Detection Of Alzheimer's Disease Research Articles

Artificial Intelligence Model for Alzheimer's Disease Detection with Convolution Neural Network for Magnetic Resonance Images.

Alzheimer's disease (AD) is a neurodegenerative disease that drastically affects brain cells. Early detection of this disease can reduce the brain cell damage rate and improve the prognosis of the patient to a great extent. The patients affected with AD tend to depend on their children and relatives for their daily chores. This research study utilizes the latest technologies of artificial intelligence and computation power to aid the medical industry. The study aims at early detection of AD to enable doctors to treat patients with the appropriate medication in the early stages of the disease condition. In this research study, convolutional neural networks, an advanced deep learning technique, are adopted to classify AD patients with their MRI images. Deep learning models with customized architecture are precise in the early detection of diseases with images retrieved by neuroimaging techniques. The convolution neural network model classifies the patients as diagnosed with AD or cognitively normal. Standard metrics evaluate the model performance to compare with the state-of-the-art methodologies. The experimental study of the proposed model shows promising results with an accuracy of 97%, precision of 94%, recall rate of 94%, and f1-score of 94%. This study leverages powerful technologies like deep learning to aid medical practitioners in diagnosing AD. It is crucial to detect AD early to control and slow down the rate at which the disease progresses.

Improved multimodal prediction of progression from MCI to Alzheimer's disease combining genetics with quantitative brain MRI and cognitive measures.

There is a pressing need for non-invasive, cost-effective tools for early detection of Alzheimer's disease (AD). Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Cox proportional models were conducted to develop a multimodal hazard score (MHS) combining age, a polygenic hazard score (PHS), brain atrophy, and memory to predict conversion from mild cognitive impairment (MCI) to dementia. Power calculations estimated required clinical trial sample sizes after hypothetical enrichment using the MHS. Cox regression determined predicted age of onset for AD pathology from the PHS. The MHS predicted conversion from MCI to dementia (hazard ratio for 80th versus 20th percentile: 27.03). Models suggest that application of the MHS could reduce clinical trial sample sizes by 67%. The PHS alone predicted age of onset of amyloid and tau. The MHS may improve early detection of AD for use in memory clinics or for clinical trial enrichment. A multimodal hazard score (MHS) combined age, genetics, brain atrophy, and memory. The MHS predicted time to conversion from mild cognitive impairment to dementia. MHS reduced hypothetical Alzheimer's disease (AD) clinical trial sample sizes by 67%. A polygenic hazard score predicted age of onset of AD neuropathology.

Applications of Nanotechnology in the Treatment of Alzheimers Disease

Alzheimers Disease (AD) is a neurodegenerative disease associated with progressive memory and cognitive impairment. Due to the devastating social and financial impacts of AD, extensive research is put into gaining a clearer understanding of its pathogenesis and risk factors, as well as the development of treatments that can slow or reverse the disease progression. Despite this, early and accurate detection of AD and the development of curative treatments are yet to be achieved. Further, major challenges remain in the symptomatic treatments available today, as the delivery of Alzheimers medications is limited by low efficiency due to difficulties in blood-brain barrier permeation and poor absorption. The incorporation of nanotechnology in current and potential treatments present unique opportunities for the delivery of therapeutic agents with increased specificity, lower toxicity and controlled release. In this review, we outline the proposed pathogenesis, current diagnosis and treatment methods of AD, and discuss recent advancements in nanomaterial-based systems that target major hallmarks of AD through different mechanisms, including targeted drug delivery, inhibition of A aggregation, delivery of neuroprotective agents and A removal from the blood.

