Unenhanced whole-body MRI versus PET-CT for the detection of prostate cancer metastases after primary treatment

The aim of this study was to evaluate the accuracy of unenhanced whole-body MRI, including whole-body Diffusion Weighted Imaging (DWI), used as a diagnostic modality to detect  pathologic lymph nodes and skeletal metastases in patients with prostate cancer (PCa) undergoing restaging after primary treatment

Development of a predictive model for the shelf-life of Atlantic mackerel (Scomber scombrus)

Despite its commercial value, the shelf-life of the Atlantic mackerel (Scomber scombrus) during refrigerated storage was poorly investigated. In this regard, the Quality Index Method (QIM) was proposed as a suitable scoring system for freshness and quality sensorial estimation of fishery products. This study aims to develop a deterministic mathematical model based on dynamic temperatures conditions and a suc-cessive statistical analysis of the results obtained. This model will be exploited to predict the shelf-life of the Atlantic mackerel based on specific storage temperatures. A total of 60 fresh fishes were subdivided into two groups and respectively stored in ice for 12 days at a constant temperature of 1±0.5°C (Group A) and a fluctuating temperature ranging between 1 and 7°C (Group B). Microbiological analysis and sensory evaluation through the QIM were performed on each fish at regular time inter-vals. A critical value of 6 Log cfu/g of spoilage bacteria (mainly psychotropic) associated with a significant decay of the sensorial characteristics was exceeded after 9 days of storage for Group A and 3 days for Group B. A reliable prediction of fish freshness was obtained by modelling the QIM as a function of the spoilage bacteria behaviour. A coefficient β of correlation was determined to convert the spoilage bacteria load into a Quality Index score. The adoption of mathematical predictive models to assess microbial behaviour under different environmental conditions is an interest-ing tool for food industries to maximize production and reduce waste

Network intelligence vs. jamming in underwater networks: how learning can cope with misbehavior

In this paper, we present a machine-learning technique to counteract jamming attacks in underwater networks. Indeed, this is relevant in security applications where sensor devices are located in critical regions, for example, in the case of national border surveillance or for identifying any unauthorized intrusion. To this aim, a multi-hop routing protocol that relies on the exploitation of a Q-learning methodology is presented with a focus on increasing reliability in data communication and network lifetime. Performance results assess the effectiveness of the proposed solution as compared to other efficient state-of-the-art approaches

Drug–drug interactions and pharmacogenomic evaluation in colorectal cancer patients. The new drug-pin® system comprehensive approach

Drug–drug interactions (DDIs) can affect both treatment efficacy and toxicity. We used Drug-PIN® (Personalized Interactions Network) software in colorectal cancer (CRC) patients to evaluate drug–drug–gene interactions (DDGIs), defined as the combination of DDIs and individual genetic polymorphisms. Inclusion criteria were: (i) stage II-IV CRC; (ii) ECOG PS (Performance status sec. Eastern coperative oncology group) ≤2; (iii) ≥5 concomitant drugs; and (iv) adequate renal, hepatic, and bone marrow function. The Drug-PIN® system analyzes interactions between active and/or pro-drug forms by integrating biochemical, demographic, and genomic data from 110 SNPs. We selected DDI, DrugPin1, and DrugPin2 scores, resulting from concomitant medication interactions, concomitant medications, and SNP profiles, and DrugPin1 added to chemotherapy drugs, respectively. Thirty-four patients, taking a median of seven concomitant medications, were included. The median DrugPin1 and DrugPin2 scores were 42.6 and 77.7, respectively. In 13 patients, the DrugPin2 score was two-fold higher than the DrugPin1 score, with 7 (54%) of these patients experiencing severe toxicity that required hospitalization. On chi-squared testing for any toxic-ity, a doubled DrugPin2 score (p = 0.001) was significantly related to G3–G4 toxicity. Drug-PIN® software may prevent severe adverse events, decrease hospitalizations, and improve survival in cancer patients

Turning round the telescope. Centre-right parties and immigration and integration policy in Europe

This is an Author's Original Manuscript of 'Turning round the telescope. Centre-right parties and immigration and integration policy in Europe', whose final and definitive form, the Version of Record, has been published in the Journal of European Public Policy 15(3):315-330, 2008 [copyright Taylor & Francis], available online at: http://www.tandfonline.com/doi.org/10.1080/13501760701847341

Recent developments on micrometric fission chambers for high neutron fluxes

ISBN 9781424452071International audiencewith the development of innovative nuclear systems and new generation neutron sources, the nuclear instrumentation should be adapted. Since several years, we developed microscopic fission chambers to study the transmutation of minor actinides in high thermal-neutron fluxes. The recent developments done to fulfill the drastic conditions of irradiations are described in this paper together with the feedback from the measurements. Two installations were used: the HFR of the ILL for its highest thermal neutron flux of the world and the MEGAPIE target which was the first 1 MW liquid Pb-Bi spallation target in the world

Evaluation of Methods for Estimating Time to Steady State with Examples from Phase 1 Studies

An overview is provided of the methodologies used in determining the time to steady state for Phase 1 multiple dose studies. These methods include NOSTASOT (no-statistical-significance-of-trend), Helmert contrasts, spline (quadratic) regression, effective half life for accumulation, nonlinear mixed effects modeling, and Bayesian approach using Markov Chain Monte Carlo (MCMC) methods. For each methodology we describe its advantages and disadvantages. The first two methods do not require any distributional assumptions for the pharmacokinetic (PK) parameters and are limited to average assessment of steady state. Also spline regression which provides both average and individual assessment of time to steady state does not require any distributional assumptions for the PK parameters. On the other hand, nonlinear mixed effects modeling and Bayesian hierarchical modeling which allow for the estimation of both population and subject-specific estimates of time to steady state do require distributional assumptions on PK parameters. The current investigation presents eight case studies for which the time to steady state was assessed using the above mentioned methodologies. The time to steady state estimates obtained from nonlinear mixed effects modeling, Bayesian hierarchal approach, effective half life, and spline regression were generally similar