Neuropathological alterations in Alzheimer disease.

By A. Serrano‐Pozo, Matthew P. Frosch, E. Masliah, Bradley T. Hyman. Cold Spring Harbor Perspectives in Medicine, 2011

3,148 citations recorded by the source corpus.

Subfields: Physiology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/875ea5aa27548adac085b276cf2ba404af4ae139

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Citations in the corpus

A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease (2018, background): “…can be identi ﬁ ed several years before frank AD clinical manifestation (Braak and Braak, 1996; Delacourte et al., 1999; Morris et al., 1996; Serrano-Pozo et al., 2011; Mosconi et al., 2007), and this suggests that the development of AD might be predicted before clinical onset via in vivo…”
The Major Risk Factors for Alzheimer’s Disease: Age, Sex, and Genes Modulate the Microglia Response to Aβ Plaques (2019, background): “…disease (AD) is characterized by typical biochemical lesions (β-amyloid peptide [Ab] plaques and tau tangles) accompanied by extensive cellular changes (neuronal dystrophic alterations, neuronal cell loss, astrogliosis, and microgliosis) (De Strooper and Karran, 2016; Serrano-Pozo et al., 2011).”
Molecular mechanisms of cell death in neurological diseases (2021, background): “Hallmark pathological features of AD include the accumulation of amyloidβ-containing neuritic plaques derived from aggregates produced by sequential cleavages of the amyloid precursor protein (APP), neurofibrillary tangles and dystrophic neurites containing hyperphosphorylated tau [38].”
Reconsideration of Amyloid Hypothesis and Tau Hypothesis in Alzheimer's Disease (2018, background): “In AD brains, 3R and 4R tau is accumulated in a hyperphosphorylated state in the pathological inclusions (Goedert, 1993; Goedert et al., 1996; Serrano-Pozo et al., 2011; Iqbal et al., 2016).”
PET Imaging of Tau Deposition in the Aging Human Brain (2016, neutral): “The patterns of AV-1451 retention parallel findings in neuropathological studies (Braak and Braak, 1991, 1997; Serrano-Pozo et al., 2011), as well as a recent report indicating that the visual appearance of AV-1451 retention parallels Braak staging (Johnson et al., 2016).”
Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline (2018, background): “…emerging from these machine learning introspection techniques—dense compact amyloid centers for cored plaques, ill-defined amorphous amyloid deposits for diffuse plaques, and amyloid within the media of the cortical vessels for CAAs — all a gree with key features used by experts 1,11,62,63 .”
A review on neuroimaging‐based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages (2017, neutral): “While SVM could have the advantage of achieving high classification accuracy with small training sample size compared to other classification algorithms such as neural networks (Shao and Lunetta, 2012), they might have the disadvantage of the need for parameter tuning (Chapelle et al., 2002).”
Epidemiology of Alzheimer Disease (2012, neutral): “The pathological manifestations of Alzheimer disease include diffuse and neuritic extracellular amyloid plaques and intracellular neurofibrillary tangles accompanied by reactive microgliosis, dystrophic neurites , and loss of neurons and synapses (see Serrano-Pozo et al. 2011).”
Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects (2014, neutral): “The progression of the neuropathology in AD can be observed many years before clinical symptoms of the disease become apparent (Braak and Braak, 1996; Delacourte et al., 1999; Morris et al., 1996; Serrano-Pozo et al., 2011; Mosconi et al., 2007).”