Positional therapy for obstructive sleep apnea: an objective measurement of patients' usage and efficacy at home.

BACKGROUND: Positional therapy that prevents patients from sleeping supine has been used for many years to manage positional obstructive sleep apnea (OSA). However, patients' usage at home and the long term efficacy of this therapy have never been objectively assessed. METHODS: Sixteen patients with positional OSA who refused or could not tolerate continuous positive airway pressure (CPAP) were enrolled after a test night study (T0) to test the efficacy of the positional therapy device. The patients who had a successful test night were instructed to use the device every night for three months. Nightly usage was monitored by an actigraphic recorder placed inside the positional device. A follow-up night study (T3) was performed after three months of positional therapy. RESULTS: Patients used the device on average 73.7 ± 29.3% (mean ± SD) of the nights for 8.0 ± 2.0 h/night. 10/16 patients used the device more than 80% of the nights. Compared to the baseline (diagnostic) night, mean apnea-hypopnea index (AHI) decreased from 26.7 ± 17.5 to 6.0 ± 3.4 with the positional device (p<0.0001) during T0 night. Oxygen desaturation (3%) index also fell from 18.4 ± 11.1 to 7.1 ± 5.7 (p = 0.001). Time spent supine fell from 42.8 ± 26.2% to 5.8 ± 7.2% (p < 0.0001). At three months (T3), the benefits persisted with no difference in AHI (p = 0.58) or in time spent supine (p = 0.98) compared to T0 night. The Epworth sleepiness scale showed a significant decrease from 9.4 ± 4.5 to 6.6 ± 4.7 (p = 0.02) after three months. CONCLUSIONS: Selected patients with positional OSA can be effectively treated by a positional therapy with an objective compliance of 73.7% of the nights and a persistent efficacy after three months.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Modelling hydrodynamics in the Rio Parana, Argentina : an evaluation and inter-comparison of reduced-complexity and physics based models applied to a large sand-bed river

Depth-averaged velocities and unit discharges within a 30 km reach of one of the world's largest rivers, the Rio Parana, Argentina, were simulated using three hydrodynamic models with different process representations: a reduced complexity (RC) model that neglects most of the physics governing fluid flow, a two-dimensional model based on the shallow water equations, and a three-dimensional model based on the Reynolds-averaged Navier-Stokes equations. Row characteristics simulated using all three models were compared with data obtained by acoustic Doppler current profiler surveys at four cross sections within the study reach. This analysis demonstrates that, surprisingly, the performance of the RC model is generally equal to, and in some instances better than, that of the physics based models in terms of the statistical agreement between simulated and measured flow properties. In addition, in contrast to previous applications of RC models, the present study demonstrates that the RC model can successfully predict measured flow velocities. The strong performance of the RC model reflects, in part, the simplicity of the depth-averaged mean flow patterns within the study reach and the dominant role of channel-scale topographic features in controlling the flow dynamics. Moreover, the very low water surface slopes that typify large sand-bed rivers enable flow depths to be estimated reliably in the RC model using a simple fixed-lid planar water surface approximation. This approach overcomes a major problem encountered in the application of RC models in environments characterised by shallow flows and steep bed gradients. The RC model is four orders of magnitude faster than the physics based models when performing steady-state hydrodynamic calculations. However, the iterative nature of the RC model calculations implies a reduction in computational efficiency relative to some other RC models. A further implication of this is that, if used to simulate channel morphodynamics, the present RC model may offer only a marginal advantage in terms of computational efficiency over approaches based on the shallow water equations. These observations illustrate the trade off between model realism and efficiency that is a key consideration in RC modelling. Moreover, this outcome highlights a need to rethink the use of RC morphodynamic models in fluvial geomorphology and to move away from existing grid-based approaches, such as the popular cellular automata (CA) models, that remain essentially reductionist in nature. In the case of the world's largest sand-bed rivers, this might be achieved by implementing the RC model outlined here as one element within a hierarchical modelling framework that would enable computationally efficient simulation of the morphodynamics of large rivers over millennial time scales. (C) 2012 Elsevier B.V. All rights reserved.

In vivo brain macromolecule signals in healthy and glioblastoma mouse models: (1) H magnetic resonance spectroscopy, post-processing and metabolite quantification at 14.1 T.

In (1) H magnetic resonance spectroscopy, macromolecule signals underlay metabolite signals, and knowing their contribution is necessary for reliable metabolite quantification. When macromolecule signals are measured using an inversion-recovery pulse sequence, special care needs to be taken to correctly remove residual metabolite signals to obtain a pure macromolecule spectrum. Furthermore, since a single spectrum is commonly used for quantification in multiple experiments, the impact of potential macromolecule signal variability, because of regional differences or pathologies, on metabolite quantification has to be assessed. In this study, we introduced a novel method to post-process measured macromolecule signals that offers a flexible and robust way of removing residual metabolite signals. This method was applied to investigate regional differences in the mouse brain macromolecule signals that may affect metabolite quantification when not taken into account. However, since no significant differences in metabolite quantification were detected, it was concluded that a single macromolecule spectrum can be generally used for the quantification of healthy mouse brain spectra. Alternatively, the study of a mouse model of human glioma showed several alterations of the macromolecule spectrum, including, but not limited to, increased mobile lipid signals, which had to be taken into account to avoid significant metabolite quantification errors.

