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PubMed1985

Social networks and social support: living with chronic renal disease.

Individuals with chronic renal disease who receive dialysis treatment are continually faced with major adjustments. These may include dealing with changes in work and economic status, social roles, activity levels, self-image, health status, and normal routines, as well as learning to live with uncertainty and loss. The individual's social network plays a key role as the individual experiences and moves through various stages of adjustment. Networks with certain characteristics (e.g. provision of affective support, reciprocal ties) may be more effective than others lacking these characteristics in meeting the individual's changing needs during the process of adjusting to chronic renal disease. This paper examines this relationship between the characteristics of an individual's social network and adjustment to chronic renal illness. The discussion focuses on the impact of chronic renal disease on the individual, the composition and characteristics of the social network, and on the relationships between network members. How the social network affects a person's adjustment to stages of adaptation to chronic renal disease is also addressed. Finally, suggestions are presented for how health care professionals can intervene at the individual, network, and organizational level to strengthen and enlarge social networks in order to enhance social support.

Humans↗
PubMed2005

Derivation and validation of a Bayesian network to predict pretest probability of venous thromboembolism.

STUDY OBJECTIVE: A Bayesian network can estimate a numeric pretest probability of venous thromboembolism on the basis of values of clinical variables. We determine the accuracy with which a Bayesian network can identify patients with a low pretest probability of venous thromboembolism, defined as less than or equal to 2%. METHODS: Using commercial software, we derived a population of Bayesian networks from 25 input variables collected on 3,145 emergency department (ED) patients with suspected venous thromboembolism who underwent standardized testing, including pulmonary vascular imaging, and 90-day follow-up (11.0% of patients were venous thromboembolism positive). The best-fit Bayesian network was selected using a genetic algorithm. The selected Bayesian network was tested in a validation population of 1,423 ED patients prospectively evaluated for venous thromboembolism, including 90-day follow-up (8.0% were venous thromboembolism positive). The Bayesian network probability estimate was normalized to a score of 0% to 100%. RESULTS: Of 1,423 patients in the validation cohort, 711 (50%; 95% confidence interval [CI] 47% to 52%) had a score less than or equal to 2% that predicted a low pretest probability. Of these 711 patients, 700 (98.5%; 95% CI 97.2% to 99.2%) had no venous thromboembolism at follow-up. CONCLUSION: A Bayesian network, derived and independently validated in ED populations, identified half of the validation cohort as having a low pretest probability (< or =2%); 98.5% of these patients were correctly classified by the network.

Adult↗
PubMed2005

Dynamical properties of model gene networks and implications for the inverse problem.

We study the inverse problem, or the "reverse-engineering" problem, for two abstract models of gene expression dynamics, discrete-time Boolean networks and continuous-time switching networks. Formally, the inverse problem is similar for both types of networks. For each gene, its regulators and its Boolean dynamics function must be identified. However, differences in the dynamical properties of these two types of networks affect the amount of data that is necessary for solving the inverse problem. We derive estimates for the average amounts of time series data required to solve the inverse problem for randomly generated Boolean and continuous-time switching networks. We also derive a lower bound on the amount of data needed that holds for both types of networks. We find that the amount of data required is logarithmic in the number of genes for Boolean networks, matching the general lower bound and previous theory, but are superlinear in the number of genes for continuous-time switching networks. We also find that the amount of data needed scales as 2(K), where K is the number of regulators per gene, rather than 2(2K), as previous theory suggests.

Models, Genetic↗
PubMed2005

Modelling periodic oscillation in gene regulatory networks by cyclic feedback systems.

In this paper, we develop a new methodology to analyze and design periodic oscillators of biological networks, in particular gene regulatory networks with multiple genes, proteins and time delays, by using negative cyclic feedback systems. We show that negative cyclic feedback networks have no stable equilibria but stable periodic orbits when certain conditions are satisfied. Specifically, we first prove the basic properties of the biological networks composed of cyclic feedback loops, and then extend our results to general cyclic feedback network with less restriction, thereby making our theoretical analysis and design of oscillators easy to implement, even for large-scale systems. Finally, we use one circadian network formed by a period protein (PER) and per mRNA, and one biologically plausible synthetic gene network, to demonstrate the theoretical results. Since there is less restriction on the network structure, the results of this paper can be expected to apply to a wide variety of areas on modelling, analyzing and designing of biological systems.

Animals↗
PubMed2006

Social network as a predictor of hospital readmission and mortality among older patients with heart failure.

