Early Detection Of Alzheimer's Disease Research Articles

Portable Electrochemical Impedance Spectroscopy Device Implementation for Alzheimer's Disease-Related Protein

Electrochemical impedance spectroscopy (EIS) is an electrochemical technique to measure the impedance varieties of a system based on the alternating current (AC) potential frequency. This technique is high accuracy and frequently used to distinguish the influences between different components. The charge transfer resistance and electric double layer phenomenon commonly occur in these components in an electrochemical system. A slight sinusoidal excitation with multiple frequency has been used to process the phenomenon to impedance values. These values can be simulated as an equivalent circuit to analyze the resistance, amplitude, and phase angle in the actual electrochemical cell [1].Alzheimer's disease (AD) is the most common form of amnesia. However, AD symptoms are difficult to be conscious of in the early stage, and current screening methods are hard to popularize. Current research generally agrees that AD has a considerable association with beta-amyloid (Aβ) precipitation [2]. Aβ is the proteolytic fragment derived from amyloid precursor proteins (APP). These proteins can be used to screen the pathogenesis of AD. Biological detection chips capture these proteins and then use commercial instruments for detection. The biological detection chips are fabricated using a screen-print-electrode (SPE) chip which contains three gold electrodes, and the fabrication steps show in Figure 1(A). 11-Mercaptoundecaoic acid (11-MUA) was used as a self-assembled monolayer (SAM) to functionalize the biological chip. 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDC)/N-hydroxysuccinimide (NHS) have been used to activate the functional group on SAM to bond the antibody for specific detection of Aβ. Finally, block the residual activated groups with ethanolamine. After modifying the sensors, the protein was applied for measuring immediately.This research aims to popularize a portable device that can achieve rapid and accurate monitoring of early AD. In this study, an AD5933 chip was utilized as the main chip for a portable EIS device to compare with the commercial instrument CHI920D. This integrated circuit chip can be applied as an impedance meter. The AD5933 is embedded on a board that contains an analog front-end (AFE) circuit. This AFE circuit can bias the direct current (DC) output from 1.48 V to 0 V and AC output from 1.98V p-p to 0.1V p-p. Also, we applied a 25 kHz clock signal for AD5933 to sweep frequency in 10 Hz to 20 Hz with 1 Hz resolution. This portable device is controlled by NI Myrio1900, and NI LabVIEW system with a laptop, as shown in Figure 1(B).To analyze the signal gathered from the Aβ, a simulated actual electrochemical cell with an equivalent current is needed. The test frequency has been fixed between 10 to 20 Hz due to the limitation of AD5933. The original Randles circuit is the frequently used circuit applied to impedance calculation [1]. To optimize the current there are some conditions that can be ignored. For instance, the resistance of redox solution (RΩ), which the resistance is too small compared with the whole electrochemical cell. The Warburg element (Zw) gives a low impact on the frequency interval. Thus, the circuit can be optimized and simplified as a simple parallel resistor-capacitor (RC) circuit. To evaluate system performance, the same biological detection chips were used for both portable devices (Figure 1(B)) and CHI920D (Figure 1(C)). Both calculation method is sample charge transfer resistance (Rct) divided by blocking charge transfer resistance (Rct). The correlation coefficient R2 of these sensors is 0.97 (Figure 1(D)), which means the portable system result is identical to the CHI920D.In conclusion, our research accomplished the system with low frequency and low AC and DC measuring, indicating a higher sensitivity for early AD detection. The limitation of this research is the biological detection chips. These modified chips are time-sensitivities that lead to conservation becoming difficult. The experimental results suggest that this portable is a potential candidate for wide screening of AD, and arranging the portable device in pairs might accomplish the point of care of AD. Furthermore, this device can also develop with different biomarkers, depending on additional antibody modification. Bard, A.J., et al., Fundamentals and applications. Electrochemical Methods, 2001. 2(482): p. 580-632.Hardy, J.A., et al., Alzheimer's disease: the amyloid cascade hypothesis. Science, 1992. 256(5054): p. 184-186. Figure 1

DE-JANet: A unified network based on dual encoder and joint attention for Alzheimer’s disease classification using multi-modal data

