SEARCH · Search PubMed
Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A unique health identification number may be heralded as the solution to unique patient identification within the Australian health care sector; however, there is scope for individual health care facilities to improve the data integrity of their Patient Master Index (PMI) to improve patient identification at a local level.
In the relatively few years since their inception, DNA microarrays and Affymetrix GeneChips have gained increasing use and acceptance in the study of genetic and cellular processes. This is evident from the rising number of published literature citing microarrays each year. With time, gene chips and microarrays have matured into complex technologies as biologists have teamed with applied mathematicians and statisticians to increase the rigor of experimentation and address the problems associated with the manipulation of large data sets. Several complementary microarray technologies for measuring gene expression are now routinely employed. This review will discuss the similarities and differences among these technologies and cover recent efforts to integrate data from cross-platform comparative studies.
Mechanisms of ionic control of insulin secretion in beta-cells of the pancreatic islets of Langerhans are reviewed. The focus is on aspects that have been treated by mathematical models, especially those related to bursting electrical activity. The study of these mechanisms is difficult because of the need to consider ionic fluxes, calcium handling, metabolism, and electrical coupling with other cells in the islet. The data come either from islets, where experimental maneuvers tend to have multiple effects, or from isolated cells, which have degraded electrical activity and secretory sensitivity. Modeling aids in the process by integrating data on individual components such as channels and calcium handling and testing hypotheses for coherence and quantitative plausibility. The study of a variety of models has led to some general mathematical results that have yielded qualitative model-independent insights.
Clinical trial management and quality assurance is a complex activity that, when manually executed, is prone to errors and delays, and organizations involved in the conduct of clinical drug trials must rely on database systems to ensure adequate data integrity and timely reporting. We report the design and implementation of an integrated computer system for the management and control of multiple phase II to IV clinical drug trials, and for automated generation of monitoring and statistical analysis reports that are fully compliant with international guidelines. This Windows-based system incorporates a number of third-party software tools and applications, and its major components are COATI (Control, Assessment and Tracking of Therapeutic Investigations), a client-server database application; DART (Data Analysis and Reporting Tool) for automated data abstraction and reporting; and PANDA (Data Analysis Package) for automated statistical analysis. The system is in production for two years and was used in 15 clinical trials in a diversity of medical conditions and study designs.
Relational database software has become popular for the management of certain types of commercial data. Its use is being given serious consideration in the management of data from clinical trials. Relational systems have a number of advantages over hierarchical or network systems for some types of data. However, as illustrated by an example, the data from clinical trials typically have an inherent hierarchical structure. The incorporation of hierarchically structured data into a relational database raises difficult problems of data integrity versus the complexity of the database structure. These problems, together with the long execution times of many relational operations, indicate that relational systems are not necessarily well suited for clinical trials data management.
Methyl bromide (MB) is used to fumigate diverse commodities. During fumigation, the commodity can sorb a substantial mass of MB, which does not chemically react and is termed a residue. During subsequent commodity handling, the residue offgasses and can lead to MB inhalation exposure among processing workers. Although MB has a low 1 ppm 8-hr TLV-TWA as recommended by the American Conference of Governmental Industrial Hygienists (ACGIH(R)) and is considered a potential occupational carcinogen by the National Institute for Occupational Safety and Health, the recent industrial hygiene literature contains no pertinent exposure data. Limited measurements made in 1992 by the California Department of Pesticide Regulation are summarized here, but associated information on exposure determinants is lacking. In this article, mathematical models are used to integrate data on MB residue offgassing with several processing scenarios to estimate potential exposure levels. The main finding is that if a large volume of commodity rapidly offgasses MB and is handled under conditions of low ventilation, the potential exists for MB exposures above the 1 ppm TLV-TWA value. However, the combination of handling a smaller commodity volume and less rapid offgassing may be the more typical scenario. It is recommended that a pilot study be conducted to measure current MB exposure levels, test the validity of the mathematical models, and collect industry-wide data on exposure determinants. By using the latter data as inputs for validated models, public health scientists could estimate the distribution of MB exposure levels across the commodity processing industry.
The general population is experiencing lifelong exposure to old and new hazardous substances. By using data collected from a subject's own teeth, accuracy in determining the effects of exposure is assured since extrapolation is excluded. The establishment of a common tooth bank can provide means to integrate data from multiple sources. Comprehensive pollution information shared by the environmental, scientific and medical communities can lead to a more efficient approach to a worldwide problem.
Successful planning for capital investment relies upon the ability of the management team to establish a cogent and comprehensive direction for facility development. The selection of an appropriate strategy integrates multiple issues: mission, service needs of the community, the external environment, the organization's ethos, current physical resources, operational systems, and vision. This paper will identify and discuss key components and data integral to formulating a facility strategy that outlines the basic direction for developing a facility master plan. The process itself will be presented as a working methodology that can be applied to the organization's resources and vision to generate a coherent facility strategy.
