Faculty Opinions recommendation of Detection of a biomarker for Alzheimer's disease from synthetic and clinical samples using a nanoscale optical biosensor.

2005 ◽  
Author(s):  
Jon Zubieta
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Optical Biosensor ◽  
Clinical Samples

scholarly journals FINANCIAL DECISION MAKING SELF-EFFICACY IN COGNITIVELY AND FINANCIALLY VULNERABLE OLDER ADULTS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S477-S478
Author(s):  
Evan Z Gross ◽  
Rebecca J Campbell ◽  
LaToya Hall ◽  
Peter Lichtenberg
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
Decision Making ◽  
Cognitive Impairment ◽  
Self Efficacy ◽  
Clinical Samples ◽  
Financial Decisions ◽  
Financial Decision Making ◽  
Financial Decision

Abstract Financial decision making self-efficacy (FDMSE) is a novel construct that may influence how older adults make financial decisions. Our previous research with a community sample of older adults demonstrated that cognitive functioning and suspected history of financial exploitation were both associated with low FDMSE. We sought to replicate these findings in two clinical samples of older adults: people with mild cognitive impairment (MCI) or probable Alzheimer’s disease (PAD) and current victims of scams or exploitation as determined by a financial coach. Samples were obtained from the Michigan Alzheimer’s Disease Center and a financial coaching intervention study. All participants completed a 4-item FDMSE measure. One-way ANOVAs, t-tests and chi-square tests were conducted to test for group differences with controls on demographics. There was a main effect of cognitive status on FDMSE, F(2,138) = 8.10, p < .001, which was driven by higher FDMSE in the healthy group (N = 63) than the MCI (N = 76) or PAD (N = 28) groups. Similarly, scam victims (N = 25) had significantly lower FDMSE than non-exploited (N = 25) peers, t(48)=2.33, p < 05. Cognitive impairment and current financial scams are both associated with low FDMSE levels. Low FDMSE may exacerbate cognitive and psychosocial vulnerabilities that contribute to risk for poor financial decisions among older adults. Future interventions to enhance FDMSE may help older adults make better decisions despite changes in thinking abilities or previous negative financial experiences.

scholarly journals Diagnostic Biomarkers for Alzheimer’s Disease Using Non-Invasive Specimens

2020 ◽  
Vol 9 (6) ◽  
pp. 1673 ◽  
Author(s):  
Maria Paraskevaidi ◽  
David Allsop ◽  
Salman Karim ◽  
Francis L. Martin ◽  
StJohn Crean
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Clinical Samples ◽  
Great Promise ◽  
Non Invasive ◽  
To Come ◽  
Sampling Procedures ◽  
T Tau ◽  
Analytical Approaches

Studies in the field of Alzheimer’s disease (AD) have shown the emergence of biomarkers in biologic fluids that hold great promise for the diagnosis of the disease. A diagnosis of AD at a presymptomatic or early stage may be the key for a successful treatment, with clinical trials currently investigating this. It is anticipated that preventative and therapeutic strategies may be stage-dependent, which means that they have a better chance of success at a very early stage—before critical neurons are lost. Several studies have been investigating the use of cerebrospinal fluid (CSF) and blood as clinical samples for the detection of AD with a number of established core markers, such as amyloid beta (Aβ), total tau (T-tau) and phosphorylated tau (P-tau), being at the center of clinical research interest. The use of oral samples—including saliva and buccal mucosal cells—falls under one of the least-investigated areas in AD diagnosis. Such samples have great potential to provide a completely non-invasive alternative to current CSF and blood sampling procedures. The present work is a thorough review of the results and analytical approaches, including proteomics, metabolomics, spectroscopy and microbiome analyses that have been used for the study and detection of AD using salivary samples and buccal cells. With a few exceptions, most of the studies utilizing oral samples were performed in small cohorts, which in combination with the existence of contradictory results render it difficult to come to a definitive conclusion on the value of oral markers. Proteins such as Aβ, T-tau and P-tau, as well as small metabolites, were detected in saliva and have shown some potential as future AD diagnostics. Future large-cohort studies and standardization of sample preparation and (pre-)analytical factors are necessary to determine the use of these non-invasive samples as a diagnostic tool for AD.

scholarly journals Mild behavioral impairment: measurement and clinical correlates of a novel marker of preclinical Alzheimer’s disease

2022 ◽  
Vol 14 (1) ◽  
Author(s):  
Byron Creese ◽  
Zahinoor Ismail
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Studies ◽  
Neuropsychiatric Symptoms ◽  
Clinical Samples ◽  
Future Research ◽  
Preclinical Alzheimer’S Disease ◽  
Behavioral Impairment ◽  
Dementia Risk ◽  
Diagnostic Framework

Abstract Background Late-life onset neuropsychiatric symptoms are established risk factors for dementia. The mild behavioral impairment (MBI) diagnostic framework was designed to standardize assessment to determine dementia risk better. In this Mini Review, we summarize the emerging clinical and biomarker evidence, which suggests that for some, MBI is a marker of preclinical Alzheimer’s disease. Main MBI is generally more common in those with greater cognitive impairment. In community and clinical samples, frequency is around 10–15%. Mounting evidence in cognitively normal samples links MBI symptoms with known AD biomarkers for amyloid, tau, and neurodegeneration, as well as AD risk genes. Clinical studies have found detectable differences in cognition associated with MBI in cognitively unimpaired people. Conclusion The emerging evidence from biomarker and clinical studies suggests MBI can be an early manifestation of underlying neurodegenerative disease. Future research must now further validate MBI to improve identification of those at the very earliest stages of disease.

scholarly journals Applying Artificial Intelligence Techniques to Improve Clinical Diagnosis of Alzheimer’s Disease

