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 & Context (language use). 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.

Helical Superstructures from Charged Poly(styrene)-Poly(isocyanodipeptide) Block Copolymers

Abstract: Amphiphilic block copolymers containing a poly(styrene) tail and a charged helical poly(isocyanide) headgroup derived from isocyano-L-alanine-L-alanine and isocyano-L-alanine-L-histidine were prepared. Analogous to low-molecular mass surfactants, these block copolymers self-assembled in aqueous systems to form micelles, vesicles, and bilayer aggregates. The morphology of these aggregates can be controlled by variation of the length of the poly(isocyanide) block, the pH, and the anion-headgroup interactions. The chirality of the macromolecules results in the formation of helical superstructures that have a helical sense opposite to that of the constituent block copolymers. The great variety of morphologies displayed by these block copolymers and the fact that they are easily accessible from poly(styrene) and different types of peptides open new opportunities for applications in the fields of life and materials sciences.

British Thoracic Society guidelines for the investigation and management of pulmonary nodules: accredited by NICE

Abstract: This guideline is based on a comprehensive review of the literature on pulmonary nodules and expert opinion. Although the management pathway for the majority of nodules detected is straightforward it is sometimes more complex and this is helped by the inclusion of detailed and specific recommendations and the 4 management algorithms below. The Guideline Development Group (GDG) wanted to highlight the new research evidence which has led to significant changes in management recommendations from previously published guidelines. These include the use of two malignancy prediction calculators (section ‘Initial assessment of the probability of malignancy in pulmonary nodules’, algorithm 1) to better characterise risk of malignancy. There are recommendations for a higher nodule size threshold for follow-up (≥5 mm or ≥80 mm3) and a reduction of the follow-up period to 1 year for solid pulmonary nodules; both of these will reduce the number of follow-up CT scans (sections ‘Initial assessment of the probability of malignancy in pulmonary nodules’ and ‘Imaging follow-up’, algorithms 1 and 2). Volumetry is recommended as the preferred measurement method and there are recommendations for the management of nodules with extended volume doubling times (section ‘Imaging follow-up’, algorithm 2). Acknowledging the good prognosis of sub-solid nodules (SSNs), there are recommendations for less aggressive options for their management (section ‘Management of SSNs’, algorithm 3). The guidelines provide more clarity in the use of further imaging, with ordinal scale reporting for PET-CT recommended to facilitate incorporation into risk models (section ‘Further imaging in management of pulmonary nodules’) and more clarity about the place of biopsy (section ‘Non-imaging tests and non-surgical biopsy’, algorithm 4). There are recommendations for the threshold for treatment without histological confirmation (sections ‘Surgical excision biopsy’ and ‘Non-surgical treatment without pathological confirmation of malignancy’, algorithm 4). Finally, and possibly most importantly, there are evidence-based recommendations about the information that people …

Inclusion of geometrical uncertainties in radiotherapy treatment planning by means of coverage probability

Abstract: Purpose: Following the ICRU-50 recommendations, geometrical uncertainties in tumor position during radiotherapy treatments are generally included in the treatment planning by adding a margin to the clinical target volume (CTV) to yield the planning target volume (PTV). We have developed a method for automatic calculation of this margin. Methods and Materials: Geometrical uncertainties of a specific patient group can normally be characterized by the standard deviation of the distribution of systematic deviations in the patient group (Σ) and by the average standard deviation of the distribution of random deviations (σ). The CTV of a patient to be planned can be represented in a 3D matrix in the treatment room coordinate system with voxel values one inside and zero outside the CTV. Convolution of this matrix with the appropriate probability distributions for translations and rotations yields a matrix with coverage probabilities (CPs) which is defined as the probability for each point to be covered by the CTV. The PTV can then be chosen as a volume corresponding to a certain iso-probability level. Separate calculations are performed for systematic and random deviations. Iso-probability volumes are selected in such a way that a high percentage of the CTV volume (on average > 99%) receives a high dose (> 95%). The consequences of systematic deviations on the dose distribution in the CTV can be estimated by calculation of dose histograms of the CP matrix for systematic deviations, resulting in a so-called dose probability histogram (DPH). A DPH represents the average dose volume histogram (DVH) for all systematic deviations in the patient group. The consequences of random deviations can be calculated by convolution of the dose distribution with the probability distributions for random deviations. Using the convolved dose matrix in the DPH calculation yields full information about the influence of geometrical uncertainties on the dose in the CTV. Results: The model is demonstrated to be fast and accurate for a prostate, cervix, and lung cancer case. A CTV-to-PTV margin size which ensures at least 95% dose to (on average) 99% of the CTV, appears to be equal to about 2Σ + 0.7σ for three all cases. Because rotational deviations are included, the resulting margins can be anisotropic, as shown for the prostate cancer case. Conclusion: A method has been developed for calculation of CTV-to-PTV margins based on the assumption that the CTV should be adequately irradiated with a high probability.

EEG and MEG data analysis in SPM8.

Abstract: SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools.