传染病网络直报若干问题探讨

疫情网络直报告别了过去在疾病预防控制工作中信息不畅的弊端,但在传染病管理和传染病报告技术规范方面仍存在一些问题,探讨如下。     

医院传染病防治管理工作中存在的问题及对策

目的:探讨医院传染病防治管理工作中存在的问题及对策.方法:对本市二级综合医院的传染病防治管理工作中存在的问题进行回顾性分析,针对工作中出现及可能出现的问题探寻出相应的解决预防措施.结果:综合医院的传染病防治管理工作中存在诸多问题,医院需加强传染病防治方法及相关制度及法律、法规宣传及人员培训,加大医院在相关硬件设施上的投入,健全医院传染病防治管理体系,提高医院传染病学术及诊治水平,进一步完善医院传染病防治管理工作.结论:综合型医院需对传染病防治管理工作存在的不足需定期检查监督,有针对性的制定相关解决办法,提高综合医院对传染性疾病的应对能力.     

医院传染病管理及控制措施

目的：探讨医院传染病管理制度对控制和预防感染的效果。方法：我院自2011年1月起实施医院传染病管理制度,将该年度病例列入研究组,取2010年1月至12月间病例列入对照组,分别比较两组传染病发生情况和抗生素应用情况。结果：研究组传染病发生率明显低于对照组,数据经统计学比较具有显著差异（P〈0．05）。研究组发生抗生素滥用的科室率明显低于对照组,数据经统计学比较具有显著差异（P〈0．05）。结论：传染病管理制度可有效降低院内传染病发生率,减少抗生素的临床滥用事件发生,从而提高了医疗工作的安全性,也提升了医院的管理水平。     

医院信息系统在传染病医院管理中的应用

目的:目前医院信息系统在全国多家医院得到普遍使用,它不仅能提高医务工作人员的工作质量和效率,还能提高医院的医疗水平,而且它在传染病医院的管理中也发挥着巨大的作用.本文旨在通过医院信息管理系统的反馈实践研究分析医院信息系统在传染病疫情报告中的重要作用.方法:随机抽取我院建立信息系统前后两个时间段内的传染病报告卡,然后分析比较这两个时间段内的传染病报告卡情况.结果:信息系统应用后漏报数和迟报数明显比应用前降低并且应用后的传染病报告卡的填写质量比应用前好,应用前后相比较,差异有统计学意义(P＜0.05).结论:医院信息系统的应用不仅能提高医务工作人员的工作质量和效率,提高传染病管理水平,为患者更好的服务,还能够促进医院的整体现代化建设,是每一个医院建设的发展方向.每家医院都应该加强医院的信息系统建设,提高医院的管理水平,特别是传染病医院更应该加强医院的信息化建设,以便提高传染病管理.     

提高医院传染病管理的实践

目的：总结解放军第302医院在防控传染病方面所践采取的措施,提高医院传染病管理相关工作。方法：完善传染病管理组织,明确责任;认真落实传染病管理相关制度;加强相关知识培训,强化医护人员责任意识,提高能动性、自觉性。总结分析以上相关措施实施后,2005年-2010年期间传染病报告情况。结果：2005年-2010年每年传染病疫情报告卡填写完整率逐渐提高,医院传染病报告漏报率呈逐年下降趋势,近两年呈现填写完整无漏报的情况。结论：提高医院传染病管理的相关措施的实施,使医护人员对的传染病防控意识有所提高,做到早发现、早报告、早隔离、早治疗,降低传染病传播风险,有效减少了医院交叉感染的发生,保障人们的生命健康和社会的发展稳定。     

加强传染病管理与医院感染管理

目的:探讨传染病防治管理措施的应用价值与效果.方法:以2009年1月至2010年6月间的院内管理为对照组;以2010年7月至2011年12月间的院内管理措施为研究组,采用传染病防治管理措施.结果:两组管理方法传染病患者发生率无显著差异(P＞0.05);研究组传染病预防管理措施院内感染发生率明显低于对照组,数据经统计学比较具有显著差异(P＜0.05).研究组管理方法下患者临床满意度明显高于对照组,数据经统计学比较具有显著差异(P＜0.05).结论:传染病预防管理措施有利于有效阻断院内感染源的传播,进而减少交叉感染发生率,提高患者满意度.     

