Detection Of Alzheimer's Disease Research Articles

Reinforcement-Learning-Based Localization of Hippocampus for Alzheimer's Disease Detection.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder primarily impacting memory and cognitive functions. The hippocampus serves as a key biomarker associated with AD. In this study, we present an end-to-end automated approach for AD detection by introducing a reinforcement-learning-based technique to localize the hippocampus within structural MRI images. Subsequently, this localized hippocampus serves as input for a deep convolutional neural network for AD classification. We model the agent-environment interaction using a Deep Q-Network (DQN), encompassing both a convolutional Target Net and Policy Net. Furthermore, we introduce an integrated loss function that combines cross-entropy and contrastive loss to effectively train the classifier model. Our approach leverages a single optimal slice extracted from each subject's 3D sMRI, thereby reducing computational complexity while maintaining performance comparable to volumetric data analysis methods. To evaluate the effectiveness of our proposed localization and classification framework, we compare its performance to the results achieved by supervised models directly trained on ground truth hippocampal regions as input. The proposed approach demonstrates promising performance in terms of classification accuracy, F1-score, precision, and recall. It achieves an F1-score within an error margin of 3.7% and 1.1% and an accuracy within an error margin of 6.6% and 1.6% when compared to the supervised models trained directly on ground truth masks, all while achieving the highest recall score.

Time-series visual explainability for Alzheimer’s disease progression detection for smart healthcare

Artificial intelligence (AI)-based diagnostic systems provide less error-prone and safer support to clinicians, enhancing the medical decision-making process. This study presents a smart and reliable healthcare framework for detecting Alzheimer's disease (AD) progression. Early detection of AD before the onset of clinical symptoms is the most crucial step in starting timely treatment. To predict the conversion of cognitively normal patients to those with AD, three-dimensional (3D) magnetic resonance imaging (MRI) whole-brain neuroimaging methods have been extensively studied. However, depending on the 3D volume, this method is computationally expensive. To solve this problem, we used an approximate rank pooling method originally designed for video action recognition with a 3D MRI volume to obtain a compressed representation of multiple two-dimensional (2D) MRI slices. This study proposes a hybrid multimodal CNN-Bi-LSTM deep model for AD progression detection, in which the resulting dynamic 2D images are fused with cognitive features. Moreover, a novel explainable AI approach is proposed to provide visual explanations using the resulting longitudinal 2D dynamic images. Temporal explanations were provided by visualizing the affected brain regions captured using longitudinal 2D MRIs. By utilizing a sample of 1,692 subjects with multimodal data from the Alzheimer’s Disease Neuroimaging Initiative dataset, our method was assessed using a 10-fold cross-validation process. The model achieved an area under the receiver operating characteristics curve (AUC) of 94% using longitudinal 2D three-time-step dynamic image data. The fusion of 2D dynamic images with cognitive features enhanced the performance by 2% in terms of the AUC. Patients who gradually develop AD, show changes in various brain regions. For such patients, our system highlights the critical role of the hippocampus, medial amygdala, caudal hippocampus, and lateral amygdala at the initial time steps. In the late stages of AD, the system detects abnormalities in extra brain regions such as the medial temporal gyrus, superior temporal gyrus, fusiform gyrus, and caudal hippocampus; indicating that patients have completely progressed to AD.

Bioinformatic Analysis and Experimental Validation of Ubiquitin-Proteasomal System-Related Hub Genes as Novel Biomarkers for Alzheimer's Disease.

