中药治疗高血压的研究进展

高血压是当今社会最常见的心血管疾病,研究表明高血压与冠心病、脑卒中、心力衰竭和肾功能障碍密切相关,是导致多种心血管疾病的主要危险因素之一。笔者通过对高血压病的发病机制进行分析,总结了常见的降压中药及其作用机制。     

基于随机森林算法的心血管疾病预测研究

血管疾病严重威胁着人类的健康,高发病率、高致残率、高死亡率是心血管疾病的主要特点,因此心血管疾病的预测研究显得尤为重要.本文探讨了随机森林算法在心血管疾病预测中的应用效果.在Kaggle网站上下载关于心血管疾病的数据集,用随机森林算法进行训练,实验结果由准确性、精度、召回率、F1-score评价标准来评价其性能的好坏(评价就包括好坏).本文将其与逻辑回归(Logistic Regression)、K近邻分类器(K-nearest neighbor classifier)、支持向量机(SVM)进行了比较,实验结果表明,随机森林算法的性能优于其他算法,其准确率为73.55,精度为75.51,召回率为70.11,F1-Score为72.71.通过基尼重要性评价能从多因素中识别出影响心血管疾病的重要因素,这意味着随机森林算法在心血管疾病预测中具有较大的优势,从而对心血管疾病的预测研究和早期病人的及时有效治疗具有重要意义.     

浅谈环境地质质量重点实验室的管理模式

环境地质的实验室在地质的监测过程中发挥着重要的作用,它能够以科学的研究技术来深入的对环境地质进行研究和调查。实验室的质量管理则与地质监测中数据的科学与否有着直接的关系,下面就地质实验室的管理模式展开详细的探讨。     

高纯水技术发展的新趋势

随着科学技术的不断发展,高纯水在现代科学、现代工业、尤其是在电子工业生产中的重要作用,正在日趋突出、日益显著。它不仅与电子产品的质量及成品率有着直接的关系,而且对     

小鼠胚胎整合素β1受体表达的免疫电镜观察

生殖机理的研究是人类长期关注的重要课题，受精、胚胎着床、生长发育是多因数、多步骤的复杂过程，其分子机理尚不完全清楚。目前认为与激素、细胞免疫、细胞因子、整合素等综合因数有关。目前整合素在细胞黏附、受精、着床、发育生殖过程中的重要作用已经逐步得到人们的肯定。研究整合素表达与胚胎细胞表达的关系，对受孕机理的研究具有重要的临床意义。     

35kV以下输电线路工程安全危险辨识与管理方法探讨

随着我国经济的不断发展，在输电线方面也得到了长足的进步，输电线路工程，是目前我国较为重要的基础设施建设，是需要不断的进行改进的，但是在改进的过程中，还是会存在着一些较为突出的问题，所以，对于输电线工程安全的危险辨识和管理是目前重要的现实问题，它能否顺利的进行也是和国家的经济发展，社会稳定有着密切的联系。本文就针对35KV以下的变电站进行研究，对其安全危险的辨识和管理有着紧密关注。     

生产测井地面系统测试软件开发

石油在现代社会发展中占据着重要的作用,是一种与人们日常生活和工业生产都有着很大关系的一种不可再生能源,如何对其加以充分的开发和利用,已经成为了现代科学技术积极研究的一个课题。在生产测井作业当中,其地面系统的作用是非常重要的,其测试软件的应用大大提升了生产作业的效率。本文首先对生产测井地面系统测试软件的功能和特征进行了简要的论述,继而对它的开发进行了具体的探讨。     

免疫细胞因子网络模型设计与仿真研究

针对免疫细胞因子网络,提出CKSIM模型,并利用Net Logo对其进行可视化仿真。该模型主要研究的是免疫细胞、抗原、抗体、细胞因子之间的相互作用关系,并且给出了仿真的具体步骤和仿真结果。研究表明,计算机仿真比传统的手工实验具有可视化程度高、容易控制、参数易调节等优点,利用计算机仿真可以研究整个的细胞因子对免疫细胞共同作用所涌现出来的规律。今后的研究可以以此模型为基础,不断进行改进,以促进对免疫细胞因子网络的研究。     

