The preanalytic phase. An important component of laboratory medicine. (1/33)

The preanalytic phase is an important component of total laboratory quality. A wide range of variables that affect the result for a patient from whom a specimen of blood or body fluid has been collected, including the procedure for collection, handling, and processing before analysis, constitute the preanalytic phase. Physiologic variables, such as lifestyle, age, and sex, and conditions such as pregnancy and menstruation, are some of the preanalytic phase factors. Endogenous variables such as drugs or circulating antibodies might interact with a specific method to yield spurious analytic results. The preanalytic phase variables affect a wide range of laboratory disciplines.  (+info)

Strategies for the physiome project. (2/33)

The physiome is the quantitative description of the functioning organism in normal and pathophysiological states. The human physiome can be regarded as the virtual human. It is built upon the morphome, the quantitative description of anatomical structure, chemical and biochemical composition, and material properties of an intact organism, including its genome, proteome, cell, tissue, and organ structures up to those of the whole intact being. The Physiome Project is a multicentric integrated program to design, develop, implement, test and document, archive and disseminate quantitative information, and integrative models of the functional behavior of molecules, organelles, cells, tissues, organs, and intact organisms from bacteria to man. A fundamental and major feature of the project is the databasing of experimental observations for retrieval and evaluation. Technologies allowing many groups to work together are being rapidly developed. Internet II will facilitate this immensely. When problems are huge and complex, a particular working group can be expert in only a small part of the overall project. The strategies to be worked out must therefore include how to pull models composed of many submodules together even when the expertise in each is scattered amongst diverse institutions. The technologies of bioinformatics will contribute greatly to this effort. Developing and implementing code for large-scale systems has many problems. Most of the submodules are complex, requiring consideration of spatial and temporal events and processes. Submodules have to be linked to one another in a way that preserves mass balance and gives an accurate representation of variables in nonlinear complex biochemical networks with many signaling and controlling pathways. Microcompartmentalization vitiates the use of simplified model structures. The stiffness of the systems of equations is computationally costly. Faster computation is needed when using models as thinking tools and for iterative data analysis. Perhaps the most serious problem is the current lack of definitive information on kinetics and dynamics of systems, due in part to the almost total lack of databased observations, but also because, though we are nearly drowning in new information being published each day, either the information required for the modeling cannot be found or has never been obtained. "Simple" things like tissue composition, material properties, and mechanical behavior of cells and tissues are not generally available. The development of comprehensive models of biological systems is a key to pharmaceutics and drug design, for the models will become gradually better predictors of the results of interventions, both genomic and pharmaceutic. Good models will be useful in predicting the side effects and long term effects of drugs and toxins, and when the models are really good, to predict where genomic intervention will be effective and where the multiple redundancies in our biological systems will render a proposed intervention useless. The Physiome Project will provide the integrating scientific basis for the Genes to Health initiative, and make physiological genomics a reality applicable to whole organisms, from bacteria to man.  (+info)

Ontology recapitulates physiology. (3/33)

High-content information experiments in the post-genomic era hold the promise of deciphering age-old questions in biology and new ones in the biomedical arena. In response, researchers are devising computationally intensive and novel strategies to extract answers from multidimensional data sets.  (+info)

Older individuals have increased oro-nasal breathing during sleep. (4/33)

Breathing route during sleep has been studied very little, however, it has potential importance in the pathophysiology of sleep disordered breathing. Using overnight polysomnography, with separate nasal and oral thermocouple probes, data were obtained from 41 subjects (snorers and nonsnorers; 25 male and 16 female; aged 20-66 yrs). Awake, upright, inspiratory nasal resistance (Rn) was measured using posterior rhinomanometry. Each 30-s sleep epoch (not affected by apnoeas/hypopnoeas) was scored for presence of nasal and/or oral breathing. Overnight, seven subjects breathed nasally, one subject oro-nasally and the remainder switched between nasal and oro-nasal breathing. Oral-only breathing rarely occurred. Nasal breathing epochs were 55.79 (69.78) per cent of total sleep epochs (%TSE; median (interquartile range)), a value not significantly different to that for oro-nasal (TSE: 44.21 (68.66)%). Oro-nasal breathing was not related to snoring, sleep stage, posture, body mass index, height, weight, Rn (2.19 (1.77) cm H2O x L(-1) x sec(-1)) or sex, but was positively associated with age. Subjects > or = 40 yrs were approximately six times more likely than younger subjects to spend >50% of sleep epochs utilising oro-nasal breathing. Ageing is associated with an increasing occurrence of oro-nasal breathing during sleep.  (+info)

