scholarly journals Alzheimer’s Dementia Recognition From Spontaneous Speech Using Disfluency and Interactional Features

2021 ◽  
Vol 3 ◽  
Author(s):  
Shamila Nasreen ◽  
Morteza Rohanian ◽  
Julian Hough ◽  
Matthew Purver
Keyword(s):  
Neurodegenerative Disorder ◽  
Memory Loss ◽  
Cost Effective ◽  
Support Vector ◽  
Linguistic Features ◽  
Clinical Assessments ◽  
Visuospatial Abilities ◽  
Advanced Stages ◽  
Age Range ◽  
Interactional Features

Alzheimer’s disease (AD) is a progressive, neurodegenerative disorder mainly characterized by memory loss with deficits in other cognitive domains, including language, visuospatial abilities, and changes in behavior. Detecting diagnostic biomarkers that are noninvasive and cost-effective is of great value not only for clinical assessments and diagnostics but also for research purposes. Several previous studies have investigated AD diagnosis via the acoustic, lexical, syntactic, and semantic aspects of speech and language. Other studies include approaches from conversation analysis that look at more interactional aspects, showing that disfluencies such as fillers and repairs, and purely nonverbal features such as inter-speaker silence, can be key features of AD conversations. These kinds of features, if useful for diagnosis, may have many advantages: They are simple to extract and relatively language-, topic-, and task-independent. This study aims to quantify the role and contribution of these features of interaction structure in predicting whether a dialogue participant has AD. We used a subset of the Carolinas Conversation Collection dataset of patients with AD at moderate stage within the age range 60–89 and similar-aged non-AD patients with other health conditions. Our feature analysis comprised two sets: disfluency features, including indicators such as self-repairs and fillers, and interactional features, including overlaps, turn-taking behavior, and distributions of different types of silence both within patient speech and between patient and interviewer speech. Statistical analysis showed significant differences between AD and non-AD groups for several disfluency features (edit terms, verbatim repeats, and substitutions) and interactional features (lapses, gaps, attributable silences, turn switches per minute, standardized phonation time, and turn length). For the classification of AD patient conversations vs. non-AD patient conversations, we achieved 83% accuracy with disfluency features, 83% accuracy with interactional features, and an overall accuracy of 90% when combining both feature sets using support vector machine classifiers. The discriminative power of these features, perhaps combined with more conventional linguistic features, therefore shows potential for integration into noninvasive clinical assessments for AD at advanced stages.

scholarly journals Article Commentary: Alzheimer's Disease, Diagnosis and the Need for Biomarkers

2008 ◽  
Vol 3 ◽  
pp. BMI.S682 ◽  
Author(s):  
Claudie Hooper ◽  
Simon Lovestone ◽  
Ricardo Sainz-Fuertes
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Memory Loss ◽  
Cost Effective ◽  
Predictive Biomarker ◽  
Disease Diagnosis ◽  
Response To Therapy ◽  
Histopathological Analysis

Alzheimer's disease (AD) is a progressive neurodegenerative disorder of aging that presents with memory loss, disorientation, confusion and a reduction in cognitive ability. Although a definite diagnosis of the disorder can only be made post-mortem by histopathological analysis, a number of methods are currently available for the in vivo assessment of AD including psycho-metric tests and neuro-imaging. However, these clinical assessments are relatively nonspecific and imaging is very costly. Genetic testing can be performed if familial AD is suspected, although such cases represent a very small minority of total AD cases. Apolipoprotein E genotype provides a measure for analysing the risk of developing AD, but does not act as an absolute predictive biomarker for AD. Therefore there is a need for an accurate, universal, specific and cost-effective biomarker to facilitate not only ante-mortem diagnosis of AD, but also to allow progression of the disease and response to therapy to be monitored. This is the ultimate goal that our group is pursuing through the pan-European AddNeuroMed project.

A Chemical Perspective to the Anti-Amyloid Action of Compounds and a Nanoparticle Based Assay for Screening Amyloid Inhibitors

2020 ◽  
Author(s):  
Nidhi Gour ◽  
Bharti Koshti
Keyword(s):  
Memory Loss ◽  
Cost Effective ◽  
Structural Motif ◽  
Rapid Screening ◽  
Stacking Interactions ◽  
Aromatic Rings ◽  
Amyloid Fibers ◽  
Aromatic Stacking ◽  
Cost Effective Technique ◽  
The Brain

