Reliable tidal volume estimates at the airway opening with an infant monitor during high-frequency oscillatory ventilation.

OBJECTIVE: To assess the suitability of a hot-wire anemometer infant monitoring system (Florian, Acutronic Medical Systems AG, Hirzel, Switzerland) for measuring flow and tidal volume (Vt) proximal to the endotracheal tube during high-frequency oscillatory ventilation. DESIGN: In vitro model study. SETTING: Respiratory research laboratory. SUBJECT: In vitro lung model simulating moderate to severe respiratory distress. INTERVENTION: The lung model was ventilated with a SensorMedics 3100A ventilator. Vt was recorded from the monitor display (Vt-disp) and compared with the gold standard (Vt-adiab), which was calculated using the adiabatic gas equation from pressure changes inside the model. MEASUREMENTS AND MAIN RESULTS: A range of Vt (1-10 mL), frequencies (5-15 Hz), pressure amplitudes (10-90 cm H2O), inspiratory times (30% to 50%), and Fio2 (0.21-1.0) was used. Accuracy was determined by using modified Bland-Altman plots (95% limits of agreement). An exponential decrease in Vt was observed with increasing oscillatory frequency. Mean DeltaVt-disp was 0.6 mL (limits of agreement, -1.0 to 2.1) with a linear frequency dependence. Mean DeltaVt-disp was -0.2 mL (limits of agreement, -0.5 to 0.1) with increasing pressure amplitude and -0.2 mL (limits of agreement, -0.3 to -0.1) with increasing inspiratory time. Humidity and heating did not affect error, whereas increasing Fio2 from 0.21 to 1.0 increased mean error by 6.3% (+/-2.5%). CONCLUSIONS: The Florian infant hot-wire flowmeter and monitoring system provides reliable measurements of Vt at the airway opening during high-frequency oscillatory ventilation when employed at frequencies of 8-13 Hz. The bedside application could improve monitoring of patients receiving high-frequency oscillatory ventilation, favor a better understanding of the physiologic consequences of different high-frequency oscillatory ventilation strategies, and therefore optimize treatment.

Hypertension is an independent predictor of mean platelet volume in patients with acute ischaemic stroke.

Introduction: Mean platelet volume (MPV) was shown to be significantly increased in patients with acute ischaemic stroke, especially in non-lacunar strokes. Moreover, some studies concluded that increased MPV is related to poor functional outcome after ischaemic stroke, although this association is still controversial. However, the determinants of MPV in patients with acute ischaemic stroke have never been investigated. Subjects and methods: We recorded the main demographic, clinical and laboratory data of consecutive patients with acute (admitted within 24 h after stroke onset) ischaemic stroke admitted in our Neurology Service between January 2003 and December 2008. MPV was generated at admission by the Sysmex XE-2100 automated cell counter (Sysmex Corporation, Kobe, Japan) from ethylenediaminetetraacetic acid blood samples stored at room temperature until measurement. The association of these parameters with MPV was investigated in univariate and multivariate analysis. Results: A total of 636 patients was included in our study. The median MPV was 10.4 ± 0.82 fL. In univariate analysis, glucose (β= 0.03, P= 0.05), serum creatinine (β= 0.002, P= 0.02), haemoglobin (β= 0.009, P < 0.001), platelet count (β=-0.002, P < 0.001) and history of arterial hypertension (β= 0.21, P= 0.005) were found to be significantly associated with MPV. In multivariate robust regression analysis, only hypertension and platelet count remained as independent determinants of MPV. Conclusions: In patients with acute ischaemic stroke, platelet count and history of hypertension are the only determinants of MPV.

Impact of prolonged antihypertensive duration of action on predicted clinical outcomes in imperfectly adherent patients: comparison of aliskiren, irbesartan and ramipril.