Structural magnetic resonance imaging (sMRI), which can reflect cerebral atrophy, plays an important role in the early detection of Alzheimer’s disease (AD). However, the information provided by analyzing only the morphological changes in sMRI is relatively limited, and the assessment of the atrophy degree is subjective. Therefore, it is meaningful to combine sMRI with other clinical information to acquire complementary diagnosis information and achieve a more accurate classification of AD. Nevertheless, how to fuse these multi-modal data effectively is still challenging. In this paper, we propose DE-JANet, a unified AD classification network that integrates image data sMRI with non-image clinical data, such as age and Mini-Mental State Examination (MMSE) score, for more effective multi-modal analysis. DE-JANet consists of three key components: (1) a dual encoder module for extracting low-level features from the image and non-image data according to specific encoding regularity, (2) a joint attention module for fusing multi-modal features, and (3) a token classification module for performing AD-related classification according to the fused multi-modal features. Our DE-JANet is evaluated on the ADNI dataset, with a mean accuracy of 0.9722 and 0.9538 for AD classification and mild cognition impairment (MCI) classification, respectively, which is superior to existing methods and indicates advanced performance on AD-related diagnosis tasks.

Breakthroughs in Alzheimer's Research: A Path to a More Promising Future?

Alzheimer's disease (AD) is a widespread neurodegenerative disorder with a significant global impact, affecting approximately 50 million individuals, and projections estimate that up to 152 million people will be affected by 2050. AD is characterized by beta-amyloid plaques and tau tangles in the brain, leading to cognitive decline. Recent research on AD has made significant strides, including the development of an "amyloid clock" biomarker that tracks AD progression through positron emission tomography (PET) scans. Surf4 and other genes have been discovered to play a role in regulating beta-amyloid toxicity, while inhibiting the enzyme hexokinase-2 has shown positive results in preclinical studies. New brain mapping techniques have identified early brain-based causes of cognitive changes in AD, and biomarkers such as neuronal pentraxin protein Nptx2 and astrocytic 7-subunit of the nicotinic acetylcholine receptors (7nAChRs) show potential for early detection. Other approaches, such as replenishing the enzyme Tip60, selectively degrading the modified protein p-p38 with PRZ-18002, and targeting the protein voltage-dependent anion channel-1 (VDAC1), have shown promise in enhancing cognitive function and preventing pathophysiological alterations linked to AD. Baseline blood samples and other biomarkers such as urine formic acid, p-tau 198, microRNAs, and glial fibrillary acidic protein (GFAP) have also been discovered for early detection and intervention of AD. Additionally, recent FDA approvals for medications such as aducanumab and lecanemab provide options for reducing AD symptoms and improving function, while clinical trials for dementia vaccines show promise for the nasal and beta-amyloid 40 vaccines as well as vaccinations targeting tau. These advancements in AD research, including biomarker discovery and the development of disease-modifying treatments, are crucial steps towards improving the lives of those affected by AD and finding a cure for this debilitating disease.

Non-invasive Biomarkers for Early Detection of Alzheimer's Disease: a New-Age Perspective.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects the elderly population. It gradually leads to memory loss, loss of thinking ability, and an overall cognitive decline. However, exhaustive literature is available to suggest that pathological changes in the brain occur decades before the first clinical symptoms appear. This review provides insight into the non-invasive biomarkers for early detection of AD that have been successfully studied in populations across the globe. These biomarkers have been detected in the blood, saliva, breath, and urine samples. Retinal imaging techniques are also reported. In this study, PubMed and Google scholar were the databases employed using keywords "Alzheimer's disease," "neurodegeneration," "non-invasive biomarkers," "early diagnosis," "blood-based biomarkers," and "preclinical AD," among others. The evaluation of these biomarkers will provide early diagnosis of AD in the preclinical stages due to their positive correlation with brain pathology in AD. Early diagnosis with reliable and timely intervention can effectively manage this disease.

Advanced brain imaging for the diagnosis of Alzheimer disease.

The purpose is to review the latest advances of brain imaging for the diagnosis of Alzheimer disease (AD). Brain imaging techniques provide valuable and complementary information to support the diagnosis of Alzheimer disease in clinical and research settings. The recent FDA accelerated approvals of aducanumab, lecanemab and donanemab made amyloid-PET critical in helping determine the optimal window for anti-amyloid therapeutic interventions. Tau-PET, on the other hand, is considered of key importance for the tracking of disease progression and for monitoring therapeutic interventions in clinical trials. PET imaging for microglial activation, astrocyte reactivity and synaptic degeneration are still new techniques only used in the research field, and more studies are needed to validate their use in the clinical diagnosis of AD. Finally, artificial intelligence has opened new prospective in the early detection of AD using MRI modalities. Brain imaging techniques using PET improve our understanding of the different AD-related pathologies and their relationship with each other along the course of disease. With more robust validation, machine learning and deep learning algorithms could be integrated with neuroimaging modalities to serve as valuable tools for clinicians to make early diagnosis and prognosis of AD.