Aggregation of amyloid beeta 1-42 (Aβ<sub>42</sub>) peptide causes the formation of clustered deposits knows as amyloid plaques in the brain which leads to neuronal dysfunction and memory loss and associated with many neurological disorders including Alzheimer’s and Parkinson’s. Aβ<sub>42</sub> has core structural motif with phenylalanine at the 19 and 20 positions. The diphenylalanine (FF) residue plays a crucial role in the formation of amyloid fibers and serves as model peptide for studying Aβ<sub>42 </sub>aggregation. FF self-assembles to well-ordered tubular morphology via aromatic pi-pi stackings. Our studies, suggest that the aromatic rings present in the anti-amyloidogenic compounds may interact with the pi-pi stacking interactions present in the FF. Even the compounds which do not have aromatic rings, like cyclodextrin and cucurbituril show anti-amyloid property due to the binding of aromatic ring inside the guest cavity. Hence, our studies also suggest that compounds which may have a functional moiety capable of interacting with the aromatic stacking interactions might be tested for their anti-amyloidogenic properties. Further, in this manuscript, we have proposed two novel nanoparticle based assays for the rapid screening of amyloid inhibitors. In the first assay, interaction between biotin-tagged FF peptide and the streptavidin labelled gold nanoparticles (s-AuNPs) were used. In another assay, thiol-Au interactions were used to develop an assay for detection of amyloid inhibitors. It is envisaged that the proposed analytical method will provide a simple, facile and cost effective technique for the screening of amyloid inhibitors and may be of immense practical implications to find the therapeutic remedies for the diseases associated with the protein aggregation.

Current Therapeutic Strategies and Perspectives for Neuroprotection in Parkinson’s Disease

2020 ◽  
Vol 26 (37) ◽  
pp. 4738-4746
Author(s):  
Mohan K. Ghanta ◽  
P. Elango ◽  
Bhaskar L. V. K. S.
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Neurodegenerative Disorder ◽  
Therapeutic Strategies ◽  
Neuroprotective Agents ◽  
Striatal Neurons ◽  
The Status ◽  
Advanced Stages ◽  
Dopaminergic Pathways

Parkinson’s disease is a progressive neurodegenerative disorder of dopaminergic striatal neurons in basal ganglia. Treatment of Parkinson’s disease (PD) through dopamine replacement strategies may provide improvement in early stages and this treatment response is related to dopaminergic neuronal mass which decreases in advanced stages. This treatment failure was revealed by many studies and levodopa treatment became ineffective or toxic in chronic stages of PD. Early diagnosis and neuroprotective agents may be a suitable approach for the treatment of PD. The essentials required for early diagnosis are biomarkers. Characterising the striatal neurons, understanding the status of dopaminergic pathways in different PD stages may reveal the effects of the drugs used in the treatment. This review updates on characterisation of striatal neurons, electrophysiology of dopaminergic pathways in PD, biomarkers of PD, approaches for success of neuroprotective agents in clinical trials. The literature was collected from the articles in database of PubMed, MedLine and other available literature resources.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

Machine Learning Models for Finger Bend Evaluation using Implemented Low cost Flex Sensor

2021 ◽  
Vol 9 (VI) ◽  
pp. 3605-3611
Author(s):  
Pratyush Kaware
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Low Cost ◽  
Learning Algorithms ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

In this paper a cost-effective sensor has been implemented to read finger bend signals, by attaching the sensor to a finger, so as to classify them based on the degree of bent as well as the joint about which the finger was being bent. This was done by testing with various machine learning algorithms to get the most accurate and consistent classifier. Finally, we found that Support Vector Machine was the best algorithm suited to classify our data, using we were able predict live state of a finger, i.e., the degree of bent and the joints involved. The live voltage values from the sensor were transmitted using a NodeMCU micro-controller which were converted to digital and uploaded on a database for analysis.

scholarly journals Neuroprotective Potential of Peroxisome Proliferator Activated Receptor-αAgonist in Cognitive Impairment in Parkinson’s Disease: Behavioral, Biochemical, and PBPK Profile

PPAR Research ◽  
2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Dedeepya Uppalapati ◽  
Nihar R. Das ◽  
Rahul P. Gangwal ◽  
Mangesh V. Damre ◽  
Abhay T. Sangamwar ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Neurodegenerative Disorder ◽  
Memory Loss ◽  
Pbpk Modeling ◽  
Pharmacokinetic Parameters ◽  
Peroxisome Proliferator ◽  
Fenofibric Acid ◽  
Peroxisome Proliferator Activated Receptor