Hepatitis C virus infection: in vivo and in vitro models.

Major advances in the understanding of the molecular biology of hepatitis C virus (HCV) have been made recently. While the chimpanzee is the only established animal model of HCV infection, several in vivo and in vitro models have been established that allow us to study various aspects of the viral life cycle. In particular, the replicon system and the production of recombinant infectious virions revolutionized the investigation of HCV-RNA replication and rendered all steps of the viral life cycle, including entry and release of viral particles, amenable to systematic analysis. In the following we will review the different in vivo and in vitro models of HCV infection.

Establishment and characterization of models of chemotherapy resistance in colorectal cancer: Towards a predictive signature of chemoresistance.

Current standard treatments for metastatic colorectal cancer (CRC) are based on combination regimens with one of the two chemotherapeutic drugs, irinotecan or oxaliplatin. However, drug resistance frequently limits the clinical efficacy of these therapies. In order to gain new insights into mechanisms associated with chemoresistance, and departing from three distinct CRC cell models, we generated a panel of human colorectal cancer cell lines with acquired resistance to either oxaliplatin or irinotecan. We characterized the resistant cell line variants with regards to their drug resistance profile and transcriptome, and matched our results with datasets generated from relevant clinical material to derive putative resistance biomarkers. We found that the chemoresistant cell line variants had distinctive irinotecan- or oxaliplatin-specific resistance profiles, with non-reciprocal cross-resistance. Furthermore, we could identify several new, as well as some previously described, drug resistance-associated genes for each resistant cell line variant. Each chemoresistant cell line variant acquired a unique set of changes that may represent distinct functional subtypes of chemotherapy resistance. In addition, and given the potential implications for selection of subsequent treatment, we also performed an exploratory analysis, in relevant patient cohorts, of the predictive value of each of the specific genes identified in our cellular models.

Examining the virulence of Candida albicans transcription factor mutants using Galleria mellonella and mouse infection models.

The aim of the present study was to identify Candida albicans transcription factors (TFs) involved in virulence. Although mice are considered the gold-standard model to study fungal virulence, mini-host infection models have been increasingly used. Here, barcoded TF mutants were first screened in mice by pools of strains and fungal burdens (FBs) quantified in kidneys. Mutants of unannotated genes which generated a kidney FB significantly different from that of wild-type were selected and individually examined in Galleria mellonella. In addition, mutants that could not be detected in mice were also tested in G. mellonella. Only 25% of these mutants displayed matching phenotypes in both hosts, highlighting a significant discrepancy between the two models. To address the basis of this difference (pool or host effects), a set of 19 mutants tested in G. mellonella were also injected individually into mice. Matching FB phenotypes were observed in 50% of the cases, highlighting the bias due to host effects. In contrast, 33.4% concordance was observed between pool and single strain infections in mice, thereby highlighting the bias introduced by the "pool effect." After filtering the results obtained from the two infection models, mutants for MBF1 and ZCF6 were selected. Independent marker-free mutants were subsequently tested in both hosts to validate previous results. The MBF1 mutant showed impaired infection in both models, while the ZCF6 mutant was only significant in mice infections. The two mutants showed no obvious in vitro phenotypes compared with the wild-type, indicating that these genes might be specifically involved in in vivo adapt.

Sensing the light environment in plants: photoreceptors and early signaling steps.

Plants must constantly adapt to a changing light environment in order to optimize energy conversion through the process of photosynthesis and to limit photodamage. In addition, plants use light cues for timing of key developmental transitions such as initiation of reproduction (transition to flowering). Plants are equipped with a battery of photoreceptors enabling them to sense a very broad light spectrum spanning from UV-B to far-red wavelength (280-750nm). In this review we briefly describe the different families of plant photosensory receptors and the mechanisms by which they transduce environmental information to influence numerous aspects of plant growth and development throughout their life cycle.

Systematic difference between blood pressure readings caused by cuff type.

In this study we determine whether blood pressure readings using a cuff of fixed size systematically differed from readings made with a triple-bladder cuff (Tricuff) that automatically adjusts bladder width to arm circumference and assessed subsequent clinical and epidemiological effects. Blood pressure was measured with a standard cuff or a Tricuff in 454 patients visiting an outpatient clinic in the Seychelles (Indian Ocean). Overall means of within-individual standard cuff-Tricuff differences in systolic and diastolic blood pressures were examined in relation to arm circumference and sex. The standard cuff-Tricuff difference in systolic and diastolic blood pressures increased monotonically with circumference (from 4.7 +/- 0.8/3.2 +/- 0.7 mm Hg for arm circumference of 30 to 31 cm to 10.0 +/- 1.1/8.0 +/- 0.9 mm Hg for arm circumference &gt; or = 36 cm) and was larger in women than men. Multivariate linear regression indicated independent effects of arm circumference and sex. Forty percent of subjects with a diastolic blood pressure of &gt; or = 95 mm Hg measured with a standard cuff had values less than 95 mm Hg measured with a Tricuff. Extrapolation to the entire population of the Seychelles decreased the prevalence of blood pressure greater than or equal to 160/95 mm Hg by 11.5% and 24.0% in men and women, respectively, aged 35 to 64 years. The age-adjusted effect of body mass index on systolic and diastolic blood pressures decreased twofold using blood pressure readings made with a Tricuff instead of a standard cuff.(ABSTRACT TRUNCATED AT 250 WORDS)

