分子模拟方法及其在分子生物学中的应用

常用的分子模拟方法有 :量子力学法、分子力学方法、蒙特卡洛法和分子动力学法。四种方法各有优势 ,共同成为分子模拟的组成部分。综述了分子模拟法在分子生物学中的应用 ,最后介绍了分子模拟的发展方向 ,并预测了其未来的发展趋势。常用的分子模拟方法有 :量子力学法、分子力学方法、蒙特卡洛法和分子动力学法。四种方法各有优势 ,共同成为分子模拟的组成部分。综述了分子模拟法在分子生物学中的应用 ,最后介绍了分子模拟的发展方向 ,并预测了其未来的发展趋势。     

公立医院临床医生模块化绩效分配模型构建研究

目的 在新医改政策背景下体现公立医院公益性、调动积极性,研究构建临床医生模块化绩效分配模型。方法 通过文献查阅法和专家咨询法进行了模型构建和评价指标遴选。结果 构建了以“岗位能力、医疗工作量、医疗质量、患者满意”为关键模块的模块化临床医生绩效分配模型。结论 该绩效分配模型符合医疗行业特点,适用于公立医院临床医师的绩效分配制度改革实践。     

基于电子病历的急性ST段抬高型心肌梗死临床路径与决策辅助系统

目的:研究在结构化电子病历上实现计算机化的急性ST段抬高型心肌梗死临床路径,进一步评价该方法的试用结果,为辅助临床医生治疗决策、规范治疗行为,并借此提高医疗质量,合理控制医疗费用提供有效的手段。方法:选取最新的、具有权威性的关于急性ST段抬高型心肌梗死诊治指南作为制定治疗决策方案及编写临床路径的依据;通过程序设计将临床路径编写入医学知识库系统,并将医学知识库系统与结构化电子病历进行无缝连接;将20例无严重并发症的急性ST段抬高型心肌梗死病人均分为两组,其中观察组推行计算机化临床路径,在患者住院天数、住院费用、患者及家属满意度以及医生对该系统的评价方面与对照组进行比较。结果:观察组患者平均住院天数及平均住院费用明显低于对照组(p＜0．000);患者及家属满意度普遍提高(p＜0．05);该系统得到所有被调查医生的认可。结论:运用计算机化临床路径管理无严重并发症的急性ST段抬高型心肌梗死患者能在维持甚至提高医疗质量的前提下,减少患者平均住院天数、平均住院费用,提高患者及家属对医疗行为的满意度,增强医生对治疗指南的顺从性。     

米桑(酪酸梭菌活菌胶囊)全国论文征集活动CSCD

米桑作为新一代微生物制剂,2004年作为中国人自己的原籍菌得到批准上市。到目前为止,其在治疗由各种原因引起的肠道菌群紊乱、肠道损伤相关疾病方面已经逐步得到广大医生患者认可和接受;为了总结米桑在临床上的应用经验,重庆泰平药业联合《中国微生态学杂志》举办米桑论文、典型病例征集活动,诚邀广大临床医生参加。     

基于马尔科夫链模型的长江源区土地覆盖格局变化特征

利用长江源区1986、2000与2014年3期的遥感影像,结合实地野外考察获得该地区在这3个时间点的土地覆盖类型图。根据各时期之间的土地覆盖格局的变化确定土地类型之间的转移概率,进一步完成对该地区马尔科夫链模型的构建、检验与预测。结果表明:1986—2014年,长江源区的土地覆盖格局的变化特征符合马尔科夫过程,通过马尔科夫链模型能够对该地区的覆盖格局变化过程进行有效的模拟;长江源区的土地覆被退化趋势明显,湿地、中高覆盖草地等面积不断下降,裸地、沙地以及低覆盖草地等面积则一直在增加;2000年以后,由于三江源区自然保护区的建立以及降水量的增加等因素影响,长江源区的植被退化状况得到明显改善。     

米桑(酪酸梭菌活菌胶囊)全国论文征集活动

正米桑作为新一代微生物制剂,2004年作为中国人自己的原籍菌得到批准上市。到目前为止,其在治疗由各种原因引起的肠道菌群紊乱、肠道损伤相关疾病方面已经逐步得到广大医生患者认可和接受;为了总结米桑在临床上的应用经验,重庆泰平药业联合《中国微生态学杂志》举办米桑论文、典型病例征集活动,诚邀广大临床医生参加。文章参选领域:1.儿科领域(腹泻、手足口病、功能性胃肠道疾病、乳糖不耐受、湿疹等)     

