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Clinical features and multidisciplinary approaches to dementia care

TLDR
The cumulative efforts of multidisciplinary healthcare teams have advanced the understanding of dementia beyond basic descriptions, towards a more complete elucidation of risk factors, clinical symptoms, and neuropathological correlates.
Abstract
Dementia is a clinical syndrome of widespread progressive deterioration of cognitive abilities and normal daily functioning. These cognitive and behavioral impairments pose considerable challenges to individuals with dementia, along with their family members and caregivers. Four primary dementia classifications have been defined according to clinical and research criteria: 1) Alzheimer's disease; 2) vascular dementias; 3) frontotemporal dementias; and 4) dementia with Lewy bodies/Parkinson's disease dementia. The cumulative efforts of multidisciplinary healthcare teams have advanced our understanding of dementia beyond basic descriptions, towards a more complete elucidation of risk factors, clinical symptoms, and neuropathological correlates. The characterization of disease subtypes has facilitated targeted management strategies, advanced treatments, and symptomatic care for individuals affected by dementia. This review briefly summarizes the current state of knowledge and directions of dementia research and clinical practice. We provide a description of the risk factors, clinical presentation, and differential diagnosis of dementia. A summary of multidisciplinary team approaches to dementia care is outlined, including management strategies for the treatment of cognitive impairments, functional deficits, and behavioral and psychological symptoms of dementia. The needs of individuals with dementia are extensive, often requiring care beyond traditional bounds of medical practice, including pharmacologic and non-pharmacologic management interventions. Finally, advanced research on the early prodromal phase of dementia is reviewed, with a focus on change-point models, trajectories of cognitive change, and threshold models of pathological burden. Future research goals are outlined, with a call to action for social policy initiatives that promote preventive lifestyle behaviors, and healthcare programs that will support the growing number of individuals affected by dementia.

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Cholinesterase Inhibitors in Alzheimer's Disease

TL;DR: Data indicate that aminoacridines, as a class of compounds, have a statistically significant effect to diminish, slightly, some of the cognitive symptoms of Alzheimer's Disease, and encourage the use of drugs that will reverse multiple neurotransmitter deficits.
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MicroRNA biomarkers of Parkinson’s disease in serum exosome-like microvesicles

TL;DR: Analysis of the expression levels of miR-19b, mi-24, and mi-195 in serum may be useful for the diagnosis of PD, and it is validated that the downregulation ofmiR- 19b and the upregulation ofMiR-195 and mi R-24 in patients with PD are validated.
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Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging.

TL;DR: In this article, a deep convolutional neural network (CNN) was used to predict future cognitive decline in mild cognitive impairment (MCI) patients using flurodeoxyglucose and florbetapir positron emission tomography (PET).
Journal ArticleDOI

Effectiveness of a Virtual Reality Forest on People With Dementia: A Mixed Methods Pilot Study.

TL;DR: This initial, small-scale study represents the first to introduce the VRF activity and describe the impact on people with dementia, and it is suggested virtual reality may have the potential to improve quality of life.
Journal ArticleDOI

Comparison of neuroimaging modalities for the prediction of conversion from mild cognitive impairment to Alzheimer's dementia

TL;DR: Multivariate modeling found that among individual modalities, MRI had the highest predictive accuracy, which increased by 9% to 76% when combined with PIB-PET, producing the highest accuracy among any biomarker combination.
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Journal ArticleDOI

Alzheimer's disease: the amyloid cascade hypothesis

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