Edgar Delgado-Eckert

A virtual look at Epstein-Barr virus infection: simulation mechanism.

Epstein-Barr virus (EBV) is an important human pathogen that establishes a life-long persistent infection and for which no precise animal model exists. In this paper, we describe in detail an agent-based model and computer simulation of EBV infection. Agents representing EBV and sets of B and T lymphocytes move and interact on a three-dimensional grid approximating Waldeyers ring, together with abstract compartments for lymph and blood. The simulation allows us to explore the development and resolution of virtual infections in a manner not possible in actual human experiments. Specifically, we identify parameters capable of inducing clearance, persistent infection, or death.

Volumetric Capnography in Infants with Bronchopulmonary Dysplasia

OBJECTIVES To assess the feasibility of using volumetric capnography in spontaneously breathing small infants and its ability to discriminate between infants with and without bronchopulmonary dysplasia (BPD). STUDY DESIGN Lung function variables for 231 infants (102 term, 52 healthy preterm, 77 BPD), matched for post-conceptional age of 44 weeks, were collected. BPD was defined as supplemental oxygen requirement at 36 weeks post-menstrual age. Tidal breath-by-breath volume capnograms were obtained by mainstream capnography. The capnographic slope of phase II (SII) and slope of phase III (SIII) were calculated and compared between study groups. The effect of BPD, tidal volume (VT), respiratory rate (RR), and prematurity on the magnitude of the slopes was assessed. RESULTS SII was steeper in infants with BPD (100 ± 28/L) compared with healthy preterm (88 ± 22/L; P = .007) and term infants (79 ± 18/L; P < .001), but this finding was attributed to differences in VT, RR, and gestational age. SIII was steeper in the BPD group (26.8 ± 14.1/L) compared with healthy preterm (16.2 ± 6.2/L; P < .001) and term controls (14.8 ± 5.4/L; P < .001). BPD was a significant predictor of SIII independently of VT, RR, and gestational age. The ability of SIII to discriminate between BPD and controls was significantly higher compared with lung clearance index (area under the curve 0.83 vs 0.56; P < .001). CONCLUSIONS Volumetric capnography may provide valuable information regarding functional lung alterations related to BPD and might be considered as an alternative to more involved lung function techniques for monitoring chronic lung disease during early infancy.

The Cycle of EBV Infection Explains Persistence, the Sizes of the Infected Cell Populations and Which Come under CTL Regulation

Previous analysis of Epstein-Barr virus (EBV) persistent infection has involved biological and immunological studies to identify and quantify infected cell populations and the immune response to them. This led to a biological model whereby EBV infects and activates naive B-cells, which then transit through the germinal center to become resting memory B-cells where the virus resides quiescently. Occasionally the virus reactivates from these memory cells to produce infectious virions. Some of this virus infects new naive B-cells, completing a cycle of infection. What has been lacking is an understanding of the dynamic interactions between these components and how their regulation by the immune response produces the observed pattern of viral persistence. We have recently provided a mathematical analysis of a pathogen which, like EBV, has a cycle of infected stages. In this paper we have developed biologically credible values for all of the parameters governing this model and show that with these values, it successfully recapitulates persistent EBV infection with remarkable accuracy. This includes correctly predicting the observed patterns of cytotoxic T-cell regulation (which and by how much each infected population is regulated by the immune response) and the size of the infected germinal center and memory populations. Furthermore, we find that viral quiescence in the memory compartment dictates the pattern of regulation but is not required for persistence; it is the cycle of infection that explains persistence and provides the stability that allows EBV to persist at extremely low levels. This shifts the focus away from a single infected stage, the memory B-cell, to the whole cycle of infection. We conclude that the mathematical description of the biological model of EBV persistence provides a sound basis for quantitative analysis of viral persistence and provides testable predictions about the nature of EBV-associated diseases and how to curb or prevent them.

