Dataset Information

A Novel Serum-Based Diagnosis of Alzheimer's Disease Using an Advanced Phage-Based Biochip.

ABSTRACT: 55 million people worldwide suffer from Alzheimer's disease (AD). A definitive diagnosis of AD is made postmortem after a neuropathological examination of the brain. There is an urgent need for an innovative, noninvasive methodology that allows for an early and reliable diagnosis. Several engineered phages that recognized Aβ-autoantibodies present in the sera of AD patients are previously identified. Here, novel phages are tested for their ability to accurately discriminate AD sera using immunophage-polymerase chain reaction in a miniatured biochip. It is found that five of the six phages analyzed discriminate between healthy controls and AD patients. Further, by combining the response of two phages, non-AD and severe AD cases are identified with 100% accuracy and mild-to-moderate cases with 90% accuracy. While the number of cases used here are relatively small and can be confirmed in larger cohorts, this first-of-a-kind system represents an innovative methodology with the potential of having a major impact in the AD field: from a clinical perspective, it can aid physicians in making an accurate AD diagnosis; from a research perspective, it can be used as a surrogate for AD clinical trials.

SUBMITTER: Rizzo MG

PROVIDER: S-EPMC10375152 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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A Novel Serum-Based Diagnosis of Alzheimer's Disease Using an Advanced Phage-Based Biochip.

Rizzo Maria Giovanna MG De Plano Laura Maria LM Palermo Nicoletta N Franco Domenico D Nicolò Marco M Sciuto Emanuele Luigi EL Calabrese Giovanna G Oddo Salvatore S Conoci Sabrina S Guglielmino Salvatore P P SPP

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20230507 21

55 million people worldwide suffer from Alzheimer's disease (AD). A definitive diagnosis of AD is made postmortem after a neuropathological examination of the brain. There is an urgent need for an innovative, noninvasive methodology that allows for an early and reliable diagnosis. Several engineered phages that recognized Aβ-autoantibodies present in the sera of AD patients are previously identified. Here, novel phages are tested for their ability to accurately discriminate AD sera using immunop ...[more]

PMID: 37150869

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Project description:Structural magnetic resonance imaging (MRI) has been proven to be an effective tool for Alzheimer's disease (AD) diagnosis. While conventional MRI-based AD diagnosis typically uses images acquired at a single time point, a longitudinal study is more sensitive in detecting early pathological changes of AD, making it more favorable for accurate diagnosis. In general, there are two challenges faced in MRI-based diagnosis. First, extracting features from structural MR images requires time-consuming nonlinear registration and tissue segmentation, whereas the longitudinal study with involvement of more scans further exacerbates the computational costs. Moreover, the inconsistent longitudinal scans (i.e., different scanning time points and also the total number of scans) hinder extraction of unified feature representations in longitudinal studies. In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which does not require nonlinear registration or tissue segmentation in the application stage and is also robust to inconsistencies among longitudinal scans. Specifically, first, the discriminative landmarks are automatically discovered from the whole brain using training images, and then efficiently localized using a fast landmark detection method for testing images, without the involvement of any nonlinear registration and tissue segmentation; and second, high-level statistical spatial features and contextual longitudinal features are further extracted based on those detected landmarks, which can characterize spatial structural abnormalities and longitudinal landmark variations. Using these spatial and longitudinal features, a linear support vector machine is finally adopted to distinguish AD subjects or mild cognitive impairment (MCI) subjects from healthy controls (HCs). Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate the superior performance and efficiency of the proposed method, with classification accuracies of 88.30% for AD versus HC and 79.02% for MCI versus HC, respectively.

Dataset Information

A Novel Serum-Based Diagnosis of Alzheimer's Disease Using an Advanced Phage-Based Biochip.

Publications

A Novel Serum-Based Diagnosis of Alzheimer's Disease Using an Advanced Phage-Based Biochip.

Similar Datasets

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