Repeated bedside echocardiography in children with respiratory failure

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to verify the benefits and limitations of repeated bedside echocardiographic examinations in children during mechanical ventilation. For the purposes of this study, we selected the data of over a time period from 2006 to 2010.</p> <p>Methods</p> <p>A total of 235 children, average age 3.21 (SD 1.32) years were included into the study and divided into etiopathogenic groups. High-risk groups comprised: Acute lung injury and acute respiratory distress syndrome (ALI/ARDS), return of spontaneous circulation after cardiopulmonary resuscitation (ROSC), bronchopulmonary dysplasia (BPD), cardiomyopathy (CMP) and cardiopulmonary disease (CPD). Transthoracic echocardiography was carried out during mechanical ventilation. The following data were collated for statistical evaluation: right and left ventricle myocardial performance indices (RV MPI; LV MPI), left ventricle shortening fraction (SF), cardiac output (CO), and the mitral valve ratio of peak velocity of early wave (E) to the peak velocity of active wave (A) as E/A ratio. The data was processed after a period of recovery, i.e. one hour after the introduction of invasive lines (time-1) and after 72 hours of comprehensive treatment (time-2). The overall development of parameters over time was compared within groups and between groups using the distribution-free Wilcoxons and two-way ANOVA tests.</p> <p>Results</p> <p>A total of 870 echocardiographic examinations were performed. At time-1 higher average values of RV MPI (0.34, SD 0.01 vs. 0.21, SD 0.01; p < 0.001) were found in all groups compared with reference values. Left ventricular load in the high-risk groups was expressed by a higher LV MPI (0.39, SD 0.13 vs. 0.29, SD 0.02; p < 0.01) and lower E/A ratio (0.95, SD 0.36 vs. 1.36, SD 0.64; p < 0.001), SF (0.37, SD 0.11 vs. 0.47, SD 0.02; p < 0.01) and CO (1.95, SD 0.37 vs. 2.94, SD 1.03; p < 0.01). At time-2 RV MPI were lower (0.25, SD 0.02 vs. 0.34, SD 0.01; p < 0.001), but remained higher compared with reference values (0.25, SD 0.02 vs. 0.21, SD 0.01; p < 0.05). Other parameters in high-risk groups were improved, but remained insignificantly different compared with reference values.</p> <p>Conclusion</p> <p>Echocardiography complements standard monitoring of valuable information regarding cardiac load in real time. Chest excursion during mechanical ventilation does not reduce the quality of the acquired data.</p

The Galleria mellonella Hologenome Supports Microbiota-Independent Metabolism of Long-Chain Hydrocarbon Beeswax

The greater wax moth, Galleria mellonella, degrades wax and plastic molecules. Despite much interest, the genetic basis of these hallmark traits remains poorly understood. Herein, we assembled high-quality genome and transcriptome data from G. mellonella to investigate long-chain hydrocarbon wax metabolism strategies. Specific carboxylesterase and lipase and fatty-acid-metabolism-related enzymes in the G. mellonella genome are transcriptionally regulated during feeding on beeswax. Strikingly, G. mellonella lacking intestinal microbiota successfully decomposes long-chain fatty acids following wax metabolism, although the intestinal microbiome performs a supplementary role in short-chain fatty acid degradation. Notably, final wax derivatives were detected by gas chromatography even in the absence of gut microbiota. Our findings provide insight into wax moth adaptation and may assist in the development of unique wax-degradation strategies with a similar metabolic approach for a plastic molecule polyethylene biodegradation using organisms without intestinal microbiota. The evolutionarily expanded long-chain fatty acid degradation gene products of Galleria mellonella decompose long-chain hydrocarbons independently of intestinal microorganisms. Kong et al. show that beeswax and degradation products are detected equally in larvae in the presence or absence of intestinal microbes

From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes

Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment model with hundreds of markers. We subsequently tested this model on an independent Illumina-genotyped dataset with imputed genotypes (1,008 cases and 1,000 controls), as well as a separate Affymetrix-genotyped dataset (1,529 cases and 1,458 controls), resulting in area under ROC curve (AUC) of ∼0.84 in both datasets. In contrast, poor performance was achieved when limited to dozens of known susceptibility loci in the SVM model or logistic regression model. Our study suggests that improved disease risk assessment can be achieved by using algorithms that take into account interactions between a large ensemble of markers. We are optimistic that genotype-based disease risk assessment may be feasible for diseases where a notable proportion of the risk has already been captured by SNP arrays

It’s not the size, it’s the relationship: from ‘small states’ to asymmetry

Debate about the definition of “small state” has produced more fragmentation than consensus, even as the literature has demonstrated its subjects’ roles in joining international organizations propagating norms, executing creative diplomacy, influencing allies, avoiding and joining conflicts, and building peace. However, work on small states has struggled to identify commonalities in these states’ international relations, to cumulate knowledge, or to impact broader IR theory. This paper advocates a changed conceptual and definitional framework. Analysis of “small states” should pivot to examine the dynamics of the asymmetrical relationships in which these states are engaged. Instead of seeking an overall metric for size as the relevant variable—falling victim in a different way Dahl’s “lump-of-power fallacy,” we can recognize the multifaceted, variegated nature of power, whether in war or peacetime