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In the last decade, the development of new radiopharmaceuticals for the imaging and therapy of prostate cancer has been a highly active and important area of research, especially focusing on the prostate‐specific membrane antigen (PSMA), an antigen which is upregulated in prostate, as well as in other tumor cells. A large variety of PSMA ligands have been radiolabeled, to date. Among the various derivatives, PSMA‐617 resulted to be one of the most interesting in terms of interaction with the antigen and clinical properties, and its lutetium‐177 labeled version has recently been approved by regulatory agencies for therapeutic purposes. For this reasons, the radiolabeling with fluorine‐18 of a PSMA‐617 derivative might be of interest. Beside other methodologies to radiolabel macromolecules with fluorine‐18, the "click‐chemistry" approach resulted to be very useful, and the copper‐catalyzed azide‐alkyne cycloaddition (CuAAC) is considered one of most efficient and reliable. This paper proposes the synthesis of a suitable precursor for the radiolabeling with fluorine‐18 of a new PSMA‐617 derivative. The whole radiosynthetic procedure has been fully automated, and the final product, which proved to be stable in plasma, has been obtained with radiochemical yield and purity suitable for subsequent preclinical studies. [ABSTRACT FROM AUTHOR]

Biomarkers able to characterise and predict multifactorial diseases are still one of the most important targets for all the “omics” investigations. In this context, Matrix-Assisted Laser Desorption/Ionisation-Mass Spectrometry Imaging (MALDI-MSI) has gained considerable attention in recent years, but it also led to a huge amount of complex data to be elaborated and interpreted. For this reason, computational and machine learning procedures for biomarker discovery are important tools to consider, both to reduce data dimension and to provide predictive markers for specific diseases. For instance, the availability of protein and genetic markers to support thyroid lesion diagnoses would impact deeply on society due to the high presence of undetermined reports (THY3) that are generally treated as malignant patients. In this paper we show how an accurate classification of thyroid bioptic specimens can be obtained through the application of a state-of-the-art machine learning approach (i.e., Support Vector Machines) on MALDI-MSI data, together with a particular wrapper feature selection algorithm (i.e., recursive feature elimination). The model is able to provide an accurate discriminatory capability using only 20 out of 144 features, resulting in an increase of the model performances, reliability, and computational efficiency. Finally, tissue areas rather than average proteomic profiles are classified, highlighting potential discriminating areas of clinical interest. [ABSTRACT FROM AUTHOR]

The biological functions of prion protein (PrPC) and its possible interaction with other specific molecular membrane partners remain largely unknown. The aim of this study is to gain information on the molecular environment of PrPC by analyzing the lipid and protein composition of a PrPC-enriched membrane subfraction, called prion domain, PrD. This domain was obtained by immunoprecipitation of detergent-resistant microdomains (DRM) of rat cerebellar granule cells under conditions designed to preserve lipid-mediated membrane organization. The electrophoretic pattern of PrD, after staining with Coomassie blue, showed the enrichment of some protein bands in comparison with DRM. μLiquid cromatography-electrospray ionization-mass spectrometry (μLC-ESI-MS)/MS analysis showed that Thy-1 and different types of myosin were strongly enriched in PrD and, in a lesser extent, also OBCAM, LSAMP and tubulin, present altogether in a single band. Experiments using the chemical cross-linker BS3 suggested the existence of an interaction between PrPC and neural cell adhesion molecule (NCAM). Concerning lipids, the comparison between PrD and DRM showed a similar phospholipid/sphingolipid ratio, a phospholipid/cholesterol ratio doubled, and a strong decrease of plasmenilethanolamine (19.7 ± 3.5% vs. 38.3 ± 1.2%). In conclusion, the peculiar lipid composition and in particular the presence of proteins involved in synaptic plasticity, cell adhesion, cytoskeleton regulation and signalling, suggest an important physiological role in neurons of Prion Domain. [ABSTRACT FROM AUTHOR]