2020 ◽  
Vol 3 (2) ◽  
pp. 58-79
Author(s):  
Ahmed Abdullah Farid ◽  
Gamal Ibrahim Selim ◽  
Hatem Awad A. Khater
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Diagnosis ◽  
Human Error ◽  
False Negative ◽  
False Negative Rate ◽  
Clinical Samples ◽  
Support Vector ◽  
Proposed Model ◽  
Better Than

Alzheimer's disease (AD) is a significant regular type of dementia that causes damage in brain cells. Early detection of AD acting as an essential role in global health care due to misdiagnosis and sharing many clinical sets with other types of dementia, and costly monitoring the progression of the disease over time by magnetic reasoning imaging (MRI) with consideration of human error in manual reading. Our proposed model in the first stage, apply the medical dataset to a composite hybrid feature selection (CHFS) to extract new features for select the best features to improve the performance of the classification process due to eliminating obscures. In the second stage, we applied a dataset to a stacked hybrid classification system to combine Jrip and random forest classifiers with six model evaluations as meta-classifier individually to improve the prediction of clinical diagnosis. All experiments conducted on a laptop with an Intel Core i7- 8750H CPU at 2.2 GHz and 16 G of ram running on windows 10 (64 bits). The dataset evaluated using an explorer set of WEKA data mining software for the analysis purpose. The experimental show that the proposed model of (CHFS) feature extraction performs better than proncipal component analysis (PCA), and lead to effectively reduced the false-negative rate with a relatively high overall accuracy with support vector machine (SVM) as meta-classifier of 96.50% compared to 68.83% which is considerably better than the previous state-of-the-art result. The receiver operating characteristic (ROC) curve was equal to 95.5%. Also, the experiment on MRI images Kaggle dataset of CNN classification process with 80.21% accuracy result. The results of the proposed model show an accurate classify Alzheimer's clinical samples against MRI neuroimaging for diagnoses AD at a low cost.

scholarly journals Recall and recognition of verbal paired associates in early Alzheimer's disease

2008 ◽  
Vol 14 (4) ◽  
pp. 591-600 ◽  
Author(s):  
G.J. LOWNDES ◽  
M.M. SALING ◽  
D. AMES ◽  
E. CHIU ◽  
L.M. GONZALEZ ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Medial Temporal Lobe ◽  
Empirical Support ◽  
Cued Recall ◽  
Clinical Samples ◽  
Clinical Value ◽  
Associate Learning ◽  
Early Alzheimer’S Disease ◽  
Associate Recognition

The primary impairment in early Alzheimer's disease (AD) is encoding/consolidation, resulting from medial temporal lobe (MTL) pathology. AD patients perform poorly on cued-recall paired associate learning (PAL) tasks, which assess the ability of the MTLs to encode relational memory. Since encoding and retrieval processes are confounded within performance indexes on cued-recall PAL, its specificity for AD is limited. Recognition paradigms tend to show good specificity for AD, and are well tolerated, but are typically less sensitive than recall tasks. Associate-recognition is a novel PAL task requiring a combination of recall and recognition processes. We administered a verbal associate-recognition test and cued-recall analogue to 22 early AD patients and 55 elderly controls to compare their ability to discriminate these groups. Both paradigms used eight arbitrarily related word pairs (e.g., pool-teeth) with varying degrees of imageability. Associate-recognition was equally effective as the cued-recall analogue in discriminating the groups, and logistic regression demonstrated classification rates by both tasks were equivalent. These preliminary findings provide support for the clinical value of this recognition tool. Conceptually it has potential for greater specificity in informing neuropsychological diagnosis of AD in clinical samples but this requires further empirical support. (JINS, 2008, 14, 591–600.)

scholarly journals A Coordinated Approach by Public Domain Bioinformatics Resources to Aid the Fight Against Alzheimer’s Disease Through Expert Curation of Key Protein Targets

2020 ◽  
Vol 77 (1) ◽  
pp. 257-273
Author(s):  
Lionel Breuza ◽  
Cecilia N. Arighi ◽  
Ghislaine Argoud-Puy ◽  
Cristina Casals-Casas ◽  
Anne Estreicher ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Ontology ◽  
Molecular Interaction ◽  
Public Domain ◽  
Research Community ◽  
Biological Data ◽  
Functional Enrichment ◽  
Clinical Samples ◽  
Free Text

Background: The analysis and interpretation of data generated from patient-derived clinical samples relies on access to high-quality bioinformatics resources. These are maintained and updated by expert curators extracting knowledge from unstructured biological data described in free-text journal articles and converting this into more structured, computationally-accessible forms. This enables analyses such as functional enrichment of sets of genes/proteins using the Gene Ontology, and makes the searching of data more productive by managing issues such as gene/protein name synonyms, identifier mapping, and data quality. Objective: To undertake a coordinated annotation update of key public-domain resources to better support Alzheimer’s disease research. Methods: We have systematically identified target proteins critical to disease process, in part by accessing informed input from the clinical research community. Results: Data from 954 papers have been added to the UniProtKB, Gene Ontology, and the International Molecular Exchange Consortium (IMEx) databases, with 299 human proteins and 279 orthologs updated in UniProtKB. 745 binary interactions were added to the IMEx human molecular interaction dataset. Conclusion: This represents a significant enhancement in the expert curated data pertinent to Alzheimer’s disease available in a number of biomedical databases. Relevant protein entries have been updated in UniProtKB and concomitantly in the Gene Ontology. Molecular interaction networks have been significantly extended in the IMEx Consortium dataset and a set of reference protein complexes created. All the resources described are open-source and freely available to the research community and we provide examples of how these data could be exploited by researchers.

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