BACKGROUND: This study examines the relationship between social network and hospital readmission and mortality in older patients with heart failure. METHODS AND RESULTS: Prospective study conducted with 371 patients, age 65 and older, admitted for heart failure-related emergencies at 4 Spanish hospitals. Social network was measured at baseline with a 4-item questionnaire that ascertained whether subjects were married, lived with another person(s), saw or had telephone contact with family members daily or almost daily, and were at home alone for less than 2 hours per day. Social network was deemed "high" where all 4 items were present, "moderate" where 3 were present, and "low" where 2 or fewer were present. Analyses were performed using Cox models, and adjusted for the main confounders. A total of 55% of patients had high or moderate social networks. During a median follow-up of 6.5 months, 135 (36.4%) patients underwent a first emergency rehospitalization and 68 (18.3%) died. Compared with patients with high social network, hospital readmission was more frequent among those who had moderate (hazard ratio [HR] 1.87; 95% confidence interval [CI] 1.06-3.29; P < .05) and low social networks (HR 1.98; 95% CI 1.07-3.68; P < .05). This relationship showed a positive dose-response (p for linear trend 0.042). The magnitude of this association was comparable to that of other important predictors of readmission, such as previous hospitalization. No relationship was observed between social network and death. CONCLUSION: A very simple questionnaire measuring social network can identify patients with a higher short-term risk of hospital readmission.

Aged↗
PubMed2004

Single cell profiling of potentiated phospho-protein networks in cancer cells.

Altered growth factor responses in phospho-protein-driven signaling networks are crucial to cancer cell survival and pathology. Profiles of cancer cell signaling networks might therefore identify mechanisms by which such cells interpret environmental cues for continued growth. Using multiparameter flow cytometry, we monitored phospho-protein responses to environmental cues in acute myeloid leukemia at the single cell level. By exposing cancer cell signaling networks to potentiating inputs, rather than relying upon the basal levels of protein phosphorylation alone, we could discern unique cancer network profiles that correlated with genetics and disease outcome. Strikingly, individual cancers manifested multiple cell subsets with unique network profiles, reflecting cancer heterogeneity at the level of signaling response. The results revealed a dramatic remodeling of signaling networks in cancer cells. Thus, single cell measurements of phospho-protein responses reveal shifts in signaling potential of a phospho-protein network, allowing for categorizing of cell network phenotypes by multidimensional molecular profiles of signaling.

Antineoplastic Agents↗
PubMed2004

Scale-free networks versus evolutionary drift.

Recent studies of properties of various biological networks revealed that many of them display scale-free characteristics. Since the theory of scale-free networks is applicable to evolving networks, one can hope that it provides not only a model of a biological network in its current state but also sheds some insight into the evolution of the network. In this work, we investigate the probability distributions and scaling properties underlying some models for biological networks and protein domain evolution. The analysis of evolutionary models for domain similarity networks indicates that models which include evolutionary drift are typically not scale free. Instead they adhere quite closely to the Yule distribution. This finding indicates that the direct applicability of scale-free models in understanding the evolution of biological network may not be as wide as it has been hoped for.

Computational Biology↗
PubMed2026

STX1B variant-specific synaptic dysfunction is associated with network hyperexcitability in human iPSC-derived neurons.

BACKGROUND: Variants in STX1B/syntaxin-1B are linked to a spectrum of fever-associated epilepsy syndromes. While studies in murine models have provided mechanistic insights, their relevance to human disease in a heterozygous context may be limited. METHODS: We investigated two pathogenic STX1B variants using isolated single neurons and neuronal network cultures derived from patient-specific induced pluripotent stem cells. These carried either a de novo p.G226R variant, associated with severe developmental epilepsy, or an InDel variant (p.K45delinsRCMIE/p.L46M) linked to a transient familial seizure syndrome. Synaptic function and network excitability were assessed using patch-clamp and multi-electrode array recordings, alongside morphological and transcriptomic profiling. FINDINGS: G226R exhibited both gain- and loss-of-function characteristics, with increased miniature excitatory postsynaptic current frequency in networks but not in autapses, and synaptic failure during sustained high-frequency stimulation. For the InDel variant, the predicted loss-of-function phenotype based on reduced syntaxin-1B levels was not detectable at the single-cell level, likely masked by compensatory synaptic upregulation. At the network level, however, both variants were associated with neuronal hyperexcitability, characterised by more frequent and prolonged bursting activity, with a much stronger phenotype in G226R-containing networks. Transcriptomic profiling revealed a differential dysregulation of synaptic and other neuronal genes. INTERPRETATION: The divergence between morphological, electrophysiological and transcriptomic findings suggests that compensatory mechanisms may contribute to network hyperexcitability. Initially engaged to maintain homoeostasis, they may ultimately contribute to a pathological network state. The graded severity of network alterations across STX1B variants correlates with the clinical phenotypes. FUNDING: BMBF (Treat ION-01GM2210A, SNAREopathies-01EW1809A), 2023 FEBS Summer Fellowship, Fort&#xfc;ne programme (2610-0-0), EKFS college precise.net, Open Access Publishing Fund of University of T&#xfc;bingen.