Disrupted Balance of Gray Matter Volume and Directed Functional Connectivity in Mild Cognitive Impairment and Alzheimer's Disease.

Alterations in functional connectivity have been demonstrated in Alzheimer's disease (AD), an age-progressive neurodegenerative disorder that affects cognitive function; however, directional information flow has never been analyzed. This study aimed to determine changes in resting-state directional functional connectivity measured using a novel approach, granger causality density (GCD), in patients with AD, and mild cognitive impairment (MCI) and explore novel neuroimaging biomarkers for cognitive decline detection. In this study, structural MRI, resting-state functional magnetic resonance imaging, and neuropsychological data of 48 Alzheimer's Disease Neuroimaging Initiative participants were analyzed, comprising 16 patients with AD, 16 with MCI, and 16 normal controls. Volume-based morphometry (VBM) and GCD were used to calculate the voxel-based gray matter (GM) volumes and directed functional connectivity of the brain. We made full use of voxel-based between-group comparisons of VBM and GCD values to identify specific regions with significant alterations. In addition, Pearson's correlation analysis was conducted between directed functional connectivity and several clinical variables. Furthermore, receiver operating characteristic (ROC) analysis related to classification was performed in combination with VBM and GCD. In patients with cognitive decline, abnormal VBM and GCD (involving inflow and outflow of GCD) were noted in default mode network (DMN)-related areas and the cerebellum. GCD in the DMN midline core system, hippocampus, and cerebellum was closely correlated with the Mini- Mental State Examination and Functional Activities Questionnaire scores. In the ROC analysis combining VBM with GCD, the neuroimaging biomarker in the cerebellum was optimal for the early detection of MCI, whereas the precuneus was the best in predicting cognitive decline progression and AD diagnosis. Changes in GM volume and directed functional connectivity may reflect the mechanism of cognitive decline. This discovery could improve our understanding of the pathology of AD and MCI and provide available neuroimaging markers for the early detection, progression, and diagnosis of AD and MCI.

Precision medicine in neurodegeneration: the IHI-PROMINENT project.

Neurodegenerative diseases are one of the most important contributors to morbidity and mortality in the elderly. In Europe, over 14 million people are currently living with dementia, at a cost of over 400 billion EUR annually. Recent advances in diagnostics and approval for new pharmaceutical treatments for Alzheimer's disease (AD), the most common etiology of dementia, heralds the beginning of precision medicine in this field. However, their implementation will challenge an already over-burdened healthcare systems. There is a need for innovative digital solutions that can drive the related clinical pathways and optimize and personalize care delivery. Public-private partnerships are ideal vehicles to tackle these challenges. Here we describe the Innovative Health Initiative (IHI) public-private partnership project PROMINENT that has been initiated by connecting leading dementia researchers, medical professionals, dementia patients and their care partners with the latest innovative health technologies using a precision medicine based digital platform. The project builds upon the knowledge and already implemented digital tools from several collaborative initiatives that address new models for early detection, diagnosis, and monitoring of AD and other neurodegenerative disorders. The project aims to provide support to improvement efforts to each aspect of the care pathway including diagnosis, prognosis, treatment, and data collection for real world evidence and cost effectiveness studies. Ultimately the PROMINENT project is expected to lead to cost-effective care and improved health outcomes.

Nuclear Medicine-based Early Diagnosis of Alzheimer's Disease

The advent of pathology-specific diagnostic methods such as amyloid positron emission tomography (PET) and tau PET has enabled early and pre-symptomatic diagnosis of Alzheimer's disease. Imaging biomarkers in nuclear medicine play a key role in elucidating the pathogenesis of Alzheimer's disease and accelerating the development of disease-modifying drugs. In this study, we describe the current status and future prospects of nuclear medicine diagnostics aimed at early detection of Alzheimer's disease to delay and prevent disease progression.

