幼儿不同跑步着地方式的生物力学特征

文题释义：跑：跑是双脚交替接触地面的周期性运动，但跑有一个双脚都离开地面的腾空期。幼儿在 1 岁多开始学习跑步，最初是走跑结合的移动方式，由于身体发育不完善，下肢力量弱，平衡能力差，容易摔倒；到 2.5岁，幼儿跑步的腾空阶段明显；到 6岁，早期跑步的特点基本消失。 着地方式：指的是人体在跑步着地阶段足部接触地面的方式，一般分为3种方式：分别为足跟着地(fore foot strike)，跟骨先接触地面；全足着地(mid foot strike)，全脚掌着地，即足跟与前足同时接触地面；前足着地(rear foot strike)：前足部首先接触地面。 背景：成年人跑步着地方式一直是国内外学者研究的重点，而幼儿跑步的着地方式也是不容忽视的内容。 目的：运用生物力学方法探究幼儿在跑步过程中，不同着地方式下的运动学和动力学指标的差异，为幼儿正确的跑步着地方式提供科学依据。 方法：在北京市海淀区某公立幼儿园中随机抽取幼儿74名，按年龄分为3岁组、4岁组、5岁组，采用BTS红外动作捕捉系统、Kistler三维测力台和VIXTA录像解析系统同步采集幼儿跑步过程中不同着地方式下的运动学、动力学数据；运用Anybody 5.2仿真建模软件计算下肢肌肉力量指标。试验前向受试者父母详细解释并签署知情同意书，试验方案符合北京师范大学的相关伦理要求。 结果与结论：①3岁组全足着地的比例最高，足跟着地的比例最低，5岁组全足着地的比例最低，足跟着地的比例最高；前足着地者的蹬伸时间大于足跟着地(P < 0.01)和全足着地(P < 0.05)；②着地时刻，踝屈曲角度足跟着地者大于前足着地(P < 0.01)和全足着地者(P < 0.05)，全足着地者大于前足着地(P < 0.05)；前足着地者髋内收-外展角度、最大髋内收-外展角、髋内-收外展的关节变化量及最大膝内收-外展角速度大于足跟着地(P < 0.01)和全足着地者(P < 0.05)；前足着地者的踝屈伸最小值大于足跟着地者(P < 0.05)，而最大髋内收-外展角速度小于足跟着地者(P < 0.05)；③足跟着地和全足着地者的腓骨短肌、腓骨长肌、第三腓骨肌的肌力大于前足着地者(P < 0.05)，前足着地者的股中间肌、股外侧肌下束、股外侧肌上束、股内侧肌下束、股内侧肌上束、股内侧肌中束肌力均大于足跟着地(P < 0.01)和全足着地者(P < 0.05)；④结果提示：在3-6岁阶段，幼儿多采用足跟或全足着地模式进行奔跑，以满足自己在跑步过程的稳定性，随着年龄的增长，逐渐出现前足着地方式的跑步模式；前足着地能够动用更多髋关节和膝关节额状面的运动来维持人体运动中的稳定，足跟着地和全足着地能够动用更多的小腿前侧和后侧的肌力，而前足着地动用更多的大腿前侧肌力。 ORCID: 0000-0002-8337-3931(赵盼超) 中国组织工程研究杂志出版内容重点：组织构建；骨细胞；软骨细胞；细胞培养；成纤维细胞；血管内皮细胞；骨质疏松；组织工程     

