Radboud University Nijmegen

About: Radboud University Nijmegen is a education organization based out in Nijmegen, Gelderland, Netherlands. It is known for research contribution in the topics: Population & Randomized controlled trial. The organization has 35417 authors who have published 83035 publications receiving 3285064 citations. The organization is also known as: Catholic University of Nijmegen & Radboud University.

Biting and Chewing in Overdentures, Full Dentures, and Natural Dentitions

Abstract: It has been suggested that the provision of dental implants can improve the oral function of subjects with severely resorbed mandibles, possibly restoring function to the level experienced by satisfied wearers of conventional complete dentures. Nevertheless, a quantitative comparison has never been made and can be drawn from the literature only with difficulty, since studies differ greatly in methodology. To make such a comparison, we measured bite force and chewing efficiency by using identical methods in subjects with overdentures, complete full dentures, and natural dentitions. Our results indicated that bite forces achieved with overdentures on dental implants were between those achieved with artificial and natural dentitions. Chewing efficiency was significantly greater than that of subjects with full dentures (low mandible), but was still lower than that of subjects with full dentures (high mandible) and overdentures on bare roots. Differences in the height of the mandible revealed significant differences in chewing efficiency between the two full-denture groups. Furthermore, subjects with a shortened dental arch exerted bite forces similar to those of subjects with a complete-natural dentition, but their chewing efficiency was limited due to the reduced occlusal area. For all groups combined, a significant correlation was found between maximum bite force and chewing efficiency. Nearly half of the variation in chewing efficiency was explained by bite force alone.

Fluid flow increases mineralized matrix deposition in 3D perfusion culture of marrow stromal osteoblasts in a dose-dependent manner

Abstract: Bone is a complex highly structured mechanically active 3D tissue composed of cellular and matrix elements. The true biological environment of a bone cell is thus derived from a dynamic interaction between responsively active cells experiencing mechanical forces and a continuously changing 3D matrix architecture. To investigate this phenomenon in vitro, marrow stromal osteoblasts were cultured on 3D scaffolds under flow perfusion with different rates of flow for an extended period to permit osteoblast differentiation and significant matrix production and mineralization. With all flow conditions, mineralized matrix production was dramatically increased over statically cultured constructs with the total calcium content of the cultured scaffolds increasing with increasing flow rate. Flow perfusion induced de novo tissue modeling with the formation of pore-like structures in the scaffolds and enhanced the distribution of cells and matrix throughout the scaffolds. These results represent reporting of the long-term effects of fluid flow on primary differentiating osteoblasts and indicate that fluid flow has far-reaching effects on osteoblast differentiation and phenotypic expression in vitro. Flow perfusion culture permits the generation and study of a 3D, actively modeled, mineralized matrix and can therefore be a valuable tool for both bone biology and tissue engineering.

Gaussian interaction profile kernels for predicting drug–target interaction

Abstract: Motivation: The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug–target pairs in current datasets are experimentally validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Results: We show that a simple machine learning method that uses the drug–target network as the only source of information is capable of predicting true interaction pairs with high accuracy. Specifically, we introduce interaction profiles of drugs (and of targets) in a network, which are binary vectors specifying the presence or absence of interaction with every target (drug) in that network. We define a kernel on these profiles, called the Gaussian Interaction Profile (GIP) kernel, and use a simple classifier, (kernel) Regularized Least Squares (RLS), for prediction drug–target interactions. We test comparatively the effectiveness of RLS with the GIP kernel on four drug–target interaction networks used in previous studies. The proposed algorithm achieves area under the precision–recall curve (AUPR) up to 92.7, significantly improving over results of state-of-the-art methods. Moreover, we show that using also kernels based on chemical and genomic information further increases accuracy, with a neat improvement on small datasets. These results substantiate the relevance of the network topology (in the form of interaction profiles) as source of information for predicting drug–target interactions. Availability: Software and Supplementary Material are available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2011/. Contact:tvanlaarhoven@cs.ru.nl; elenam@cs.ru.nl Supplementary Information:Supplementary data are available at Bioinformatics online.