Predicting Alzheimer’s Disease from Clinical Dementia Rating, Estimated Total Intracranial Volume, Atlas Scaling Factor: Incorporating Ensemble Approach into Automated Machine Learning (P12-6.010)

<h3>Objective:</h3> With artificial intelligence (particularly machine learning) exhibiting superior predictive and prognosticative capabilities when compared with conventional statistical techniques, for neurological as well as non-neurological conditions, we explored automated Machine Learning to predict AD using variables such as socioeconomic status, Normalized Whole Brain Volume and Atlas Scaling Factor. <h3>Background:</h3> Alzheimer’s Disease (AD) is a debilitating disease associated with the pathognomonic deposition of neurofibrillary tangles and neuritic plaques along with characteristic amyloid angiopathy-driven hippocampal granulovacuolar degeneration and Meynert’s nucleus neuronal loss. <h3>Design/Methods:</h3> The data was adopted as shared on Kaggle. The study population comprised 373 human subjects (Female subjects = 57%; Range of age = 60–98). Variables include years of education, socioeconomic status and MMSE scores. The current state of the art for aML was adopted to develop classification models using algorithms including Extreme Gradient Boosting, Random Forest and Neural Network. The models were stacked after implementation of hyperparameter tuning, via insertion of random features and boosting on errors. Ensemble approach, which is the amalgamation of two or more than two algorithmic models to develop a model better than either of its computive components, was superimposed on stacked models. The mwA-AUROC area gauged the discriminating ability of models, with the highest value of 1 indicating perfect discriminative classification ability. <h3>Results:</h3> An ensemble of CatBoost, Nearest Neighbours and Neural Network algorithmic models with incorporated K-means features and boost on errors achieved an mwA-AUROC of 0.96 with a close-to perfect AD detection score. The respective model did not perform well in classifying converted patients. The ensemble model exhibited the least log loss among all developed algorithmic models. <h3>Conclusions:</h3> Such predictive models, when deployed on cloud, would serve to improve the management of the respective patient as well as the resource allocation, ultimately translating into significant reduction of morbidity and mortality associated with this enfeebling condition. <b>Disclosure:</b> Mr. Bangash has nothing to disclose. Miss Batool has nothing to disclose.

Review of Risk Factors Associated With Biomarkers for Alzheimer Disease.

BACKGROUND: Alzheimer disease (AD) is a neurodegenerative disease with no cure. The number of individuals living with AD doubles every 5 years. The current clinical practice relies on clinical history, mental status tests, cerebrum imaging, and physical and neurological examinations; however, recent advances in the field of biomarkers have provided clues for the early detection of AD. High levels of tau and low levels of amyloid-β (Aβ) in cerebrospinal fluid are well-known biomarkers for AD. METHODS: A database search of PubMed, Ovid MEDLINE, and CINAHL was conducted to identify relevant articles published within the last 5 years. The search was limited to articles concerning adults 65 years or older and published in the English language. Twelve articles were included in the review. RESULTS: Risk factors of sleep disruption, depression, and motor function are implicated. Cerebrospinal fluid parameters for biomarkers of tau and Aβ were universally lower among Blacks compared with Whites, raising concern that norm reference may not be accurate for all populations. Older adults are more at risk for AD. Results are inconclusive regarding whether depression is related to Aβ and tau pathology. CONCLUSION: Nurses should screen for sleep architecture, depression, and motor function in their patients and educate them on good sleep hygiene. Sleep studies should be advocated for people with suspected sleep apnea to mitigate the risk factor related to abnormal Aβ and tau pathology. Falls and decreased motor function require screening because they may be early indicators of abnormal biomarkers leading to AD.

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning.

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.

Evaluation of Plasma Biomarkers for A/T/N Classification of Alzheimer Disease Among Adults of Caribbean Hispanic Ethnicity