Background: Most patients miss occasional doses of antihypertensives. The use of 'forgiving' drugs (i.e. drugs with duration of action longer than the 24-h dosing interval) may allow an adequate blood pressure (BP) reduction to be maintained despite missed doses. Aim:To quantify the effects of adherence level and duration of action on estimated mean systolic BP (SBP) reduction and cardiovascular disease (CVD) risk. Method:For 1250 patients, we simulated 256-day dosing histories with realistically distributed drug holidays based on a study of electronically monitored dosing records. Adherence was set to the desired level by altering the proportion of doses missed. Mean office SBP-lowering effect (aliskiren 300 mg, -14.1 mmHg; irbesartan 300 mg, -13.3; ramipril 10 mg, -10.1 mmHg) and the rate of SBP increase after stopping treatment (off-rate; aliskiren, 1.0 mmHg/day; irbesartan, 3.6 mmHg/day; ramipril, 4.0 mmHg/day) were taken from the results of a randomised, double-blind trial. SBP was averaged over time and patient to estimate mean reductions in SBP and 10-year CVD risk (Framingham risk equation, baseline absolute 10-year CVD risk: 27%). Results:Predicted reductions in SBP and CVD risk with aliskiren were larger and less affected by imperfect adherence than the reductions with irbesartan or ramipril. For aliskiren, reducing adherence from 90% to 60% led to a predicted rise in SBP of 1.0 mmHg and three additional CVD events per 1000 treated patients; larger predicted differences were observed for irbesartan (2.5 mmHg; 7.5 events/1000 treated patients) and ramipril (2.2 mmHg; 6.7 events/1000 treated patients). Conclusion:To offset the effects of imperfect adherence, a common challenge with antihypertensives, for better BP management it may be prudent to prescribe 'forgiving' drugs.

A test of Jessor's problem behavior theory in a Eurasian and a Western European developmental context.

PURPOSE: The current study tested the applicability of Jessor's problem behavior theory (PBT) in national probability samples from Georgia and Switzerland. Comparisons focused on (1) the applicability of the problem behavior syndrome (PBS) in both developmental contexts, and (2) on the applicability of employing a set of theory-driven risk and protective factors in the prediction of problem behaviors. METHODS: School-based questionnaire data were collected from n = 18,239 adolescents in Georgia (n = 9499) and Switzerland (n = 8740) following the same protocol. Participants rated five measures of problem behaviors (alcohol and drug use, problems because of alcohol and drug use, and deviance), three risk factors (future uncertainty, depression, and stress), and three protective factors (family, peer, and school attachment). Final study samples included n = 9043 Georgian youth (mean age = 15.57; 58.8% females) and n = 8348 Swiss youth (mean age = 17.95; 48.5% females). Data analyses were completed using structural equation modeling, path analyses, and post hoc z-tests for comparisons of regression coefficients. RESULTS: Findings indicated that the PBS replicated in both samples, and that theory-driven risk and protective factors accounted for 13% and 10% in Georgian and Swiss samples, respectively in the PBS, net the effects by demographic variables. Follow-up z-tests provided evidence of some differences in the magnitude, but not direction, in five of six individual paths by country. CONCLUSION: PBT and the PBS find empirical support in these Eurasian and Western European samples; thus, Jessor's theory holds value and promise in understanding the etiology of adolescent problem behaviors outside of the United States.

Mismatch of arterial and central venous blood gas analysis during haemorrhage.

BACKGROUND AND OBJECTIVE: Arterial base excess and lactate levels are key parameters in the assessment of critically ill patients. The use of venous blood gas analysis may be of clinical interest when no arterial blood is available initially. METHODS: Twenty-four pigs underwent progressive normovolaemic haemodilution and subsequent progressive haemorrhage until the death of the animal. Base excess and lactate levels were determined from arterial and central venous blood after each step. In addition, base excess was calculated by the Van Slyke equation modified by Zander (BE(z)). Continuous variables were summarized as mean +/- SD and represent all measurements (n = 195). RESULTS: Base excess according to National Committee for Clinical Laboratory Standards for arterial blood was 2.27 +/- 4.12 versus 2.48 +/- 4.33 mmol(-l) for central venous blood (P = 0.099) with a strong correlation (r(2) = 0.960, P < 0.001). Standard deviation of the differences between these parameters (SD-DIFBE) did not increase (P = 0.355) during haemorrhage as compared with haemodilution. Arterial lactate was 2.66 +/- 3.23 versus 2.71 +/- 2.80 mmol(-l) in central venous blood (P = 0.330) with a strong correlation (r(2) = 0.983, P < 0.001). SD-DIFLAC increased (P < 0.001) during haemorrhage. BE(z) for central venous blood was 2.22 +/- 4.62 mmol(-l) (P = 0.006 versus arterial base excess according to National Committee for Clinical Laboratory Standards) with strong correlation (r(2) = 0.942, P < 0.001). SD-DIFBE(z)/base excess increased (P < 0.024) during haemorrhage. CONCLUSION: Central venous blood gas analysis is a good predictor for base excess and lactate in arterial blood in steady-state conditions. However, the variation between arterial and central venous lactate increases during haemorrhage. The modification of the Van Slyke equation by Zander did not improve the agreement between central venous and arterial base excess.