Humans↗
PubMed2006

Life cycle assessment of second generation (2G) and third generation (3G) mobile phone networks.

The environmental performance of presently operated GSM and UMTS networks was analysed concentrating on the environmental effects of the End-of-Life (EOL) phase using the Life Cycle Assessment (LCA) method. The study was performed based on comprehensive life cycle inventory and life cycle modelling. The environmental effects were quantified using the IMPACT2002+ method. Based on technological forecasts, the environmental effects of forthcoming mobile telephone networks were approximated. The results indicate that a parallel operation of GSM and UMTS networks is environmentally detrimental and the transition phase should be kept as short as possible. The use phase (i.e. the operation) of the radio network components account for a large fraction of the total environmental impact. In particular, there is a need to lower the energy consumption of those network components. Seen in relation to each other, UMTS networks provide an environmentally more efficient mobile communication technology than GSM networks. In assessing the EOL phase, recycling the electronic scrap of mobile phone networks was shown to have clear environmental benefits. Under the present conditions, material recycling could help lower the environmental impact of the production phase by up to 50%.

Cell Phone↗
PubMed2005

Social networks and health service utilization.

While social networks have been examined in the context of many economic choices and outcomes, this study is the first to investigate the effects of social networks on health service utilization decisions. Networks can affect utilization decisions in many ways. They can provide information on institutional details of the health care system, and can reduce the search costs of locating an appropriate health care provider. Networks can even alter the demand for services by affecting the perceived efficacy or desirability of the available services. Using health service utilization decisions to study networks has two main advantages over work that studies other public programs. First, because health care in Canada is universal, there are no questions of eligibility. Second, by studying the different measures of utilization, it is possible to observe how the network effects vary across measures that reflect visits primarily instigated by the patient, to measures that reflect visits instigated by both patients and their physician. Using data from three cycles of the Canadian National Population Health Survey, this work exploits regional and language group variation to identify network effects. Strong and robust evidence of networks effects is found on the decision to utilize services reflecting initial contact with the health care system. As well, this work presents novel evidence that utilization of health services by immigrants increases with the number of doctors that speak their language in their neighborhood.

Adult↗
PubMed2005

A connection rule for alpha-carbon coarse-grained elastic network models using chemical bond information.

A sparser but more efficient connection rule (called a bond-cutoff method) for a simplified alpha-carbon coarse-grained elastic network model is presented. One of conventional connection rules for elastic network models is the distance-cutoff method, where virtual springs connect an alpha-carbon with all neighbor alpha-carbons within predefined distance-cutoff value. However, though the maximum interaction distance between alpha-carbons is reported as 7 angstroms, this cutoff value can make the elastic network unstable in many cases of protein structures. Thus, a larger cutoff value (>11 angstroms) is often used to establish a stable elastic network model in previous researches. To overcome this problem, a connection rule for backbone model is proposed, which satisfies the minimum condition to stabilize an elastic network. Based on the backbone connections, each type of chemical interactions is considered and added to the elastic network model: disulfide bonds, hydrogen bonds, and salt-bridges. In addition, the van der Waals forces between alpha-carbons are modeled by using the distance-cutoff method. With the proposed connection rule, one can make an elastic network model with less than 7 angstroms distance cutoff, which can reveal protein flexibility more sharply. Moreover, the normal modes from the new elastic network model can reflect conformational changes of a given protein better than ones by the distance-cutoff method. This method can save the computational cost when calculating normal modes of a given protein structure, because it can reduce the total number of connections. As a validation, six example proteins are tested. Computational times and the overlap values between the conformational change and infinitesimal motion calculated by normal mode analysis are presented. Those animations are also available at UMass Morph Server (http://biomechanics.ecs.umass.edu/umms.html).

Carbon↗
PubMed2006

Evaluation of a Bayesian decision network for diagnosing pyloric stenosis.