歼(强)击机飞行员心血管危险因素调查

目的探讨我军歼（强）击机飞行员心血管疾病危险因素的流行病学特征。方法随机选取2008年度住院体检的277名飞行员临床资料,按年龄分为Ⅰ组〈30岁,Ⅱ组≥30岁〈40岁,Ⅲ组≥40岁,比较各组血压、血脂、血尿酸、身体质量指数（BMI）指标。结果Ⅲ组收缩压（SBP）及舒张压（DBP）、低密度脂蛋白（LDL）水平、BMI、高血压比例、正常血压比例、高甘油三酯血症患病率与Ⅰ、Ⅱ组比较有统计学差异（P〈0．05）;Ⅲ组高密度脂蛋白（HDL）水平低于Ⅰ组,血胆固醇（CH）高于Ⅱ组（P〈0．05）。结论各年龄段飞行员均存在心血管疾病危险因素,应加强健康教育与监督,进行心血管疾病的防控。     

基于二次谐波成像定量评估主动脉增龄性改变

衰老是引起主动脉硬化进而引发各种心血管疾病的主要独立风险因素。对主动脉增龄性改变进行定量评估有望为心血管疾病研究提供重要线索。采用二次谐波成像技术，结合三维灰度共生矩阵纹理分析算法，对不同周龄大鼠主动脉血管壁内外表面的胶原纤维进行了定量评估；提取出多种可量化表征主动脉增龄性改变的特征参数，从胶原纤维微结构角度揭示了主动脉增龄性变化规律。上述方法及提取出的特征参数有望为评估血管老化程度提供有力工具和重要参考指标，并进一步应用于与老化相关的心血管疾病的研究。     

基于双模算法的脉搏波速度测量方法研究

人体脉搏波速度(PWV)被认为是体现心脑血管健康和血管壁弹性变化的重要因素之一。医学上对脉搏波速的研究也变得越来越热,许多诸如糖尿病、高血压、冠心病、动脉硬化等疾病也都与之有密切关系。因此,对脉搏波速度(PWV)的检测具有重要的、特殊的意义。该文主要从信号提取和信号分析方面入手研究脉搏波,利用标准时钟信号插入所提取的肱动脉与桡动脉信号之间以及多点脉搏波信号相位差之间取平均值的双模算法,从而更精确地计算出PWV,并且其求解的PWV标准差为0.06~0.12。该文所使用的双模算法在实时性、测量精度和稳定性方面,优于传统的PWV测量方法,可应用于脉搏波相关的医学研究和实验中。     

Machine learning techniques as a helpful tool toward determination of plaque vulnerability

Atherosclerotic cardiovascular disease results in millions of sudden deaths annually, and coronary artery disease accounts for the majority of this toll. Plaque rupture plays main role in the majority of acute coronary syndromes. Rupture has been usually associated with stress concentrations, which are determined mainly by tissue properties and plaque geometry. The aim of this study is develop a tool, using machine learning techniques to assist the clinical professionals on decisions of the vulnerability of the atheroma plaque. In practice, the main drawbacks of 3-D finite element analysis to predict the vulnerability risk are the huge main memories required and the long computation times. Therefore, it is essential to use these methods which are faster and more efficient. This paper discusses two potential applications of computational technologies, artificial neural networks and support vector machines, used to assess the role of maximum principal stress in a coronary vessel with atheroma plaque as a function of the main geometrical features in order to quantify the vulnerability risk.     