Mucosal healing and deep remission: What does it mean?

The use of specific terms under different meanings and varying definitions has always been a source of confusion in science. When we point our efforts towards an evidence based medicine for inflammatory bowel diseases (IBD) the same is true: Terms such as "mucosal healing" or "deep remission" as endpoints in clinical trials or treatment goals in daily patient care may contribute to misconceptions if meanings change over time or definitions are altered. It appears to be useful to first have a look at the development of terms and their definitions, to assess their intrinsic and context-independent problems and then to analyze the different relevance in present-day clinical studies and trials. The purpose of such an attempt would be to gain clearer insights into the true impact of the clinical findings behind the terms. It may also lead to a better defined use of those terms for future studies. The terms "mucosal healing" and "deep remission" have been introduced in recent years as new therapeutic targets in the treatment of IBD patients. Several clinical trials, cohort studies or inception cohorts provided data that the long term disease course is better, when mucosal healing is achieved. However, it is still unclear whether continued or increased therapeutic measures will aid or improve mucosal healing for patients in clinical remission. Clinical trials are under way to answer this question. Attention should be paid to clearly address what levels of IBD activity are looked at. In the present review article authors aim to summarize the current evidence available on mucosal healing and deep remission and try to highlight their value and position in the everyday decision making for gastroenterologists.

Electrical colonic stimulation reduces mean transit time in a porcine model.

Abstract Electrical stimulation is a new way to treat digestive disorders such as constipation. Colonic propulsive activity can be triggered by battery operated devices. This study aimed to demonstrate the effect of direct electrical colonic stimulation on mean transit time in a chronic porcine model. The impact of stimulation and implanted material on the colonic wall was also assessed. Three pairs of electrodes were implanted into the caecal wall of 12 anaesthetized pigs. Reference colonic transit time was determined by radiopaque markers for each pig before implantation. It was repeated 4 weeks after implantation with sham stimulation and 5 weeks after implantation with electrical stimulation. Aboral sequential trains of 1-ms pulse width (10 V; 120 Hz) were applied twice daily for 6 days, using an external battery operated stimulator. For each course of markers, a mean value was computed from transit times obtained from individual pig. Microscopic examination of the caecum was routinely performed after animal sacrifice. A reduction of mean transit time was observed after electrical stimulation (19 +/- 13 h; mean +/- SD) when compared to reference (34 +/- 7 h; P = 0.045) and mean transit time after sham stimulation (36 +/- 9 h; P = 0.035). Histological examination revealed minimal chronic inflammation around the electrodes. Colonic transit time measured in a chronic porcine model is reduced by direct sequential electrical stimulation. Minimal tissue lesion is elicited by stimulation or implanted material. Electrical colonic stimulation could be a promising approach to treat specific disorders of the large bowel.

