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Quantitative MRI of a Cerebral Cryptococcoma Mouse Model for In Vivo Distinction between Different Cryptococcal Molecular Types
Quantitative MRI of a Cerebral Cryptococcoma Mouse Model for In Vivo Distinction between Different Cryptococcal Molecular Types
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Quantitative MRI of a Cerebral Cryptococcoma Mouse Model for In Vivo Distinction between Different Cryptococcal Molecular Types
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Quantitative MRI of a Cerebral Cryptococcoma Mouse Model for In Vivo Distinction between Different Cryptococcal Molecular Types
Quantitative MRI of a Cerebral Cryptococcoma Mouse Model for In Vivo Distinction between Different Cryptococcal Molecular Types

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Quantitative MRI of a Cerebral Cryptococcoma Mouse Model for In Vivo Distinction between Different Cryptococcal Molecular Types
Quantitative MRI of a Cerebral Cryptococcoma Mouse Model for In Vivo Distinction between Different Cryptococcal Molecular Types
Journal Article

Quantitative MRI of a Cerebral Cryptococcoma Mouse Model for In Vivo Distinction between Different Cryptococcal Molecular Types

2024
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Overview
The controversially discussed taxonomy of the Cryptococcus neoformans/Cryptococcus gattii species complex encompasses at least eight major molecular types. Cerebral cryptococcomas are a common manifestation of cryptococcal neurological disease. In this study, we compared neurotypical symptoms and differential neurovirulence induced by one representative isolate for each of the eight molecular types studied. We compared single focal lesions caused by the different isolates and evaluated the potential relationships between the fungal burden and properties obtained with quantitative magnetic resonance imaging (qMRI) techniques such as diffusion MRI, T2 relaxometry and magnetic resonance spectroscopy (MRS). We observed an inverse correlation between parametric data and lesion density, and we were able to monitor longitudinally biophysical properties of cryptococcomas induced by different molecular types. Because the MRI/MRS techniques are also clinically available, the same approach could be used to assess image-based biophysical properties that correlate with fungal cell density in lesions in patients to determine personalized treatments.