传染病医院住院部医院感染管理现状及对策

目的：对传染病医院住院部感染管理的现状进行探讨分析,并研究相应的对策。方法：根据卫生部颁布的《医院感染诊断标准》对我院患者进行调查统计,重点对2009、2010、2011年的住院患者的感染率以及患者感染部位进行回顾性统计分析。结果：2009、2010、2011年医院感染率分别为3.07%、2.94%、3.05%。且这三年中,患者感染部位以呼吸道与腹腔为主。结论：通过切实加强传染病医院的医院感染的管理、监督以及控制,有效阻断了医院的院内所存在与潜在的传染源,从而有效降低了患者因医源性感染而患病的几率。     

传染病医院血液净化中心的医院感染管理

目的：探讨传染病医院血液净化中心的医院感染管理方法。方法：我院血液净化中心自2010年1月～2012年1月加强医院感染管理,制定了相关的制度及规范,现对感染管理措施进行总结分析,并统计医院感染发生率。结果：通过加强血液净化中心的医院感染管理,可有效防止医院感染的发生,医院感染的发生率为11.68%。结论：完善医院管理制度和规范,制定严格的管理措施,提高医务人员风险意识等可有效提高医院感染的管理水平,预防医院感染的发生。     

北京市延庆县非结核病防治机构网络直报肺结核患者就诊及追踪分析

目的:分析2006-2010年北京市延庆县非结核病防治机构网络直报肺结核患者就诊及追踪情况,为进一步推进结核病防控工作提供依据。方法:对2006-2010年延庆县非结核病防治机构网络直报信息管理系统数据进行回顾性描述性分析。结果:2006-2010年延庆县非结核病防治机构网络直报患者主动就诊到位率为90.18%,追踪到位率为67.35%。在主动就诊到位的450例病人中,106人(23.56%)诊断为非结核,被诊断为初治痰涂片阳性、复治痰涂片阳性和初治痰涂片阴性患者分别占主动就诊到位病人的56.67%、1.78%和18.00%。结论:非结防机构是肺结核患者接受治疗的主要场所,在肺结核患者的发现中起着重要的作用。应针对非结核病防治机构网络直报工作中存在的问题采取相应的措施,以提高主动就诊率和追踪到位率。     

绩效考核在传染病医院管理中的应用

目的：探讨医院绩效质量考核在专科性传染病医院的效果。方法：自2011年来,我院积极开展绩效考核制度：组建质量绩效考核管理小组、制定质量绩效考核管理制度、认真落实制度。结果：自实施绩效考核制度以来,我院临床医疗服务水平、医护人员个人服务水平、业务实施能力均得到了稳步提升。结论：绩效考核制度有利于医院整体水平提高,专科性传染病医院需根据自身情况制定出符合自己发展的绩效考核制度。     

Infectious disease and amphibian population declines

Abstract. A series of recent papers have implicated pathogens and parasites in amphibian population declines. Here, we review evidence on the link between infectious disease and amphibian population declines. We conclude that available data provide the clearest link for the fungal disease amphibian chytridiomycosis, although other pathogens are also implicated. We suggest additional experimental and observational data that need to be collected to provide further support that these other pathogens are associated with declines. We suggest that, in common with many emerging infectious diseases (EIDs) of humans, domestic animals and other wildlife species, emergence of chytridiomycosis may be driven by anthropogenic introduction (pathogen pollution). Finally, we review a number of recent advances in the host–parasite ecology of chytridiomycosis that help explain its emergence and impact.     

Origin of cells and network information

All cells are derived from one cell, and the origin of different cell types is a subject of curiosity. Cells construct life through appropriately timed networks at each stage of development. Communication among cells and intracellular signaling are essential for cell differentiation and for life processes. Cellular molecular networks establish cell diversity and life. The investigation of the regulation of each gene in the genome within the cellular network is therefore of interest. Stem cells produce various cells that are suitable for specific purposes. The dynamics of the information in the cellular network changes as the status of cells is altered. The components of each cell are subject to investigation.     

文本挖掘技术在整合蛋白与疾病关系资源中的应用

为了整合文献中大量的人类蛋白质与疾病相互关系的信息,通过文本挖掘和通路分析的方法从PubMed中的摘要提取出对应关系后,利用KEGG中的通路信息构建出人类蛋白质和疾病相互的一个网络效应,并构建了查询数据库,用户可以根据蛋白质名称、疾病名称、通路名称来进行多方面的查询。     

积极的防护意识与健全的防护体系在传染病医院内的影响与效应

目的:通过提高防护意识和进一步完善防护体系实现对传染病医院的全面管理.方法:随机将我院2009年3月至2011年11月的两个病区患者及医护人员作为研究对象,向所有患者发放调查表对政策调整前后的满意程度进行调查,通过针对医护人员防护意识的强化教育和医院设施及防护体系的调整与完善以评估对患者住院期间的临床效应与影响.结果:全院上下的积极防护意识普遍增强并完善健全了防护体系,从而显著降低了感染率,患者满意度反馈良好.结论:通过提高医护人员防护意识和健全医院防护体系有助于进一步减少院内感染,提高医院医疗质量和服务水平.     