The GDB Human Genome Data Base refers collectively to GDB and OMIM, Online Mendelian Inheritance in Man. GDB and OMIM are linked databases that provide an international repository for information generated by the Human Genome Initiative. GDB contains human gene mapping data, while OMIM offers the text of Dr. Victor A. McKusick's catalog of genetic disease and phenotype descriptions. These databases, updated and edited continuously, integrate bibliographic and full-text information with several types of mapping data. They are accessible through a flexible interface and are available through SprintNet and the Internet to the scientific community without cost. This paper provides an overview of the context, development, structure, content, and use of these databases.
Systems biology is a holistic approach to biological sciences that combines experimental and computational strategies, aimed at integrating information from different scales of biological processes to unravel pathophysiological mechanisms and behaviours. In this scenario, high-throughput technologies have been playing a major role in providing huge amounts of omics data, whose integration would offer unprecedented possibilities in gaining insights on diseases and identifying potential biomarkers. In the present review, we focus on strategies that have been applied in literature to integrate genomics, transcriptomics, proteomics, and metabolomics in the year range 2018-2024. Integration approaches were divided into three main categories: statistical-based approaches, multivariate methods, and machine learning/artificial intelligence techniques. Among them, statistical approaches (mainly based on correlation) were the ones with a slightly higher prevalence, followed by multivariate approaches, and machine learning techniques. Integrating multiple biological layers has shown great potential in uncovering molecular mechanisms, identifying putative biomarkers, and aid classification, most of the time resulting in better performances when compared to single omics analyses. However, significant challenges remain. The high-throughput nature of omics platforms introduces issues such as variable data quality, missing values, collinearity, and dimensionality. These challenges further increase when combining multiple omics datasets, as the complexity and heterogeneity of the data increase with integration. We report different strategies that have been found in literature to cope with these challenges, but some open issues still remain and should be addressed to disclose the full potential of omics integration.
The human immune system is a highly complex, dynamic, and heterogeneous network shaped by genetic, environmental, and temporal influences. Advances in high-throughput omics technologies have transformed our ability to study this complexity directly and comprehensively in human cohorts. These developments have positioned systems immunology as a powerful framework for investigating coordinated immune responses, identifying regulatory mechanisms, and linking molecular patterns to clinical phenotypes. However, the analytical challenges inherent to large-scale, multimodal datasets-including batch effects, small sample sizes, high dimensionality, and substantial interindividual heterogeneity-require rigorous study design, robust statistical modeling, and thoughtful data analysis strategies. In this review, we summarize key technological foundations enabling modern human systems immunology, outline common analytical pitfalls and effective mitigation approaches, discuss data integration concepts, and highlight emerging opportunities in the field. Together, these technological and analytical advances are redefining how immune function is measured and interpreted in real-world human biology and hold significant promise for enhancing mechanistic insight, biomarker discovery, and precision medicine across immunological diseases and interventions.
The security of Electronic Medical Records can be enhanced by the addition of digital signatures that guarantee data integrity, authenticate the signer, and establish non-repudiation through the use of public key encryption. The task is complicated by the contribution of multiple providers to an encounter and the entry of data at multiple points in time Dividing encounters into an episode of care and redesigning the data model of the EMR will facilitate full signature capabilities. Generation of digital signatures is best accomplished using microprocessors on smart cards that control visibility of the private keys and assist in user authentication. The Java Programming Language including cryptography extensions and a smart card API is a useful tool for adding digital signature to an EMR. Inter-operability of signatures and continuity of signature will require attention to standards and preservation of cryptography and authentication certificate archives. Digital signatures will need to accommodate changes in data storage formats when information is transported between EMR systems using XML or other transaction standards because the original signatures will not validate if the data storage format changes. The costs of adding digital signature to EMR mandates serious examination of the business case for digital signature within an EMR as compared with transactions such as electronic prescriptions. At present, there is no regulatory requirement for digital signature of an EMR.
Despite promising results, computer-aided diagnosis in acute abdominal pain is rarely used in the clinic. We therefore developed an expert system for acute abdominal pain to be used in clinical routine. The system is based on a new approach integrating a data dictionary, a clinical database and the knowledge base. A data dictionary editor has been developed (C++, WINDOWS, IBM-compatible PC) and a data dictionary for acute abdominal pain has been built up. The clinical database has been linked to a documentation program providing three modes of data entry. The documentation program has been evaluated extensively by clinicians. The integrated approach clearly separates clinical data from knowledge, but guarantees high consistency of data.
