Comparison of Normal Saline,Hypertonic Saline Albumin and Terlipressin plus Hypertonic Saline Albumin in an Infant Animal Model of Hypovolemic Shock

Introduction

In series of cases and animal models suffering hemorrhagic shock, the use of vasopressors has shown potential benefits regarding hemodynamics and tissue perfusion. Terlipressin is an analogue of vasopressin with a longer half-life that can be administered by bolus injection. We have previously observed that hypertonic albumin improves resuscitation following controlled hemorrhage in piglets. The aim of the present study was to analyze whether the treatment with the combination of terlipressin and hypertonic albumin can produce better hemodynamic and tissular perfusion parameters than normal saline or hypertonic albumin alone at early stages of hemorrhagic shock in an infant animal model.

Methods

Experimental, randomized animal study including 39 2-to-3-month-old piglets. Thirty minutes after controlled 30 ml/kg bleed, pigs were randomized to receive either normal saline (NS) 30 ml/kg (n = 13), 5% albumin plus 3% hypertonic saline (AHS) 15 ml/kg (n = 13) or single bolus of terlipressin 15 μg/kg i.v. plus 5% albumin plus 3% hypertonic saline 15 ml/kg (TAHS) (n = 13) over 30 minutes. Global hemodynamic and tissular perfusion parameters were compared.

Results

After controlled bleed a significant decrease of blood pressure, cardiac index, central venous saturation, carotid and peripheral blood flow, brain saturation and an increase of heart rate, gastric PCO₂ and lactate was observed. After treatment no significant differences in most hemodynamic (cardiac index, mean arterial pressure) and perfusion parameters (lactate, gastric PCO₂, brain saturation, cutaneous blood flow) were observed between the three therapeutic groups. AHS and TAHS produced higher increase in stroke volume index and carotid blood flow than NS.

Conclusions

In this pediatric animal model of hypovolemic shock, albumin plus hypertonic saline with or without terlipressin achieved similar hemodynamics and perfusion parameters than twice the volume of NS. Addition of terlipressin did not produce better results than AHS.     

Interferon-Gamma Promotes Infection of Astrocytes by Trypanosoma cruzi

The inflammatory cytokine interferon-gamma (IFNγ) is crucial for immunity against intracellular pathogens such as the protozoan parasite Trypanosoma cruzi, the causative agent of Chagas disease (CD). IFNγ is a pleiotropic cytokine which regulates activation of immune and non-immune cells; however, the effect of IFNγ in the central nervous system (CNS) and astrocytes during CD is unknown. Here we show that parasite persists in the CNS of C3H/He mice chronically infected with the Colombian T. cruzi strain despite the increased expression of IFNγ mRNA. Furthermore, most of the T. cruzi-bearing cells were astrocytes located near IFNγ⁺ cells. Surprisingly, in vitro experiments revealed that pretreatment with IFNγ promoted the infection of astrocytes by T. cruzi increasing uptake and proliferation of intracellular forms, despite inducing increased production of nitric oxide (NO). Importantly, the effect of IFNγ on T. cruzi uptake and growth is completely blocked by the anti-tumor necrosis factor (TNF) antibody Infliximab and partially blocked by the inhibitor of nitric oxide synthesis L-NAME. These data support that IFNγ fuels astrocyte infection by T. cruzi and critically implicate IFNγ-stimulated T. cruzi-infected astrocytes as sources of TNF and NO, which may contribute to parasite persistence and CNS pathology in CD.     

Personal Networks and Mortality Risk in Older Adults: A Twenty-Year Longitudinal Study

Background

Research on aging has consistently demonstrated an increased chance of survival for older adults who are integrated into rich networks of social relationships. Theoretical explanations state that personal networks offer indirect psychosocial and direct physiological pathways. We investigate whether effects on and pathways to mortality risk differ between functional and structural characteristics of the personal network. The objective is to inquire which personal network characteristics are the best predictors of mortality risk after adjustment for mental, cognitive and physical health.

Methods and Findings

Empirical tests were carried out by combining official register information on mortality with data from the Longitudinal Aging Study Amsterdam (LASA). The sample included 2,911 Dutch respondents aged 54 to 85 at baseline in 1992 and six follow-ups covering a time span of twenty years. Four functional characteristics (emotional and social loneliness, emotional and instrumental support) and four structural characteristics (living arrangement, contact frequency, number of contacts, number of social roles) of the personal network as well as mental, cognitive and physical health were assessed at all LASA follow-ups. Statistical analyses comprised of Cox proportional hazard regression models. Findings suggest differential effects of personal network characteristics on survival, with only small gender differences. Mortality risk was initially reduced by functional characteristics, but disappeared after full adjustment for the various health variables. Mortality risk was lowest for older adults embedded in large (HR = 0.986, 95% CI 0.979—0.994) and diverse networks (HR = 0.948, 95% CI 0.917—0.981), and this effect continued to show in the fully adjusted models.

Conclusions

Functional characteristics (i.e. emotional and social loneliness) are indirectly associated with a reduction in mortality risk, while structural characteristics (i.e. number of contacts and number of social roles) have direct protective effects. More research is needed to understand the causal mechanisms underlying these relations.     

