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Ways of Enhancing Preclinical Imaging. Preclinical models and the accompanying modalities are special tools that are used in the study of particular aspects of disease such as neurodegeneration in the case of diseases of the nervous system, well, the following tips help to ensure imaging in a preclinical space generates comprehensible data across all disease states and thus assisting drug developers to better design and monitor clinical trials and bring drugs to the market faster. Prior to engaging in a study, connect preclinical imaging specialists if possible with specialists from elsewhere within the field (preclinical translation and clinical development) to evaluate the translatability of all the components of the model. The relevance of a certain particular disease model and the nature of the disease being studied must always be taken into account because without a clear model to serve as a reference, one will not be able to understand one aspect of the disease being studied.
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Once you factor in several things, then scaling from rodents to humans may not be as easy as it seems but there has to be a very straightforward translational aspects that rely on certain parameters.
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More often than not imaging constitutes a single aspect of the modeling paradigm together with function, behavior, electrophysiological and histopathological aspects, well, the paradigm ought to be examined with respect to the timing and outcome of the assays relative to imaging endpoints. Whenever possible, it is critical that prior data is availed by the imaging group that shows either a discernible deficit within the modeling paradigm or that the parameters under study can actually be examined by the body conducting the study. The next step is to validate newly established paradigms to discover new methodologies and designs which are more cost-effective than those currently used for the subject, well, it is important to remember that data from one scanner and the representative data coming from the team is crucial for a study and utilizing data from a different scanner may present conflicting results. When studying data, parties must use “test-retest” measures to create the study’s estimates and determine possible variations to the subject, also, the selected animal model and imaging methodologies applied in the study and the type of data analysis selected may influence the overall results of the study thus the necessity of doing multiple retests to get accurate results. Such measures would work wonders for those studying the changes in drug treatments. Take the disease model and look at all the limitations and possibilities of the model which will help you find the best exclusion criteria in the study for the subject animal at hand. Looking for high quality throughout, then do not be worries, because these terms can co-exist, but you have to do it in a careful manner where you only extract relevant information while understanding the capability of retention of the imaging. Imaging studies can be difficult, however, if you are working hand in hand with experts then you have a higher and better chance to understand it through some easier and quicker interpretation.