An intelligent system for prognosis of noncommunicable diseases’ risk factors

Noncommunicable diseases are the main reason to the rise of diseases incidence in the developed world. The management and prevention of these diseases can be done by controlling the behavioral and biological risk factors which are related to them. ChronicPrediction is an intelligent system for noncommunicable diseases care which determines in real time the impact on risk factors due to actions taken by users. Based on impact information, the system presents on users’ smartphones strategic messages to help in their treatment. ChronicPrediction applies Bayesian Networks (BNs) which use risk factors for mapping the causes of noncommunicable diseases worsening. The support to multiple chronic diseases and the integrated use of multiple BNs based on risk factors are the main contributions of this work and differentiate the proposed system from related work. We have built a functional prototype that allowed us to conduct two experiments. The first one successfully tested the main functionalities provided by ChronicPrediction to support BNs based on risk factors and the sending of messages to users’ smartphones. The evaluation involved the building of a BN for predicting coronary artery disease made with real world data obtained in a prospective cohort study. The study involved 302 patients from a hospital localized in southern Brazil. The second experiment assessed the ChronicPrediction support to multiple BNs at same time. The test involved the previous BN and another from a thirty part research work to map risk factors of diabetes. The results were encouraging and show potential for implementing ChronicPrediction in real-life situations.     

冠心病患者焦虑抑郁现状及影响因素调查

目的调查冠心病患者伴发焦虑、抑郁负性情绪的患病率及影响因素。方法随机抽样冠心病患者226例和体检健康者220例,采用自制一般情况调查表、焦虑自评量表(SAS)、抑郁自评量表(SDS)、症状自评量表(SCL)进行问卷调查,并进行统计学分析。结果冠心病患者焦虑发生率为71.68%,抑郁发生率为62.83%,两者兼有发生率为29.20%,体检健康者焦虑发生率为4.09%,抑郁发生率为6.82%,两者兼有发生率为2.73%,两者比较有显著差异(p0.05);离婚或丧偶、既往有其他疾病、过度积极应对冠心病、家庭朋友对自己关注度差、经济收入较差等因素与焦虑抑郁呈正相关。结论冠心病患者焦虑、抑郁患病率较高,离婚或丧偶、既往有其他疾病、过度积极应对、家庭朋友对自己关注度差、经济收入较差等因素是引起焦虑抑郁情绪的主要因素,应引起临床医护人员高度重视与关怀。     

Background millimeter radiation influence in cardiology on patients with metabolic and pre-metabolic syndrome

The effects of background millimeter radiations (BMR) in patients with coronary artery disease (CAD), hypertension and in subjects with Inherited real risk of CAD, were investigated through invariant statistic measures, typical of nonlinear dynamics analysis of biological systems. The experimental evidences show that BMR ameliorate the nonlinear complexity in biosystems, recognized sign of physiological behavior, by increasing both the rate of unpredictability of heart rate variability (HRV) in patients with metabolic syndrome and the fractal dimension of coronary microvessel oscillations in subjects with pre-metabolic syndrome, healing their genetic alteration and CAD Inherited real risk.     

太赫兹技术的液体中同型半胱氨酸的定量识别研究

同型半胱氨酸是一种含硫氨基酸,为蛋氨酸和半胱氨酸代谢过程中产生的重要中间产物,它在人体血液或尿液中浓度含量过高时会大幅增加心血管疾病、老年痴呆、骨折等疾病的发病风险。基于太赫兹时域光谱系统,针对液体状态下同型半胱氨酸的检测提出了浓缩富集再烘干的处理方法,有效测量了不同浓度下同型半胱氨酸的太赫兹吸收光谱。经过与拉曼光谱方法的结果比对,证明太赫兹检测的精度比拉曼光谱检测的精度提升了3倍。该结果对临床医学中同型半胱氨酸相关疾病的准确、快速诊断具有重要意义。     

Closed-loop drug infusion for control of heart-rate trajectory inpharmacological stress tests

The diagnosis of coronary artery disease (CAD) is an important task in the management of cardiology patients. Recently, the use of pharmacological stress testing has become available as an alternative to exercise stress testing (ETT). A new system (device-drug combination) was developed specifically for the diagnosis of coronary artery disease. The system uses a novel catecholamine, arbutamine, which is infused intravenously to increase heart rate (HR) and cardiac contractility in order to evoke signs of ischemia. The development of a closed-loop control algorithm for the delivery of this drug and a pharmacodynamic (PD) model representing the HR response to arbutamine infusions are presented. Model parameters are estimated from clinical data on normal volunteers and patients. Based on this mathematical model, a rule-based control algorithm is designed. The structure of the control algorithm is discussed and testing of the algorithm based on simulations and animal and human trials are summarized. Results from clinical trials shows that the algorithm controls the HR increase according to a selected trajectory. The automated delivery of the drug can provide the cardiologist with an efficient, effective, and safe method for administering a pharmacological stress test     

Validation of high gradient magnetic field based drug delivery to magnetizable implants under flow.

