Diagnosis Of Alzheimer's Disease Research Articles

A probe for NIR-II imaging and multimodal analysis of early Alzheimer’s disease by targeting CTGF

To date, earlier diagnosis of Alzheimer’s disease (AD) is still challenging. Recent studies revealed the elevated expression of connective tissue growth factor (CTGF) in AD brain is an upstream regulator of amyloid-beta (Aβ) plaque, thus CTGF could be an earlier diagnostic biomarker of AD than Aβ plaque. Herein, we develop a peptide-coated gold nanocluster that specifically targets CTGF with high affinity (KD ~ 21.9 nM). The probe can well penetrate the blood-brain-barrier (BBB) of APP/PS1 transgenic mice at early-stage (earlier than 3-month-old) in vivo, allowing non-invasive NIR-II imaging of CTGF when there is no appearance of Aβ plaque deposition. Notably, this probe can also be applied to measuring CTGF on postmortem brain sections by multimodal analysis, including fluorescence imaging, peroxidase-like chromogenic imaging, and ICP-MS quantitation, which enables distinguishment between the brains of AD patients and healthy people. This probe possesses great potential for precise diagnosis of earlier AD before Aβ plaque formation.

Acceptable performance of blood biomarker tests of amyloid pathology - recommendations from the Global CEO Initiative on Alzheimer's Disease.

Anti-amyloid treatments for early symptomatic Alzheimer disease have recently become clinically available in some countries, which has greatly increased the need for biomarker confirmation of amyloid pathology. Blood biomarker (BBM) tests for amyloid pathology are more acceptable, accessible and scalable than amyloid PET or cerebrospinal fluid (CSF) tests, but have highly variable levels of performance. The Global CEO Initiative on Alzheimer's Disease convened a BBM Workgroup to consider the minimum acceptable performance of BBM tests for clinical use. Amyloid PET status was identified as the reference standard. For use as a triaging test before subsequent confirmatory tests such as amyloid PET or CSF tests, the BBM Workgroup recommends that a BBM test has a sensitivity of ≥90% with a specificity of ≥85% in primary care and ≥75-85% in secondary care depending on the availability of follow-up testing. For use as a confirmatory test without follow-up tests, a BBM test should have performance equivalent to that of CSF tests - a sensitivity and specificity of ~90%. Importantly, the predictive values of all biomarker tests vary according to the pre-test probability of amyloid pathology and must be interpreted in the complete clinical context. Use of BBM tests that meet these performance standards could enable more people to receive an accurate and timely Alzheimer disease diagnosis and potentially benefit from new treatments.

Extracellular Vesicle-Enriched miRNA-Biomarkers Show Improved Utility for Detecting Alzheimer's Disease Dementia and Medial Temporal Atrophy.

Emerging diagnostic modalities suggest that miRNA profiles within extracellular vesicles (EVs) isolated from peripheral blood specimens may provide a non-invasive diagnostic alternative for dementia and neurodegenerative disorders. Given that EVs confer a protective environment against miRNA enzymatic degradation, the miRNAs enriched in the EV fraction of blood samples could serve as more stable and clinically relevant biomarkers compared to those obtained from serum. To compare miRNAs isolated from EVs versus serum in blood taken from Alzheimer's disease (AD) dementia patients and control cohorts. We compared 25 AD patients to 34 individuals who exhibited no cognitive impairments (NCI). Subjects were Singapore residents with Chinese heritage. miRNAs purified from serum versus blood-derived EVs were analyzed for associations with AD dementia and medial temporal atrophy detected by magnetic resonance imaging. Compared to serum-miRNAs, we identified almost twice as many EV-miRNAs associated with AD dementia, and they also correlated more significantly with medial temporal atrophy, a neuroimaging marker of AD-brain pathology. Wefurther developed combination panels of serum-miRNAs and EV-miRNAs with improved performance in identifying AD dementia. Dominant in both panels was miRNA-1290. This data indicates that miRNA profiling from EVs offers diagnostic superiority. This underscores the role of EVs as vectors harboring prognostic biomarkers for neurodegenerative disorders and suggests their potential in yielding novel biomarkers for AD diagnosis.