Alzheimer's disease (AD) is a common progressive neurodegenerative disease. The Ubiquitin-Protease system (UPS), which plays important roles in maintaining protein homeostasis in eukaryotic cells, is involved in the development of AD. This study sought to identify differential UPS-related genes (UPGs) in AD patients by using bioinformatic methods, reveal potential biomarkers for early detection of AD, and investigate the association between the identified biomarkers and immune cell infiltration in AD. The differentially expressed UPGs were screened with bioinformatics analyses using the Gene Expression Omnibus (GEO) database. A weighted gene co-expression network analysis (WGCNA) analysis was performed to explore the key gene modules associated with AD. A Single-sample Gene Set Enrichment Analysis (ssGSEA) analysis was peformed to explore the patterns of immune cells in the brain tissue of AD patients. Real-time quantitative PCR (RT-qPCR) was performed to examine the expression of hub genes in blood samples from healthy controls and AD patients. In this study, we identified four UPGs (USP3, HECW2, PSMB7, and UBE2V1) using multiple bioinformatic analyses. Furthermore, three UPGs (USP3, HECW2, PSMB7) that are strongly correlated with the clinical features of AD were used to construct risk score prediction markers to diagnose and predict the severity of AD. Subsequently, we analyzed the patterns of immune cells in the brain tissue of AD patients and the associations between immune cells and the three key UPGs. Finally, the risk score model was verified in several datasets of AD and showed good accuracy. Three key UPGs are identified as potential biomarker for AD patients. These genes may provide new targets for the early identification of AD patients.

Class-Balanced Deep Learning with Adaptive Vector Scaling Loss for Dementia Stage Detection.

Alzheimer's disease (AD) leads to irreversible cognitive decline, with Mild Cognitive Impairment (MCI) as its prodromal stage. Early detection of AD and related dementia is crucial for timely treatment and slowing disease progression. However, classifying cognitive normal (CN), MCI, and AD subjects using machine learning models faces class imbalance, necessitating the use of balanced accuracy as a suitable metric. To enhance model performance and balanced accuracy, we introduce a novel method called VS-Opt-Net. This approach incorporates the recently developed vector-scaling (VS) loss into a machine learning pipeline named STREAMLINE. Moreover, it employs Bayesian optimization for hyperparameter learning of both the model and loss function. VS-Opt-Net not only amplifies the contribution of minority examples in proportion to the imbalance level but also addresses the challenge of generalization in training deep networks. In our empirical study, we use MRI-based brain regional measurements as features to conduct the CN vs MCI and AD vs MCI binary classifications. We compare the balanced accuracy of our model with other machine learning models and deep neural network loss functions that also employ class-balanced strategies. Our findings demonstrate that after hyperparameter optimization, the deep neural network using the VS loss function substantially improves balanced accuracy. It also surpasses other models in performance on the AD dataset. Moreover, our feature importance analysis highlights VS-Opt-Net's ability to elucidate biomarker differences across dementia stages.

A Concatenated Deep Feature Extraction Architecture For Multi-Class Alzheimer Disease Prediction

Alzheimer's disease (AD), particularly affects adults, is among the most common brain disorders, causing memory loss and losing information to varied degrees. is a progressive neurological condition that gradually impairs cognitive performance and can lead to death. With today's technological advancements, Magnetic Resonance Imaging (MRI) scanning can diagnose AD. As a result, MRI is the most often utilized tool for diagnosing and monitoring the progression of AD. Image identification in the early detection of AD can be accomplished automatically utilizing machine learning (ML) with this technique. Although ML has several advantages, deep learning (DL) is currently more extensively used because it has superior learning ability and is more suited to tackling image identification difficulties. However, there are significant hurdles to implementing DL, such as the requirement for huge datasets, large computational resources, and careful parameter tuning to avoid overfitting or underfitting. In order to solve the problem of classifying AD using DL, this research provides randomised concatenated feature representations obtained by two pre-trained network models that learn feature information in deep from brain functional schemes of MRI scans simultaneously. To execute the task of AD multiclass classification, we experimented using concatenated CNN of DenseNet and MobileNet. To highlight the discriminating regions from scans for the suggested model prediction, a gradient class activation map has been used. In multiclass classification, the suggested model achieved 98.87 percent accuracy, 98.95 percent precision, and 98.99 percent recall, according to the experimental results. The findings suggest that advanced DL techniques combined with MRI data can be utilised to classify and forecast neurodegenerative brain illnesses such as Alzheimer's.

