Identify Compounds' Target Against Alzheimer's Disease Based on In-Silico Approach

2019 ◽  
Vol 16 (3) ◽  
pp. 193-208 ◽  
Author(s):  
Yan Hu ◽  
Guangya Zhou ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Qin Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Small Molecule ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Learner Model ◽  
Mao B ◽  
Number Of Patients ◽  
Sar Model ◽  
Set Up

Background: Alzheimer's disease swept every corner of the globe and the number of patients worldwide has been rising. At present, there are as many as 30 million people with Alzheimer's disease in the world, and it is expected to exceed 80 million people by 2050. Consequently, the study of Alzheimer’s drugs has become one of the most popular medical topics. Methods: In this study, in order to build a predicting model for Alzheimer’s drugs and targets, the attribute discriminators CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval are combined with search methods such as BestFirst, GeneticSearch and Greedystepwise to filter the molecular descriptors. Then the machine learning algorithms such as BayesNet, SVM, KNN and C4.5 are used to construct the 2D-Structure Activity Relationship(2D-SAR) model. Its modeling results are utilized for Receiver Operating Characteristic curve(ROC) analysis. Results: The prediction rates of correctness using Randomforest for AChE, BChE, MAO-B, BACE1, Tau protein and Non-inhibitor are 77.0%, 79.1%, 100.0%, 94.2%, 93.2% and 94.9%, respectively, which are overwhelming as compared to those of BayesNet, BP, SVM, KNN, AdaBoost and C4.5. Conclusion: In this paper, we conclude that Random Forest is the best learner model for the prediction of Alzheimer’s drugs and targets. Besides, we set up an online server to predict whether a small molecule is the inhibitor of Alzheimer's target at http://47.106.158.30:8080/AD/. Furthermore, it can distinguish the target protein of a small molecule.

Easy Screening for Mild Alzheimer's Disease and Mild Cognitive Impairment from Elderly Speech

2018 ◽  
Vol 15 (2) ◽  
pp. 104-110 ◽  
Author(s):  
Shohei Kato ◽  
Akira Homma ◽  
Takuto Sakuma
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Characteristic Curve ◽  
Linear Regression Analysis ◽  
Speech Sound ◽  
The Elderly ◽  
Speech Sounds ◽  
Mel Frequency Cepstral Coefficients

Objective: This study presents a novel approach for early detection of cognitive impairment in the elderly. The approach incorporates the use of speech sound analysis, multivariate statistics, and data-mining techniques. We have developed a speech prosody-based cognitive impairment rating (SPCIR) that can distinguish between cognitively normal controls and elderly people with mild Alzheimer's disease (mAD) or mild cognitive impairment (MCI) using prosodic signals extracted from elderly speech while administering a questionnaire. Two hundred and seventy-three Japanese subjects (73 males and 200 females between the ages of 65 and 96) participated in this study. The authors collected speech sounds from segments of dialogue during a revised Hasegawa's dementia scale (HDS-R) examination and talking about topics related to hometown, childhood, and school. The segments correspond to speech sounds from answers to questions regarding birthdate (T1), the name of the subject's elementary school (T2), time orientation (Q2), and repetition of three-digit numbers backward (Q6). As many prosodic features as possible were extracted from each of the speech sounds, including fundamental frequency, formant, and intensity features and mel-frequency cepstral coefficients. They were refined using principal component analysis and/or feature selection. The authors calculated an SPCIR using multiple linear regression analysis. Conclusion: In addition, this study proposes a binary discrimination model of SPCIR using multivariate logistic regression and model selection with receiver operating characteristic curve analysis and reports on the sensitivity and specificity of SPCIR for diagnosis (control vs. MCI/mAD). The study also reports discriminative performances well, thereby suggesting that the proposed approach might be an effective tool for screening the elderly for mAD and MCI.

scholarly journals Caretaker Obstreperous Behavior Rating Scale

1997 ◽  
Vol 8 (S3) ◽  
pp. 321-324 ◽  
Author(s):  
Joan M. Swearer ◽  
David A. Drachman
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Rating Scale ◽  
Late Onset ◽  
Senile Dementia ◽  
Medical Personnel ◽  
Original Description ◽  
Behavior Rating Scale ◽  
Large Patient ◽  
Number Of Patients

Although Alzheimer's original description of the dementing disorder that bears his name emphasized the prominence of troublesome and disruptive behaviors, a systematic investigation of behavioral disturbances of dementia did not begin in earnest until the 1980s. At that time, as the neuropathologic identity of presenile Alzheimer's disease and late-onset “senile dementia” was recognized, the redefinition of Alzheimer's disease abruptly increased the number of patients diagnosed with this condition. Physicians and other medical personnel working with Alzheimer's disease patients recognized both the importance of abnormal behaviors in this now large patient population and the need to describe, classify, and quantify these behaviors.