A deep learning model for generating [18F]FDG PET Images from early-phase [18F]Florbetapir and [18F]Flutemetamol PET images

IntroductionAmyloid-β (Aβ) plaques is a significant hallmark of Alzheimer's disease (AD), detectable via amyloid-PET imaging. The Fluorine-18-Fluorodeoxyglucose ([18F]FDG) PET scan tracks cerebral glucose metabolism, correlated with synaptic dysfunction and disease progression and is complementary for AD diagnosis. Dual-scan acquisitions of amyloid PET allows the possibility to use early-phase amyloid-PET as a biomarker for neurodegeneration, proven to have a good correlation to [18F]FDG PET. The aim of this study was to evaluate the added value of synthesizing the later from the former through deep learning (DL), aiming at reducing the number of PET scans, radiation dose, and discomfort to patients.MethodsA total of 166 subjects including cognitively unimpaired individuals (N = 72), subjects with mild cognitive impairment (N = 73) and dementia (N = 21) were included in this study. All underwent T1-weighted MRI, dual-phase amyloid PET scans using either Fluorine-18 Florbetapir ([18F]FBP) or Fluorine-18 Flutemetamol ([18F]FMM), and an [18F]FDG PET scan. Two transformer-based DL models called SwinUNETR were trained separately to synthesize the [18F]FDG from early phase [18F]FBP and [18F]FMM (eFBP/eFMM). A clinical similarity score (1: no similarity to 3: similar) was assessed to compare the imaging information obtained by synthesized [18F]FDG as well as eFBP/eFMM to actual [18F]FDG. Quantitative evaluations include region wise correlation and single-subject voxel-wise analyses in comparison with a reference [18F]FDG PET healthy control database. Dice coefficients were calculated to quantify the whole-brain spatial overlap between hypometabolic ([18F]FDG PET) and hypoperfused (eFBP/eFMM) binary maps at the single-subject level as well as between [18F]FDG PET and synthetic [18F]FDG PET hypometabolic binary maps.ResultsThe clinical evaluation showed that, in comparison to eFBP/eFMM (average of clinical similarity score (CSS) = 1.53), the synthetic [18F]FDG images are quite similar to the actual [18F]FDG images (average of CSS = 2.7) in terms of preserving clinically relevant uptake patterns. The single-subject voxel-wise analyses showed that at the group level, the Dice scores improved by around 13% and 5% when using the DL approach for eFBP and eFMM, respectively. The correlation analysis results indicated a relatively strong correlation between eFBP/eFMM and [18F]FDG (eFBP: slope = 0.77, R2 = 0.61, P-value < 0.0001); eFMM: slope = 0.77, R2 = 0.61, P-value < 0.0001). This correlation improved for synthetic [18F]FDG (synthetic [18F]FDG generated from eFBP (slope = 1.00, R2 = 0.68, P-value < 0.0001), eFMM (slope = 0.93, R2 = 0.72, P-value < 0.0001)).ConclusionWe proposed a DL model for generating the [18F]FDG from eFBP/eFMM PET images. This method may be used as an alternative for multiple radiotracer scanning in research and clinical settings allowing to adopt the currently validated [18F]FDG PET normal reference databases for data analysis.

Research Progress on the Pathogenesis, Diagnosis, and Drug Therapy of Alzheimer's Disease.

As the population ages worldwide, Alzheimer's disease (AD), the most prevalent kind of neurodegenerative disorder among older people, has become a significant factor affecting quality of life, public health, and economies. However, the exact pathogenesis of Alzheimer's remains elusive, and existing highly recognized pathogenesis includes the amyloid cascade hypothesis, Tau neurofibrillary tangles hypothesis, and neuroinflammation hypothesis. The major diagnoses of Alzheimer's disease include neuroimaging positron emission computed tomography, magnetic resonance imaging, and cerebrospinal fluid molecular diagnosis. The therapy of Alzheimer's disease primarily relies on drugs, and the approved drugs on the market include acetylcholinesterase drugs, glutamate receptor antagonists, and amyloid-β monoclonal antibodies. Still, the existing drugs can only alleviate the symptoms of the disease and cannot completely reverse it. This review aims to summarize existing research results on Alzheimer's disease pathogenesis, diagnosis, and drug therapy, with the objective of facilitating future research in this area.