Estimation of jump-diffusion processes via empirical characteristic functions

Preface The starting point for this work and eventually the subject of the whole thesis was the question: how to estimate parameters of the affine stochastic volatility jump-diffusion models. These models are very important for contingent claim pricing. Their major advantage, availability T of analytical solutions for characteristic functions, made them the models of choice for many theoretical constructions and practical applications. At the same time, estimation of parameters of stochastic volatility jump-diffusion models is not a straightforward task. The problem is coming from the variance process, which is non-observable. There are several estimation methodologies that deal with estimation problems of latent variables. One appeared to be particularly interesting. It proposes the estimator that in contrast to the other methods requires neither discretization nor simulation of the process: the Continuous Empirical Characteristic function estimator (EGF) based on the unconditional characteristic function. However, the procedure was derived only for the stochastic volatility models without jumps. Thus, it has become the subject of my research. This thesis consists of three parts. Each one is written as independent and self contained article. At the same time, questions that are answered by the second and third parts of this Work arise naturally from the issues investigated and results obtained in the first one. The first chapter is the theoretical foundation of the thesis. It proposes an estimation procedure for the stochastic volatility models with jumps both in the asset price and variance processes. The estimation procedure is based on the joint unconditional characteristic function for the stochastic process. The major analytical result of this part as well as of the whole thesis is the closed form expression for the joint unconditional characteristic function for the stochastic volatility jump-diffusion models. The empirical part of the chapter suggests that besides a stochastic volatility, jumps both in the mean and the volatility equation are relevant for modelling returns of the S&P500 index, which has been chosen as a general representative of the stock asset class. Hence, the next question is: what jump process to use to model returns of the S&P500. The decision about the jump process in the framework of the affine jump- diffusion models boils down to defining the intensity of the compound Poisson process, a constant or some function of state variables, and to choosing the distribution of the jump size. While the jump in the variance process is usually assumed to be exponential, there are at least three distributions of the jump size which are currently used for the asset log-prices: normal, exponential and double exponential. The second part of this thesis shows that normal jumps in the asset log-returns should be used if we are to model S&P500 index by a stochastic volatility jump-diffusion model. This is a surprising result. Exponential distribution has fatter tails and for this reason either exponential or double exponential jump size was expected to provide the best it of the stochastic volatility jump-diffusion models to the data. The idea of testing the efficiency of the Continuous ECF estimator on the simulated data has already appeared when the first estimation results of the first chapter were obtained. In the absence of a benchmark or any ground for comparison it is unreasonable to be sure that our parameter estimates and the true parameters of the models coincide. The conclusion of the second chapter provides one more reason to do that kind of test. Thus, the third part of this thesis concentrates on the estimation of parameters of stochastic volatility jump- diffusion models on the basis of the asset price time-series simulated from various "true" parameter sets. The goal is to show that the Continuous ECF estimator based on the joint unconditional characteristic function is capable of finding the true parameters. And, the third chapter proves that our estimator indeed has the ability to do so. Once it is clear that the Continuous ECF estimator based on the unconditional characteristic function is working, the next question does not wait to appear. The question is whether the computation effort can be reduced without affecting the efficiency of the estimator, or whether the efficiency of the estimator can be improved without dramatically increasing the computational burden. The efficiency of the Continuous ECF estimator depends on the number of dimensions of the joint unconditional characteristic function which is used for its construction. Theoretically, the more dimensions there are, the more efficient is the estimation procedure. In practice, however, this relationship is not so straightforward due to the increasing computational difficulties. The second chapter, for example, in addition to the choice of the jump process, discusses the possibility of using the marginal, i.e. one-dimensional, unconditional characteristic function in the estimation instead of the joint, bi-dimensional, unconditional characteristic function. As result, the preference for one or the other depends on the model to be estimated. Thus, the computational effort can be reduced in some cases without affecting the efficiency of the estimator. The improvement of the estimator s efficiency by increasing its dimensionality faces more difficulties. The third chapter of this thesis, in addition to what was discussed above, compares the performance of the estimators with bi- and three-dimensional unconditional characteristic functions on the simulated data. It shows that the theoretical efficiency of the Continuous ECF estimator based on the three-dimensional unconditional characteristic function is not attainable in practice, at least for the moment, due to the limitations on the computer power and optimization toolboxes available to the general public. Thus, the Continuous ECF estimator based on the joint, bi-dimensional, unconditional characteristic function has all the reasons to exist and to be used for the estimation of parameters of the stochastic volatility jump-diffusion models.

Alcohol drinking, the metabolic syndrome and diabetes in a population with high mean alcohol consumption.

