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Updated: May 20, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Searching for predictive blood biomarkers: misfolded p53 in mild cognitive impairment
Serena Stanga1, Cristina Lanni, Elena Sinforiani
1Department of Drug Sciences, University of Pavia, Pavia, Italy.
Abstract:
The identification and validation of biomarkers for preclinical patients with mild cognitive impairment (MCI) at-risk for Alzheimer's disease (AD) development is increasingly important. We used the cytofluorimetric analysis of unfolded p53 to determine the prognostic ability of the protein as predictive signature from MCI to AD in a longitudinal study of a population of presymptomatic patients with the clinical diagnosis of MCI. Venous blood samples from 24 healthy subjects, 28 MCI and 15 AD were analyzed with the cytofluorimetric method for unfolded p53 protein detection. Twenty-four MCI patients had clinical follow-up subsequent to the analysis for unfolded p53. Elevated levels of the conformationally altered protein were able to discriminate both MCI and AD patients comparing with healthy subjects. Longitudinal follow-up revealed that 7/24 MCI patients progressed to AD. All converters (100%) were predicted by elevated levels of unfolded p53, with a positive predictive value of 87.5%. These data support and extend our previous observation that the cytofluorimetric approach for unfolded p53 protein was able to discriminate AD patients from healthy subjects and to predict the progression from MCI to AD in presymptomatic patients before clinical diagnosis for AD was evident.
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