Role of Neuropsychological Tests in Early Detection of Alzheimer's Disease

Longitudinal studies using neuropsychological tests have reported that cognitive changes may be observed up to 20 years before diagnosis of dementia. These findings suggest that neuropsychological tests may serve as potential screening tools during the preclinical phase of Alzheimer's disease. However, routine cognitive screening tests show limited sensitivity for prediction of dementia. Therefore, careful selection of appropriate neuropsychological tests is essential for effective screening of dementia during the preclinical stages.

How Can We Suspect a Preclinical Alzheimer's Disease Only Through History Taking and Neurological Examination?

Investigations of clinical biomarkers for discovering a preclinical Alzheimer's disease (AD) remain behind those of CSF and PET biomarkers. Almost all of today's checklists for early detection of AD are not generally useful for recognizing preclinical AD. Conversely, activities of daily living or language ability should be evaluated. In addition, focusing on risk factors for onset of AD, such as living alone/social isolation, hearing loss, hyposmia, loss of teeth, body weight changes, apathy/depression, and their combination, is valuable for the clinical investigation of preclinical AD.

Eye-tracking paradigms for the assessment of mild cognitive impairment: a systematic review.

Mild cognitive impairment (MCI), representing the 'transitional zone' between normal cognition and dementia, has become a novel topic in clinical research. Although early detection is crucial, it remains logistically challenging at the same time. While traditional pen-and-paper tests require in-depth training to ensure standardized administration and accurate interpretation of findings, significant technological advancements are leading to the development of procedures for the early detection of Alzheimer's disease (AD) and facilitating the diagnostic process. Some of the diagnostic protocols, however, show significant limitations that hamper their widespread adoption. Concerns about the social and economic implications of the increasing incidence of AD underline the need for reliable, non-invasive, cost-effective, and timely cognitive scoring methodologies. For instance, modern clinical studies report significant oculomotor impairments among patients with MCI, who perform poorly in visual paired-comparison tasks by ascribing less attentional resources to novel stimuli. To accelerate the Global Action Plan on the Public Health Response to Dementia 2017-2025, this work provides an overview of research on saccadic and exploratory eye-movement deficits among older adults with MCI. The review protocol was drafted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Electronic databases were systematically searched to identify peer-reviewed articles published between 2017 and 2022 that examined visual processing in older adults with MCI and reported gaze parameters as potential biomarkers. Moreover, following the contemporary trend for remote healthcare technologies, we reviewed studies that implemented non-commercial eye-tracking instrumentation in order to detect information processing impairments among the MCI population. Based on the gathered literature, eye-tracking-based paradigms may ameliorate the screening limitations of traditional cognitive assessments and contribute to early AD detection. However, in order to translate the findings pertaining to abnormal gaze behavior into clinical applications, it is imperative to conduct longitudinal investigations in both laboratory-based and ecologically valid settings.

Preliminary Analysis on the Effect of Different Denoising Techniques towards Texture Features of MRI Images of Alzheimer’s Disease

Early detection of Alzheimer’s disease (AD) has become one of the major research topics nowadays. The utilization of the computerized system may help medical experts to better understand and analyse the magnetic resonance imaging (MRI) images of AD patients for early detection. One of the commonly steps taken for the analysis of the image is image denoising using certain filters. However, finding shows that previous researchers use different approaches. This study aims to analyse the effect of different denoising techniques towards detection of Alzheimer’s disease. Data of two different groups (AD patients and Normal Control) were collected from Alzheimer's Disease Neuroimaging Initiative (ADNI). Then brain extraction and skull stripping were performed. Several image denoising techniques were implemented for both groups namely median filter, Wiener filter, histogram equalization filter and Gaussian low pass filter. After that, all images underwent texture feature extraction process and analysis were made to see the effect of those denoising techniques towards the features of Gray-Level Co-occurrence Matrix (GLCM) extracted which are the contract, correlation, energy and homogeneity features. The result shows that the use of mentioned denoising filters do not give effect to the extracted features. All values of contrast, correlation, energy and homogeneity cannot clearly distinguish between AD and NC groups. Without any filters on the other hand, contrast feature gives the best output in distinguishing between AD and NC groups with the normalized value of 0.1. The result from this study may help in thorough investigation of other features or hybrid features that could be used for the purpose of detection and classification of AD.