The Memory Alteration Test Is Correlated with Clinical, Cerebrospinal Fluid, and Brain Imaging Markers of Alzheimer Disease in Lima, Peru

Introduction: As disease-modifying therapies become available for Alzheimer’s disease (AD), detection of AD in early stages of illness (mild cognitive impairment [MCI], early dementia) becomes increasingly important. Biomarkers for AD in low- and middle-income countries (LMICs) are costly and not widely available; hence, it is important to identify cognitive tests that correlate well with AD biomarker status. In this study, we evaluated the memory alteration test (M@T) to detect biomarker-proven AD and quantify its correlation with neurodegeneration and cerebrospinal fluid (CSF) AD biomarkers in a cohort of participants from Lima, Peru. Methods: This is a secondary analysis of a cohort of 185 participants: 63 controls, 53 with amnestic MCI (aMCI), and 69 with dementia due to AD. Participants underwent testing with M@T and a gold standard neuropsychological battery. We measured total tau (t-tau), phosphorylated tau (p-tau), and beta-amyloid (β-amyloid) in CSF, and evaluated neurodegeneration via medial temporal atrophy score in MRI. We used receiver-operator curves to determine the discriminative capacity of the total M@T score and its subdomains. We used the Pearson coefficient to correlate M@T score and CSF biomarkers. Results: The M@T had an area under the curve (AUC) of 0.994 to discriminate between controls and cognitively impaired (aMCI or AD) patients, and an AUC of 0.98 to differentiate between aMCI and AD patients. Free-recall and cued recall had the highest AUCs of all subdomains. Total score was strongly correlated with t-tau (−0.77) and p-tau (−0.72), and moderately correlated with β-amyloid (0.66). The AUC for discrimination of neurodegeneration was 0.87. Conclusion: The M@T had excellent discrimination of aMCI and dementia due to AD. It was strongly correlated with CSF biomarkers and had good discrimination of neurodegeneration. In LMICs, the M@T may be a cost-effective screening tool for aMCI and dementia caused by AD.

Lexical Speech Features of Spontaneous Speech in Older Persons With and Without Cognitive Impairment: Reliability Analysis.

Speech analysis data are promising digital biomarkers for the early detection of Alzheimer disease. However, despite its importance, very few studies in this area have examined whether older adults produce spontaneous speech with characteristics that are sufficiently consistent to be used as proxy markers of cognitive status. This preliminary study seeks to investigate consistency across lexical characteristics of speech in older adults with and without cognitive impairment. A total of 39 older adults from a larger, ongoing study (age: mean 81.1, SD 5.9 years) were included. Participants completed neuropsychological testing and both picture description tasks and expository tasks to elicit speech. Participants with T-scores of ≤40 on ≥2 cognitive tests were categorized as having mild cognitive impairment (MCI). Speech features were computed automatically by using Python and the Natural Language Toolkit. Reliability indices based on mean correlations for picture description tasks and expository tasks were similar in persons with and without MCI (with r ranging from 0.49 to 0.65 within tasks). Intraindividual variability was generally preserved across lexical speech features. Speech rate and filler rate were the most consistent indices for the cognitively intact group, and speech rate was the most consistent for the MCI group. Our findings suggest that automatically calculated lexical properties of speech are consistent in older adults with varying levels of cognitive impairment. These findings encourage further investigation of the utility of speech analysis and other digital biomarkers for monitoring cognitive status over time.

Blood-Based Transcriptomic Biomarkers Are Predictive of Neurodegeneration Rather Than Alzheimer's Disease.

Alzheimer's disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS). Our analyses showed that genes involved in spliceosome assembly, RNA binding, transcription, protein synthesis, mitoribosomes, and NADH dehydrogenase were the best-performing genes for identifying AD patients relative to cognitively healthy controls. This transcriptomic signature, however, was not unique to AD, and subsequent machine learning showed that this signature could also predict PD and ALS relative to controls without neurodegenerative disease. Combined, our results suggest that mRNA from whole blood can indeed be used to screen for patients with neurodegeneration but may be less effective in diagnosing the specific neurodegenerative disease.

Abnormalities in EEG as alzheimer marker

Alzheimer’s Disease (AD) is a prevalent neurodegenerative disorder characterized by protein accumulation, cognitive decline, and irreversible brain damage. With the aging population, AD’s prevalence is rising, necessitating effective diagnostic tools. Electroencephalography (EEG), a non-invasive neuroimaging method, has emerged as a promising tool for early AD detection.

A novel optimal wavelet filter banks for automated diagnosis of Alzheimer’s disease and mild cognitive impairment using Electroencephalogram signals

Electroencephalogram (EEG) of Alzheimer’s disease (AD) patients show a slowing effect and less synchronization. EEG signal’s transient and abrupt nature is captured from various mother wavelets. However, better performance can be obtained by balancing time–frequency localization in wavelet filters. We propose a new approach for designing filter banks based on optimal time–frequency localization of a four-step lifting structure (FSLS). First, we design a FSLS using Euler’s Frobenius half-band polynomial (EFHBP). The perfect reconstruction condition and vanishing moments are imposed on EFHBP to achieve maximum flat half-band filters (HBFs). HBFs are optimized using a balanced uncertainty metric to obtain a time–frequency spread balance. Afterward, these HBFs are used in the FSLS to obtain the synthesis and analysis filter banks. The proposed time–frequency optimized biorthogonal wavelet filter banks (TFOBWFBs) achieved a balance between time–frequency localization. Further, these TFOBWFBs are applied to decompose the EEG signals of AD patients. Twenty different features were extracted from decomposed EEG subbands, of which twelve significant features were selected using Kruskal Walli’s test. The machine learning models were trained and tested using obtained features with 10-fold cross-validation and leave-one-subject-out cross-validation. To validate this study, the proposed TFOBWFBs have been applied to two publicly available EEG datasets of mild cognitive impairment (MCI), AD, and healthy control (HC) subjects. The proposed TFOBWFBs achieved 98.90% accuracy for 2-way (AD vs. HC) and 96.50% for 3-way (AD vs. MCI vs. HC) classification using a support vector machine model with a 10-fold cross-validation. The proposed method outperforms the existing AD and MCI detection techniques. The performance of the proposed machine learning framework with optimization of wavelet filter banks is more significant and fast compared to previous studies. Also, the proposed framework can be applied to detect other neurodegenerative disorders.

An effective feature selection using improved marine predators algorithm for Alzheimer’s disease classification

<span lang="EN-US">Alzheimer’s disease (AD) is an irremediable neurodegenerative illness developed by the fast deterioration of brain cells. AD is mostly common in elder people and it extremely disturbs the physical and mental health of patients, therefore early detection is essential to prevent AD development. However, the precise detection of AD and mild cognitive impairment (MCI) is difficult during classification. In this paper, the Residual network i.e., ResNet-18 is used for extracting the features, and the proposed improved marine predators algorithm (IMPA) is developed for choosing the optimum features to perform an effective classification of AD. The multi-verse optimizer (MVO) used in the IMPA helps to balance exploration and exploitation, which leads to the selection of optimal relevant features. Further, the classification of AD is accomplished using the multiclass support vector machine (MSVM). Open access series of imaging studies-1 (OASIS-1) and Alzheimer disease neuroimaging initiative (ADNI) datasets are used to evaluate the IMPA-MSVM method. The performance of the IMPA-MSVM method is analyzed using accuracy, sensitivity, specificity, positive predictive value (PPV) and matthews correlation coefficient (MCC). The existing methods such as the <a name="_Hlk134426295"></a>deep learning-based segmenting method using SegNet (DLSS), mish activation function (MAF) with spatial transformer network (STN) and BrainNet2D are used to evaluate the IMPA-MSVM method. The accuracy of IMPA-MSVM for the ADNI dataset is 98.43% which is more when compared to the DLSS and MAF-STN.</span>

The "when" matters: Evidence from memory markers in the clinical continuum of Alzheimer's disease.

Cognitive assessment able to detect impairments in the early neuropathological stages of Alzheimer's disease (AD) is urgently needed. The visual short-term memory binding task (VSTMBT) and the Free and Cued Selective Reminding Test (FCSRT) have been recommended by the neurodegenerative disease working group as promising tests to aid in the early detection of AD. In this study, we investigated their complementary value across the clinical stages of the AD continuum. One hundred and seventeen older adults with subjective cognitive complaint (SCC), 79 with mild cognitive impairment (MCI), 31 patients with AD dementia (ADD), and 37 cognitively unimpaired (CU) subjects, underwent assessment with the VSTMBT and the picture version of the Spanish FCSRT. After controlling for multiple comparisons, significant differences were found across groups. The VSTMBT was the only test that "marginally" differentiated between CU and SCC (d = 0.47, p = .052). Moreover, whereas the FCSRT showed a gradient (CU = SCC) > MCI > ADD, the VSTMBT gradient was CU > SCC > (MCI = ADD) suggesting that conjunctive binding deficits assessed by the latter may be sensitive to the very early stages of the disease. Our results suggest that the VSTMBT and the FCSRT are sensitive to the clinical continuum of AD. Whereas the former detects changes in the early prodromal stages, the latter is more sensitive to the advanced prodromal stages of AD. These novel tests can aid in the early detection, monitor disease progression and response to treatment, and thus support drug development programs. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Deep Multi-Branch CNN Architecture for Early Alzheimer's Detection from Brain MRIs.

Alzheimer's disease (AD) is a neurodegenerative disease that can cause dementia and result in a severe reduction in brain function, inhibiting simple tasks, especially if no preventative care is taken. Over 1 in 9 Americans suffer from AD-induced dementia, and unpaid care for people with AD-related dementia is valued at USD 271.6 billion. Hence, various approaches have been developed for early AD diagnosis to prevent its further progression. In this paper, we first review other approaches that could be used for the early detection of AD. We then give an overview of our dataset and propose a deep convolutional neural network (CNN) architecture consisting of 7,866,819 parameters. This model comprises three different convolutional branches, each having a different length. Each branch is comprised of different kernel sizes. This model can predict whether a patient is non-demented, mild-demented, or moderately demented with a 99.05% three-class accuracy. In summary, the deep CNN model demonstrated exceptional accuracy in the early diagnosis of AD, offering a significant advancement in the field and the potential to improve patient care.

Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data

Alzheimer’s disease (AD) is the most common form of dementia. Early and accurate detection of AD is crucial to plan for disease modifying therapies that could prevent or delay the conversion to sever stages of the disease. As a chronic disease, patient’s multivariate time series data including neuroimaging, genetics, cognitive scores, and neuropsychological battery provides a complete profile about patient’s status. This data has been used to build machine learning and deep learning (DL) models for the early detection of the disease. However, these models still have limited performance and are not stable enough to be trusted in real medical settings. Literature shows that DL models outperform classical machine learning models, but ensemble learning has proven to achieve better results than standalone models. This study proposes a novel deep stacking framework which combines multiple DL models to accurately predict AD at an early stage. The study uses long short-term memory (LSTM) models as base models over patient’s multivariate time series data to learn the deep longitudinal features. Each base LSTM classifier has been optimized using the Bayesian optimizer using different feature sets. As a result, the final optimized ensembled model employed heterogeneous base models that are trained on heterogeneous data. The performance of the resulting ensemble model has been explored using a cohort of 685 patients from the University of Washington's National Alzheimer’s Coordinating Center dataset. Compared to the classical machine learning models and base LSTM classifiers, the proposed ensemble model achieves the highest testing results (i.e., 82.02, 82.25, 82.02, and 82.12 for accuracy, precision, recall, and F1-score, respectively). The resulting model enhances the performance of the state-of-the-art literature, and it could be used to build an accurate clinical decision support tool that can assist domain experts for AD progression detection.