AIMS: To investigate the relationship of alcohol consumption with the metabolic syndrome and diabetes in a population-based study with high mean alcohol consumption. Few data exist on these conditions in high-risk drinkers. METHODS: In 6172 adults aged 35-75 years, alcohol consumption was categorized as 0, 1-6, 7-13, 14-20, 21-27, 28-34 and ≥ 35 drinks/week or as non-drinkers (0), low-risk (1-13), medium-to-high-risk (14-34) and very-high-risk (≥ 35) drinkers. Alcohol consumption was objectively confirmed by biochemical tests. In multivariate analysis, we assessed the relationship of alcohol consumption with adjusted prevalence of the metabolic syndrome, diabetes and insulin resistance, determined with the homeostasis model assessment of insulin resistance (HOMA-IR). RESULTS: Seventy-three per cent of participants consumed alcohol, 16% were medium-to-high-risk drinkers and 2% very-high-risk drinkers. In multivariate analysis, the prevalence of the metabolic syndrome, diabetes and mean HOMA-IR decreased with low-risk drinking and increased with high-risk drinking. Adjusted prevalence of the metabolic syndrome was 24% in non-drinkers, 19% in low-risk (P<0.001 vs. non-drinkers), 20% in medium-to-high-risk and 29% in very-high-risk drinkers (P=0.005 vs. low-risk). Adjusted prevalence of diabetes was 6.0% in non-drinkers, 3.6% in low-risk (P<0.001 vs. non-drinkers), 3.8% in medium-to-high-risk and 6.7% in very-high-risk drinkers (P=0.046 vs. low-risk). Adjusted HOMA-IR was 2.47 in non-drinkers, 2.14 in low-risk (P<0.001 vs. non-drinkers), 2.27 in medium-to-high-risk and 2.53 in very-high-risk drinkers (P=0.04 vs. low-risk). These relationships did not differ according to beverage types. CONCLUSIONS: Alcohol has a U-shaped relationship with the metabolic syndrome, diabetes and HOMA-IR, without differences between beverage types.

Brain Connectivity Analysis in Children. Effects of Prematurity. and Prenatal Growth Restriction

