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oagulation pathway changes in patients with POCD.The detection of plasma tau and its phosphorylation is technically challenging due to the relatively low sensitivity. However, in Alzheimer's disease and other tauopathies, it is hypothesized that tau in the biofluid may serve as a biomarker. In recent years, several ultrasensitive assays have been developed, which can successfully detect tau and its phosphorylation in various biofluids, and collectively demonstrated the prognostic and diagnostic value of plasma tau/phosphorylated tau. Here we have summarized the principle of four ultrasensitive assays newly developed suitable for plasma tau detection, namely single-molecule array, immunomagnetic reduction assay, enhanced immunoassay using multi-arrayed fiber optics, and meso scale discovery assay, with their advantages and applications. We have also compared these assays with traditional enzyme-linked-immunosorbent serologic assay, hoping to facilitate future tau-based biomarker discovery for Alzheimer's disease and other neurodegenerative diseases.In this paper, we review state-of-the-art approaches that apply signal processing (SP) and machine learning (ML) to automate the detection of Alzheimer's disease (AD) and its prodromal stages. In the first part of the document, we describe the economic and social implications of the disease, traditional diagnosis techniques, and the fundaments of automated AD detection. Then, we present electroencephalography (EEG) as an appropriate alternative for the early detection of AD, owing to its reduced cost, portability, and non-invasiveness. We also describe the main time and frequency domain EEG features that are employed in AD detection. Subsequently, we examine some of the main studies of the last decade that aim to provide an automatic detection of AD and its previous stages by means of SP and ML. In these studies, brain data was acquired using multiple medical techniques such as magnetic resonance imaging, positron emission tomography, and EEG. The main aspects of each approach, namely feature extraction, classification model, validation approach, and performance metrics, are compiled and discussed. Lastly, a set of conclusions and recommendations for future research on AD automatic detection are drawn in the final section of the paper.This longitudinal study evaluates the prognostic impact of amyloid PET in patients suspected of Alzheimer's disease and presenting with isolated cerebrospinal fluid (CSF) increases in P-Tau proteins (NCT02556502). The rate of conversion, based on the DSM-5 criteria and all collected data (average follow-up of 39.2±13.2 months), was determined by a panel of experts blinded to the PET results and was 75%(6/8) for positive and 35%(6/17) for negative baseline amyloid PET. In this population with isolated CSF increases in P-Tau, a positive baseline amyloid PET was associated with greater than twice the proportion of dementia conversions within the following three years.As an established treatment for movement disorders, deep brain stimulation (DBS) has been adapted for the treatment of Alzheimer's disease (AD) by modulating fornix activity. learn more Although it is generally regarded as a safe intervention in patients over 65 years of age, the complex neurophysiology and interconnection within circuits connected to the fornix warrants a careful ongoing evaluation of the true benefit and risk potential of DBS on slowing cognitive decline in AD patients. Here we report on a patient who died long after being implanted with a DBS device who donated her brain for neuropathologic study. The autopsy confirmed multiple proteinopathies including AD-related change, diffuse neocortical Lewy body disease, TDP-43 proteinopathy, and a nonspecific tauopathy. We discuss the possible mechanisms of these overlapping neurodegenerative disorders and caution that future studies of DBS for AD will need to take these findings into consideration.

Age-related cerebrovascular and neuroinflammatory processes have been independently identified as key mechanisms of Alzheimer's disease (AD), although their interactive effects have yet to be fully examined.

The current study examined 1) the influence of pulse pressure (PP) and inflammatory markers on AD protein levels and 2) links between protein biomarkers and cognitive function in older adults with and without mild cognitive impairment (MCI).

This study included 218 ADNI (81 cognitively normal [CN], 137 MCI) participants who underwent lumbar punctures, apolipoprotein E (APOE) genotyping, and cognitive testing. Cerebrospinal (CSF) levels of eight pro-inflammatory markers were used to create an inflammation composite, and amyloid-beta 1-42 (Aβ42), phosphorylated tau (p-tau), and total tau (t-tau) were quantified.

Multiple regression analyses controlling for age, education, and APOE ɛ4 genotype revealed significant PP x inflammation interactions for t-tau (B = 0.88, p = 0.01) and p-tau (B = 0.84, p = 0.02); higher inflammation was associated with higher levels of tau within the MCI group. However, within the CN group, analyses revealed a significant PP x inflammation interaction for Aβ42 (B = -1.01, p = 0.02); greater inflammation was associated with higher levels of Aβ42 (indicative of lower cerebral amyloid burden) in those with lower PP. Finally, higher levels of tau were associated with poorer memory performance within the MCI group only (p s < 0.05).

PP and inflammation exert differential effects on AD CSF proteins and provide evidence that vascular risk is associated with greater AD pathology across our sample of CN and MCI older adults.

PP and inflammation exert differential effects on AD CSF proteins and provide evidence that vascular risk is associated with greater AD pathology across our sample of CN and MCI older adults.

Structural brain magnetic resonance imaging (MRI) scans may provide reliable neuroimaging markers for defining amnestic mild cognitive impairment (aMCI).

We sought to characterize global and regional brain structures of aMCI among rural-dwelling older adults with limited education in China.

This population-based study included 180 participants (aged≥65 years, 42 with aMCI and 138 normal controls) in the Shandong Yanggu Study of Aging and Dementia during 2014-2016. We defined aMCI following the Petersen's criteria. Global and regional brain volumes were automatically segmented on MRI scans and compared using a region-of-interest approach. Data were analyzed using general linear regression models.

Multi-adjusted β-coefficient (95% confidence interval) of brain volumes (cm3) associated with aMCI was -12.07 (-21.49, -2.64) for global grey matter (GM), -18.31 (-28.45, -8.17) for global white matter (WM), 28.17 (12.83, 44.07) for cerebrospinal fluid (CSF), and 2.20 (0.24, 4.16) for white matter hyperintensities (WMH).