Relationship between Spectral Characteristics of Spontaneous Postural Sway and Motion Sickness Susceptibility

Motion sickness (MS) usually occurs for a narrow band of frequencies of the imposed oscillation. It happens that this frequency band is close to that which are spontaneously produced by postural sway during natural stance. This study examined the relationship between reported susceptibility to motion sickness and postural control. The hypothesis is that the level of MS can be inferred from the shape of the Power Spectral Density (PSD) profile of spontaneous sway, as measured by the displacement of the center of mass during stationary, upright stance. In Experiment 1, postural fluctuations while standing quietly were related to MS history for inertial motion. In Experiment 2, postural stability measures registered before the onset of a visual roll movement were related to MS symptoms following the visual stimulation. Study of spectral characteristics in postural control showed differences in the distribution of energy along the power spectrum of the antero-posterior sway signal. Participants with MS history provoked by exposure to inertial motion showed a stronger contribution of the high frequency components of the sway signal. When MS was visually triggered, sick participants showed more postural sway in the low frequency range. The results suggest that subject-specific PSD details may be a predictor of the MS level. Furthermore, the analysis of the sway frequency spectrum provided insight into the intersubject differences in the use of postural control subsystems. The relationship observed between MS susceptibility and spontaneous posture is discussed in terms of postural sensory weighting and in relation to the nature of the provocative stimulus.     

Changes in Body Composition in Anorexia Nervosa: Predictors of Recovery and Treatment Outcome

The restoration of body composition (BC) parameters is considered to be one of the most important goals in the treatment of patients with anorexia nervosa (AN). However, little is known about differences between AN diagnostic subtypes [restricting (AN-R) and binge/purging (AN-BP)] and weekly changes in BC during refeeding treatment. Therefore, the main objectives of our study were twofold: 1) to assess the changes in BC throughout nutritional treatment in an AN sample and 2) to analyze predictors of BC changes during treatment, as well as predictors of treatment outcome. The whole sample comprised 261 participants [118 adult females with AN (70 AN-R vs. 48 AN-BP), and 143 healthy controls]. BC was measured weekly during 15 weeks of day-hospital treatment using bioelectrical impedance analysis (BIA). Assessment measures also included the Eating Disorders Inventory-2, as well as a number of other clinical indices. Overall, the results showed that AN-R and AN-BP patients statistically differed in all BC measures at admission. However, no significant time×group interaction was found for almost all BC parameters. Significant time×group interactions were only found for basal metabolic rate (p = .041) and body mass index (BMI) (p = .035). Multiple regression models showed that the best predictors of pre-post changes in BC parameters (namely fat-free mass, muscular mass, total body water and BMI) were the baseline values of BC parameters. Stepwise predictive logistic regressions showed that only BMI and age were significantly associated with outcome, but not with the percentage of body fat. In conclusion, these data suggest that although AN patients tended to restore all BC parameters during nutritional treatment, only AN-BP patients obtained the same fat mass values as healthy controls. Put succinctly, the best predictors of changes in BC were baseline BC values, which did not, however, seem to influence treatment outcome.     

Variable-Selection Emerges on Top in Empirical Comparison of Whole-Genome Complex-Trait Prediction Methods

Accurate prediction of complex traits based on whole-genome data is a computational problem of paramount importance, particularly to plant and animal breeders. However, the number of genetic markers is typically orders of magnitude larger than the number of samples (p >> n), amongst other challenges. We assessed the effectiveness of a diverse set of state-of-the-art methods on publicly accessible real data. The most surprising finding was that approaches with feature selection performed better than others on average, in contrast to the expectation in the community that variable selection is mostly ineffective, i.e. that it does not improve accuracy of prediction, in spite of p >> n. We observed superior performance despite a somewhat simplistic approach to variable selection, possibly suggesting an inherent robustness. This bodes well in general since the variable selection methods usually improve interpretability without loss of prediction power. Apart from identifying a set of benchmark data sets (including one simulated data), we also discuss the performance analysis for each data set in terms of the input characteristics.     

A Bayesian Hierarchical Model for Estimation of Abundance and Spatial Density of Aedes aegypti

Strategies to minimize dengue transmission commonly rely on vector control, which aims to maintain Ae. aegypti density below a theoretical threshold. Mosquito abundance is traditionally estimated from mark-release-recapture (MRR) experiments, which lack proper analysis regarding accurate vector spatial distribution and population density. Recently proposed strategies to control vector-borne diseases involve replacing the susceptible wild population by genetically modified individuals’ refractory to the infection by the pathogen. Accurate measurements of mosquito abundance in time and space are required to optimize the success of such interventions. In this paper, we present a hierarchical probabilistic model for the estimation of population abundance and spatial distribution from typical mosquito MRR experiments, with direct application to the planning of these new control strategies. We perform a Bayesian analysis using the model and data from two MRR experiments performed in a neighborhood of Rio de Janeiro, Brazil, during both low- and high-dengue transmission seasons. The hierarchical model indicates that mosquito spatial distribution is clustered during the winter (0.99 mosquitoes/premise 95% CI: 0.80–1.23) and more homogeneous during the high abundance period (5.2 mosquitoes/premise 95% CI: 4.3–5.9). The hierarchical model also performed better than the commonly used Fisher-Ford’s method, when using simulated data. The proposed model provides a formal treatment of the sources of uncertainty associated with the estimation of mosquito abundance imposed by the sampling design. Our approach is useful in strategies such as population suppression or the displacement of wild vector populations by refractory Wolbachia-infected mosquitoes, since the invasion dynamics have been shown to follow threshold conditions dictated by mosquito abundance. The presence of spatially distributed abundance hotspots is also formally addressed under this modeling framework and its knowledge deemed crucial to predict the fate of transmission control strategies based on the replacement of vector populations.