We analyze key proteomic issues and cutting-edge technologies that will spearhead inroads into functional interpretations of human diseases and their therapeutic rectification, following the availability of the predicted human proteome. We contrast the distinctions between high quality data that are low throughput, (e.g., 3-D proteomic reconstructions in embryogenic and nervous system contexts, and multigenerational transgenic studies), versus automated data harvesting that is more distant from human disease phenotypes and currently fulfills a diagnostic role, (e.g., molecular portraits of human diseases via transcriptomic analyses). We examine the extent to which these approaches impinge upon a realistic understanding of human diseases, namely how close they come to revealing the causal events involved in the initiation of disease. While tissue sources from human embryogenesis, foetal development and the brain remain the absolute priority, the pragmatic approaches utilize judicious data integration from selected proteomic studies of model organisms. The role of genome-wide disease-related screens, "humanized" transgenic analyses, multigenerational gene interference methods, and analyses of post-translational modifications in epigenetic contexts from Drosophila will be crucial, since these avenues are far too slow and transgenically cumbersome in mammals. Finally, the implementation of multi compartment electrolyzers (MCE) and multi photon detection (MPD) systems will be pivotal for the proteomic profiling of human tissue samples.
In systems biology, biologically relevant quantitative modelling of physiological processes requires the integration of experimental data from diverse sources. Recent developments in high-throughput methodologies enable the analysis of the transcriptome, proteome, interactome, metabolome and phenome on a previously unprecedented scale, thus contributing to the deluge of experimental data held in numerous public databases. In this review, we describe some of the databases and simulation tools that are relevant to systems biology and discuss a number of key issues affecting data integration and the challenges these pose to systems-level research.
The links between poverty and health have been investigated in a number of independent studies, as well as by the Poverty Commission in the 1970s and, more recently, the National Health Strategy. However, much of the poverty research suffers from the lack of detailed information on health status, while the work conducted in the public health sphere has used rather rudimentary poverty measures. The research reported here attempts to overcome these limitations by using unit record data from two national household surveys conducted by the Australian Bureau of Statistics in 1990. Together, these two data sets contain an enormous amount of detailed information on household incomes and the health status of individuals. Data from the two surveys are combined in a way which allows the links between poverty and health to be explored in greater detail than has hitherto been possible in Australia. Analysis of the integrated data set focuses on the links between poverty and several measures of stress-related poor health. The results from a variety of different methods point to the existence of significant differences between the reported incidence of stress of those whose incomes place them either side of a poverty threshold. The size of the statistical association between poverty and stress is of both numerical and statistical significance, although further work, preferably using longitudinal data, is needed on the important issues of causation.
Query and interpretation of time-oriented medical data involves two subtasks: Temporal-reasoning--intelligent analysis of time-oriented data, and temporal-maintenance--effective storage, query, and retrieval of these data. Integration of these tasks into one system, known as temporal-mediator, has been proven to be beneficial to biomedical applications such as monitoring, therapy, quality assessment, visualization and exploration of time-oriented data. One potential problem in existing temporal-mediation approaches is lack of sufficient responsiveness when querying or continuously monitoring the database for complex abstract concepts that are derived from the raw data, especially regarding a large patient group. We propose a new approach: the knowledge-based time-oriented active database, a temporal extension of the active-database concept, and a merger of temporal reasoning and temporal maintenance within a persistent database framework. The approach preserves the efficiency of databases in handling data storage and retrieval, while enabling specification and performance of complex temporal reasoning using an incremental-computation approach. We implemented our approach within the Momentum system. Initial experiments are encouraging; an evaluation is underway
BACKGROUND/AIMS: The principle of integral photography was first proposed by Lippman in 1908. Recently, the three-dimensional display system based on the principle of integral photography was developed. It projects three-dimensional objects using a lens array called a fly's eye lens and photographic film. Some groups have used it for surgical navigation in brain surgery. In this study, we tried to generate integral photographic images of intraductal papillary mucinous tumors of the pancreas, and evaluated the feasibility of this method. METHODOLOGY: Five patients with an intraductal papillary mucinous tumor of pancreas were studied. We used MRI data as three-dimensional data. MRI data were acquired with a 1.5-T clinical imager (Signal.5; GE Medical Systems, U.S.A). We used multi-slab single-shot fast spin-echo sequences. Section thickness was between 2 and 3mm in the coronal plane. From these data, integral photographic images were generated on a liquid crystal display by a three-dimensional rendering algorithm. RESULTS: Three-dimensional images using the principle of integral photography could be generated in all 5 cases. We could recognize these images as three-dimensional images by slightly moving the viewpoint to up, down, left, and right. From these images, we were able to grasp the three-dimensional relationship between the tumor, bile duct, and pancreatic duct. Using these three-dimensional images, a variety of minimally invasive surgical procedures were performed for intraductal papillary mucinous tumor of the pancreas, more safely and speedily than formerly. CONCLUSIONS: The images based on integral photography are suitable for medical imaging and are useful for surgical planning.