Cerebrospinal fluid (CSF) and plasma biomarkers can detect biological evidence of Alzheimer disease (AD), but their use in low-resource environments and among minority ethnic groups is limited. To assess validated plasma biomarkers for AD among adults of Caribbean Hispanic ethnicity. In this decision analytical modeling study, adults were recruited between January 1, 2018, and April 30, 2022, and underwent detailed clinical assessments and venipuncture. A subsample of participants also consented to lumbar puncture. Established CSF cut points were used to define AD biomarker-positive status, allowing determination of optimal cut points for plasma biomarkers in the same individuals. The performance of a panel of 6 plasma biomarkers was then assessed with respect to the entire group. Data analysis was performed in January 2023. Main outcomes were the association of plasma biomarkers amyloid-β 1-42 (Aβ42), amyloid-β 1-40 (Aβ40), total tau (T-tau), phosphorylated tau181 (P-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) with AD diagnosis. These biomarkers allow assessment of amyloid (A), neurofibrillary degeneration (T), and neurodegeneration (N) aspects of AD. Statistical analyses performed included receiver operating characteristics, Pearson and Spearman correlations, t tests, and Wilcoxon rank-sum, chi-square, and Fisher exact tests. Exposures included age, sex, education, country of residence, apolipoprotein-ε4 (APOE-ε4) allele number, serum creatinine, blood urea nitrogen, and body mass index. This study included 746 adults. Participants had a mean (SD) age of 71.0 (7.8) years, 480 (64.3%) were women, and 154 (20.6%) met clinical criteria for AD. Associations were observed between CSF and plasma P-tau181 (r = .47 [95% CI, 0.32-0.60]), NfL (r = 0.57 [95% CI, 0.44-0.68]), and P-tau181/Aβ42 (r = 0.44 [95% CI, 0.29-0.58]). For AD defined by CSF biomarkers, plasma P-tau181 and P-tau181/Aβ42 provided biological evidence of AD. Among individuals judged to be clinically healthy without dementia, biomarker-positive status was determined by plasma P-tau181 for 133 (22.7%) and by plasma P-tau181/Aβ42 for 104 (17.7%). Among individuals with clinically diagnosed AD, 69 (45.4%) had plasma P-tau181 levels and 89 (58.9%) had P-tau181/Aβ42 levels that were inconsistent with AD. Individuals with biomarker-negative clinical AD status tended to have lower levels of education, were less likely to carry APOE-ε4 alleles, and had lower levels of GFAP and NfL than individuals with biomarker-positive clinical AD. In this cross-sectional study, plasma P-tau181 and P-tau181/Aβ42 measurements correctly classified Caribbean Hispanic individuals with and without AD. However, plasma biomarkers identified individuals without dementia with biological evidence of AD, and a portion of those with dementia whose AD biomarker profile was negative. These results suggest that plasma biomarkers can augment detection of preclinical AD among asymptomatic individuals and improve the specificity of AD diagnosis.

The promise of convolutional neural networks for the early diagnosis of the Alzheimer’s disease

Alzheimer’s Disease (AD) is one of the most devastating neurologic disorders, if not the most, as there is no cure for this disease, and its symptoms eventually become severe enough to interfere with daily tasks. The early diagnosis of AD, which might be up to 8 years before the onset of dementia symptoms, comes with many promises. To this end, we propose a novel Convolutional Neural Network (CNN) as a cheap, fast, yet accurate solution. First, a gold-standard dataset, namely DARWIN, that was proposed for the detection of AD through handwriting and consisted of 1D features, was used to generate the 2D features, which were yielded into the proposed novel model. Then, the proposed novel model was trained and evaluated on this dataset. According to the experimental result, the proposed novel model obtained an accuracy as high as 90.4%, which was higher than the accuracies obtained by the state-of-the-art baselines, which covered a total of 17 widely-used classifiers.

Three-round learning strategy based on 3D deep convolutional GANs for Alzheimer’s disease staging