Finding the probability of infection in an SIR network is NP-Hard

It is the purpose of this article to review results that have long been known to communications network engineers and have direct application to epidemiology on networks. A common approach in epidemiology is to study the transmission of a disease in a population where each individual is initially susceptible (S), may become infective (I) and then removed or recovered (R) and plays no further epidemiological role. Much of the recent work gives explicit consideration to the network of social interactions or disease-transmitting contacts and attendant probability of transmission for each interacting pair. The state of such a network is an assignment of the values {S,I,R} to its members. Given such a network, an initial state and a particular susceptible individual, we would like to compute their probability of becoming infected in the course of an epidemic. It turns out that this and related problems are NP-hard. In particular, it belongs in a class of problems for which no efficient algorithms for their solution are known. Moreover, finding an efficient algorithm for the solution of any problem in this class would entail a major breakthrough in computer science.

Air pollution modelling for birth cohorts: a time-space regression model.

BackgroundTo investigate air pollution effects during pregnancy or in the first weeks of life, models are needed that capture both the spatial and temporal variability of air pollution exposures.MethodsWe developed a time-space exposure model for ambient NO2 concentrations in Bern, Switzerland. We used NO2 data from passive monitoring conducted between 1998 and 2009: 101 rural sites (24,499 biweekly measurements) and 45 urban sites (4350 monthly measurements). We evaluated spatial predictors (land use; roads; traffic; population; annual NO2 from a dispersion model) and temporal predictors (meteorological conditions; NO2 from continuous monitoring station). Separate rural and urban models were developed by multivariable regression techniques. We performed ten-fold internal cross-validation, and an external validation using 57 NO2 passive measurements obtained at study participant’s homes.ResultsTraffic related explanatory variables and fixed site NO2 measurements were the most relevant predictors in both models. The coefficient of determination (R2) for the log transformed models were 0.63 (rural) and 0.54 (urban); cross-validation R2s were unchanged indicating robust coefficient estimates. External validation showed R2s of 0.54 (rural) and 0.67 (urban).ConclusionsThis approach is suitable for air pollution exposure prediction in epidemiologic research with time-vulnerable health effects such as those occurring during pregnancy or in the first weeks of life.

Electronic monitoring of adherence to inhaled corticosteroids: an essential tool in identifying severe asthma in children

International guidelines recommend that severe asthma can only be diagnosed after contributory factors, including adherence, have been addressed. Accurate assessment of adherence is difficult in clinical practice. We hypothesised that electronic monitoring in children would identify nonadherence, thus delineating the small number with true severe asthma. Asthmatic children already prescribed inhaled corticosteroids were prospectively recruited and persistence of adherence assessed using electronic monitoring devices. Spirometry, airway inflammation and asthma control were measured at the start and end of the monitoring period. 93 children (62 male; median age 12.4 years) were monitored for a median of 92 days. Median (range) monitored adherence was 74% (21–99%). We identified four groups: 1) good adherence during monitoring with improved control, 24% (likely previous poor adherence); 2) good adherence with poor control, 18% (severe therapy-resistant asthma); 3) poor adherence with good control, 26% (likely overtreated); and 4) poor adherence with poor control, 32%. No clinical parameter prior to monitoring distinguished these groups. Electronic monitoring is a useful tool for identifying children in whom a step up in treatment is indicated. Different approaches are needed in those who are controlled when adherent or who are nonadherent. Electronic monitoring is essential in a paediatric severe asthma clinic. Children with true, severe therapy-resistant asthma cannot be identified without electronic adherence monitoring http://ow.ly/RMoU30fk1wu

Reverse engineering time discrete finite dynamical systems: a feasible undertaking?

With the advent of high-throughput profiling methods, interest in reverse engineering the structure and dynamics of biochemical networks is high. Recently an algorithm for reverse engineering of biochemical networks was developed by Laubenbacher and Stigler. It is a top-down approach using time discrete dynamical systems. One of its key steps includes the choice of a term order, a technicality imposed by the use of Gröbner-bases calculations. The aim of this paper is to identify minimal requirements on data sets to be used with this algorithm and to characterize optimal data sets. We found minimal requirements on a data set based on how many terms the functions to be reverse engineered display. Furthermore, we identified optimal data sets, which we characterized using a geometric property called “general position”. Moreover, we developed a constructive method to generate optimal data sets, provided a codimensional condition is fulfilled. In addition, we present a generalization of their algorithm that does not depend on the choice of a term order. For this method we derived a formula for the probability of finding the correct model, provided the data set used is optimal. We analyzed the asymptotic behavior of the probability formula for a growing number of variables n (i.e. interacting chemicals). Unfortunately, this formula converges to zero as fast as , where and . Therefore, even if an optimal data set is used and the restrictions in using term orders are overcome, the reverse engineering problem remains unfeasible, unless prodigious amounts of data are available. Such large data sets are experimentally impossible to generate with todays technologies.