A Comparative Study of Alzheimer Detection Techniques

2021 ◽  
Vol 9 (8) ◽  
pp. 2877-2883
Author(s):  
Adwait Patil
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Comparative Study ◽  
Fuzzy System ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Detection Techniques

Abstract: Alzheimer’s disease is one of the neurodegenerative disorders. It initially starts with innocuous symptoms but gradually becomes severe. This disease is so dangerous because there is no treatment, the disease is detected but typically at a later stage. So it is important to detect Alzheimer at an early stage to counter the disease and for a probable recovery for the patient. There are various approaches currently used to detect symptoms of Alzheimer’s disease (AD) at an early stage. The fuzzy system approach is not widely used as it heavily depends on expert knowledge but is quite efficient in detecting AD as it provides a mathematical foundation for interpreting the human cognitive processes. Another more accurate and widely accepted approach is the machine learning detection of AD stages which uses machine learning algorithms like Support Vector Machines (SVMs) , Decision Tree , Random Forests to detect the stage depending on the data provided. The final approach is the Deep Learning approach using multi-modal data that combines image , genetic data and patient data using deep models and then uses the concatenated data to detect the AD stage more efficiently; this method is obscure as it requires huge volumes of data. This paper elaborates on all the three approaches and provides a comparative study about them and which method is more efficient for AD detection. Keywords: Alzheimer’s Disease (AD), Fuzzy System , Machine Learning , Deep Learning , Multimodal data

Using Fractional Anisotropy Imaging to Detect Mild Cognitive Impairment and Alzheimer’s Disease among Mexican Americans and Non-Hispanic Whites: A HABLE Study

2021 ◽  
pp. 1-8
Author(s):  
James R. Hall ◽  
Leigh A. Johnson ◽  
Fan Zhang ◽  
Melissa Petersen ◽  
Arthur W. Toga ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Fractional Anisotropy ◽  
Mexican Americans ◽  
Diffusion Tensor ◽  
Characteristic Curve ◽  
Aging Brain ◽  
Community Based

<b><i>Introduction:</i></b> Alzheimer’s disease (AD) is the most frequently occurring neurodegenerative disease; however, little work has been conducted examining biomarkers of AD among Mexican Americans. Here, we examined diffusion tensor MRI marker profiles for detecting mild cognitive impairment (MCI) and dementia in a multi-ethnic cohort. <b><i>Methods:</i></b> 3T MRI measures of fractional anisotropy (FA) were examined among 1,636 participants of the ongoing community-based Health &amp; Aging Brain among Latino Elders (HABLE) community-based study (Mexican American <i>n</i> = 851; non-Hispanic white <i>n</i> = 785). <b><i>Results:</i></b> The FA profile was highly accurate in detecting both MCI (area under the receiver operating characteristic curve [AUC] = 0.99) and dementia (AUC = 0.98). However, the FA profile varied significantly not only between diagnostic groups but also between Mexican Americans and non-Hispanic whites. <b><i>Conclusion:</i></b> Findings suggest that diffusion tensor imaging markers may have a role in the neurodiagnostic process for detecting MCI and dementia among diverse populations.

scholarly journals Alzheimer’s Disease and Parkinson Dementia Distinguished by Cognitive Marker

2020 ◽  
Author(s):  
Irina Kozlova ◽  
Mario A Parra ◽  
Nataliya Titova ◽  
Maria Gantman ◽  
Sergio Della Sala
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
High Sensitivity ◽  
Task Demands ◽  
Memory Binding ◽  
Parkinson Dementia ◽  
Temporary Memory

Abstract Background Temporary memory binding (TMB) has been shown to be specifically affected by Alzheimer’s disease (AD) when it is assessed via free recall and titrating the task demands to equate baseline performance across patients. Methods Patients with Parkinson’s disease (PD) were subdivided into patients with and without cognitive impairment and compared with AD and amnestic mild cognitive impairment (aMCI) patients on their performance on the TMB. Results The results show that only patients with AD dementia present with impaired TMB performance. Receiver operating characteristic curve analyses showed that TMB holds high sensitivity and specificity for aMCI and AD relative to PD groups and healthy controls. Conclusion The TMB is sensitive to the neurodegenerative mechanisms leading to AD dementia but not to those underpinning PD dementia. As such, TMB task can aid the differential diagnosis of these common forms of dementia.

What Does Imaging Add to the Management of Alzheimer's Disease?

CNS Spectrums ◽  
2004 ◽  
Vol 9 (S5) ◽  
pp. 20-23 ◽  
Author(s):  
Gary W. Small
Keyword(s):  
Quality Of Life ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Imaging Techniques ◽  
Screening Tools ◽  
Surrogate Markers ◽  
Genetic Disposition ◽  
Number Of Patients

AbstractThe prevalence of Alzheimer's disease (AD) and dementia continues to rise. However, a significant number of patients are undiagnosed or untreated. Given the complexities of detecting cognitive impairment and the early signs of AD, this review discusses how advances in brain imaging can help assist in improving overall management. Imaging techniques and surrogate markers may provide unique opportunities to diagnose accurately AD in presymptomatic stages with practical consequences for patients, caregivers, and physicians. The possible outcomes for using imaging and surrogate markers as adjuncts to clinical examination and as screening tools for AD, as well as tangible and intangible advantages to early diagnosis and treatment, will be discussed. The specific value of using advanced serial imaging in patients with a genetic disposition to AD will be evaluated. If neurons can be protected from neurodegenerative damage in early stages, this may preserve patient cognition, function, and quality of life, and may confer considerable societal healthcare benefits.

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