and finger flexion strength were significantly worse. Remaining outcomes did not show significant improvement.ConclusionsOccupational therapy with surgical intervention can improve strength, range, and upper limb function with people following traumatic BPI. Further investigations into impact on participation, pain, and quality of life are required.     

A Multi-Scale DNN Algorithm for Nonlinear Elliptic Equations with Multiple Scales

Algorithms based on deep neural networks (DNNs) have attracted increasing attention from the scientific computing community. DNN based algorithms are easy to implement, natural for nonlinear problems, and have shown great potential to overcome the curse of dimensionality. In this work, we utilize the multi-scale DNN-based algorithm (MscaleDNN) proposed by Liu, Cai and Xu (2020) to solve multi-scale elliptic problems with possible nonlinearity, for example, the p-Laplacian problem. We improve the MscaleDNN algorithm by a smooth and localized activation function. Several numerical examples of multi-scale elliptic problems with separable or non-separable scales in low-dimensional and high-dimensional Euclidean spaces are used to demonstrate the effectiveness and accuracy of the MscaleDNN numerical scheme.