Sigh‐induced changes of breathing pattern in preterm infants

Sighs are thought to play an important role in control of breathing. It is unclear how sighs are triggered, and whether preterm birth and lung disease influence breathing pattern prior to and after a sigh in infants. To assess whether frequency, morphology, size, and short‐term variability in tidal volume (VT) before, during, and after a sigh are influenced by gestational age at birth and lung disease (bronchopulmonary dysplasia, BPD) in former preterm infants and healthy term controls measured at equivalent postconceptional age (PCA). We performed tidal breathing measurements in 143 infants during quiet natural sleep at a mean (SD) PCA of 44.8 (1.3) weeks. A total of 233 sighs were analyzed using multilevel, multivariable regression. Sigh frequency in preterm infants increased with the degree of prematurity and severity of BPD, but was not different from that of term controls when normalized to respiratory rate. After a sigh, VT decreased remarkably in all infants (paired t‐test: P < 0.001). There was no major effect of prematurity or BPD on various indices of sigh morphology and changes in VT prior to or after a sigh. Short‐term variability in VT modestly increased with maturity at birth and infants with BPD showed an earlier return to baseline variability in VT following a sigh. In early infancy, sigh‐induced changes in breathing pattern are moderately influenced by prematurity and BPD in preterm infants. The major determinants of sigh‐related breathing pattern in these infants remain to be investigated, ideally using a longitudinal study design.

Long-term smoking cessation and heart rate dynamics in an aging healthy cohort : is it possible to fully recover?

AIM To evaluate the long-term influence of smoking cessation on the regulation of the autonomic cardiovascular system in an aging general population, using the subpopulation of lifelong non-smokers as control group. METHODS We analyzed 1481 participants aged ≥50 years from the SAPALDIA cohort. In each participant, heart rate variability and heart rate dynamics were characterized by means of various quantitative analyzes of the inter-beat interval time series generated from 24-hour electrocardiogram recordings. Each parameter obtained was then used as the outcome variable in multivariable linear regression models in order to evaluate the association with smoking status and time elapsed since smoking cessation. The models were adjusted for known confounding factors and stratified by the time elapsed since smoking cessation. RESULTS Our findings indicate that smoking triggers adverse changes in the regulation of the cardiovascular system, even at low levels of exposure since current light smokers exhibited significant changes as compared to lifelong non-smokers. Moreover, there was evidence for a dose-response effect. Indeed, the changes observed in current heavy smokers were more marked as compared to current light smokers. Furthermore, full recovery was achieved in former smokers (i.e., normalization to the level of lifelong non-smokers). However, while light smokers fully recovered within the 15 first years of cessation, heavy former smokers might need up to 15-25 years to fully recover. CONCLUSION This study supports the substantial benefits of smoking cessation, but also warns of important long-term alterations caused by heavy smoking.

SATURATION EFFECTS ON T-CELL ACTIVATION IN A MODEL OF A MULTI-STAGE PATHOGEN

In previous work, we studied host response to a pathogen which uses a cycle of immunologically distinct stages to establish and maintain infection. We showed that for generic parameter values, the system has a unique biologically meaningful stable fixed point. That paper used a simplified model of T-cell activation, making proliferation depend linearly on antigen-T-cell encounters. Here we generalize the way in which T-cell proliferation depends on the sizes of the antigenic populations. In particular, we allow this response to become saturated at high levels of antigen. As a result, we show that this family of generalized models shares the same steady-state behavior properties with the simpler model contemplated in our previous work.