[18F]EF3 is not superior to [18F]FMISO for PET-based hypoxia evaluation as measured in a rat rhabdomyosarcoma tumour model

PURPOSE: The aim of this investigation was to quantitatively compare the novel positron emission tomography (PET) hypoxia marker 2-(2-nitroimidazol-1-yl)-N-(3[(18)F],3,3-trifluoropropyl)acetamide ([(18)F]EF3) with the reference hypoxia tracer [(18)F]fluoromisonidazole ([(18)F]FMISO). METHODS: [(18)F]EF3 or [(18)F]FMISO was injected every 2 days into two separate groups of rats bearing syngeneic rhabdomyosarcoma tumours. In vivo PET analysis was done by drawing regions of interest on the images of selected tissues. The resulting activity data were quantified by the percentage of injected radioactivity per gram tissue (%ID/g) and tumour to blood (T/B) ratio. The spatial distribution of radioactivity was defined by autoradiography on frozen tumour sections. RESULTS: The blood clearance of [(18)F]EF3 was faster than that of [(18)F]FMISO. The clearance of both tracers was slower in tumour tissue compared with other tissues. This results in increasing T/B ratios as a function of time post tracer injection (p.i.). The maximal [(18)F]EF3 tumour uptake, compared to the maximum [(18)F]FMISO uptake, was significantly lower at 2 h p.i. but reached similar levels at 4 h p.i. The tumour uptake for both tracers was independent of the tumour volume for all investigated time points. Both tracers showed heterogeneous intra-tumoural distribution. CONCLUSIONS: [(18)F]EF3 tumour uptake reached similar levels at 4 h p.i. compared with tumour retention observed after injection of [(18)F]FMISO at 2 h p.i. Although [(18)F]EF3 is a promising non-invasive tracer, it is not superior over [(18)F]FMISO for the visualisation of tumour hypoxia. No significant differences between [(18)F]EF3 and [(18)F]FMISO were observed with regard to the intra-tumoural distribution and the extra-tumoural tissue retention

An ecosystem service approach to support integrated pond management: a case study using Bayesian belief networks: highlighting opportunities and risks

Freshwater ponds deliver a broad range of ecosystem services (ESS). Taking into account this broad range of services to attain cost-effective ESS delivery is an important challenge facing integrated pond management. To assess the strengths and weaknesses of an ESS approach to support decisions in integrated pond management, we applied it on a small case study in Flanders, Belgium. A Bayesian belief network model was developed to assess ESS delivery under three alternative pond management scenarios: intensive fish farming (IFF), extensive fish farming (EFF) and nature conservation management (NCM). A probabilistic cost-benefit analysis was performed that includes both costs associated with pond management practices and benefits associated with ESS delivery. Whether or not a particular ESS is included in the analysis affects the identification of the most preferable management scenario by the model. Assessing the delivery of a more complete set of ecosystem services tends to shift the results away from intensive management to more biodiversity-oriented management scenarios. The proposed methodology illustrates the potential of Bayesian belief networks. BBNs facilitate knowledge integration and their modular nature encourages future model expansion to more encompassing sets of services. Yet, we also illustrate the key weaknesses of such exercises, being that the choice whether or not to include a particular ecosystem service may determine the suggested optimal management practice

An ecosystem service approach to support integrated pond management: A case study using Bayesian belief networks - Highlighting opportunities and risks

Freshwater ponds deliver a broad range of ecosystem services (ESS). Taking into account this broad range of services to attain cost-effective ESS delivery is an important challenge facing integrated pond management. To assess the strengths and weaknesses of an ESS approach to support decisions in integrated pond management, we applied it on a small case study in Flanders, Belgium. A Bayesian belief network model was developed to assess ESS delivery under three alternative pond management scenarios: intensive fish farming (IFF), extensive fish farming (EFF) and nature conservation management (NCM). A probabilistic cost-benefit analysis was performed that includes both costs associated with pond management practices and benefits associated with ESS delivery. Whether or not a particular ESS is included in the analysis affects the identification of the most preferable management scenario by the model. Assessing the delivery of a more complete set of ecosystem services tends to shift the results away from intensive management to more biodiversity-oriented management scenarios. The proposed methodology illustrates the potential of Bayesian belief networks. BBNs facilitate knowledge integration and their modular nature encourages future model expansion to more encompassing sets of services. Yet, we also illustrate the key weaknesses of such exercises, being that the choice whether or not to include a particular ecosystem service may determine the suggested optimal management practice.publisher: Elsevier articletitle: An ecosystem service approach to support integrated pond management: A case study using Bayesian belief networks – Highlighting opportunities and risks journaltitle: Journal of Environmental Management articlelink: http://dx.doi.org/10.1016/j.jenvman.2014.06.015 content_type: article copyright: Copyright © 2014 Elsevier Ltd. All rights reserved.status: publishe

Postoperative intensity-modulated radiotherapy in sinonasal carcinoma - Clinical results in 39 patients

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