Effects of intensive lifestyle changes on the progression of mild cognitive impairment or early dementia due to Alzheimer’s disease: a randomized, controlled clinical trial

BackgroundEvidence links lifestyle factors with Alzheimer’s disease (AD). We report the first randomized, controlled clinical trial to determine if intensive lifestyle changes may beneficially affect the progression of mild cognitive impairment (MCI) or early dementia due to AD.MethodsA 1:1 multicenter randomized controlled phase 2 trial, ages 45-90 with MCI or early dementia due to AD and a Montreal Cognitive Assessment (MoCA) score of 18 or higher. The primary outcome measures were changes in cognition and function tests: Clinical Global Impression of Change (CGIC), Alzheimer’s Disease Assessment Scale (ADAS-Cog), Clinical Dementia Rating–Sum of Boxes (CDR-SB), and Clinical Dementia Rating Global (CDR-G) after 20 weeks of an intensive multidomain lifestyle intervention compared to a wait-list usual care control group. ADAS-Cog, CDR-SB, and CDR-Global scales were compared using a Mann-Whitney-Wilcoxon rank-sum test, and CGIC was compared using Fisher’s exact test. Secondary outcomes included plasma Aβ42/40 ratio, other biomarkers, and correlating lifestyle with the degree of change in these measures.ResultsFifty-one AD patients enrolled, mean age 73.5. No significant differences in any measures at baseline. Only two patients withdrew. All patients had plasma Aβ42/40 ratios <0.0672 at baseline, strongly supporting AD diagnosis. After 20 weeks, significant between-group differences in the CGIC (p= 0.001), CDR-SB (p= 0.032), and CDR Global (p= 0.037) tests and borderline significance in the ADAS-Cog test (p= 0.053). CGIC, CDR Global, and ADAS-Cog showed improvement in cognition and function and CDR-SB showed significantly less progression, compared to the control group which worsened in all four measures. Aβ42/40 ratio increased in the intervention group and decreased in the control group (p = 0.003). There was a significant correlation between lifestyle and both cognitive function and the plasma Aβ42/40 ratio. The microbiome improved only in the intervention group (p <0.0001).ConclusionsComprehensive lifestyle changes may significantly improve cognition and function after 20 weeks in many patients with MCI or early dementia due to AD.Trial registrationApproved by Western Institutional Review Board on 12/31/2017 (#20172897) and by Institutional Review Boards of all sites. This study was registered retrospectively with clinicaltrials.gov on October 8, 2020 (NCT04606420, ID: 20172897).

An effectual recommendation model using hybrid learning models for early detection of Alzheimer’s disease

Alzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of individuals worldwide, causing progressive cognitive decline. Early prediction and diagnosis the AD accurately is crucial for effective intervention and treatment. In this study, we propose a comprehensive framework for AD prediction using various techniques, including preprocessing and denoising with Multilayer Perceptron (MLP) and Ant Colony Optimization (ACO), segmentation using U-Net, and classification with Spatial Pyramid Pooling Network (SPPNet). Furthermore, we employ Convolutional Neural Network (CNN) with SPPNet for training and develop a chatbot for recommendation based on MRI data input. The preprocessing and denoising techniques play a vital role in enhancing the quality of the input data. MLP is utilized for preprocessing, where it effectively handles feature extraction and noise reduction. ACO is employed for denoising, optimizing the data to improve the signal-to-noise ratio, and enhancing the overall performance of subsequent stages. For accurate segmentation of brain regions, we employ the U-Net architecture, which has shown remarkable success in medical image segmentation tasks. U-Net effectively identifies the regions of interest, aiding in subsequent classification stages. The classification phase utilizes SPPNet, a deep learning model known for its ability to capture spatial information at multiple scales. SPPNet extracts features from segmented brain regions, enabling robust classification of AD and non-AD cases. To enhance the training process, we employ CNN with SPPNet, leveraging the power of convolutional layers to capture intricate patterns and improve predictive accuracy. The CNN-SPPNet model is trained on a large dataset of MRI scans, enabling it to learn complex representations and make accurate predictions. Hence the proposed work can be integrated with a chatbot that takes MRI data as input and provides recommendations based on the predicted AD probability. Experimental evaluation shows that the combination of preprocessing, denoising, segmentation, and classification offers a comprehensive solution for accurate and efficient AD diagnosis and management.

