Computational tools to explore oxidative and immunological stress in dementia
December 2014
San Raffaele Scientific Institute, Milan, Italy
Mild cognitive impairment can increase the risk of developing Alzheimer's disease. Therefore, prediction tools are needed to know the prognosis of the disease. Pro-oxidative state and neuroinflammation are increasingly linked to dementia. So, a new computational model based on artificial neural networks is used in this study to decipher the relationship between oxidative stress and inflammation in Alzheimer's disease and mild cognitive impairment. The results show that machine learning was able to build an algorithm that, using a small amount of immunological and oxidative stress parameters, accurately classified Alzheimer's disease and mild cognitive impairment. Also, it was possible to establish a correlation between global immune deficit and cognitive impairment with a new non-linear mathematical model. Overall, this study proposes a new method to discriminate between different types of dementia, to accurately predict the possible prognosis of these cases and also to decipher new mechanisms of the pathophysiology of these disorders, making it a potentially valuable tool for clinical applications.
A global immune deficit in Alzheimer’s disease and mild cognitive impairment disclosed by a novel data mining process
Maira Gironi
Added on: 08-05-2021
[1] https://content.iospress.com/articles/journal-of-alzheimers-disease/jad141116[2] https://data.jrc.ec.europa.eu/dataset/a8fd26ef-b113-47ab-92ba-fd2be449c7eb