Accurately diagnosing of Alzheimer's disease (AD) and its early stages is critical for prompt treatment or potential intervention to delay the the disease’s progression. Convolutional neural networks (CNNs) models have shown promising results in structural MRI (sMRI)-based diagnosis, but their performance, particularly for 3D models, is constrained by the lack of labeled training samples. To address the overfitting problem brought on by the insufficient training sample size, we propose a three-round learning strategy that combines transfer learning with generative adversarial learning. In the first round, a 3D Deep Convolutional Generative Adversarial Networks (DCGAN) model was trained with all available sMRI data to learn the common feature of sMRI through unsupervised generative adversarial learning. The second round involved transferring and fine-tuning, and the pre-trained discriminator (D) of the DCGAN learned more specific features for the classification task between AD and cognitively normal (CN). In the final round, the weights learned in the AD versus CN classification task were transferred to the MCI diagnosis. By highlighting brain regions with high prediction weights using 3D Grad-CAM, we further enhanced the model's interpretability. The proposed model achieved accuracies of 92.8%, 78.1%, and 76.4% in the classifications of AD versus CN, AD versus MCI, and MCI versus CN, respectively. The experimental results show that our proposed model avoids overfitting brought on by a paucity of sMRI data and enables the early detection of AD.

Auditory Event-Related Potentials in Older Adults with Subjective Memory Complaints.

Auditory event-related potentials (AERPs) have been suggested as possible biomarkers for the early diagnosis of Alzheimer's disease (AD). However, no study has investigated AERP measures in individuals with subjective memory complaints (SMCs), who have been suggested to be at a pre-clinical stage of AD. This study investigated whether AERPs in older adults with SMC can be used to objectively identify those at high risk of developing AD. AERPs were measured in older adults. Presence of SMC was determined using the Memory Assessment Clinics Questionnaire (MAC-Q). Hearing thresholds using pure-tone audiometry, neuropsychological data, levels of amyloid-β burden and Apolipoprotein E (APOE)ɛ genotype were also obtained A classic two-tone discrimination (oddball) paradigm was used to elicit AERPs (i.e., P50, N100, P200, N200, and P300). Sixty-two individuals (14 male, mean age 71.9±5.2 years) participated in this study, of which, 43 (11 male, mean age 72.4±5.5 years) were SMC and 19 (3 male, mean age 70.8±4.3 years) were non-SMC (controls). P50 latency was weakly but significantly correlated with MAC-Q scores. In addition, P50 latencies were significantly longer in Aβ+ individuals compared to Aβ- individuals. Results suggest that P50 latencies may be a useful tool to identify individuals at higher risk (i.e., participants with high Aβ burden) of developing measurable cognitive decline. Further longitudinal and cross-sectional studies in a larger cohort on SMC individuals are warranted to determine if AERP measures could be of significance for the detection of pre-clinical AD.

Increased plasma DYRK1A with aging may protect against neurodegenerative diseases

Early markers are needed for more effective prevention of Alzheimer’s disease. We previously showed that individuals with Alzheimer’s disease have decreased plasma DYRK1A levels compared to controls. We assessed DYRK1A in the plasma of cognitively healthy elderly volunteers, individuals with either Alzheimer’s disease (AD), tauopathies or Down syndrome (DS), and in lymphoblastoids from individuals with DS. DYRK1A levels were inversely correlated with brain amyloid β burden in asymptomatic elderly individuals and AD patients. Low DYRK1A levels were also detected in patients with tauopathies. Individuals with DS had higher DYRK1A levels than controls, although levels were lower in individuals with DS and with dementia. These data suggest that plasma DYRK1A levels could be used for early detection of at risk individuals of AD and for early detection of AD. We hypothesize that lack of increase of DYRK1A at middle age (40–50 years) could be a warning before the cognitive decline, reflecting increased risk for AD.

Swin Transformer-Based Segmentation and Multi-Scale Feature Pyramid Fusion Module for Alzheimer’s Disease with Machine Learning

