Evidence-Based Health Care for Children: What Are We Missing?

Proposes a new framework for evaluating evidence in health care that takes into account interventions in child health promotion, which aim to change children's physical, social, or emotional environment and may take longer for the effects to show

Modulating active sites in MOFs for improved Lewis acid or base catalysis

International audienc

Automatic Segmentation of Thoracic Aorta Segments in Low-Dose Chest CT

Morphological analysis and identification of pathologies in the aorta are important for cardiovascular diagnosis and risk assessment in patients. Manual annotation is time-consuming and cumbersome in CT scans acquired without contrast enhancement and with low radiation dose. Hence, we propose an automatic method to segment the ascending aorta, the aortic arch and the thoracic descending aorta in low-dose chest CT without contrast enhancement. Segmentation was performed using a dilated convolutional neural network (CNN), with a receptive field of 131X131 voxels, that classified voxels in axial, coronal and sagittal image slices. To obtain a final segmentation, the obtained probabilities of the three planes were averaged per class, and voxels were subsequently assigned to the class with the highest class probability. Two-fold cross-validation experiments were performed where ten scans were used to train the network and another ten to evaluate the performance. Dice coefficients of 0.83, 0.86 and 0.88, and Average Symmetrical Surface Distances (ASSDs) of 2.44, 1.56 and 1.87 mm were obtained for the ascending aorta, the aortic arch, and the descending aorta, respectively. The results indicate that the proposed method could be used in large-scale studies analyzing the anatomical location of pathology and morphology of the thoracic aorta

Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions

Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening

A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration

Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for \textit{unsupervised} affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster.Comment: Accepted: Medical Image Analysis - Elsevie

The SOS response of Listeria monocytogenes is involved in stress resistance and mutagenesis

The SOS response is a conserved pathway that is activated under certain stress conditions and is regulated by the repressor LexA and the activator RecA. The food-borne pathogen Listeria monocytogenes contains RecA and LexA homologs, but their roles in Listeria have not been established. In this study, we identified the SOS regulon in L. monocytogenes by comparing the transcription profiles of the wild-type strain and the DeltarecA mutant strain after exposure to the DNA damaging agent mitomycin C. In agreement with studies in other bacteria, we identified an imperfect palindrome AATAAGAACATATGTTCGTTT as the SOS operator sequence. The SOS regulon of L. monocytogenes consists of 29 genes in 16 LexA regulated operons, encoding proteins with functions in translesion DNA synthesis and DNA repair. We furthermore identified a role for the product of the LexA regulated gene yneA in cell elongation and inhibition of cell division. As anticipated, RecA of L. monocytogenes plays a role in mutagenesis; DeltarecA cultures showed considerably lower rifampicin and streptomycin resistant fractions than the wild-type cultures. The SOS response is activated after stress exposure as shown by recA- and yneA-promoter reporter studies. Subsequently, stress survival studies showed DeltarecA mutant cells to be less resistant to heat, H(2)O(2), and acid exposure than wild-type cells. Our results indicate that the SOS response of L. monocytogenes contributes to survival upon exposure to a range of stresses, thereby likely contributing to its persistence in the environment and in the hos