Toward Reagent-Free Discrimination of Alzheimer's Disease Using Blood Plasma Spectral Digital Biomarkers and Machine Learning.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. The detection of early-stage AD is particularly desirable because it would allow early intervention. However, a minimally invasive, low-cost, and accurate discrimination or diagnostic method for AD is especially difficult in the earliest stage of AD. The aim of this research is to discover blood plasma spectral digital biomarkers of AD, develop a novel intelligent method for the discrimination of AD and accelerate the translation of Fourier transform infrared (FTIR) spectral-based disease discrimination methods from the laboratory to clinical practice. Since vibration spectroscopy can provide the structure and chemical composition information of biological samples at the molecular level, we investigated the potential of FTIR spectral biomarkers of blood plasma to differentiate between AD patients and healthy controls. Combined with machine learning technology, we designed a hierarchical discrimination system that provides reagent-free and accurate AD discrimination based on blood plasma spectral digital biomarkers of AD. Accurate segregation between AD patients and healthy controls was achieved with 89.3% sensitivity and 85.7% specificity for early-stage AD patients, 92.8% sensitivity and 87.5% specificity for middle-stage AD patients, and 100% sensitivity and 100% specificity for late-stage AD patients. Our results show that blood plasma spectral digital biomarkers hold great promise as discrimination markers of AD, indicating the potential for the development of an inexpensive, reagent-free, and less laborious clinical test. As a result, our research outcome will accelerate the clinical application of spectral digital biomarkers and machine learning.

Analyzing subcortical structures in Alzheimer's disease using ensemble learning

Alzheimer's disease (AD) is a neurological condition that causes significant cognitive deterioration within the brain. Early detection can lead to an early diagnosis of the illness. Structural and subcortical analyses are required for better diagnosis of disease and detection to gain a deeper understanding of the connections between neuroregions. As a result, in this paper, we have included the fusion of PET and MRI modalities, which can provide better visualization of the result. Second, we used the Ensemble Model (EM) and other machine learning (ML) methods to analyze the different subcortical structures to determine which region is more critical in the detection of AD when compared to other subtypes. The results revealed that the Hippocampus, the Amygdala in the left and right Hemispheres, and the neuroregion were the most effective in detecting AD, Mild Cognitive Impairment (MCI), and Cognitive Normal (CN).

Deep Learning of Speech Data for Early Detection of Alzheimer's Disease in the Elderly.

Alzheimer's disease (AD) is the most common form of dementia, which makes the lives of patients and their families difficult for various reasons. Therefore, early detection of AD is crucial to alleviating the symptoms through medication and treatment. Given that AD strongly induces language disorders, this study aims to detect AD rapidly by analyzing the language characteristics. The mini-mental state examination for dementia screening (MMSE-DS), which is most commonly used in South Korean public health centers, is used to obtain negative answers based on the questionnaire. Among the acquired voices, significant questionnaires and answers are selected and converted into mel-frequency cepstral coefficient (MFCC)-based spectrogram images. After accumulating the significant answers, validated data augmentation was achieved using the Densenet121 model. Five deep learning models, Inception v3, VGG19, Xception, Resnet50, and Densenet121, were used to train and confirm the results. Considering the amount of data, the results of the five-fold cross-validation are more significant than those of the hold-out method. Densenet121 exhibits a sensitivity of 0.9550, a specificity of 0.8333, and an accuracy of 0.9000 in a five-fold cross-validation to separate AD patients from the control group. The potential for remote health care can be increased by simplifying the AD screening process. Furthermore, by facilitating remote health care, the proposed method can enhance the accessibility of AD screening and increase the rate of early AD detection.

Pipelined deep learning architecture for the detection of Alzheimer’s disease

The progressive neurodegenerative disease in the human brain causes Alzheimer's disease (AD).The earlier detection helps to slowdown the progression of AD using continuous medical support system. The research work aims in the detection of Alzheimer’s disease (AD) using brain images. The AD images are detected using fusion-based deep learning method. This works also adopts Pipelined LeNet (PLN) architecture. The brain Magnetic Resonance Imaging (MRI) images obtained are resized in the preprocessing stage and the internal regions are enhanced using the image fusion method. Therefore, the proposed AD detection system produces high classification rate even using low resolution brain MRI images. The ternary features are computed from the fused image and these features are classified by the proposed PLN architecture for identification of Alzheimer’s brain image. High efficient and high speed novel PLN architecture is proposed in this AD detection system using brain MRI images due to the consumption time of classification and requirement of brain image dataset by conventional methods. Evaluation of the work is carried out using Kaggle Alzheimer’s Classification Dataset (KACD). Performance of the AD image detection system in terms of sensitivity (Se), specificity (Sp), precision (Pr), and accuracy (Acc) are evaluated. The proposed AD image detection system obtains 99.9% Se, 99.8% Sp, 99.8% Pr and 99.5% Acc using PLN architecture. The proposed AD detection system using PLN architecture consumed 0.65 ms as an average execution span. By comparing the execution span of all other similar methods, the proposed system consumes less execution span due to its Pipelined architecture. The experimental results of the image detection system are compared with similar works in the literatures.