Reduced slow-wave activity and autonomic dysfunction during sleep precede cognitive deficits in Alzheimer’s disease transgenic mice

Occurrence of amyloid-β (Aβ) aggregation in brain begins before the clinical onset of Alzheimer’s disease (AD), as preclinical AD. Studies have reported that sleep problems and autonomic dysfunction associate closely with AD. However, whether they, especially the interaction between sleep and autonomic function, play critical roles in preclinical AD are unclear. Therefore, we investigated how sleep patterns and autonomic regulation at different sleep–wake stages changed and whether they were related to cognitive performance in pathogenesis of AD mice. Polysomnographic recordings in freely-moving APP/PS1 and wild-type (WT) littermates were collected to study sleep patterns and autonomic function at 4 (early disease stage) and 8 months of age (advanced disease stage), cognitive tasks including novel object recognition and Morris water maze were performed, and Aβ levels in brain were measured. APP/PS1 mice at early stage of AD pathology with Aβ aggregation but without significant differences in cognitive performance had frequent sleep–wake transitions, lower sleep-related delta power percentage, lower overall autonomic activity, and lower parasympathetic activity mainly during sleep compared with WT mice. The same phenomenon was observed in advanced-stage APP/PS1 mice with significant cognitive deficits. In mice at both disease stages, sleep-related delta power percentage correlated positively with memory performance. At early stage, memory performance correlated positively with sympathetic activity during wakefulness; at advanced stage, memory performance correlated positively with parasympathetic activity during both wakefulness and sleep. In conclusion, sleep quality and distinction between wake- and sleep-related autonomic function may be biomarkers for early AD detection.

Longitudinal changes in hippocampal texture from healthy aging to Alzheimer's disease.

Early detection of Alzheimer's disease is essential to develop preventive treatment strategies. Detectible change in brain volume emerges relatively late in the pathogenic progression of disease, but microstructural changes caused by early neuropathology may cause subtle changes in the MR signal, quantifiable using texture analysis. Texture analysis quantifies spatial patterns in an image, such as smoothness, randomness and heterogeneity. We investigated whether the MRI texture of the hippocampus, an early site of Alzheimer's disease pathology, is sensitive to changes in brain microstructure before the onset of cognitive impairment. We also explored the longitudinal trajectories of hippocampal texture across the Alzheimer's continuum in relation to hippocampal volume and other biomarkers. Finally, we assessed the ability of texture to predict future cognitive decline, over and above hippocampal volume. Data were acquired from the Alzheimer's Disease Neuroimaging Initiative. Texture was calculated for bilateral hippocampi on 3T T1-weighted MRI scans. Two hundred and ninety-three texture features were reduced to five principal components that described 88% of total variance within cognitively unimpaired participants. We assessed cross-sectional differences in these texture components and hippocampal volume between four diagnostic groups: cognitively unimpaired amyloid-β- (n = 406); cognitively unimpaired amyloid-β+ (n = 213); mild cognitive impairment amyloid-β+ (n = 347); and Alzheimer's disease dementia amyloid-β+ (n = 202). To assess longitudinal texture change across the Alzheimer's continuum, we used a multivariate mixed-effects spline model to calculate a 'disease time' for all timepoints based on amyloid PET and cognitive scores. This was used as a scale on which to compare the trajectories of biomarkers, including volume and texture of the hippocampus. The trajectories were modelled in a subset of the data: cognitively unimpaired amyloid-β- (n = 345); cognitively unimpaired amyloid-β+ (n = 173); mild cognitive impairment amyloid-β+ (n = 301); and Alzheimer's disease dementia amyloid-β+ (n = 161). We identified a difference in texture component 4 at the earliest stage of Alzheimer's disease, between cognitively unimpaired amyloid-β- and cognitively unimpaired amyloid-β+ older adults (Cohen's d = 0.23, Padj = 0.014). Differences in additional texture components and hippocampal volume emerged later in the disease continuum alongside the onset of cognitive impairment (d = 0.30-1.22, Padj < 0.002). Longitudinal modelling of the texture trajectories revealed that, while most elements of texture developed over the course of the disease, noise reduced sensitivity for tracking individual textural change over time. Critically, however, texture provided additional information than was provided by volume alone to more accurately predict future cognitive change (d = 0.32-0.63, Padj < 0.0001). Our results support the use of texture as a measure of brain health, sensitive to Alzheimer's disease pathology, at a time when therapeutic intervention may be most effective.