基于机器学习的医学影像分析在药物研发和精准医疗方面的应用

MRI,PET,和CT等医学影像在新药研发和精准医疗中起着越来越重要的作用。影像技术可以被用来诊断疾病,评估药效,选择适应患者,或者确定用药剂量。 随着人工智能技术的发展,特别是机器学习以及深度学习技术在医学影像中的应用,使得我们可以用更短的时间,更少的放射剂量获取更高质量的影像。这些技术还可以帮助放射科医生缩短读片时间,提高诊断准确率。除此之外,机器学习技术还可以提高量化分析的可行性和精度,帮助建立影像与基因以及疾病的临床表现之间的关系。首先根据不同形态的医学影像,简单介绍他们在药物研发和精准医疗中的应用。并对机器学习在医学影像中的功能作一概括总结。最后讨论这个领域的挑战和机遇。     

基因组编辑技术研究新进展

基因组编辑是一种针对目的基因组进行定点改造的技术,其主要方法是通过对目的基因组的改造,从而达到对未知功能基因进行研究和基因治疗的目的。人工核酸内切酶介导的基因组编辑技术是目前应用前景最为广泛的技术,其包括ZFNs、TALENs和CRISPR/Cas技术。本文针对3种不同的基因组编辑技术的原理作了简要介绍,比较分析了它们各自的优缺点,并对不同生物中基因组编辑技术的研究现状进行了总结,对疾病模型动物的建立中的应用作了探讨,同时对该技术在疾病基因治疗中的应用前景进行了展望。     

生态系统服务建模技术研究进展

在生态系统服务评估模型的数量、类型及应用大量增加的背景下,为将生态系统服务评估有效整合到决策中,系统比较、甄别不同建模工具并筛选出适合决策需求的生态系统服务评估和模拟方法尤为必要。因此,归纳并总结了国内外现有的生态系统服务评估模型的建模技术,包括:相关关系法、生物-物理过程法以及专家知识法;分别对其原理、差异、优缺点以及适用性进行了详尽阐释。大多数相关模型侧重于统计关系,相对容易创建和扩展,适用于生态系统服务的初始评估;生物-物理过程模型难以构建且不易获取,但提供了探索人-地系统相互作用和长期变化的有效机制;专家知识法有效结合了多种类型的知识体系,关注人类社会与自然系统之间反馈和交互动态的系统整合,但当评估地点发生变化时难以验证。在此基础上,介绍了基于上述3种建模技术的典型生态系统服务综合评估模型的发展和应用现状。各类建模技术面临着实用性和科学准确性之间的权衡。通过对不同建模技术的梳理与整合分析旨在提升当前生态系统服务研究的决策支撑能力,并为国内相关研究提供参考和借鉴。     

对心血管外科进修生的教学体会

为造福更多的心血管病患者,进一步提高治疗心血管疾病专业人才的质量,建立科学、健全的心血管外科进修生管理和培养模式很有必要。我科室通过总结多年的进修生教学经验,树立"双赢"理念,规范培训制度,实行导师负责制,实施PBL教学方式,进行多学科协作培训,定期对进修医生考核等方法措施,切实提高进修生的医疗水平,更好地为基层医院服务、为心血管病患者服务。     

Withholding the artificial administration of fluids and food from elderly patients with dementia: ethnographic study

ObjectiveTo clarify the practice of withholding the artificial administration of fluids and food from elderly patients with dementia in nursing homes.DesignQualitative, ethnographic study in two phases.Setting10 wards in two nursing homes in the Netherlands.Participants35 patients with dementia, eight doctors, 43 nurses, and 32 families.ResultsThe clinical course of dementia was considered normal and was rarely reason to begin the artificial administration of fluids and food in advanced disease. Fluids and food seemed to be given mainly when there was an acute illness or a condition that needed medical treatment and which required hydration to be effective. The medical condition of the patient, the wishes of the family, and the interpretations of the patients'' quality of life by their care providers were considered more important than living wills and policy agreements.ConclusionsDoctors'' decisions about withholding the artificial administration of fluids and food from elderly patients with dementia are influenced more by the clinical course of the illness, the presumed quality of life of the patient, and the patient''s medical condition than they are by advanced planning of care. In an attempt to understand the wishes of the patient doctors try to create the broadest possible basis for the decision making process and its outcome, mainly by involving the family.