Alzheimer Disease (AD) is the ordinary type of dementia which does not have any proper and efficient medication. Accurate classification and detection of AD helps to diagnose AD in an earlier stage, for that purpose machine learning and deep learning techniques are used in AD detection which observers both normal and abnormal brain and accurately detect AD in an early. For accurate detection of AD, we proposed a novel approach for detecting AD using MRI images. The proposed work includes three processes such as tri-level pre-processing, swin transfer based segmentation, and multi-scale feature pyramid fusion module-based AD detection.In pre-processing, noises are removed from the MRI images using Hybrid Kuan Filter and Improved Frost Filter (HKIF) algorithm, skull stripping is performed by Geodesic Active Contour (GAC) algorithm which removes the non-brain tissues that increases detection accuracy. Here, bias field correction is performed by Expectation-Maximization (EM) algorithm which removes the intensity non-uniformity. After completed pre-processing, we initiate segmentation process using Swin Transformer based Segmentation using Modified U-Net and Generative Adversarial Network (ST-MUNet) algorithm which segments the gray matter, white matter, and cerebrospinal fluid from the brain images by considering cortical thickness, color, texture, and boundary information which increases segmentation accuracy. After that, multi-scale feature extraction is performed by Multi-Scale Feature Pyramid Fusion Module using VGG16 (MSFP-VGG16) which extract the features in multi-scale which increases the detection and classification accuracy, based on the extracted features the brain image is classified into three classes such as Alzheimer Disease (AD), Mild Cognitive Impairment, and Normal. The simulation of this research is conducted by Matlab R2020a simulation tool, and the performance of this research is evaluated by ADNI dataset in terms of accuracy, specificity, sensitivity, confusion matrix, and positive predictive value.&#x0D;

Uncorrected errors and correct saccades in the antisaccade task distinguish between early-stage Alzheimer’s disease dementia, amnestic mild cognitive impairment, and normal aging

ABSTRACT Alzheimer’s disease (AD) dementia is a degenerative illness that is characterized by a gradual decline in cognitive abilities. Amnestic mild cognitive impairment (aMCI) is seen as a precursor to AD. The changes in antisaccade performance that can be seen in MCI may provide important clues in the early detection of AD. Therefore, the antisaccade deficits in AD and aMCI remain a research question. This study aimed to examine antisaccade responses and the relationship between antisaccade and cognitive function in AD, aMCI, and healthy controls (HC). This study included 30 patients with early-stage AD, 34 with aMCI, and 32 HC. Patients with AD showed higher rates of uncorrected error, anticipatory saccades and corrected errors, as well as decreased correct saccade rates, and shortened saccade latency compared to aMCI and HC in this study. Patients with aMCI exhibited increased rates of express saccades relative to HC. The antisaccade task and cognitive domains were found to be significantly related. Our study showed that the rate of correct saccades has the capacity to distinguish AD from HC with 87% sensitivity and 86% specificity (AUC = 0.93, p < 0.001). In addition, the rate of uncorrected errors was found to be capable of distinguishing AD from HC with 84% sensitivity and 83% specificity (AUC = 0.91, p < 0.001). This study presented promising findings that these parameters can be used clinically to differentiate AD and aMCI from healthy older individuals.

Alzheimer’s detection through neuro imaging and subsequent fusion for clinical diagnosis

In recent years, vast improvement has been observed in the field of medical research. Alzheimer's is the most common cause for dementia. Alzheimer's disease (AD) is a chronic disease with no cure, and it continues to pose a threat to millions of lives worldwide. The main purpose of this study is to detect the presence of AD from magnetic resonance imaging (MRI) scans through neuro imaging and to perform fusion process of both MRI and positron emission tomography (PET) scans of the same patient to obtain a fused image with more detailed information. Detection of AD is done by calculating the gray matter and white matter volumes of the brain and subsequently, a ratio of calculated volume is taken which helps the doctors in deciding whether the patient is affected with or without the disease. Image fusion is carried out after preliminary detection of AD for MRI scan along with PET scan. The main objective is to combine these two images into a single image which contains all the possible information together. The proposed approach yields better results with a peak signal to noise ratio of 60.6 dB, mean square error of 0.0176, entropy of 4.6 and structural similarity index of 0.8.

Functional Magnetic Resonance Spectroscopy of Lactate in Alzheimer Disease: A Comprehensive Review of Alzheimer Disease Pathology and the Role of Lactate.