Navigating the future of Alzheimer’s care in Ireland - a service model for disease-modifying therapies in small and medium-sized healthcare systems

BackgroundA new class of antibody-based drug therapy with the potential for disease modification is now available for Alzheimer’s disease (AD). However, the complexity of drug eligibility, administration, cost, and safety of such disease modifying therapies (DMTs) necessitates adopting new treatment and care pathways. A working group was convened in Ireland to consider the implications of, and health system readiness for, DMTs for AD, and to describe a service model for the detection, diagnosis, and management of early AD in the Irish context, providing a template for similar small-medium sized healthcare systems.MethodsA series of facilitated workshops with a multidisciplinary working group, including Patient and Public Involvement (PPI) members, were undertaken. This informed a series of recommendations for the implementation of new DMTs using an evidence-based conceptual framework for health system readiness based on [1] material resources and structures and [2] human and institutional relationships, values, and norms.ResultsWe describe a hub-and-spoke model, which utilises the existing dementia care ecosystem as outlined in Ireland’s Model of Care for Dementia, with Regional Specialist Memory Services (RSMS) acting as central hubs and Memory Assessment and Support Services (MASS) functioning as spokes for less central areas. We provide criteria for DMT referral, eligibility, administration, and ongoing monitoring.ConclusionsHealthcare systems worldwide are acknowledging the need for advanced clinical pathways for AD, driven by better diagnostics and the emergence of DMTs. Despite facing significant challenges in integrating DMTs into existing care models, the potential for overcoming challenges exists through increased funding, resources, and the development of a structured national treatment network, as proposed in Ireland’s Model of Care for Dementia. This approach offers a replicable blueprint for other healthcare systems with similar scale and complexity.

Improved Affinity: A Customized Fluorescent Probe for the Rapid Detection of Butyrylcholinesterase

This article presents the distinctive butyrylcholinesterase (BChE) fluorescent probe P5, designed via a targeting-site method. This method was proposed to enhance the affinity of the probe for BChE by targeting the peripheral anionic site (PAS) of BChE. By mimicking the natural substrate butyrylcholine, the structure of the probe was optimized by introducing a positive charge. Fluorescent probe P5, selected from a series of designed fluorescent probes P1–P6, exhibited excellent affinity and specificity towards BChE, enabling rapid detection within 5 min with a low detection limit of 16.7 ng/mL. Furthermore, this probe can distinguish between normal cells and Alzheimer’s disease (AD) model cells, and demonstrated good imaging results in a P12 cell AD model. The results of this study indicate that this novel fluorescent probe could serve as a promising tool for the rapid detection of BChE and accurate AD diagnosis.

Oligomeric amyloid-β targeted contrast agent for MRI evaluation of Alzheimer's disease mouse models.

Oligomeric amyloid beta (oAβ) is a toxic factor that acts in the early stage of Alzheimer's disease (AD) and may initiate the pathologic cascade. Therefore, detecting oAβ has a crucial role in the early diagnosis, monitoring, and treatment of AD. The purpose of this study was to evaluate MRI signal changes in different mouse models and the time-dependent signal changes using our novel gadolinium (Gd)-dodecane tetraacetic acid (DOTA)- ob5 aptamer contrast agent. We developed an MRI contrast agent by conjugating Gd-DOTA-DNA aptamer called ob5 to evaluate its ability to detect oAβ deposits in the brain using MRI. A total of 10 control mice, 9 3xTg AD mice, and 11 APP/PS/Tau AD mice were included in this study, with the age of each model being 16 or 36 weeks. A T1-weighted image was acquired at the time points before (0min) and after injection of the contrast agent at 5, 10, 15, 20, and 25min. The analyses were performed to compare MRI signal differences among the three groups and the time-dependent signal differences in different mouse models. Both 3xTg AD and APP/PS/Tau AD mouse models had higher signal enhancement than control mice at all scan-time points after injection of our contrast media, especially in bilateral hippocampal areas. In particular, all Tg AD mouse models aged 16 weeks showed a higher contrast enhancement than those aged 36 weeks. For 3xTg AD and APP/PS/Tau AD groups, the signal enhancement was significantly different among the five time points (0min, 5min, 10min, 15min, 20min, and 25min) in multiple ROI areas, typically in the bilateral hippocampus, left thalamus, and left amygdala. The findings of this study suggest that the expression of the contrast agent in different AD models demonstrates its translational flexibility across different species. The signal enhancement peaked around 15-20min after injection of the contrast agent. Therefore, our novel contrast agent targeting oAβ has the potential ability to diagnose early AD and monitor the progression of AD.