What is already known on this topic

Debate has focused on whether it is beneficial to withhold the artificial administration of fluids and food from patients with advanced dementia

What this study adds

The course of dementia, the patient''s quality of life, and the patient''s current medical condition influence doctors'' decision making more than advanced planning of careDoctors try to create the broadest possible basis for the decision making process and its outcome, mainly by involving the family     

Latent class model diagnosis

In many areas of medical research, such as psychiatry and gerontology, latent class variables are used to classify individuals into disease categories, often with the intention of hierarchical modeling. Problems arise when it is not clear how many disease classes are appropriate, creating a need for model selection and diagnostic techniques. Previous work has shown that the Pearson chi 2 statistic and the log-likelihood ratio G2 statistic are not valid test statistics for evaluating latent class models. Other methods, such as information criteria, provide decision rules without providing explicit information about where discrepancies occur between a model and the data. Identifiability issues further complicate these problems. This paper develops procedures for assessing Markov chain Monte Carlo convergence and model diagnosis and for selecting the number of categories for the latent variable based on evidence in the data using Markov chain Monte Carlo techniques. Simulations and a psychiatric example are presented to demonstrate the effective use of these methods.     

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics

Background

Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques. This is true of Bayesian models for genome-enabled selection, which uses whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes. In this regard, parallel computing can overcome the bottlenecks that can arise from series computing. Hence, a major goal of the present study is to bridge the gap to high-performance Bayesian computation in the context of animal breeding and genetics.

Results

Parallel Monte Carlo Markov chain algorithms and strategies are described in the context of animal breeding and genetics. Parallel Monte Carlo algorithms are introduced as a starting point including their applications to computing single-parameter and certain multiple-parameter models. Then, two basic approaches for parallel Markov chain Monte Carlo are described: one aims at parallelization within a single chain; the other is based on running multiple chains, yet some variants are discussed as well. Features and strategies of the parallel Markov chain Monte Carlo are illustrated using real data, including a large beef cattle dataset with 50K SNP genotypes.

Conclusions

Parallel Markov chain Monte Carlo algorithms are useful for computing complex Bayesian models, which does not only lead to a dramatic speedup in computing but can also be used to optimize model parameters in complex Bayesian models. Hence, we anticipate that use of parallel Markov chain Monte Carlo will have a profound impact on revolutionizing the computational tools for genomic selection programs.     

Bayesian phylogeny analysis via stochastic approximation Monte Carlo

Monte Carlo methods have received much attention in the recent literature of phylogeny analysis. However, the conventional Markov chain Monte Carlo algorithms, such as the Metropolis–Hastings algorithm, tend to get trapped in a local mode in simulating from the posterior distribution of phylogenetic trees, rendering the inference ineffective. In this paper, we apply an advanced Monte Carlo algorithm, the stochastic approximation Monte Carlo algorithm, to Bayesian phylogeny analysis. Our method is compared with two popular Bayesian phylogeny software, BAMBE and MrBayes, on simulated and real datasets. The numerical results indicate that our method outperforms BAMBE and MrBayes. Among the three methods, SAMC produces the consensus trees which have the highest similarity to the true trees, and the model parameter estimates which have the smallest mean square errors, but costs the least CPU time.     

A Bayesian hierarchical approach for combining case-control and prospective studies

Motivated by the absolute risk predictions required in medical decision making and patient counseling, we propose an approach for the combined analysis of case-control and prospective studies of disease risk factors. The approach is hierarchical to account for parameter heterogeneity among studies and among sampling units of the same study. It is based on modeling the retrospective distribution of the covariates given the disease outcome, a strategy that greatly simplifies both the combination of prospective and retrospective studies and the computation of Bayesian predictions in the hierarchical case-control context. Retrospective modeling differentiates our approach from most current strategies for inference on risk factors, which are based on the assumption of a specific prospective model. To ensure modeling flexibility, we propose using a mixture model for the retrospective distributions of the covariates. This leads to a general nonlinear regression family for the implied prospective likelihood. After introducing and motivating our proposal, we present simple results that highlight its relationship with existing approaches, develop Markov chain Monte Carlo methods for inference and prediction, and present an illustration using ovarian cancer data.     