Introduction: Survival of children born prematurely or with very low birth weight has increased dramatically, but the long term developmental outcome remains unknown. Many children have deficits in cognitive capacities, in particular involving executive domains and those disabilities are likely to involve a central nervous system deficit. To understand their neurostructural origin, we use DTI. Structurally segregated and functionally regions of the cerebral cortex are interconnected by a dense network of axonal pathways. We noninvasively map these pathways across cortical hemispheres and construct normalized structural connection matrices derived from DTI MR tractography. Group comparisons of brain connectivity reveal significant changes in fiber density in case of children with poor intrauterine grown and extremely premature children (gestational age<28 weeks at birth) compared to control subjects. This changes suggest a link between cortico-axonal pathways and the central nervous system deficit. Methods: Sixty premature born infants (5-6 years old) were scanned on clinical 3T scanner (Magnetom Trio, Siemens Medical Solutions, Erlangen, Germany) at two hospitals (HUG, Geneva and CHUV, Lausanne). For each subject, T1-weighted MPRAGE images (TR/TE=2500/2.91,TI=1100, resolution=1x1x1mm, matrix=256x154) and DTI images (30 directions, TR/TE=10200/107, in-plane resolution=1.8x1.8x2mm, 64 axial, matrix=112x112) were acquired. Parent(s) provided written consent on prior ethical board approval. The extraction of the Whole Brain Structural Connectivity Matrix was performed following (Cammoun, 2009 and Hagmann, 2008). The MPARGE images were registered using an affine registration to the non-weighted-DTI and WM-GM segmentation performed on it. In order to have equal anatomical localization among subjects, 66 cortical regions with anatomical landmarks were created using the curvature information, i.e. sulcus and gyrus (Cammoun et al, 2007; Fischl et al, 2004; Desikan et al, 2006) with freesurfer software (http://surfer.nmr.mgh.harvard.edu/). Tractography was performed in WM using an algorithm especially designed for DTI/DSI data (Hagmann et al., 2007) and both information were then combined in a matrix. Each row and column of the matrix corresponds to a particular ROI. Each cell of index (i,j) represents the fiber density of the bundle connecting the ROIs i and j. Subdividing each cortical region, we obtained 4 Connectivity Matrices of different resolution (33, 66, 125 and 250 ROI/hemisphere) for each subject . Subjects were sorted in 3 different groups, namely (1) control, (2) Intrauterine Growth Restriction (IUGR), (3) Extreme Prematurity (EP), depending on their gestational age, weight and percentile-weight score at birth. Group-to-group comparisons were performed between groups (1)-(2) and (1)-(3). The mean age at examination of the three groups were similar. Results: Quantitative analysis were performed between groups to determine fibers density differences. For each group, a mean connectivity matrix with 33ROI/hemisphere resolution was computed. On the other hand, for all matrix resolutions (33,66,125,250 ROI/hemisphere), the number of bundles were computed and averaged. As seen in figure 1, EP and IUGR subjects present an overall reduction of fibers density in both interhemispherical and intrahemispherical connections. This is given quantitatively in table 1. IUGR subjects presents a higher percentage of missing fiber bundles than EP when compared to control subjects (~16% against 11%). When comparing both groups to control subjects, for the EP subjects, the occipito-parietal regions seem less interhemispherically connected whilst the intrahemispherical networks present lack of fiber density in the lymbic system. Children born with IUGR, have similar reductions in interhemispherical connections than the EP. However, the cuneus and precuneus connections with the precentral and paracentral lobe are even lower than in the case of the EP. For the intrahemispherical connections the IUGR group preset a loss of fiber density between the deep gray matter structures (striatum) and the frontal and middlefrontal poles, connections typically involved in the control of executive functions. For the qualitative analysis, a t-test comparing number of bundles (p-value<0.05) gave some preliminary significant results (figure 2). Again, even if both IUGR and EP appear to have significantly less connections comparing to the control subjects, the IUGR cohort seems to present a higher lack of fiber density specially relying the cuneus, precuneus and parietal areas. In terms of fiber density, preliminary Wilcoxon tests seem to validate the hypothesis set by the previous analysis. Conclusions: The goal of this study was to determine the effect of extreme prematurity and poor intrauterine growth on neurostructural development at the age of 6 years-old. This data indicates that differences in connectivity may well be the basis for the neurostructural and neuropsychological deficit described in these populations in the absence of overt brain lesions (Inder TE, 2005; Borradori-Tolsa, 2004; Dubois, 2008). Indeed, we suggest that IUGR and prematurity leads to alteration of connectivity between brain structures, especially in occipito-parietal and frontal lobes for EP and frontal and middletemporal poles for IUGR. Overall, IUGR children have a higher loss of connectivity in the overall connectivity matrix than EP children. In both cases, the localized alteration of connectivity suggests a direct link between cortico-axonal pathways and the central nervous system deficit. Our next step is to link these connectivity alterations to the performance in executive function tests.

Protein-ligand binding free energy estimation using molecular mechanics and continuum electrostatics. Application to HIV-1 protease inhibitors.

A method is proposed for the estimation of absolute binding free energy of interaction between proteins and ligands. Conformational sampling of the protein-ligand complex is performed by molecular dynamics (MD) in vacuo and the solvent effect is calculated a posteriori by solving the Poisson or the Poisson-Boltzmann equation for selected frames of the trajectory. The binding free energy is written as a linear combination of the buried surface upon complexation, SASbur, the electrostatic interaction energy between the ligand and the protein, Eelec, and the difference of the solvation free energies of the complex and the isolated ligand and protein, deltaGsolv. The method uses the buried surface upon complexation to account for the non-polar contribution to the binding free energy because it is less sensitive to the details of the structure than the van der Waals interaction energy. The parameters of the method are developed for a training set of 16 HIV-1 protease-inhibitor complexes of known 3D structure. A correlation coefficient of 0.91 was obtained with an unsigned mean error of 0.8 kcal/mol. When applied to a set of 25 HIV-1 protease-inhibitor complexes of unknown 3D structures, the method provides a satisfactory correlation between the calculated binding free energy and the experimental pIC5o without reparametrization.

Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.