Latent diffusion model-based MRI superresolution enhances mild cognitive impairment prognostication and Alzheimer's disease classification

IntroductionTimely diagnosis and prognostication of Alzheimer's disease (AD) and mild cognitive impairment (MCI) are pivotal for effective intervention. Artificial intelligence (AI) in neuroradiology may aid in such appropriate diagnosis and prognostication. This study aimed to evaluate the potential of novel diffusion model-based AI for enhancing AD and MCI diagnosis through superresolution (SR) of brain magnetic resonance (MR) images. Methods1.5T brain MR scans of patients with AD or MCI and healthy controls (NC) from Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) were superresolved to 3T using a novel diffusion model-based generative AI (d3T*) and a convolutional neural network-based model (c3T*). Comparisons of image quality to actual 1.5T and 3T MRI were conducted based on signal-to-noise ratio (SNR), naturalness image quality evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Voxel-based volumetric analysis was then conducted to study whether 3T* images offered more accurate volumetry than 1.5T images. Binary and multiclass classifications of AD, MCI, and NC were conducted to evaluate whether 3T* images offered superior AD classification performance compared to actual 1.5T MRI. Moreover, CNN-based classifiers were used to predict conversion of MCI to AD, to evaluate the prognostication performance of 3T* images. The classification performances were evaluated using accuracy, sensitivity, specificity, F1 score, Matthews correlation coefficient (MCC), and area under the receiver-operating curves (AUROC). ResultsAnalysis of variance (ANOVA) detected significant differences in image quality among the 1.5T, c3T*, d3T*, and 3T groups across all metrics. Both c3T* and d3T* showed superior image quality compared to 1.5T MRI in NIQE and BRISQUE with statistical significance. While the hippocampal volumes measured in 3T* and 3T images were not significantly different, the hippocampal volume measured in 1.5T images showed significant difference. 3T*-based AD classifications showed superior performance across all performance metrics compared to 1.5T-based AD classification. Classification performance between d3T* and actual 3T was not significantly different. 3T* images offered superior accuracy in predicting the conversion of MCI to AD than 1.5T images did. ConclusionsThe diffusion model-based MRI SR enhances the resolution of brain MR images, significantly improving diagnostic and prognostic accuracy for AD and MCI. Superresolved 3T* images closely matched actual 3T MRIs in quality and volumetric accuracy, and notably improved the prediction performance of conversion from MCI to AD.

Iterative Design of Near-Infrared Fluorescent Probes for Early Diagnosis of Alzheimer's Disease by Targeting Aβ Oligomers.

Amyloid-β oligomers (AβOs), crucial toxic proteins in early Alzheimer's disease (AD), precede the formation of Aβ plaques and cognitive impairment. In this context, we present our iterative process for developing novel near-infrared fluorescent (NIRF) probes specifically targeting AβOs, aimed at early AD diagnosis. An initial screening identified compound 18 as being highly selective for AβOs. Subsequent analysis revealed that compound 20 improved serum stability while retaining affinity for AβOs. The most promising iteration, compound 37, demonstrated exceptional qualities: a high affinity for AβOs, emission in the near-infrared region, and good biocompatibility. Significantly, ex vivo double staining indicated that compound 37 detected AβOs in AD mouse brain and in vivo imaging experiments showed that compound 37 could differentiate between 4-month-old AD mice and age-matched wild-type mice. Therefore, compound 37 has emerged as a valuable NIRF probe for early detection of AD and a useful tool in exploring AD's pathological mechanisms.

Analyzing electroencephalograph signals for early Alzheimer’s disease detection: deep learning vs. traditional machine learning approaches

Alzheimer’s disease (AD) stands as a progressive neurodegenerative disorder with a significant global public health impact. It is imperative to establish early and accurate diagnoses of AD to facilitate effective interventions and treatments. Recent years have witnessed the emergence of machine learning (ML) and deep learning (DL) techniques, displaying promise in various medical domains, including AD diagnosis. This study undertakes a comprehensive contrast between conventional machine learning methods and advanced deep learning strategies for early AD diagnosis. Conventional ML algorithms like support vector machines, decision trees, and K nearest neighbor have been extensively employed for AD diagnosis through relevant feature extraction from heterogeneous data sources. Conversely, deep learning techniques such as multilayer perceptron (MLP) and convolutional neural networks (CNNs) have demonstrated exceptional aptitude in autonomously uncovering intricate patterns and representations from unprocessed data like EEG data. The findings reveal that while traditional ML methods may perform adequately with limited data, deep learning techniques excel when ample data is available, showcasing their potential for early and precise AD diagnosis. In conclusion, this research paper contributes to the ongoing discourse surrounding the choice of appropriate methodologies for early Alzheimer’s disease diagnosis.