Parkinson’s disease (PD) is a common neurodegenerative disorder affecting 1% of the population by the age of 65 years and 4-5% of the population by the age of 85 years. PD affects functional capabilities of the patient by producing motor symptoms and nonmotor symptoms. Apart from this, it is also associated with a higher risk of cognitive impairment that may lead to memory loss, confusion, and decreased attention span. In this study, we have investigated the effect of fenofibrate, a PPAR-αagonist in cognitive impairment model in PD. Bilateral intranigral administration of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) (100 µg/1 µL/side) produced significant cognitive dysfunctions. Fenofibrate treatment at 10, 30, and 100 mg/kg for twenty-five days was found to be neuroprotective and improved cognitive impairment in MPTP-induced PD model as evident from behavioral, biochemical (MDA, GSH, TNF-α, and IL-6), immunohistochemistry (TH), and DNA fragmentation (TUNEL positive cells) studies. Further, physiologically based pharmacokinetic (PBPK) modeling study was performed using GastroPlus to characterize the kinetics of fenofibric acid in the brain. A good agreement was found between pharmacokinetic parameters obtained from the actual and simulated plasma concentration-time profiles of fenofibric acid. Results of this study suggest that PPAR-αagonist (fenofibrate) is neuroprotective in PD-induced cognitive impairment.

scholarly journals Breast Thermograms Asymmetry Analysis using Gabor filters

2021 ◽  
Vol 309 ◽  
pp. 01109
Author(s):  
Priyanka Yadlapalli ◽  
Madhavi K Reddy ◽  
Sunitha Gurram ◽  
J Avanija ◽  
K Meenakshi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Stage ◽  
Cost Effective ◽  
Gabor Filters ◽  
Support Vector ◽  
Svm Classifier ◽  
Infrared Thermal Imaging ◽  
Younger Women ◽  
Early Diagnosis Of Cancer ◽  
Diagnosis Of Cancer

Women are far more likely than males to acquire breast cancer, and current research indicates that this is entirely avoidable. It is also to blame for higher death rates among younger women compared to older women in nearly all developing nations. Medical imaging modalities are continuously in need of development. A variety of medical techniques have been employed to detect breast cancer in women. The most recent studies support mammography for breast cancer screening, although its sensitivity and specificity remain suboptimal, particularly in individuals with thick breast tissue, such as young women. As a result, alternative modalities, such as thermography, are required. Digital Infrared Thermal Imaging (DITI), as it is known, detects and records temperature changes on the skin’s surface. Thermography is well-known for its non-invasive, painless, cost-effective, and high recovery rates, as well as its potential to identify breast cancer at an early stage. Gabor filters are used to extract the textural characteristics of the left and right breasts. Using a support vector machine, the thermograms are then classified as normal or malignant based on textural asymmetry between the breasts (SVM). The accuracy achieved by combining Gabor features with an SVM classifier is around 84.5 percent. The early diagnosis of cancer with thermography enhances the patient’s chances of survival significantly since it may detect the disease in its early stages.

Cost Effective On-Site Fault Diagnosis Home Appliance Using a Smart Phone and Support Vector Machine

2019 ◽  
Author(s):  
Ji Min Baek ◽  
Kyeong Ha Lee ◽  
Seung Ho Lee ◽  
Ja Choon Koo
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Smart Phone ◽  
Poor Performance ◽  
Cost Effective ◽  
Consumer Electronics ◽  
Support Vector ◽  
Rotating Machines ◽  
Pass Filter ◽  
Rotating Machine

Abstract One of the common rotating machines of the consumer electronics might be a washing machine. The rotating machinery normally suffers mechanical failures even during daily operations that results in poor performance or shortening lifetime of the machine. Therefore, engineers have been interested in the earliest fault diagnosis of the rotating machine. Existing fault diagnosis methods for rotating machines have used fast fourier transform (FFT) method in frequency domain to detect abnormal frequency. However, it is difficult to diagnose using the FFT method if the normal frequency components of the rotating machines overlaps with the fault frequencies. In this paper, sets of acoustic signals generated by the washing machines are collected by using a smart phone in which an inexpensive microphone is equipped, and collected data are analyzed using a new algorithm, which combining the skewness, kurtosis, A-weighting filter, high-pass filter (HPF), and FFT. The analyzed data is applied to support vector machine (SVM) to determine defect existence. The proposed algorithm solves the disadvantages of the existing method and is accurate enough to discriminate the data collected by the cheap microphone of the smart phone.

Clinical course of Alzheimer’s disease

2011 ◽  
Author(s):  
Alberto Lleo ◽  
Rafael Blesa
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Daily Living ◽  
Clinical Course ◽  
Neurodegenerative Disorder ◽  
Memory Loss ◽  
Remote Memory ◽  
Initial Symptom ◽  
Delayed Recall ◽  
Age Related

• Alzheimer’s disease is an age-related neurodegenerative disorder, with onset usually in late life, characterized by cognitive impairment, a variety of behavioural symptoms, and restrictions in the activities of daily living• The initial symptom is episodic memory loss, in particular in delayed recall of visual and/or verbal material. Immediate and remote memory is usually preserved in early stages...

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