| Annotation: |
Neurodegenerative diseases (ND) are becoming a severe problem in developed countries. Since we currently haveno effective therapies available, early diagnosis is critical to ensure a good quality of life for ND patients. ND arecharacterized by iron accumulation and magnetite mineralization in brain tissue, with ferritin as a precursor. Due toits low relaxivity, physiological ferritin is at the edge of visibility using magnetic resonance imaging (MRI)techniques. On the contrary, "pathological" ferritin causes a significant shortening of MRI relaxation times. Thiscreates hypointense artifacts, which theoretically allow the distinguishability of both proteins. Since ironaccumulation precedes the clinical symptoms of the disease, MRI has the potential to become a non -invasivediagnostic method for the early stages of ND. At present, however, this is limited by the insufficient characterizationof the relaxation properties of biogenic iron and the uncertainty in the interpretation of clinical data. Therefore, ourbasic goal (application output) is the development of a comprehensive methodology (FERINO software tool) for theunequivocal diagnosis of the early stages of ND. To reach our goal, we will use a combination of several diagnostictechniques and artificial intelligence tools. The diagnostic techniques include in-vitro, in-silico, and in-vivocharacteristics of ferritin relaxation, structural MRI, magnetic resonance spectroscopy (MRS), neurological tests,and clinical biochemistry biomarkers. The cornerstone of the methodology will be the FerroQuant software tool,which was proposed by the principal investigator within the APVV 2012. It enables the analysis and quantificationof iron-related clinical MRI data but lacks new findings in iron MRI (false-positive artifacts, ferritin\'s mineral phases).FerroQuant also does not use artificial intelligence and does not combine different diagnostic data, whic h, however,will be an integral part of the FERINO tool. |
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