Bio-inspired deep learning-personalized ensemble Alzheimer's diagnosis model for mental well-being

Most classification models for Alzheimer's Diagnosis (AD) do not have specific strategies for individual input samples, leading to the problem of easily overlooking personalized differences between samples. This research introduces a customized dynamically ensemble convolution neural network (PDECNN), which is able to build a specific integration strategy based on the distinctiveness of the sample. In this paper, we propose a personalized dynamic ensemble alzheimer's Diagnosis classification model. This model will dynamically modify the deteriorated brain areas of interest depending on various samples since it can adjust to variations in the degeneration of sample brain areas. In clinical problems, the PDECNN model has additional diagnostic importance since it can identify sample-specific degraded brain areas based on input samples. This model considers the variability of brain region degeneration levels between input samples, evaluates the degree of degeneration of specific brain regions using an attention mechanism, and selects and integrates brain region features based on the degree of degeneration. Furthermore, by redesigning the classification accuracy performance, we respectively improve it by 4 %, 11 %, and 8 %. Moreover, the degraded brain regions identified by the model show high consistency with the clinical manifestations of AD.

Predicting Progression From Mild Cognitive Impairment to Alzheimer's Dementia With Adversarial Attacks.

Early diagnosis of Alzheimer's disease plays a crucial role in treatment planning that might slow down the disease's progression. This problem is commonly posed as a classification task performed by machine learning and deep learning techniques. Although data-driven techniques set the state-of-the-art in many domains, the scale of the available datasets in Alzheimer's research is not sufficient to learn complex models from patient data. This study proposes a simple yet promising framework to predict the conversion from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD). The proposed framework comprises a shallow neural network for binary classification and a single-step gradient-based adversarial attack to find an adversarial progression direction in the input space. The step size required for the adversarial attack to change a patient's diagnosis from MCI to AD indicates the distance to the decision boundary. The patient's diagnosis at the next visit is predicted by employing this notion of distance to the decision boundary. We also present a potential application of the proposed framework to patient subtyping. Experiments with two publicly available datasets for Alzheimer's disease research imply that the proposed framework can predict MCI-to-AD conversions and assist in subtyping by only training a shallow neural network.

Pyroptosis Signature Gene CHMP4B Regulates Microglia Pyroptosis by Inhibiting GSDMD in Alzheimer's Disease.

In this study, we aimed to work through the key genes involved in the process of pyroptosis in Alzheimer's disease (AD) to identify potential biomarkers using bioinformatics technology and further explore the underlying molecular mechanisms. The transcriptome data of brain tissue in AD patients were screened from the GEO database, and pyroptosis-related genes were analyzed. The functions of differential genes were analyzed by enrichment analysis and protein-protein interaction. The diagnostic model was established using LASSO and logistic regression analysis, and the correlation of clinical data was analyzed. Based on single-cell analysis of brain tissues of patients with AD, immunofluorescence and western blotting were used to explore the key cells affected by the hub gene. After GSEA, qRT-PCR, western blotting, LDH, ROS, and JC-1 were used to investigate the potential mechanism of the hub gene on pyroptosis. A total of 15 pyroptosis differentially expressed genes were identified. A prediction model consisting of six genes was established by LASSO and logistic regression analysis, and the area under the curve was up to 0.81. As a hub gene, CHMP4B was negatively correlated with the severity of AD. CHMP4B expression was decreased in the hippocampal tissue of patients with AD and mice. Single-cell analysis showed that CHMP4B was downregulated in AD microglia. Overexpression of CHMP4B reduced the release of LDH and ROS and restored mitochondrial membrane potential, thereby alleviating the inflammatory response during microglial pyroptosis. In summary, CHMP4B as a hub gene provides a new strategy for the diagnosis and treatment of AD.