Functional 1H magnetic resonance spectroscopy (fMRS) is a derivative of dynamic MRS imaging. This modality links physiologic metabolic responses with available activity and measures absolute or relative concentrations of various metabolites. According to clinical evidence, the mitochondrial glycolysis pathway is disrupted in many nervous system disorders, especially Alzheimer disease, resulting in the activation of anaerobic glycolysis and an increased rate of lactate production. Our study evaluates fMRS with J-editing as a cutting-edge technique to detect lactate in Alzheimer disease. In this modality, functional activation is highlighted by signal subtractions of lipids and macromolecules, which yields a much higher signal-to-noise ratio and enables better detection of trace levels of lactate compared with other modalities. However, until now, clinical evidence is not conclusive regarding the widespread use of this diagnostic method. The complex machinery of cellular and noncellular modulators in lactate metabolism has obscured the potential roles fMRS imaging can have in dementia diagnosis. Recent developments in MRI imaging such as the advent of 7 Tesla machines and new image reconstruction methods, coupled with a renewed interest in the molecular and cellular basis of Alzheimer disease, have reinvigorated the drive to establish new clinical options for the early detection of Alzheimer disease. Based on the latter, lactate has the potential to be investigated as a novel diagnostic and prognostic marker for Alzheimer disease.

Ensemble learning approach for multi-class classification of Alzheimer’s stages using magnetic resonance imaging

Alzheimer’s disease (AD) is a gradually progressing neurodegenerative irreversible disorder. Mild cognitive impairment convertible (MCIc) is the clinical forerunner of AD. Precise diagnosis of MCIc is essential for effective treatments to reduce the progressing rate of the disease. The other cognitive states included in this study are mild cognitive impairment non-convertible (MCInc) and cognitively normal (CN). MCInc is a stage in which aged people suffer from memory problems, but the stage will not lead to AD. The classification between MCIc and MCInc is crucial for the early detection of AD. In this work, an algorithm is proposed which concatenates the output layers of Xception, InceptionV3, and MobileNet pre-trained models. The algorithm is tested on the baseline T1-weighted structural magnetic resonance imaging (MRI) images obtained from Alzheimer’s disease neuroimaging initiative database. The proposed algorithm provided multi-class classification accuracy of 85%. Also, the proposed algorithm gave an accuracy of 85% for classifying MCIc vs MCInc, an accuracy of 94% for classifying AD vs CN, and an accuracy of 92% for classifying MCIc vs CN. The results exhibit that the proposed algorithm outruns other state-of-the-art methods for the multi-class classification and classification between MCIc and MCInc.

Early Detection of Alzheimer’s Using EEG

Alzheimer’s disease (AD) is a brain disorder which is irreversible as well as progressive. AD slowly destroys memory and thinking skills. The most common cause of Dementia is AD. Dementia that consists of (loss of Cognitive functioning – thinking, remembering and reasoning – and behavioural ability) is caused due to death of neurons due to AD, it has serious interference with Daily life. The chronicity of the disease is increasing rapidly in the world. The advancements in medical have initiated many research studies to automatically detect the disease by capturing the brain signals. The main aim of this paper is to help the doctors detect the Alzheimer’s disease at earliest and more number of patients can be prevented before irreversible changes occur in brain. In this paper we present some common features that will improve the detection of AD accurately from EEG signals in early stage of the disease. Apart from this, different signal decomposition methods including filtering into brain frequency bands, discrete wavelet transform (DWT) and empirical mode decomposition (EMD), and various classification algorithms including support vector machine (SVM), K-nearest neighbours (KNN) and regularized linear discriminant analysis (RLDA) are evaluated.

Prognosis of Biomarker of Alzheimer's Disease in the Function of the Retina and Secondary Molecular Structure Variation of the Retina and Brain.

