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Tips for Improving Preclinical Imaging.

In order to study the nature or diseases, such as those affecting the central nervous system, medical practitioners and scientists often use preclinical models and modalities to assist in their diagnosis, frankly, medical practitioners follow certain guidelines to ensure that the imaging recorded from preclinical models can provide a clear and interpretable data across all fields and assist drug manufacturers a better framework to work on how they can conduct their clinical trials and develop their drugs for a certain disease.

When it is possible and prior to the beginning of every study, preclinical imaging specialists from all areas of development come together to analyze the data and determine whether the models components are accurate for review.

You should take into account the particular disease model and the aspects of the different diseases that are being discussed, it may help in examining different aspects of the disease hence translating into a reliable imaging aspect.
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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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Do not forget to factor in all the components of modeling strategies, these include, the model paradigm, behavior and histopathological components, that should be processed and assessed in respect to the timing of the assays.

Always seek for data from imaging groups to determine whether there is a gap within the modeling paradigm and see if the parameters used in the study can be examined by the imaging group or those doing the study.

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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.

The particular animal model and the specific imaging techniques applied and the data analysis selected may all have an effect on the eventual results, this becomes of utmost importance when possibly subtle changes upon drug treatment are under scrutiny.

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.

These terms can definitely coexist, but it is critical to optimize time used for imaging per subject to obtain only the relevant information required with subsequent capability to retain high throughput.

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.