scholarly journals State‐of‐the‐art of lumbar puncture and its place in the journey of patients with Alzheimer's disease

2021 ◽  
Author(s):  
Harald Hampel ◽  
Leslie M. Shaw ◽  
Paul Aisen ◽  
Christopher Chen ◽  
Alberto Lleó ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Lumbar Puncture ◽  
State Of The Art

scholarly journals APOE: The New Frontier in the Development of a Therapeutic Target towards Precision Medicine in Late-Onset Alzheimer’s

2021 ◽  
Vol 22 (3) ◽  
pp. 1244
Author(s):  
Anna Yang ◽  
Boris Kantor ◽  
Ornit Chiba-Falek
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Precision Medicine ◽  
Antisense Oligonucleotides ◽  
State Of The Art ◽  
Late Onset ◽  
Base Editing ◽  
Medical Need ◽  
New Frontier ◽  
Amyloid Cascade

Alzheimer’s disease (AD) has a critical unmet medical need. The consensus around the amyloid cascade hypothesis has been guiding pre-clinical and clinical research to focus mainly on targeting beta-amyloid for treating AD. Nevertheless, the vast majority of the clinical trials have repeatedly failed, prompting the urgent need to refocus on other targets and shifting the paradigm of AD drug development towards precision medicine. One such emerging target is apolipoprotein E (APOE), identified nearly 30 years ago as one of the strongest and most reproduceable genetic risk factor for late-onset Alzheimer’s disease (LOAD). An exploration of APOE as a new therapeutic culprit has produced some very encouraging results, proving that the protein holds promise in the context of LOAD therapies. Here, we review the strategies to target APOE based on state-of-the-art technologies such as antisense oligonucleotides, monoclonal antibodies, and gene/base editing. We discuss the potential of these initiatives in advancing the development of novel precision medicine therapies to LOAD.

scholarly journals The needle has been blunt for 20 years

2010 ◽  
Vol 23 (2) ◽  
pp. 330-331
Author(s):  
Brendan Silbert ◽  
David Scott ◽  
Lisbeth Evered ◽  
Paul Maruff
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Lumbar Puncture ◽  
Sampling Unit

The growing need for lumbar puncture in order to obtain cerebrospinal fluid (CSF) for the diagnosis Alzheimer's disease is becoming increasingly apparent (Herskovits and Growdon, 2010). The concept of a CSF sampling unit specializing in lumbar puncture would seem the most plausible solution. Physicians and interns are not necessarily skilled in the procedure and neurologists perform lumbar puncture rarely.

scholarly journals Ambient Intelligence Based on IoT for Assisting People with Alzheimer’s Disease Through Context Histories

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1260
Author(s):  
Savanna Denega Machado ◽  
João Elison da Rosa Tavares ◽  
Márcio Garcia Martins ◽  
Jorge Luis Victória Barbosa ◽  
Gabriel Villarrubia González ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
State Of The Art ◽  
Physiological Data ◽  
Data Sets ◽  
Scientific Contribution ◽  
Daily Lives ◽  
Iot Applications ◽  
Methodological Aspects ◽  
Dangerous Behaviors

New Internet of Things (IoT) applications are enabling the development of projects that help with monitoring people with different diseases in their daily lives. Alzheimer’s is a disease that affects neurological functions and needs support to maintain maximum independence and security of patients during this stage of life, as the cure and reversal of symptoms have not yet been discovered. The IoT-based monitoring system provides the caregivers’ support in monitoring people with Alzheimer’s disease (AD). This paper presents an ontology-based computational model that receives physiological data from external IoT applications, allowing identification of potentially dangerous behaviors for patients with AD. The main scientific contribution of this work is the specification of a model focusing on Alzheimer’s disease using the analysis of context histories and context prediction, which, considering the state of the art, is the only one that uses analysis of context histories to perform predictions. In this research, we also propose a simulator to generate activities of the daily life of patients, allowing the creation of data sets. These data sets were used to evaluate the contributions of the model and were generated according to the standardization of the ontology. The simulator generated 1026 scenarios applied to guide the predictions, which achieved average accurary of 97.44%. The experiments also allowed the learning of 20 relevant lessons on technological, medical, and methodological aspects that are recorded in this article.

scholarly journals Appropriate use criteria for lumbar puncture and cerebrospinal fluid testing in the diagnosis of Alzheimer's disease

2018 ◽  
Vol 14 (11) ◽  
pp. 1505-1521 ◽  
Author(s):  
Leslie M. Shaw ◽  
Jalayne Arias ◽  
Kaj Blennow ◽  
Douglas Galasko ◽  
Jose Luis Molinuevo ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Lumbar Puncture ◽  
Appropriate Use Criteria ◽  
Appropriate Use

scholarly journals A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

2020 ◽  
Vol 10 (2) ◽  
pp. 84 ◽  
Author(s):  
Atif Mehmood ◽  
Muazzam Maqsood ◽  
Muzaffar Bashir ◽  
Yang Shuyuan
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Early Stage ◽  
The State ◽  
Test Accuracy ◽  
Learning Approaches

Alzheimer’s disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer’s disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.

Alzheimer’s disease: The state of the art in resting-state magnetoencephalography

2017 ◽  
Vol 128 (8) ◽  
pp. 1426-1437 ◽  
Author(s):  
M.M.A. Engels ◽  
W.M. van der Flier ◽  
C.J. Stam ◽  
A. Hillebrand ◽  
Ph. Scheltens ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Resting State ◽  
State Of The Art ◽  
The State

Classification of Alzheimer’s Disease via Eight-Layer Convolutional Neural Network with Batch Normalization and Dropout Techniques

2020 ◽  
Vol 10 (5) ◽  
pp. 1040-1048 ◽  
Author(s):  
Xianwei Jiang ◽  
Liang Chang ◽  
Yu-Dong Zhang
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
State Of The Art ◽  
Training Dataset ◽  
Batch Normalization ◽  
Heavy Burden

More than 35 million patients are suffering from Alzheimer’s disease and this number is growing, which puts a heavy burden on countries around the world. Early detection is of benefit, in which the deep learning can aid AD identification effectively and gain ideal results. A novel eight-layer convolutional neural network with batch normalization and dropout techniques for classification of Alzheimer’s disease was proposed. After data augmentation, the training dataset contained 7399 AD patient and 7399 HC subjects. Our eight-layer CNN-BN-DO-DA method yielded a sensitivity of 97.77%, a specificity of 97.76%, a precision of 97.79%, an accuracy of 97.76%, a F1 of 97.76%, and a MCC of 95.56% on the test set, which achieved the best performance in seven state-of-the-art approaches. The results strongly demonstrate that this method can effectively assist the clinical diagnosis of Alzheimer’s disease.

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