生物样品中天麻素测定方法及药代动力学研究进展＊

天麻素是从兰科植物天麻的块茎中提取分离而得，现已人工合成。天麻素药理作用广泛，如镇痛、镇静安眠、促智、保护神经元、降血压、抗惊厥、抗癫痫、抗氧化、抗衰老、改善微循环等，且安全、无明显毒副作用，其相关制剂在临床上亦被广泛用于心脑血管及神经精神系统疾病，如头痛、冠心病、高血压、后循环缺血性眩晕、突发性耳聋、癫痫、神经衰弱及脑卒中等。考虑到天麻素临床治疗上述疾病常与其他药物或治疗方法联用，故其体内过程及其药代动力学性质不容忽视。目前，有关天麻素综述多集中于药理作用和临床应用方面，而其药代动力学综述鲜见报道。随着天麻素药代动力学研究日渐深入，本研究将从其样品前处理、测定方法、药物ADME过程及影响体内过程的因素等方面较系统全面地对天麻素近年来的药代动力学研究作一综述，以期对天麻素制剂工艺及临床联合配伍应用有所裨益。     

40岁以上高龄女性体外受精-胚胎移植妊娠结局分析

目的：探讨40岁以上高龄女性体外受精-胚胎移植（IVF-ET）的妊娠结局，旨在为高龄女性提供生育咨询以及为改善高龄女性个体化辅助生殖治疗结局提供临床依据。方法：选择我院生殖中心2015年1月—2017年12月女方年龄≥40岁且使用自身卵子行体外受精的共2 467个治疗周期资料，对各项临床数据进行回顾性分析。结果：40岁及以上行辅助生殖治疗的患者，随着女性年龄增加获卵数明显减少（40～48岁女性平均获卵数分别为2.97、 2.69、2.17、2.01、1.77、1.61、1.68、1.29和1.00，44～48岁与40～43岁依次组间比较均P＜0.05），尤其是44岁以上女性胚胎发育潜能明显降低（40～48岁囊胚形成率分别为48.90%、43.72%、33.67%、34.29%、24.39%、21.14%、26.32%、16.67%和0%，44～48岁与40～43岁组间依次比较均P＜0.05）。共有518个周期行新鲜胚胎移植，结果显示，随女性年龄增加，临床妊娠率（40～48岁临床妊娠率分别为26.92%、21.15%、20.79%、10.96%、18.87%、11.11%、5.88%、0%和0%，43～48岁与40～42岁组间依次比较均P＜0.05）、种植率（40～48岁种植率分别为23.65%、19.51%、17.70%、8.54%、7.49%、10.81%、5.56%、0%和0%，43～48岁与40～42岁组间依次比较均P＜0.05）和活产率均显著降低（40～46岁活产率分别为18.46%、10.58%、9.90%、5.48%、5.66%、2.78%和5.88%，43～46岁与40～42岁组间依次比较均P＜0.05），43岁以上者结局更差。44岁以上女性自然流产率明显增高（40～45岁流产率分别为31.43%、50.00%、52.38%、50.00%、70.00%和75.00%，44～45岁与40～43岁组间依次比较均P＜0.05）。46岁女性仅1例妊娠并分娩，47岁和48岁女性均无成功妊娠。与抗苗勒管激素（AMH）＞1.0 ng/mL组相比，AMH≤1.0 ng/mL组妊娠率、种植率及活产率均显著下降（27.04% vs. 14.74%，22.99% vs. 13.50%，15.88% vs. 7.37%；均P＜0.05），流产率明显升高（41.27% vs. 50.00%，P＜0.05）。结论：≥40岁高龄女性随年龄增长生育力逐渐降低。40～43岁年龄段女性助孕仍有一定的价值，尤其是卵巢仍有一定储备者（AMH＞1.0 ng/mL），但44岁以上女性原则上不再建议ART助孕，对于46岁以上卵巢功能衰竭的女性强烈建议卵子捐赠或收养。     

沙库巴曲缬沙坦在心力衰竭治疗中的应用

心力衰竭是一种慢性疾病，是心脏无法泵出足够的血液为其他身体器官提供氧气，从而引发呼吸短促、疲劳和水肿等症状。目前其治疗包括病因治疗、一般治疗、药物治疗及非药物治疗等，其中血管紧张素受体脑啡肽酶抑制剂（ARNI）是当前新研发出的一类抗心衰药物，沙库巴曲缬沙坦是其代表药物，具有阻断肾素-血管紧张-醛固酮系统（RAAS）及抑制脑啡肽酶（NEP）的双重作用，新的欧美指南均推荐用于射血分数降低心力衰竭（HFrEF）的治疗。本文现就沙库巴曲缬沙坦在心力衰竭治疗中的作用机理、临床试验、药物安全性作一综述，旨在为该病的临床治疗提供参考。     