Alzheimer's disease (AD) is one of the most serious neurodegenerative diseases in the globe. As a result, there is an acute need to discover indications that allow for early disease detection. There is growing scientific data showing the similarities between the eye and other central nervous system components, suggesting that information obtained in ophthalmic research might be valuable in the study and diagnosis of AD. Fifty male albino Wistar rats were separated into five groups: the first group served as control, and the other four groups of animals were administrated aluminium chloride (AlCl3) in a dose of 100 mg/kg body weight (b.w.) for 2, 4, 6, and 8 weeks, respectively. Insights into the function of the retina by electroretinogram (ERG) and the changes thought to have occurred in the molecular structure of the retina and brain using Fourier transform infrared spectroscopy (FTIR) as a result of AD progression induced by AlCl3 in rats were done. Moreover, the measurement of acetylcholinesterase (AchE) was done. After 6 and 8 weeks of AlCl3 injection, there was a substantial reduction (p ≤ 0.05) in a- and b-wave amplitudes and a significant rise (p ≤ 0.05) in implicit time compared to controls. A significant elevation (p ≤ 0.05) of AchE content was observed after 4, 6, and 8 weeks. FTIR revealed a significant increase (p ≤ 0.05) of β-turn and β-sheet content associated with significant decrease (p ≤ 0.05) of α-helix content for all groups administrated with AlCl3. Our findings suggest that retinal biomarkers such as ERG of the retina may be used as a screening tool for detection of AD. Secondary structural changes in the proteins of the retina and the brain were similar in AD rats' model and precede retinal dysfunction.

Plasma-based diagnostic and screening platform using a combination of biosensing signals in Alzheimer's disease

Using biosensor to screen for Alzheimer's disease (AD) facilitates early detection of AD with high sensitivity and accuracy. This approach overcomes the limitations of conventional AD diagnostic methods, such as neuropsychological assessment and neuroimaging analysis. Here, we propose a simultaneous analysis of signal combinations generated by four crucial AD biomarkers (Amyloid beta 1–40 (Aβ40), Aβ42, total tau 441 (tTau441), and phosphorylated tau 181 (pTau181)) by inducing a dielectrophoretic (DEP) force on fabricated interdigitated microelectrode (IME) sensor. By applying an optimal DEP force, our biosensor selectively concentrates and filters the plasma-based AD biomarkers, exhibiting high sensitivity (limit of detection <100 fM) and selectivity in the plasma-based AD biomarkers detection (p < 0.0001). Consequently, it is demonstrated that a complex combined signal comprising four AD-specific biomarker signals (Aβ40− Aβ42+ tTau441− pTau181) can differentiate between patients with AD and healthy subjects with high accuracy (78.85%) and precision (80.95%) (p < 0.0001).

Accurate Detection of Alzheimer's Disease Using Lightweight Deep Learning Model on MRI Data.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive impairment and aberrant protein deposition in the brain. Therefore, the early detection of AD is crucial for the development of effective treatments and interventions, as the disease is more responsive to treatment in its early stages. It is worth mentioning that deep learning techniques have been successfully applied in recent years to a wide range of medical imaging tasks, including the detection of AD. These techniques have the ability to automatically learn and extract features from large datasets, making them well suited for the analysis of complex medical images. In this paper, we propose an improved lightweight deep learning model for the accurate detection of AD from magnetic resonance imaging (MRI) images. Our proposed model achieves high detection performance without the need for deeper layers and eliminates the use of traditional methods such as feature extraction and classification by combining them all into one stage. Furthermore, our proposed method consists of only seven layers, making the system less complex than other previous deep models and less time-consuming to process. We evaluate our proposed model using a publicly available Kaggle dataset, which contains a large number of records in a small dataset size of only 36 Megabytes. Our model achieved an overall accuracy of 99.22% for binary classification and 95.93% for multi-classification tasks, which outperformed other previous models. Our study is the first to combine all methods used in the publicly available Kaggle dataset for AD detection, enabling researchers to work on a dataset with new challenges. Our findings show the effectiveness of our lightweight deep learning framework to achieve high accuracy in the classification of AD.