A network medicine approach to human disease

High-throughput interaction discovery initiatives are providing thousands of novel protein interactions which are unveiling many unexpected links between apparently unrelated biological processes. In particular, analyses of the first draft human interactomes highlight a strong association between protein network connectivity and disease. Indeed, recent exciting studies have exploited the information contained within protein networks to disclose some of the molecular mechanisms underlying complex pathological processes. These findings suggest that both protein-protein interactions and the networks themselves could emerge as a new class of targetable entities, boosting the quest for novel therapeutic strategies.     

Fish Karyome: A karyological information network database of Indian Fishes

'Fish Karyome', a database on karyological information of Indian fishes have been developed that serves as central source for karyotype data about Indian fishes compiled from the published literature. Fish Karyome has been intended to serve as a liaison tool for the researchers and contains karyological information about 171 out of 2438 finfish species reported in India and is publically available via World Wide Web. The database provides information on chromosome number, morphology, sex chromosomes, karyotype formula and cytogenetic markers etc. Additionally, it also provides the phenotypic information that includes species name, its classification, and locality of sample collection, common name, local name, sex, geographical distribution, and IUCN Red list status. Besides, fish and karyotype images, references for 171 finfish species have been included in the database. Fish Karyome has been developed using SQL Server 2008, a relational database management system, Microsoft's ASP.NET-2008 and Macromedia's FLASH Technology under Windows 7 operating environment. The system also enables users to input new information and images into the database, search and view the information and images of interest using various search options. Fish Karyome has wide range of applications in species characterization and identification, sex determination, chromosomal mapping, karyo-evolution and systematics of fishes.     

Predicting disease associations via biological network analysis

Background

Understanding the relationship between diseases based on the underlying biological mechanisms is one of the greatest challenges in modern biology and medicine. Exploring disease-disease associations by using system-level biological data is expected to improve our current knowledge of disease relationships, which may lead to further improvements in disease diagnosis, prognosis and treatment.

Results

We took advantage of diverse biological data including disease-gene associations and a large-scale molecular network to gain novel insights into disease relationships. We analysed and compared four publicly available disease-gene association datasets, then applied three disease similarity measures, namely annotation-based measure, function-based measure and topology-based measure, to estimate the similarity scores between diseases. We systematically evaluated disease associations obtained by these measures against a statistical measure of comorbidity which was derived from a large number of medical patient records. Our results show that the correlation between our similarity measures and comorbidity scores is substantially higher than expected at random, confirming that our similarity measures are able to recover comorbidity associations. We also demonstrated that our predicted disease associations correlated with disease associations generated from genome-wide association studies significantly higher than expected at random. Furthermore, we evaluated our predicted disease associations via mining the literature on PubMed, and presented case studies to demonstrate how these novel disease associations can be used to enhance our current knowledge of disease relationships.

Conclusions

We present three similarity measures for predicting disease associations. The strong correlation between our predictions and known disease associations demonstrates the ability of our measures to provide novel insights into disease relationships.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-304) contains supplementary material, which is available to authorized users.     

Gene regulatory network inference by point-based Gaussian approximation filters incorporating the prior information

Abstract

The extended Kalman filter (EKF) has been applied to inferring gene regulatory networks. However, it is well known that the EKF becomes less accurate when the system exhibits high nonlinearity. In addition, certain prior information about the gene regulatory network exists in practice, and no systematic approach has been developed to incorporate such prior information into the Kalman-type filter for inferring the structure of the gene regulatory network. In this paper, an inference framework based on point-based Gaussian approximation filters that can exploit the prior information is developed to solve the gene regulatory network inference problem. Different point-based Gaussian approximation filters, including the unscented Kalman filter (UKF), the third-degree cubature Kalman filter (CKF₃), and the fifth-degree cubature Kalman filter (CKF₅) are employed. Several types of network prior information, including the existing network structure information, sparsity assumption, and the range constraint of parameters, are considered, and the corresponding filters incorporating the prior information are developed. Experiments on a synthetic network of eight genes and the yeast protein synthesis network of five genes are carried out to demonstrate the performance of the proposed framework. The results show that the proposed methods provide more accurate inference results than existing methods, such as the EKF and the traditional UKF.