Reactome: a knowledgebase of biological pathways. (5/33)

Reactome, located at http://www.reactome.org is a curated, peer-reviewed resource of human biological processes. Given the genetic makeup of an organism, the complete set of possible reactions constitutes its reactome. The basic unit of the Reactome database is a reaction; reactions are then grouped into causal chains to form pathways. The Reactome data model allows us to represent many diverse processes in the human system, including the pathways of intermediary metabolism, regulatory pathways, and signal transduction, and high-level processes, such as the cell cycle. Reactome provides a qualitative framework, on which quantitative data can be superimposed. Tools have been developed to facilitate custom data entry and annotation by expert biologists, and to allow visualization and exploration of the finished dataset as an interactive process map. Although our primary curational domain is pathways from Homo sapiens, we regularly create electronic projections of human pathways onto other organisms via putative orthologs, thus making Reactome relevant to model organism research communities. The database is publicly available under open source terms, which allows both its content and its software infrastructure to be freely used and redistributed.  (+info)

Ligand accumulation in autocrine cell cultures. (6/33)

Cell-culture assays are routinely used to analyze autocrine signaling systems, but quantitative experiments are rarely possible. To enable the quantitative design and analysis of experiments with autocrine cells, we develop a biophysical theory of ligand accumulation in cell-culture assays. Our theory predicts the ligand concentration as a function of time and measurable parameters of autocrine cells and cell-culture experiments. The key step of our analysis is the derivation of the survival probability of a single ligand released from the surface of an autocrine cell. An expression for this probability is derived using the boundary homogenization approach and tested by stochastic simulations. We use this expression in the integral balance equations, from which we find the Laplace transform of the ligand concentration. We demonstrate how the theory works by analyzing the autocrine epidermal growth factor receptor system and discuss the extension of our methods to other experiments with cultured autocrine cells.  (+info)

Assessing physiological complexity. (7/33)

Physiologists both admire and fear complexity, but we have made relatively few attempts to understand it. Inherently complex systems are more difficult to study and less predictable. However, a deeper understanding of physiological systems can be achieved by modifying experimental design and analysis to account for complexity. We begin this essay with a tour of some mathematical views of complexity. After briefly exploring chaotic systems, information theory and emergent behavior, we reluctantly conclude that, while a mathematical view of complexity provides useful perspectives and some narrowly focused tools, there are too few generally practical take-home messages for physiologists studying complex systems. Consequently, we attempt to provide guidelines as to how complex systems might be best approached by physiologists. After describing complexity based on the sum of a physiological system's structures and processes, we highlight increasingly refined approaches based on the pattern of interactions between structures and processes. We then provide a series of examples illustrating how appreciating physiological complexity can improve physiological research, including choosing experimental models, guiding data collection, improving data interpretations and constructing more rigorous system models. Finally, we conclude with an invitation for physiologists, applied mathematicians and physicists to collaborate on describing, studying and learning from studies of physiological complexity.  (+info)

Noise in gene expression: origins, consequences, and control. (8/33)

Genetically identical cells and organisms exhibit remarkable diversity even when they have identical histories of environmental exposure. Noise, or variation, in the process of gene expression may contribute to this phenotypic variability. Recent studies suggest that this noise has multiple sources, including the stochastic or inherently random nature of the biochemical reactions of gene expression. In this review, we summarize noise terminology and comment on recent investigations into the sources, consequences, and control of noise in gene expression.  (+info)

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