PURPOSE: Most infants undergoing an ultrasound to rule out pyloric stenosis will have a negative study, suggesting the low accuracy of clinical assessment. The purpose of this study was to evaluate the feasibility of using a Bayesian network to improve the accuracy of diagnosing pyloric stenosis. METHODS: Records of 118 infants undergoing an ultrasound to rule out pyloric stenosis were reviewed. Data from 88 (75%) infants were used to train a Bayesian decision network that predicted the probability of pyloric stenosis using risk factors, signs, and symptoms of the disease. The emergency department records of the remaining 28 (25%) infants were used to test the network. Two groups of pediatric surgeons and pediatric emergency medicine physicians were asked to predict the probability of pyloric stenosis in the testing set: (1) physicians using the network and (2) physicians using only emergency department records. Accuracy was evaluated using area under the ROC curve (discrimination) and Hosmer-Lemeshow (H-L) c-statistic (calibration). RESULTS: Physicians using the Bayesian decision network better predicted the probability of pyloric stenosis among infants in the testing set than those not using the network (ROC 0.973 vs 0.882; H-L c-statistic 3.9 [P > .05] vs 24.3 [P < .05]). Physicians using the network would have ordered 22% fewer ultrasounds and missed no cases of pyloric stenosis. CONCLUSIONS: The use of a Bayesian decision network may improve the accuracy of physicians diagnosing infants with possible pyloric stenosis. Use of this decision tool may safely reduce the need for imaging among infants with suspected pyloric stenosis.

Bayes Theorem↗
PubMed2006

Effectiveness of realistic vaccination strategies for contact networks of various degree distributions.

A "contact network" that models infection transmission comprises nodes (or individuals) that are linked when they are in contact and can potentially transmit an infection. Through analysis and simulation, we studied the influence of the distribution of the number of contacts per node, defined as degree, on infection spreading and its control by vaccination. Three random contact networks of various degree distributions were examined. In a scale-free network, the frequency of high-degree nodes decreases as the power of the degree (the case of the third power is studied here); the decrease is exponential in an exponential network, whereas all nodes have the same degree in a constant network. Aiming for containment at a very early stage of an epidemic, we measured the sustainability of a specific network under a vaccination strategy by employing the critical transmissibility larger than which the epidemic would occur. We examined three vaccination strategies: mass, ring, and acquaintance. Irrespective of the networks, mass preventive vaccination increased the critical transmissibility inversely proportional to the unvaccinated rate of the population. Ring post-outbreak vaccination increased the critical transmissibility inversely proportional to the unvaccinated rate, which is the rate confined to the targeted ring comprising the neighbors of an infected node; however, the total number of vaccinated nodes could mostly be fewer than 100 nodes at the critical transmissibility. In combination, mass and ring vaccinations decreased the pathogen's "effective" transmissibility each by the factor of the unvaccinated rate. The amount of vaccination used in acquaintance preventive vaccination was lesser than the mass vaccination, particularly under a highly heterogeneous degree distribution; however, it was not as less as that used in ring vaccination. Consequently, our results yielded a quantitative assessment of the amount of vaccination necessary for infection containment, which is universally applicable to contact networks of various degree distributions.

Communicable Diseases↗
PubMed2006

Asymptotic states and topological structure of an activation-deactivation chemical network.

The influence of the topology on the asymptotic states of a network of interacting chemical species has been studied by simulating its time evolution. Random and scale-free networks have been designed to support relevant features of activation-deactivation reactions networks (mapping signal transduction networks) and the system of ordinary differential equations associated to the dynamics has been numerically solved. We analysed stationary states of the dynamics as a function of the network's connectivity and of the distribution of the chemical species on the network; we found important differences between the two topologies in the regime of low connectivity. In particular, only for low connected scale-free networks it is possible to find zero activity patterns as stationary states of the dynamics which work as signal off-states. Asymptotic features of random and scale-free networks become similar as the connectivity increases.

Animals↗
PubMed2006

A paradigm for viewing biologic systems as scale-free networks based on energy efficiency: implications for present therapies and the future of evolution.

A network constitutes an abstract description of the relationships among entities, respectively termed links and nodes. If a power law describes the probability distribution of the number of links per node, the network is said to be scale-free. Scale-free networks feature link clustering around certain hubs based on preferential attachments that emerge due either to merit or legacy. Biologic systems ranging from sub-atomic to ecosystems represent scale-free networks in which energy efficiency forms the basis of preferential attachments. This paradigm engenders a novel scale-free network theory of evolution based on energy efficiency. As environmental flux induces fitness dislocations and compels a new meritocracy, new merit-based hubs emerge, previously merit-based hubs become legacy hubs, and network recalibration occurs to achieve system optimization. To date, Darwinian evolution, characterized by innovation sampling, variation, and selection through filtered termination, has enabled biologic progress through optimization of energy efficiency. However, as humans remodel their environment, increasing the level of unanticipated fitness dislocations and inducing evolutionary stress, the tendency of networks to exhibit inertia and retain legacy hubs engender maladaptations. Many modern diseases may fundamentally derive from these evolutionary displacements. Death itself may constitute a programmed adaptation, terminating individuals who represent legacy hubs and recalibrating the network. As memes replace genes as the basis of innovation, death itself has become a legacy hub. Post-Darwinian evolution may favor indefinite persistence to optimize energy efficiency. We describe strategies to reprogram or decommission legacy hubs that participate in human disease and death.