Evaluation of Dimercaptosuccinic Acid-Coated Iron Nanoparticles Immunotargeted to Amyloid Beta as MRI Contrast Agents for the Diagnosis of Alzheimer's Disease.

Nanoparticle-based magnetic contrast agents have opened the potential for magnetic resonance imaging (MRI) to be used for early non-invasive diagnosis of Alzheimer's disease (AD). Accumulation of amyloid pathology in the brain has shown association with cognitive decline and tauopathy; hence, it is an effective biomarker for the early detection of AD. The aim of this study was to develop a biocompatible magnetic nanoparticle targeted to amyloid beta (Aβ) plaques to increase the sensitivity of T2-weighted MRI for imaging of amyloid pathology in AD. We presented novel iron core-iron oxide nanoparticles stabilized with a dimercaptosuccinic acid coating and functionalized with an anti-Aβ antibody. Nanoparticle biocompatibility and cellular internalization were evaluated in vitro in U-251 glioblastoma cells using cellular assays, proteomics, and transmission electron microscopy. Iron nanoparticles demonstrated no significant in vitro cytotoxicity, and electron microscopy results showed their movement through the endocytic cycle within the cell over a 24 h period. In addition, immunostaining and bio-layer interferometry confirmed the targeted nanoparticle's binding affinity to amyloid species. The iron nanoparticles demonstrated favourable MRI contrast enhancement; however, the addition of the antibody resulted in a reduction in the relaxivity of the particles. The present work shows promising preliminary results in the development of a targeted non-invasive method of early AD diagnosis using contrast-enhanced MRI.

Elevated plasma sclerostin is associated with high brain amyloid-β load in cognitively normal older adults

Osteoporosis and Alzheimer’s disease (AD) mainly affect older individuals, and the possibility of an underlying link contributing to their shared epidemiological features has rarely been investigated. In the current study, we investigated the association between levels of plasma sclerostin (SOST), a protein primarily produced by bone, and brain amyloid-beta (Aβ) load, a pathological hallmark of AD. The study enrolled participants meeting a set of screening inclusion and exclusion criteria and were stratified into Aβ− (n = 65) and Aβ+ (n = 35) according to their brain Aβ load assessed using Aβ-PET (positron emission tomography) imaging. Plasma SOST levels, apolipoprotein E gene (APOE) genotype and several putative AD blood-biomarkers including Aβ40, Aβ42, Aβ42/Aβ40, neurofilament light (NFL), glial fibrillary acidic protein (GFAP), total tau (t-tau) and phosphorylated tau (p-tau181 and p-tau231) were detected and compared. It was found that plasma SOST levels were significantly higher in the Aβ+ group (71.49 ± 25.00 pmol/L) compared with the Aβ− group (56.51 ± 22.14 pmol/L) (P < 0.01). Moreover, Spearman’s correlation analysis showed that plasma SOST concentrations were positively correlated with brain Aβ load (ρ = 0.321, P = 0.001). Importantly, plasma SOST combined with Aβ42/Aβ40 ratio significantly increased the area under the curve (AUC) when compared with using Aβ42/Aβ40 ratio alone (AUC = 0.768 vs 0.669, P = 0.027). In conclusion, plasma SOST levels are elevated in cognitively unimpaired older adults at high risk of AD and SOST could complement existing plasma biomarkers to assist in the detection of preclinical AD.