Three Class Classification of Alzheimer's Disease Using Deep Neural Networks.

Alzheimer's disease (AD) is a prevalent type of dementia that can cause neurological brain disorders, poor decision making, impaired memory, mood swings, unstable emotions, and personality change. Deep neural networks are proficient in classifying Alzheimer's disease based on MRI images. This classification assists human experts in diagnosing AD and predicts its future progression. The paper proposes various Deep Neural Networks (DNN) for early AD detection to save cost and time for doctors, radiologists, and caregivers. A 3330-image-based Kaggle dataset is used to train the DNN, including 52 images of AD, 717 images of Mild Cognitive Impairment (MCI), and the remaining images of Cognitive Normal (CN). Stratified partitioning splits the dataset into 80% and 20% proportions for training and validation datasets. Proposed models include DenseNet169, DenseNet201, and Res- Net152 DNNs with additional three fully-connected layers and softmax and Kullback Leibler Divergence (KLD) loss function. These models are trained considering pre-trained, partially pre-trained, and fully re-trained extended base models. The KLD loss function reduces the error and increases accuracy for all models. The partially pre-trained DenseNet201 model outperformed all the other models. DenseNet201 gives the highest accuracy of 99.98% for training, 99.07% for validation, and 95.66% for test datasets. The DenseNet201 model has the highest accuracy in comparison to other state-of-artmethods.

Urinary Cytokines as Potential Biomarkers of Mild Cognitive Impairment and Alzheimer's Disease: A Pilot Study.

Alzheimer's disease (AD) is the most common form of dementia, accounting for 80% of all cases. Mild cognitive impairment (MCI) is a transitional state between normal aging and AD. Early detection is crucial, as irreversible brain damage occurs before symptoms manifest. This study aimed to identify potential biomarkers for early detection of AD by analyzing urinary cytokine concentrations. We investigated 37 cytokines in AD, MCI, and cognitively normal individuals (NC), assessing their associations with AD development. Urinary cytokine concentrations were measured in AD (n = 25), MCI (n = 25), and NC (n = 26) patients. IL6ST and MMP-2 levels were compared between AD and NC, while TNFRSF8, IL6ST, and IL-19 were assessed in AD versus MCI. Diagnostic models distinguished AD from NC, and in-silico analysis explored molecular mechanisms related to AD. Significant perturbations in IL6ST and MMP-2 concentrations were observed in AD urine compared to NC, suggesting their potential as biomarkers. TNFRSF8, IL6ST, and IL-19 differed significantly between AD and MCI, implicating them in disease progression. Diagnostic models exhibited promising performance (AUC: 0.59-0.79, sensitivity: 0.72-0.80, specificity: 0.56-0.78) in distinguishing AD from NC. In-silico analysis revealed molecular insights, including relevant non-coding RNAs, microRNAs, and transcription factors. This study establishes significant associations between urinary cytokine concentrations and AD and MCI. IL6ST, MMP-2, TNFRSF8, IL6ST, and IL-19 emerge as potential biomarkers for early detection of AD. In-silico analysis enhances understanding of molecular mechanisms in AD. Further validation and exploration of these biomarkers in larger cohorts are warranted to assess their clinical utility.

SiC Electrochemical Sensor Validation for Alzheimer Aβ42 Antigen Detection.

Alzheimer's disease (AD) is a neurodegenerative disease with only late-stage detection; thus, diagnosis is made when it is no longer possible to treat the disease, only its symptoms. Consequently, this often leads to caregivers who are the patient's relatives, which adversely impacts the workforce along with severely diminishing the quality of life for all involved. It is, therefore, highly desirable to develop a fast, effective and reliable sensor to enable early-stage detection in an attempt to reverse disease progression. This research validates the detection of amyloid-beta 42 (Aβ42) using a Silicon Carbide (SiC) electrode, a fact that is unprecedented in the literature. Aβ42 is considered a reliable biomarker for AD detection, as reported in previous studies. To validate the detection with a SiC-based electrochemical sensor, a gold (Au) electrode-based electrochemical sensor was used as a control. The same cleaning, functionalization and Aβ1-28 antibody immobilization steps were used on both electrodes. Sensor validation was carried out by means of Cyclic Voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS) aiming to detect an 0.5 µg·mL-1 Aβ42 concentration in 0.1 M buffer solution as a proof of concept. A repeatable peak directly related to the presence of Aβ42 was observed, indicating that a fast SiC-based electrochemical sensor was constructed and may prove to be a useful approach for the early detection of AD.