Animals↗
PubMed2005

Measuring temporal dynamics of functional networks using phase spectrum of fMRI data.

We present a novel method to measure relative latencies between functionally connected regions using phase-delay of functional magnetic resonance imaging data. Derived from the phase component of coherency, this quantity estimates the linear delay between two time-series. In conjunction with coherence, derived from the magnitude component of coherency, phase-delay can be used to examine the temporal properties of functional networks. In this paper, we apply coherence and phase-delay methods to fMRI data in order to investigate dynamics of the motor network during task and rest periods. Using the supplementary motor area (SMA) as a reference region, we calculated relative latencies between the SMA and other regions within the motor network including the dorsal premotor cortex (PMd), primary motor cortex (M1), and posterior parietal cortex (PPC). During both the task and rest periods, we measured significant delays that were consistent across subjects. Specifically, we found significant delays between the SMA and the bilateral PMd, bilateral M1, and bilateral PPC during the task condition. During the rest condition, we found that the temporal dynamics of the network changed relative to the task period. No significant delays were measured between the SMA and the left PM and left M1; however, the right PM, right M1, and bilateral PPC were significantly delayed with respect to the SMA. Additionally, we observed significant map-wise differences in the dynamics of the network at task compared to the network at rest. These differences were observed in the interaction between the SMA and the left M1, left superior frontal gyrus, and left middle frontal gyrus. These temporal measurements are important in determining how regions within a network interact and provide valuable information about the sequence of cognitive processes within a network.

Adult↗
PubMed2004

Development of attentional networks in childhood.

Recent research in attention has involved three networks of anatomical areas that carry out the functions of orienting, alerting and executive control (including conflict monitoring). There have been extensive cognitive and neuroimaging studies of these networks in adults. We developed an integrated Attention Network Test (ANT) to measure the efficiency of the three networks with adults. We have now adapted this test to study the development of these networks during childhood. The test is a child-friendly version of the flanker task with alerting and orienting cues. We studied the development of the attentional networks in a cross-sectional experiment with four age groups ranging from 6 through 9 (Experiment 1). In a second experiment, we compared children (age 10 years) and adult performance in both child and adults versions of the ANT. Reaction time and accuracy improved at each age interval and positive values were found for the average efficiency of each of the networks. Alertness showed evidence of change up to and beyond age 10, while conflict scores appear stable after age seven and orienting scores do not change in the age range studied. A final experiment with forty 7-year-old children suggested that children like adults showed independence between the three networks under some conditions.

Adult↗
PubMed2004

Emergent properties of CNS neuronal networks as targets for pharmacology: application to anticonvulsant drug action.

CNS drugs may act by modifying the emergent properties of complex CNS neuronal networks. Emergent properties are network characteristics that are not predictably based on properties of individual member neurons. Neuronal membership within networks is controlled by several mechanisms, including burst firing, gap junctions, endogenous and exogenous neuroactive substances, extracellular ions, temperature, interneuron activity, astrocytic integration and external stimuli. The effects of many CNS drugs in vivo may critically involve actions on specific brain loci, but this selectivity may be absent when the same neurons are isolated from the network in vitro where emergent properties are lost. Audiogenic seizures (AGS) qualify as an emergent CNS property, since in AGS the acoustic stimulus evokes a non-linear output (motor convulsion), but the identical stimulus evokes minimal behavioral changes normally. The hierarchical neuronal network, subserving AGS in rodents is initiated in inferior colliculus (IC) and progresses to deep layers of superior colliculus (DLSC), pontine reticular formation (PRF) and periaqueductal gray (PAG) in genetic and ethanol withdrawal-induced AGS. In blocking AGS, certain anticonvulsants reduce IC neuronal firing, while other agents act primarily on neurons in other AGS network sites. However, the NMDA receptor channel blocker, MK-801, does not depress neuronal firing in any network site despite potently blocking AGS. Recent findings indicate that MK-801 actually enhances firing in substantia nigra reticulata (SNR) neurons in vivo but not in vitro. Thus, the MK-801-induced firing increases in SNR neurons observed in vivo may involve an indirect effect via disinhibition, involving an action on the emergent properties of this seizure network.

Acoustic Stimulation↗