The drug-eluting stent's increasingly frequent occurrence late stage thrombosis have created a need for new strategies for intervention in coronary artery disease. This paper demonstrates further development of our minimally invasive, targeted drug delivery system that uses induced magnetism to administer repeatable and patient specific dosages of therapeutic agents to specific sites in the human body. Our first aim is the use of magnetizable stents for the prevention and treatment of coronary restenosis; however, future applications include the targeting of tumors, vascular defects, and other localized pathologies. Future doses can be administered to the same site by intravenous injection. This implant-based drug delivery system functions by placement of a weakly magnetizable stent or implant at precise locations in the cardiovascular system, followed by the delivery of magnetically susceptible drug carriers. The stents are capable of applying high local magnetic field gradients within the body, while only exposing the body to a modest external field. The local gradients created within the blood vessel create the forces needed to attract and hold drug-containing magnetic nanoparticles at the implant site. Once these particles are captured, they are capable of delivering therapeutic agents such as antineoplastics, radioactivity, or biological cells.     

Evaluation of three-dimensional segmentation algorithms for the identification of luminal and medial-adventitial borders in intravascular ultrasound images

Intravascular ultrasound (IVUS) provides direct depiction of coronary artery anatomy, including plaque and vessel area, which is important in quantitative studies on the progression or regression of coronary artery disease. Traditionally, these studies have relied on manual evaluation, which is laborious, time consuming, and subject to large interobserver and intraobserver variability. A new technique, called active surface segmentation, alleviates these limitations and makes strides toward routine analyses. However, for three-dimensional (3-D) plaque assessment or 3-D reconstruction to become a clinical reality, methods must be developed which can analyze many images quickly. Presented is a comparison between two active surface techniques for three-dimensional segmentation of luminal and medial-adventitial borders. The force-acceleration technique and the neighborhood-search technique accurately detected both borders in vivo (r2 = 0.95 and 0.99, Williams' index = 0.67 and 0.65, and r2 = 0.95 and 0.99, WI = 0.67 and 0.70, respectively). However, the neighborhood-search technique was significantly faster and required less computation. Volume calculations for both techniques (r2 = 0.99 and r2 = 0.99) also agreed with a known-volume phantom. Active surface segmentation allows 3-D assessment of coronary morphology and further developments with this technology will provide clinical analysis tools.     

Strong compound-risk factors: efficient discovery through emerging patterns and contrast sets.

Odds ratio (OR), relative risk (RR) (risk ratio), and absolute risk reduction (ARR) (risk difference) are biostatistics measurements that are widely used for identifying significant risk factors in dichotomous groups of subjects. In the past, they have often been used to assess simple risk factors. In this paper, we introduce the concept of compound-risk factors to broaden the applicability of these statistical tests for assessing factor interplays. We observe that compound-risk factors with a high risk ratio or a big risk difference have an one-to-one correspondence to strong emerging patterns or strong contrast sets-two types of patterns that have been extensively studied in the data mining field. Such a relationship has been unknown to researchers in the past, and efficient algorithms for discovering strong compound-risk factors have been lacking. In this paper, we propose a theoretical framework and a new algorithm that unify the discovery of compound-risk factors that have a strong OR, risk ratio, or a risk difference. Our method guarantees that all patterns meeting a certain test threshold can be efficiently discovered. Our contribution thus represents the first of its kind in linking the risk ratios and ORs to pattern mining algorithms, making it possible to find compound-risk factors in large-scale data sets. In addition, we show that using compound-risk factors can improve classification accuracy in probabilistic learning algorithms on several disease data sets, because these compound-risk factors capture the interdependency between important data attributes.