Cost-effectiveness of a screening strategy for Q fever among pregnant women in risk areas: a clustered randomized controlled trial

Contains fulltext : 87399.pdf (publisher's version ) (Open Access)BACKGROUND: In The Netherlands the largest human Q fever outbreak ever reported in the literature is currently ongoing with more than 2300 notified cases in 2009. Pregnant women are particularly at risk as Q fever during pregnancy may cause maternal and obstetric complications. Since the majority of infected pregnant women are asymptomatic, a screening strategy might be of great value to reduce Q fever related complications. We designed a trial to assess the (cost-)effectiveness of a screening program for Q fever in pregnant women living in risks areas in The Netherlands. METHODS/DESIGN: We will conduct a clustered randomized controlled trial in which primary care midwife centres in Q fever risk areas are randomized to recruit pregnant women for either the control group or the intervention group. In both groups a blood sample is taken around 20 weeks postmenstrual age. In the intervention group, this sample is immediately analyzed by indirect immunofluorescence assay for detection of IgG and IgM antibodies using a sensitive cut-off level of 1:32. In case of an active Q fever infection, antibiotic treatment is recommended and serological follow up is performed. In the control group, serum is frozen for analysis after delivery. The primary endpoint is a maternal (chronic Q fever or reactivation) or obstetric complication (low birth weight, preterm delivery or fetal death) in Q fever positive women. Secondary aims pertain to the course of infection in pregnant women, diagnostic accuracy of laboratory tests used for screening, histo-pathological abnormalities of the placenta of Q fever positive women, side effects of therapy, and costs. The analysis will be according to the intention-to-screen principle, and cost-effectiveness analysis will be performed by comparing the direct and indirect costs between the intervention and control group. DISCUSSION: With this study we aim to provide insight into the balance of risks of undetected and detected Q fever during pregnancy. TRIAL REGISTRATION: ClinicalTrials.gov, protocol record NL30340.042.09

Adenylyl Cyclase α and cAMP Signaling Mediate Plasmodium Sporozoite Apical Regulated Exocytosis and Hepatocyte Infection

Malaria starts with the infection of the liver of the host by Plasmodium sporozoites, the parasite form transmitted by infected mosquitoes. Sporozoites migrate through several hepatocytes by breaching their plasma membranes before finally infecting one with the formation of an internalization vacuole. Migration through host cells induces apical regulated exocytosis in sporozoites. Here we show that apical regulated exocytosis is induced by increases in cAMP in sporozoites of rodent (P. yoelii and P. berghei) and human (P. falciparum) Plasmodium species. We have generated P. berghei parasites deficient in adenylyl cyclase α (ACα), a gene containing regions with high homology to adenylyl cyclases. PbACα-deficient sporozoites do not exocytose in response to migration through host cells and present more than 50% impaired hepatocyte infectivity in vivo. These effects are specific to ACα, as re-introduction of ACα in deficient parasites resulted in complete recovery of exocytosis and infection. Our findings indicate that ACα and increases in cAMP levels are required for sporozoite apical regulated exocytosis, which is involved in sporozoite infection of hepatocytes

The Relationship of DNA Methylation with Age, Gender and Genotype in Twins and Healthy Controls

Cytosine-5 methylation within CpG dinucleotides is a potentially important mechanism of epigenetic influence on human traits and disease. In addition to influences of age and gender, genetic control of DNA methylation levels has recently been described. We used whole blood genomic DNA in a twin set (23 MZ twin-pairs and 23 DZ twin-pairs, N = 92) as well as healthy controls (N = 96) to investigate heritability and relationship with age and gender of selected DNA methylation profiles using readily commercially available GoldenGate bead array technology. Despite the inability to detect meaningful methylation differences in the majority of CpG loci due to tissue type and locus selection issues, we found replicable significant associations of DNA methylation with age and gender. We identified associations of genetically heritable single nucleotide polymorphisms with large differences in DNA methylation levels near the polymorphism (cis effects) as well as associations with much smaller differences in DNA methylation levels elsewhere in the human genome (trans effects). Our results demonstrate the feasibility of array-based approaches in studies of DNA methylation and highlight the vast differences between individual loci. The identification of CpG loci of which DNA methylation levels are under genetic control or are related to age or gender will facilitate further studies into the role of DNA methylation and disease