Microglial activation-sensitive gadolinium complex as a potential MRI contrast agent for Alzheimer’s disease diagnosis

Numerous studies have developed gadolinium (Gd)-based contrast agents (GBCAs) for in-vivo magnetic resonance imaging (MRI) with the aim of diagnosing Alzheimer’s disease (AD). Nevertheless, GBCAs that are capable of identifying microglial activation, a critical early indicator of AD pathology, for in-vivo diagnosis remain scarce. In response to this, we synthesized a novel GBCA, Gd-DO3A-Va, designed specifically to detect microglial activation using MRI. This innovative GBCA, which was conjugated with vanillic acid, demonstrated a high selectivity for microglial activation by targeting Toll-like receptor 4 (TLR4). The use of Gd-DO3A-Va in in-vivo imaging successfully highlighted microglial activation in a transgenic AD mouse model. In particular, the use of Gd-DO3A-Va for contrast-enhanced T1-weighted MRI enabled the detection of microglial activation within the hippocampus and cortex, regions notably affected by AD. The observed enhancements in the MR contrast correlated well with immunohistological evidence of microglial activation. Consequently, Gd-DO3A-Va represents a highly promising GBCA for the identification of microglial activation, providing a novel pathway for the molecular diagnosis of AD.

Neuroimaging for Early Diagnosis of Alzheimer's Disease: a Review.

Alzheimer's disease (AD) is a progressive neurodegenerative disease that primarily affects people above the age of 60 all around the world. As of now, the cause is unknown and there is no effective cure. The pathological changes of AD have occurred many years before the onset of the disease, and current treatment techniques can only delay the progression of the disease. Because disease-modifying therapies may be most beneficial in the early stages of AD, the clinical significance of an early diagnosis is emphasized. So far, a variety of imaging technologies and related biomarkers have been used to identify and monitor AD, but there are many imaging technologies; finding the most effective imaging technology can assist medical personnel in interpreting the early stages of AD and can also improve patient treatment opportunities. This is, therefore, the main purpose and back-ground of this study. PubMed and other repositories were used in this study to conduct a literature search with various keywords, and relevant articles were reviewed. In this review, different neuroimaging techniques are reviewed which are considered advanced tools to help establish the diagnosis, and in addition, the diagnostic utility, advantages, and limitations of contemporary AD imaging techniques are discussed. The results of the literature review and synthesis show that the prevalence of several in vivo biomarkers helps distinguish affected individuals from healthy controls in the early stages of the disease. Additionally, each current imaging method has its advantages and disadvantages, so no single imaging method is the best diagnostic modality. This article also reviews and draws conclusions on better ways to use the imaging techniques to improve the likelihood of an early diagnosis of AD. It is suggested that future research could focus on expanding the use of imaging technologies and on identifying novel biomarkers manifesting the earliest stages of AD pathology.

Enhancing Alzheimer's Disease Diagnosis with K-NN: A Study on Pre-processed MRI Data

Enhancing Alzheimer's Disease Diagnosis with K-NN: A Study on Pre-processed MRI Data

Cerebrovascular disease emerges with age and Alzheimer’s disease in adults with Down syndrome

Adults with Down syndrome have a genetic form of Alzheimer’s disease (AD) and evidence of cerebrovascular disease across the AD continuum, despite few systemic vascular risk factors. The onset and progression of AD in Down syndrome is highly age-dependent, but it is unknown at what age cerebrovascular disease emerges and what factors influence its severity. In the Alzheimer’s Biomarker Consortium-Down Syndrome study (ABC-DS; n = 242; age = 25–72), we estimated the age inflection point at which MRI-based white matter hyperintensities (WMH), enlarged perivascular spaces (PVS), microbleeds, and infarcts emerge in relation to demographic data, risk factors, amyloid and tau, and AD diagnosis. Enlarged PVS and infarcts appear to develop in the early 30s, while microbleeds, WMH, amyloid, and tau emerge in the mid to late 30s. Age-residualized WMH were higher in women, in individuals with dementia, and with lower body mass index. Participants with hypertension and APOE-ε4 had higher age-residualized PVS and microbleeds, respectively. Lifespan trajectories demonstrate a dramatic cerebrovascular profile in adults with Down syndrome that appears to evolve developmentally in parallel with AD pathophysiology approximately two decades prior to dementia symptoms.