颊侧多曲簧矫治器有限元模型的建立

背景颊侧多曲簧是一种新型的治疗乳、替牙期AngleⅢ类错牙合畸形的活动矫治器,其在推上颌骨向前促进其发育的同时,抑制下颌的过度向前发育.目的 探讨颊侧多曲簧活动矫治器有限元模型的建立方法.方法 用ANSYS有限元软件建立颊侧多曲簧的有限元模型.结果 成功建立了颊侧多曲簧的有限元模型.结论 本研究用有限元软件对颊侧多曲簧矫治器进行有限元模型的建立,为进一步力学分析和优化设计提供可靠途径.     

试论中医证候动物模型的研究技术

论述了建立中医诱发性证候动物模型和自发性证候动物模型的技术方法,并提出了判断两类模型的标准。     

基于多模型模糊核聚类方法的污水处理过程软测量建模

针对污水处理过程高度非线性及强耦合性的特点,基于多个模型的组合可以提高模型精度和鲁棒性的思想,提出了一种基于 模糊核聚类的多最小二乘支持向量机的软测量建模方法。该方法根据不同工况使用模糊核聚类算法对输入数据进行聚类划分,针对 每个聚类子集用最小二乘支持向量机方法建立子模型,最终通过子模型切换策略得到系统输出。在污水处理过程仿真平台展开验证 工作,对生化需氧量BOD的软测量进行建模,获得了良好的实验结果。     

基于Logistic模型的亚组识别方法

目的基于Logistic模型,提出一种适用于结局为二分类变量的阳性对照、双臂临床试验的亚组识别方法,并利用亚组相关 协变量建立亚组人群判别模型。方法首先基于两处理组,分别建立二分类Logistic回归模型,计算出每一个患者在不同处理组 内的阳性概率并进行比较,然后根据所建立的规则将患者分到各自对应的亚组中,建立多分类Logistic模型。模拟不同样本量 下的错判率、正确判断率、符合率和模型判对率且进行实例分析。结果不同样本量下,错判率均在0.07以下;正确判断率均达 到0.75以上;符合率和模型判对率分别在72%以及92%以上。结论本研究所提出的亚组识别方法是一种有效可靠的亚组识别 方法。     

人脑类器官在神经发育疾病研究中的应用

人脑类器官是由人多能干细胞(hPSC)包括人胚胎干细胞(hESC)和人诱导多功能干细胞(hiPSC)衍生而来的三维组织,能模拟人大脑的结构和功能,作为神经发育疾病的体外研究模型独具优势。本文将对神经疾病体外模型建立和发展的历程、脑类器官在神经发育疾病研究中的应用、相关前沿技术和脑类器官技术的结合等进行综述和展望。     

人类3D大脑类器官研究进展

人类3D大脑类器官为研究人脑发育和神经系统疾病提供了新的模型。体外培育的人类3D大脑类器官主要是由人多能干细胞（human pluripotent stem cell,hPSC）,包括人胚胎干细胞（embryonic stem cell,ESC）和诱导多能干细胞（induced pluripotent stem cell,iPSC）分化而来。iPSC重编程技术与3D大脑类器官技术相结合,可以获得来自患者的iPSC并分化成包括神经元及大脑类器官在内的几乎任何人体细胞或组织,是动物实验向临床试验转化的桥梁。本文回顾了从多能干细胞技术到3D大脑类器官诞生并发展的历程,介绍了以3D大脑类器官为工具构建脑发育和神经系统疾病的研究模型,讨论了大脑类器官在其他方面的应用和相关技术的研究进展,并分析了3D大脑类器官的局限性及其未来可能的发展方向。     

颅面复合体三维有限元模型的快速建立

目的:探讨利用螺旋CT建立颅面复合体三维有限元模型的全系列数字化方法,建立一个具有生物力学特性的动态颅面复合体三维有限元模型。方法:采用多排螺旋CT对恒牙早期骨性安氏Ⅲ类患者行常规头部平扫及三维数字影像重建,利用原始DICOM数据建立颅面复合体三维几何模型,在前处理软件HyperMesh中划分网格。结果:获得高生物相似性的颅面复合体三维重建生物医学模型。建立了恒牙早期骨性安氏Ⅲ类患者颅面复合体三维有限元模型,由108 818个单元和195 654个节点组成。探索出一条完全数字化、适用于活体的三维有限元快速建模方法。结论:应用螺旋CT薄层扫描、三维重建软件Mimics和前处理软件HyperMesh相结合的方法,建立颅面复合体三维有限元模型是一种快速有效的方法。     