Predicting conversion from clinically isolated syndrome to multiple sclerosis–An imaging-based machine learning approach

Magnetic resonance imaging (MRI) scans play a pivotal role in the evaluation of patients presenting with a clinically isolated syndrome (CIS), as these may depict brain lesions suggestive of an inflammatory cause. We hypothesized that it is possible to predict the conversion from CIS to multiple sclerosis (MS) based on the baseline MRI scan by studying image features of these lesions.We analyzed 84 patients diagnosed with CIS from a prospective observational single center cohort. The patients were followed up for at least three years. Conversion to MS was defined according to the 2010 McDonald criteria. Brain lesions were segmented based on 3D FLAIR and 3D T1 images. We generated brain lesion masks by a computer assisted manual segmentation. We also generated a set of automated segmentations using the Lesion Segmentation Toolbox for SPM to assess the influence of different segmentation methods. Shape and brightness features were automatically calculated from the segmented masks and used as input data to train an oblique random forest classifier. Prediction accuracies of the resulting model were validated through a three-fold cross-validation.Conversion from CIS to MS occurred in 66 of 84 patients (79%). The conversion or non-conversion was predicted correctly in 71 patients based on shape features derived from the computer assisted manual segmentation masks (84.5% accuracy). This predictor was more accurate than predicting conversion using dissemination in space at baseline according to the 2010 McDonald criteria (75% accuracy). While shape features strongly contributed to the accuracy of the predictor, including intensity features did not further improve performance.As patients who convert to definite MS benefit from early treatment, an early classification model is highly desirable. Our study shows that shape parameters of lesions can contribute to predicting the future course of CIS patients more accurately. Keywords: Multiple sclerosis, Clinically isolated syndrome, MRI, Machine learnin

A Nonparametric model for Brain Tumor Segmentation and Volumetry in Longitudinal MR Sequences

International audienceBrain tumor image segmentation and brain tumor growth assessment are inter-dependent and benet from a joint evaluation. Starting from a generative model for multimodal brain tumor segmentation, we make use of a nonparametric growth model that is implemented as a conditional random field (CRF) including directed links with infinite weight in order to incorporate growth and inclusion constraints, reflecting our prior belief on tumor occurrence in the dierent image modalities. In this study, we validate this model to obtain brain tumor segmentations and volumetry in longitudinal image data. Moreover, we extend the framework with a probabilistic approach for estimating the likelihood of disease progression, i.e. tumor regrowth, after therapy. We present experiments for longitudinal image sequences with T1, T1c, T2 and FLAIR images, acquired for ten patients with low and high grade gliomas

A Dedicated Very Low Power Analog VLSI Architecture for Smart Adaptive Systems

This paper deals with analog VLSI architectures addressed to the implementation of smart adaptive systems on silicon. In particular, we addressed the implementation of artificial neural networks with on-chip learning algorithms with the goal of efficiency in terms of scalability, modularity, computational density, real time operation and power consumption. We present the analog circuit architecture of a feed-forward network with on-chip weight perturbation learning in CMOS technology. Novelty of the approach lies in the circuit implementation of the feed-forward neural primitives and on the overall analog circuit architecture. The proposed circuits feature very low power consumption and robustness with respect to noise effects. We extensively tested the analog architecture with simulations at transistor level by using the netlist extracted from the physical design. The results compare favourably with those reported in the open literature. In particular, the architecture exhibits very high power efficiency and computational density and remarkable modularity and scalability features. The proposed approach is aimed to the implementation of embedded intelligent systems for ubiquitous computing