64Cu- and 68Ga-labelled [Nle(14),Lys(40)(Ahx-NODAGA)NH2]-exendin-4 for pancreatic beta cell imaging in rats.

PURPOSE Glucagon-like peptide-1 receptor (GLP-1R) is a molecular target for imaging of pancreatic beta cells. We compared the ability of [Nle(14),Lys(40)(Ahx-NODAGA-(64)Cu)NH2]-exendin-4 ([(64)Cu]NODAGA-exendin-4) and [Nle(14),Lys(40)(Ahx-NODAGA-(68)Ga)NH2]-exendin-4 ([(68)Ga]NODAGA-exendin-4) to detect native pancreatic islets in rodents. PROCEDURES The stability, lipophilicity and affinity of the radiotracers to the GLP-1R were determined in vitro. The biodistribution of the tracers was assessed using autoradiography, ex vivo biodistribution and PET imaging. Estimates for human radiation dosimetry were calculated. RESULTS We found GLP-1R-specific labelling of pancreatic islets. However, the pancreas could not be visualised in PET images. The highest uptake of the tracers was observed in the kidneys. Effective dose estimates for [(64)Cu]NODAGA-exendin-4 and [(68)Ga]NODAGA-exendin-4 were 0.144 and 0.012 mSv/MBq, respectively. CONCLUSION [(64)Cu]NODAGA-exendin-4 might be more effective for labelling islets than [(68)Ga]NODAGA-exendin-4. This is probably due to the lower specific radioactivity of [(68)Ga]NODAGA-exendin-4 compared to [(64)Cu]NODAGA-exendin-4. The radiation dose in the kidneys may limit the use of [(64)Cu]NODAGA-exendin-4 as a clinical tracer.

Evaluating atmospheric methane inversion model results for Pallas, northern Finland

A state-of-the-art inverse model, CarbonTracker Data Assimilation Shell (CTDAS), was used to optimize estimates of methane (CH4) surface fluxes using atmospheric observations of CH4 as a constraint. The model consists of the latest version of the TM5 atmospheric chemistry-transport model and an ensemble Kalman filter based data assimilation system. The model was constrained by atmospheric methane surface concentrations, obtained from the World Data Centre for Greenhouse Gases (WDCGG). Prior methane emissions were specified for five sources: biosphere, anthropogenic, fire, termites and ocean, of which bio-sphere and anthropogenic emissions were optimized. Atmospheric CH 4 mole fractions for 2007 from northern Finland calculated from prior and optimized emissions were compared with observations. It was found that the root mean squared errors of the posterior esti - mates were more than halved. Furthermore, inclusion of NOAA observations of CH 4 from weekly discrete air samples collected at Pallas improved agreement between posterior CH 4 mole fraction estimates and continuous observations, and resulted in reducing optimized biosphere emissions and their uncertainties in northern Finland.

Depression predicts persistence of paranoia in clinical high-risk patients to psychosis: results of the EPOS project.

BACKGROUND The link between depression and paranoia has long been discussed in psychiatric literature. Because the causality of this association is difficult to study in patients with full-blown psychosis, we aimed to investigate how clinical depression relates to the presence and occurrence of paranoid symptoms in clinical high-risk (CHR) patients. METHODS In all, 245 young help-seeking CHR patients were assessed for suspiciousness and paranoid symptoms with the structured interview for prodromal syndromes at baseline, 9- and 18-month follow-up. At baseline, clinical diagnoses were assessed by the Structured Clinical Interview for DSM-IV, childhood adversities by the Trauma and Distress Scale, trait-like suspiciousness by the Schizotypal Personality Questionnaire, and anxiety and depressiveness by the Positive and Negative Syndrome Scale. RESULTS At baseline, 54.3 % of CHR patients reported at least moderate paranoid symptoms. At 9- and 18-month follow-ups, the corresponding figures were 28.3 and 24.4 %. Depressive, obsessive-compulsive and somatoform disorders, emotional and sexual abuse, and anxiety and suspiciousness associated with paranoid symptoms. In multivariate modelling, depressive and obsessive-compulsive disorders, sexual abuse, and anxiety predicted persistence of paranoid symptoms. CONCLUSION Depressive disorder was one of the major clinical factors predicting persistence of paranoid symptoms in CHR patients. In addition, obsessive-compulsive disorder, childhood sexual abuse, and anxiety associated with paranoia. Effective pharmacological and psychotherapeutic treatment of these disorders and anxiety may reduce paranoid symptoms in CHR patients.