Role of macrophages in peripheral nerve injury and repair

Resident and inflammatory macrophages are essential effectors of the innate immune system. These cells provide innate immune defenses and regulate tissue and organ homeostasis. In addition to their roles in diseases such as cancer, obesity and osteoarthritis, they play vital roles in tissue repair and disease rehabilitation. Macrophages and other inflammatory cells are recruited to tissue injury sites where they promote changes in the microenvironment. Among the inflammatory cell types, only macrophages have both pro-inflammatory(M1) and anti-inflammatory(M2) actions, and M2 macrophages have four subtypes. The co-action of M1 and M2 subtypes can create a favorable microenvironment, releasing cytokines for damaged tissue repair. In this review, we discuss the activation of macrophages and their roles in severe peripheral nerve injury. We also describe the therapeutic potential of macrophages in nerve tissue engineering treatment and highlight approaches for enhancing M2 cell-mediated nerve repair and regeneration.     

偏瘫性肩痛患者腋囊的MRI观测及临床意义

目的通过MRI技术观察偏瘫患者肩疼痛腋囊形态变化,为影像诊断提供依据。方法对47例偏瘫性肩疼痛患者(疼痛组)和47例正常肩部(对照组),运用MRI技术对肩关节腋囊进行测量,观察腋囊下肱盂韧带信号。结果偏瘫性肩疼痛的腋囊厚度(4.1±1.45)mm,高于对照组。腋囊腔高度(8.70±1.80)mm,腋囊腔宽度(4.31±0.56)mm,小于对照组。偏瘫性肩痛下肱盂韧带呈高信号出现率达21%,腋囊厚度与VAS呈正相关,与肩关节外展外旋呈负相关。结论脑中风中后期肩周炎是引起偏瘫性肩疼痛患者的常见原因,腋囊厚度是限制肩关节活动度的重要因素。     

Comparison of the practicability of ultrasound and spiral computed tomography in the diagnosis of colon cancer: A protocol for systematic review and meta-analysis

Background:Colon cancer is a common malignant tumor of the gastrointestinal tract. Therefore, a clear diagnosis is particularly important for the treatment of colon cancer. Ultrasound and spiral computed tomography (CT) can both be used in the diagnosis, but each has its own advantages and disadvantages, which could cause confusion in clinical choice. The purpose of this study was to systematically evaluate the practicability of spiral CT and ultrasound in the diagnosis of colon cancer.Methods:A systematic search was performed by retrieving on English databases (PubMed, Embase, Web of Science, the Cochrane Library) and Chinese databases (CNKI, Wanfang, Weipu [VIP], CBM). Besides, manually search for Google and Baidu academic of diagnostic experimental study of ultrasound and spiral CT in the diagnosis of Colon Cancer. The retrieval time limit was from the establishment of the database to October 2020. Two researchers independently extracted and evaluated the quality of the data in the included study. A meta-analysis was performed using Meta Disc1.4 and RevMan5.3 software.Results:Sensitivity, specificity, positive Likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were used to determine the diagnostic efficacy of ultrasonography and helical CT in colorectal cancer.Conclusions:This study will compare the practicability of CT and ultrasound in the diagnosis of colon cancer and provide reliable evidence-based basis for clinicians to choose the appropriate or best evidence-based basis.Ethics and dissemination:The private information from individuals will not be published. This systematic review also will not involve endangering participant rights. Ethical approval is not required. The results may be published in a peer-reviewed journal or disseminated in relevant conferences.OSF Registration number:DOI 10.17605/OSF.IO/WAJHQ     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.