基于NIR法雷公藤多甙片快检方法的研究

目的应用近红外漫反射技术建立雷公藤多甙片一致性检验模型快检方法。方法收集雷公藤多甙片的近红外漫反射光谱,运用OPUS软件建立一致性检验模型,并用雷公藤多甙片伪品进行验证。结果正品雷公藤多甙片与伪品有较大差别,利用该一致性检验模型能快速、准确地判断雷公藤多甙片的真伪。结论该方法建立的模型操作简单、快速有效,能作为雷公藤多甙片真伪鉴别的快速筛选方法。     

中草药干燥加工现状及发展趋势

干燥过程是影响中草药质量和药效的重要环节, 干燥结果直接影响着产品的使用和经济价值。该文综述了中药材的干燥技术、干燥模型和干燥过程中有效成分降解规律的研究现状及发展趋势。针对中草药的干燥技术, 分别分析了阴干、晒干和烘干的传统干燥技术及气体射流冲击、真空脉动、中短波红外、射频等新型干燥技术的优缺点及对中草药物料的适用范围; 对于干燥模型, 论述了理论模型、半理论模型和经验模型在中草药干燥加工中的应用; 以生物碱类、黄酮类、多糖类和色素类为代表性中草药药用成分指标, 论述了干燥过程中降解的影响因素及变化规律。明确中草药各类干燥技术的特性和有效成分的降解规律、建立合适的干燥模型, 以期为不同类型的中草药选用合适的干燥技术及工艺提供理论依据。      

有序反应变量的连续比(Logit)模型及其在医学上的应用

本文系统地介绍了用于有序反应变量资料分析的连续比模型;从理论上探讨了当有序反应变量资料转化为多个二分类资料结构时应用一般 Logistic 模型估计连续比模型参数的方法。所建立的方法已应用于列车运行人员消化系统疾患调查资料的分析。     

Context Based Error Modeling for Lossless Compression of EEG Signals Using Neural Networks

Two-stage lossless data compression methods involving predictors and encoders are well known. This paper discusses the application of context based error modeling techniques for neural network predictors used for the compression of EEG signals. Error modeling improves the performance of a compression algorithm by removing the statistical redundancy that exists among the error signals after the prediction stage. In this paper experiments are carried out by using human EEG signals recorded under various physiological conditions to evaluate the effect of context based error modeling in the EEG compression. It is found that the compression efficiency of the neural network based predictive techniques is significantly improved by using the error modeling schemes. It is shown that the bits per sample required for EEG compression with error modeling and entropy coding lie in the range of 2.92 to 6.62 which indicates a saving of 0.3 to 0.7 bits compared to the compression scheme without error modeling.     

Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions

Use of computer based decision tools to aid clinical decision making, has been a primary goal of research in biomedical informatics. Research in the last five decades has led to the development of Medical Decision Support (MDS) applications using a variety of modeling techniques, for a diverse range of medical decision problems. This paper surveys literature on modeling techniques for diagnostic decision support, with a focus on decision accuracy. Trends and shortcomings of research in this area are discussed and future directions are provided. The authors suggest that-(i) Improvement in the accuracy of MDS application may be possible by modeling of vague and temporal data, research on inference algorithms, integration of patient information from diverse sources and improvement in gene profiling algorithms; (ii) MDS research would be facilitated by public release of de-identified medical datasets, and development of opensource data-mining tool kits; (iii) Comparative evaluations of different modeling techniques are required to understand characteristics of the techniques, which can guide developers in choice of technique for a particular medical decision problem; and (iv) Evaluations of MDS applications in clinical setting are necessary to foster physicians' utilization of these decision aids.     

核函数方法在丙烯腈收率软测量建模中的应用

介绍了核函数方法的基本原理及两种核函数统计建模方法;提出了用核函数PLS与核函数PCR建立工业丙烯腈生产过程丙烯腈收率软测量模型,以便更有效地处理过程非线性、多输入和数据共线性等复杂特性。对比研究发现,基于核函数方法的软测量模型要优于线性统计模型,而核函数PLS模型性能优于核函数PCR。     

肝脏形态建模中的图像处理技术

在肝脏形态建模中，需要用到大量的数字图像处理技术。对以下五个方面的技术进行介绍：肝脏的自动分割、血管分割、多相期和多模图像的配准、血管的平滑与重建、肝段的划分，并给出结果。     

A study on 3D Monte Carlo modeling of photon propagation through tissue

Monte Carlo techniques have become popular in modeling of the random events with advantage of powerful computing systems. Especially they have been applied to simulate processes involving random behavior such as diffusion of the Gamma rays through matter and electron concentrations in semiconductors. Recent medical innovations such as Computer Automated Tomography (CAT) and Positron Emission Tomography (PET) are ideal for Monte Carlo modeling techniques. This paper presents derivation and results of the three dimensional Monte Carlo simulation. The results are presented for tissue interactions with photons at wavelength of 207 nm X rays and 630 nm red light. Resulting values of model tissue have been interpolated and mapped into two-dimensional pattern in gray tone. Combination of two-dimensional patterns allow a reconstruction in three dimensions.     