Withholding or Withdrawing Extraordinary Life Support—Optimizing Rights and Limiting Liability

For many years physicians, ethicists and members of the legal community have attempted to minimize ambiguity and unpredictability in making decisions to withhold or withdraw extraordinary life support. Recent developments in national and California law now afford medical care providers unparalleled protection from criminal and civil liability in surrogate decision-making situations. They also reinforce the concept of patient''s rights by providing medical care consumers with new and effective mechanisms for enforcing their “right to decide,” even after they have lost decision-making capacity. A case in point is California''s new Durable Power of Attorney for Health Care, which serves as a model for other jurisdictions that do not have such legislation. Thus, the medical and legal professions, working together, can contribute immeasurably to respectful medical decision making by educating the public about these developments and by adopting policies that reinforce these rights.     

Stochastic search variable selection based on two mixture components and continuous‐scale weighting

Stochastic search variable selection (SSVS) is a Bayesian variable selection method that employs covariate‐specific discrete indicator variables to select which covariates (e.g., molecular markers) are included in or excluded from the model. We present a new variant of SSVS where, instead of discrete indicator variables, we use continuous‐scale weighting variables (which take also values between zero and one) to select covariates into the model. The improved model performance is shown and compared to standard SSVS using simulated and real quantitative trait locus mapping datasets. The decision making to decide phenotype‐genotype associations in our SSVS variant is based on median of posterior distribution or using Bayes factors. We also show here that by using continuous‐scale weighting variables it is possible to improve mixing properties of Markov chain Monte Carlo sampling substantially compared to standard SSVS. Also, the separation of association signals and nonsignals (control of noise level) seems to be more efficient compared to the standard SSVS. Thus, the novel method provides efficient new framework for SSVS analysis that additionally provides whole posterior distribution for pseudo‐indicators which means more information and may help in decision making.     

Bayesian restoration of a hidden Markov chain with applications to DNA sequencing.

Hidden Markov models (HMMs) are a class of stochastic models that have proven to be powerful tools for the analysis of molecular sequence data. A hidden Markov model can be viewed as a black box that generates sequences of observations. The unobservable internal state of the box is stochastic and is determined by a finite state Markov chain. The observable output is stochastic with distribution determined by the state of the hidden Markov chain. We present a Bayesian solution to the problem of restoring the sequence of states visited by the hidden Markov chain from a given sequence of observed outputs. Our approach is based on a Monte Carlo Markov chain algorithm that allows us to draw samples from the full posterior distribution of the hidden Markov chain paths. The problem of estimating the probability of individual paths and the associated Monte Carlo error of these estimates is addressed. The method is illustrated by considering a problem of DNA sequence multiple alignment. The special structure for the hidden Markov model used in the sequence alignment problem is considered in detail. In conclusion, we discuss certain interesting aspects of biological sequence alignments that become accessible through the Bayesian approach to HMM restoration.     

Exploratory Bayesian model selection for serial genetics data

Characterizing the process by which molecular and cellular level changes occur over time will have broad implications for clinical decision making and help further our knowledge of disease etiology across many complex diseases. However, this presents an analytic challenge due to the large number of potentially relevant biomarkers and the complex, uncharacterized relationships among them. We propose an exploratory Bayesian model selection procedure that searches for model simplicity through independence testing of multiple discrete biomarkers measured over time. Bayes factor calculations are used to identify and compare models that are best supported by the data. For large model spaces, i.e., a large number of multi-leveled biomarkers, we propose a Markov chain Monte Carlo (MCMC) stochastic search algorithm for finding promising models. We apply our procedure to explore the extent to which HIV-1 genetic changes occur independently over time.     

Reverse Monte Carlo Simulation: A New Technique for the Determination of Disordered Structures

Abstract

We have developed a new technique, based on the standard Monte Carlo simulation method with Markov chain sampling, in which a set of three dimensional particle configurations are generated that are consistent with the experimentally measured structure factor. A(Q), and radial distribution function, g(r), of a liquid or other disordered system. Consistency is determined by a standard χ² test using the experimental errors. No input potential is required, we present initial results for liquid argon. Since the technique can work directly from the structure factor it promises to be useful for modelling the structures of glasses or amorphous materials. It also has other advantages in multicomponent systems and as a tool for experimental data analysis.