Short-chain fatty acids and polyamines in the pathogenesis of necrotizing enterocolitis: Kinetics aspects in gnotobiotic quails

Despite years of investigation, pathogenesis of necrotizing enterocolitis (NEC) remains elusive. Bacterial metabolites were implicated by several authors but their roles remain controversial. The aim of our study was to investigate the role of SCFAs and polyamines through a kinetic study of histological and macroscopical digestive lesions in monobiotic quails. Germ-free quails, inoculated with a Clostridium butyricum strain involved in a NEC case, were fed or not with a diet including lactose (7%). Quails were sacrificed at various times between D7 and D24 after bacterial inoculation. NEC-like lesions, i.e. thickening, pneumatosis, and hemorrhages, occurred only in lactose-fed quails and increased with time. The main histological characteristics were infiltrates of mononuclear cells, then heterophilic cells, then gas cyst and necrosis. The first event observed, before histological and macroscopical lesions, is a high production of butyric acid, which precedes an increase of iNOS gene expression. No difference in polyamines contents depending on the diet was observed. These results show the major role of butyric acid produced by commensal bacteria in the onset of the digestive lesions.     

Optimized Null Model for Protein Structure Networks

Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by comparing various RIG definitions against a series of network models.     

Asymptomatic Carriers of Toxigenic C. difficile in Long-Term Care Facilities: A Meta-Analysis of Prevalence and Risk Factors

BackgroundThe impact of Clostridium difficile colonization in C. difficile infection (CDI) is inadequately explored. As a result, asymptomatic carriage is not considered in the development of infection control policies and the burden of carrier state in long-term care facilities (LTCFs) is unknown.PurposeTo explore the epidemiology of C. difficile colonization in LTCFs, identify predisposing factors and describe its impact on healthcare management.ResultsBased on data from 9 eligible studies that met the specified criteria and included 1,371 subjects, we found that 14.8% (95%CI 7.6%-24.0%) of LTCF residents are asymptomatic carriers of toxigenic C. difficile. Colonization estimates were significantly higher in facilities with prior CDI outbreak (30.1% vs. 6.5%, p = 0.01). Patient history of CDI (OR 6.07; 95% CI 2.06–17.88; effect derived from 3 studies), prior hospitalization (OR 2.11; 95% CI 1.08–4.13; derived from 3 studies) and antimicrobial use within previous 3 months (OR 3.68; 95% CI 2.04–6.62; derived from 4 studies) were associated with colonization. The predicted colonization rate at admission was 8.9%.ConclusionAsymptomatic carriage of toxigenic C. difficile represents a significant burden in LTCFs and is associated with prior CDI outbreaks in the facility, a history of CDI, prior hospitalization and antimicrobial use. These findings can impact infection control measures at LTCFs.     

Magnetic Resonance Imaging of the Newborn Brain: Automatic Segmentation of Brain Images into 50 Anatomical Regions

We studied methods for the automatic segmentation of neonatal and developing brain images into 50 anatomical regions, utilizing a new set of manually segmented magnetic resonance (MR) images from 5 term-born and 15 preterm infants imaged at term corrected age called ALBERTs. Two methods were compared: individual registrations with label propagation and fusion; and template based registration with propagation of a maximum probability neonatal ALBERT (MPNA). In both cases we evaluated the performance of different neonatal atlases and MPNA, and the approaches were compared with the manual segmentations by means of the Dice overlap coefficient. Dice values, averaged across regions, were 0.81±0.02 using label propagation and fusion for the preterm population, and 0.81±0.02 using the single registration of a MPNA for the term population. Segmentations of 36 further unsegmented target images of developing brains yielded visibly high-quality results. This registration approach allows the rapid construction of automatically labeled age-specific brain atlases for neonates and the developing brain.     

New Insights into Handling Missing Values in Environmental Epidemiological Studies

Missing data are unavoidable in environmental epidemiologic surveys. The aim of this study was to compare methods for handling large amounts of missing values: omission of missing values, single and multiple imputations (through linear regression or partial least squares regression), and a fully Bayesian approach. These methods were applied to the PARIS birth cohort, where indoor domestic pollutant measurements were performed in a random sample of babies'' dwellings. A simulation study was conducted to assess performances of different approaches with a high proportion of missing values (from 50% to 95%). Different simulation scenarios were carried out, controlling the true value of the association (odds ratio of 1.0, 1.2, and 1.4), and varying the health outcome prevalence. When a large amount of data is missing, omitting these missing data reduced statistical power and inflated standard errors, which affected the significance of the association. Single imputation underestimated the variability, and considerably increased risk of type I error. All approaches were conservative, except the Bayesian joint model. In the case of a common health outcome, the fully Bayesian approach is the most efficient approach (low root mean square error, reasonable type I error, and high statistical power). Nevertheless for a less prevalent event, the type I error is increased and the statistical power is reduced. The estimated posterior distribution of the OR is useful to refine the conclusion. Among the methods handling missing values, no approach is absolutely the best but when usual approaches (e.g. single imputation) are not sufficient, joint modelling approach of missing process and health association is more efficient when large amounts of data are missing.     

Enhancement of cytochrome c catalytic behaviour by affecting the heme environment using functionalized carbon-based nanomaterials

The effect of carbon-based nanomaterials (CBNs), such as multi-wall carbon nanotubes (CNTs) and graphene oxide (GO) nanomaterials functionalized with carboxyl, alkyl and amine groups, on the peroxidase-like activity and structure of cytochrome c (cyt c) was investigated. The catalytic efficiency of cyt c increases up to 78-fold in the presence of graphene oxide and up to 2.5-fold in the presence of other functionalized CBNs. Moreover, the use of functionalized CBNs enhances the thermal stability of the protein as well as its tolerance against hydrogen peroxide up to 2.5-fold. UV–vis and circular dichroism spectroscopy studies suggest that the increase in the peroxidase activity of cyt c in the presence of some functionalized GO nanomaterials, correlates to perturbations of the heme microenvironment, while the secondary structure of the enzyme remains intact. These results indicate that the beneficial effect the functionalized CBNs have on the activity and on the stability of cyt c depends on CBNs geometry and surface functionalization.     

In Vitro Model of Tumor Cell Extravasation

Tumor cells that disseminate from the primary tumor and survive the vascular system can eventually extravasate across the endothelium to metastasize at a secondary site. In this study, we developed a microfluidic system to mimic tumor cell extravasation where cancer cells can transmigrate across an endothelial monolayer into a hydrogel that models the extracellular space. The experimental protocol is optimized to ensure the formation of an intact endothelium prior to the introduction of tumor cells and also to observe tumor cell extravasation by having a suitable tumor seeding density. Extravasation is observed for 38.8% of the tumor cells in contact with the endothelium within 1 day after their introduction. Permeability of the EC monolayer as measured by the diffusion of fluorescently-labeled dextran across the monolayer increased 3.8 fold 24 hours after introducing tumor cells, suggesting that the presence of tumor cells increases endothelial permeability. The percent of tumor cells extravasated remained nearly constant from1 to 3 days after tumor seeding, indicating extravasation in our system generally occurs within the first 24 hours of tumor cell contact with the endothelium.