采用123DCatch基于照片全自动构建人体器官三维模型

三维模型库的构建是实现《人体解剖学》虚拟实验室的关键。由于人体器官形态的复杂性,传统的手工建模技术因其专业性强、投入大、周期长,难以适用。基于照片的三维建模技术,因其成本低、操作简单、逼真性高等优势,逐渐得到研究者的重视。该文在比较各类建模方法优缺点的基础上,以Autodesk 123D Catch为例,从应用的角度重点阐述了其基本方法、相应流程、操作要点、注意事项及在VRML中的应用,实现了基于照片快速构建《人体解剖学》虚拟实验室三维模型库。     

Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods

Predicting glucose values on the basis of insulin and food intakes is a difficult task that people with diabetes need to do daily. This is necessary as it is important to maintain glucose levels at appropriate values to avoid not only short-term, but also long-term complications of the illness. Artificial intelligence in general and machine learning techniques in particular have already lead to promising results in modeling and predicting glucose concentrations. In this work, several machine learning techniques are used for the modeling and prediction of glucose concentrations using as inputs the values measured by a continuous monitoring glucose system as well as also previous and estimated future carbohydrate intakes and insulin injections. In particular, we use the following four techniques: genetic programming, random forests, k-nearest neighbors, and grammatical evolution. We propose two new enhanced modeling algorithms for glucose prediction, namely (i) a variant of grammatical evolution which uses an optimized grammar, and (ii) a variant of tree-based genetic programming which uses a three-compartment model for carbohydrate and insulin dynamics. The predictors were trained and tested using data of ten patients from a public hospital in Spain. We analyze our experimental results using the Clarke error grid metric and see that 90% of the forecasts are correct (i.e., Clarke error categories A and B), but still even the best methods produce 5 to 10% of serious errors (category D) and approximately 0.5% of very serious errors (category E). We also propose an enhanced genetic programming algorithm that incorporates a three-compartment model into symbolic regression models to create smoothed time series of the original carbohydrate and insulin time series.     

大鼠心肌缺血再灌注损伤模型建立的关键性问题

心肌缺血再灌注损伤模型是研究心肌缺血预适应机制、评价抗心肌缺血及心律失常药物疗效的经典动物模型,传统实验操作仍有较多缺陷,需要对造模的关键技术不断完善。加强该模型术前处理、术中操作和术后护理3个方面的研究,分析心肌缺血再灌注损伤实验大鼠模型成功建立的关键性问题,并提出解决办法,组建心肌缺血再灌注损伤模型的规范化造模流程。     

The role of modeling methods in medical diagnosis

Modeling methods in medical diagnosis are concerned with medical information processing as it pertains to utilizing biological modeling methods to facilitate patient care. Major considerations in this particular area are (1) the classification problem related to the establishment of disease entities—the taxonomy problem, and (2) the diagnosis of diseases. Available are properties, criteria, signs, symptoms, and manifestations of diseases that have been cumulated and categorized by clinicians and researchers. The problem is to optimally utilize the information content of a sign or set of signs in the practice of patient care as pertaining to the medical diagnosis problem. Some mathematical approaches implemented to facilitate such analyses include cluster analysis, discriminant analysis, Bayesian methods, computer approaches, game theory, information theory, stochastic representations, stepwise procedures, decision analysis, and pattern recognition techniques. Each of these has been studied in depth by numerous researchers advocating computer applications in medicine. Here we discuss the scope and limitations of utilizing modeling methods as a viable approach to interpreting vast amounts of biological data collected on a single patient during an encounter. We consider the following: (1) limitations associated with modeling methodologies: (2) levels of responsibilities, ranging over logging, summarizing, reporting, monitoring, and therapy selection; and (3) operational strategies and considerations as they affect hardware logistics, the actual algorithm utilized, and implementation of these sophisticated analysis systems.     

Automatic Home Medical Product Recommendation

Web-based personal health records (PHRs) are being widely deployed. To improve PHR’s capability and usability, we proposed the concept of intelligent PHR (iPHR). In this paper, we use automatic home medical product recommendation as a concrete application to demonstrate the benefits of introducing intelligence into PHRs. In this new application domain, we develop several techniques to address the emerging challenges. Our approach uses treatment knowledge and nursing knowledge, and extends the language modeling method to (1) construct a topic-selection input interface for recommending home medical products, (2) produce a global ranking of Web pages retrieved by multiple queries, and (3) provide diverse search results. We demonstrate the effectiveness of our techniques using USMLE medical exam cases.