Developpement et application du moniteur de tabagisme passif MoNIC

A passive sampling device, called Monitor of NICotine (MoNIC), was constructed and evaluated by the laboratory of the Institute for Work and Health to determine nicotine in Environmental Tobacco Smoke (ETS). To support a large campaign on the subject of passive smoking, the CIPRET Valais gracefully distributed more than 1500 MoNIC badges to the Swiss population in order to measure ETS. Non-stimulated saliva was also collected to determine nicotine/cotinine levels of participating volunteers. The inhaled Cigarette Equivalents (CE) by non-smokers was calculated for non-smokers, based on a reference of 0.2 mg of nicotine per cigarette. Using the detected CE on the badge for non-smokers, and comparing it to the nicotine/cotinine levels in saliva, we could confirm the use of the CE concept for estimating exposure to ETS

Fähigkeit von Kondensationskeimzählern, Nanopartikel mit hydrophober und hydrophiler Oberfläche zu messen

Heute werden Nanopartikel zwar noch in relativ wenigen Produkten eingesetzt und meist kommen die Partikel im Produkt selbst als gebundene Partikel vor. Eine Exposition Endverbrauchers kann nicht ausgeschlossen werden, sie wird aber heute angesichts der geringen Verbreitung als eher unwahrscheinlich betrachtet. Was hingegen heute schon vermehrt vorkommen kann ist eine Exposition eines Arbeiters während des Herstellungs- oder Verarbeitungsprozesses eines Produkts. Auf diesen Bereich muss man daher ein grösseres Augenmerk legen. Um zu bestimmen, ob ein Arbeiter einer Nanopartikel-Exposition ausgesetzt ist, stehen heute verschiedene Messmethoden zur Verfügung. Die meisten Methoden fokussieren sich auf die Messung von Nanopartikel in der Luft, da die Aufnahme von Nanopartikel vor allem über die Atemwege stattfindet. Die heutige Technik erlaubt eine quantitative Ermittlung dieser Konzentration. Da die Technik mit dem Fokus auf Diesel- und Umweltpartikel entwickelt wurde, muss die Vertrauenswürdigkeit dieser Messgeräte für neue Industrie-Partikel mit spezifischen Eigenschaften erneut überprüft werden. Die Effizienz von drei CPC-Messgeräte (Condensation-Particle-Counters) Typen zum Nachweis der Luftkonzentration von Nanopartikel-Pulvern wurde miteinander verglichen. Als Resultat der Studie kann gefolgert werden, dass alle CPCs gleichermassen für die Abschätzung der Nanopartikel-Konzentration an Arbeitsplätzen in der Industrie verwendet werden können. Hierbei spielt es keine wesentliche Rolle, ob das Pulver eine hydrophile oder hydrophobe Oberfläche aufweist. Nur eines der drei hydrophilen Pulvern konnte mit dem Wasser CPC besser nachgewiesen werden als mit den anderen CPC desselben Herstellers.

Dynamic models of viral replication and latency.

PURPOSE OF REVIEW: HIV targets primary CD4(+) T cells. The virus depends on the physiological state of its target cells for efficient replication, and, in turn, viral infection perturbs the cellular state significantly. Identifying the virus-host interactions that drive these dynamic changes is important for a better understanding of viral pathogenesis and persistence. The present review focuses on experimental and computational approaches to study the dynamics of viral replication and latency. RECENT FINDINGS: It was recently shown that only a fraction of the inducible latently infected reservoirs are successfully induced upon stimulation in ex-vivo models while additional rounds of stimulation make allowance for reactivation of more latently infected cells. This highlights the potential role of treatment duration and timing as important factors for successful reactivation of latently infected cells. The dynamics of HIV productive infection and latency have been investigated using transcriptome and proteome data. The cellular activation state has shown to be a major determinant of viral reactivation success. Mathematical models of latency have been used to explore the dynamics of the latent viral reservoir decay. SUMMARY: Timing is an important component of biological interactions. Temporal analyses covering aspects of viral life cycle are essential for gathering a comprehensive picture of HIV interaction with the host cell and untangling the complexity of latency. Understanding the dynamic changes tipping the balance between success and failure of HIV particle production might be key to eradicate the viral reservoir.

Dynamical modeling and analysis of large cellular regulatory networks.

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.