RESUMO
In the last three decades, radiopharmaceuticals have proven their effectiveness for cancer diagnosis and therapy. In parallel, the advances in nanotechnology have fueled a plethora of applications in biology and medicine. A convergence of these disciplines has emerged more recently with the advent of nanotechnology-aided radiopharmaceuticals. Capitalizing on the unique physical and functional properties of nanoparticles, radiolabeled nanomaterials or nano-radiopharmaceuticals have the potential to enhance imaging and therapy of human diseases. This article provides an overview of various radionuclides used in diagnostic, therapeutic, and theranostic applications, radionuclide production through different techniques, conventional radionuclide delivery systems, and advancements in the delivery systems for nanomaterials. The review also provides insights into fundamental concepts necessary to improve currently available radionuclide agents and formulate new nano-radiopharmaceuticals.
RESUMO
Information dissemination has changed rapidly in recent years with the emergence of social media which provides online platforms for people worldwide to share their thoughts, activities, emotions, and build social relationships. Hence, modeling information diffusion has become an important area of research in the field of network analysis. It involves the mathematical modeling of the movement of information and study the information spread pattern. In this paper, we attempt to model information propagation in online social networks using a nature-inspired approach based on a modified forest-fire model. A slight spark can start a wildfire in a forest, and the spread of this fire depends on vegetation, weather, and topography, which may act as fuel. On similar lines, we labeled users who haven't joined the network yet as E m p t y, existing users as T r e e, and information as F i r e. The spread of information across online social networks depends upon users-followers relationships, the significance of the topic, and other such features. We introduce a novel B u r n t state to the traditional forest-fire model to represent non-spreaders in the network. We validate our method on six real-world data-sets extracted from Twitter and conclude that the proposed model performs reasonably well in predicting information diffusion.