Deep learning and image processing-based early detection of Alzheimer disease in cognitively normal individuals

Deep learning and image processing-based early detection of Alzheimer disease in cognitively normal individuals

Single-platform, attomolar detection of multiple biomarkers by flexible SERS sensor.

Early detection of Alzheimer's disease (AD), is critical for better healthcare management. Herein, we demonstrate a Surface Enhanced Raman Spectroscopy (SERS) active sensor for highly sensitive and selective detection of β-Amyloid Peptide (Aβ1-42), a biomarker of Alzheimer's disease. Polyacrylonitrile (PAN) nanofiber mats, containing purine-based ligand (L; 0mg (P1), 50mg (P2), and 100mg (P3)) were prepared by electrospinning followed by functionalization with silver nanoparticles (AgNPs). The fabricated SERS sensors were employed for the detection of Rhodamine 6G (Rh-6G) dye for optimization and the highest sensitivity was achieved on P3/AgNPs SERS sensor. The P3/AgNPs sensor was chosen for the detection of Aβ1-42 and human Insulin (HI). The limit of detection (LoD) was found to be 76 × 10-18M and 26× 10-18M for Aβ1-42 and HI, respectively. The sensitivity achieved is one order improved for Aβ1-42 and four orders for HI when compared with reported values. Also, demonstrated the selectivity of the P3/AgNPs sensor by testing a simulated cerebrospinal fluid (CSF) and achieved easily identifiable peaks of Aβ1-42 among the noise of HI and BSA. This approach could be extended to develop ultra-sensitive flexible SERS sensors for the facile detection of multiple biomarkers on a single platform with excellent sensitivity, selectivity and stability.

Effects of T217 Phosphorylation on Tau Localization and Microtubule Binding

AbstractBackgroundBiomarker‐based early detection of Alzheimer’s disease (AD) is necessary for developing therapies that counter AD symptom onset. Promising biomarkers include tau phosphorylated at T181, T217 or T231, levels of which can be measured in cerebrospinal fluid or plasma. Little is known about functional consequences of tau phosphorylation at these sites, though, especially for taupT217. We thus sought to identify where taupT217 is localized in the brains of human AD patients and AD model mice, and in cultured mouse neurons, and, in a more functional context, to determine if phosphorylation at T217 affects tau binding to microtubules.MethodBrain tissue from human AD patients and AD model mice, and cultured mouse neurons were analyzed by western blotting and immunofluorescence microscopy to compare the intracellular localization of taupT217, taupT181, taupT231 (AT180 epitope) and taupS396,pS404 (PHF1 epitope). EGFP‐tau (2N4R) was expressed in CV‐1 African green monkey kidney fibroblasts as wild type (WT), T217A non‐phosphorylatable, and T217E pseudo‐phosphorylated tau versions of the fluorescent fusion protein. Fluorescence recovery after photobleaching (FRAP) microscopy was used to assay turnover rates of the EGFP‐tau fusion proteins on microtubules, a surrogate measurement of affinity.ResultIn human AD brains and AD model mice, taupT217 was abundant in the somatodendritic compartment and co‐localized with neurofibrillary tangles. In cultured neurons, taupT217 was concentrated in somatodendritic puncta that were closely associated with dendritic spines. Both in vivo and in cultured neurons taupT217 co‐localized only partially with taupT181 and taupT231. Exposure of cultured neurons to extracellular oligomers of tau, but not amyloid‐β, increased intracellular taupT217. In CV‐1 cells, EGFP‐tauT217E recovered from photobleaching faster than EGFP‐tauT217A or EGFP‐tauT217.ConclusionIntracellular taupT217 is enriched at locations that normally lack much tau and is increased by extracellular tau oligomers. TaupT217 is enriched near dendritic spines, suggesting a possible role for T217 phosphorylation in altering synaptic activity. The faster fluorescence recovery of EGFP‐tauT217E compared to EGFP‐tauT217A or EGFP‐tauT217 implies that T217 phosphorylation reduces tau’s affinity for microtubules.