Early Diagnosis Of Alzheimer's Disease Research Articles

Prediction of Alzheimer’s Disease with Retinal Images Using Deep Learning

Alzheimer's disease (AD) is a leading cause of mortality worldwide. Early detection and accurate diagnosis of AD are crucial for effective intervention and improved patient outcomes. Retinal imaging has emerged as a non-invasive and cost-effective technique for AD prediction. This study aims to develop a deep learning model using convolutional neural networks (CNNs) architecture to predict AD from retinal images. The proposed model leverages the capabilities of CNNs to automatically learn relevant features from retinal images.

DNA tetrahedron-based dual-signal fluorescence detection of apoE4 gene sites on a microplate reader.

As a neurodegenerative disorder, Alzheimer's disease (AD) is characterized by cognitive dysfunction and behavioral impairment. Among the various genetic risk factors for AD, apoE4 gene plays a pivotal role in the onset and progression of AD, and detection of apoE4 gene holds significance for prevention and early diagnosis of AD. Herein, dual-signal fluorescence detection of fragments associated with apoE ε4 allele near codon 112 (Tc1) and codon 158 (Tc2) was achieved using DNA tetrahedron nanostructure (DTN). The Förster resonance energy transfer (FRET) process in the DTN was initiated in which the nucleic acid intercalating dye thiazole orange (TO) served as the donor and the cyanine dyes of cyanine3 (Cy3) and cyanine5 (Cy5) at the two vertices of DTN served as the acceptors. In the presence of Tc1 and Tc2, the FRET process between TO and the cyanine dyes was hindered by the enzymatic cleavage reaction, which ensures the dual-signal fluorescence assay of apoE4 gene sites. The limit of detection for Tc1 and Tc2 was estimated to be 0.82nM and 0.77nM, respectively, and the whole assay was accomplished within 1h on a microplate reader. The proposed method thus possesses the advantages of easy operation, short detection time, and high-throughput capability.

Prediction of Alzheimer's disease stages based on ResNet-Self-attention architecture with Bayesian optimization and best features selection.

Alzheimer's disease (AD) is a neurodegenerative illness that impairs cognition, function, and behavior by causing irreversible damage to multiple brain areas, including the hippocampus. The suffering of the patients and their family members will be lessened with an early diagnosis of AD. The automatic diagnosis technique is widely required due to the shortage of medical experts and eases the burden of medical staff. The automatic artificial intelligence (AI)-based computerized method can help experts achieve better diagnosis accuracy and precision rates. This study proposes a new automated framework for AD stage prediction based on the ResNet-Self architecture and Fuzzy Entropy-controlled Path-Finding Algorithm (FEcPFA). A data augmentation technique has been utilized to resolve the dataset imbalance issue. In the next step, we proposed a new deep-learning model based on the self-attention module. A ResNet-50 architecture is modified and connected with a self-attention block for important information extraction. The hyperparameters were optimized using Bayesian optimization (BO) and then utilized to train the model, which was subsequently employed for feature extraction. The self-attention extracted features were optimized using the proposed FEcPFA. The best features were selected using FEcPFA and passed to the machine learning classifiers for the final classification. The experimental process utilized a publicly available MRI dataset and achieved an improved accuracy of 99.9%. The results were compared with state-of-the-art (SOTA) techniques, demonstrating the improvement of the proposed framework in terms of accuracy and time efficiency.

Research Progress of Eye Movement Analyses and its Detection Algorithms in Alzheimer's Disease.

Alzheimer's disease (AD) has been considered one of the most challenging forms of dementia. The earlier the people are diagnosed with AD, the easier it is for doctors to find a treatment. Based on the previous literature summarizing the research results on the relationship between eye movement and AD before 2013, this paper reviewed 34 original eye movements research papers only closely related to AD published in the past ten years and pointed out that the prosaccade (4 papers) and antisaccade (5 papers) tasks, reading tasks (3 papers), visual search tasks (3 papers) are still the research objects of many researchers, Some researchers have looked at King-Devick tasks (2 papers), reading tasks (3 papers) and special tasks (8 papers), and began to use combinations of different saccade tasks to detect the relationship between eye movement and AD, which had not been done before. These reflect the diversity of eye movement tasks and the complexity and difficulty of the relationship between eye movement and AD. On this basis, the current processing and analysis methods of eye movement datasets are analyzed and discussed in detail, and we note that certain key data that may be especially important for the early diagnosis of AD by using eye movement studies cannot be miss-classified as noise and removed. Finally, we note that the development of methods that can accurately denoise and classify and quickly process massive eye movement data is quite significant for detecting eye movements in early diagnosis of AD.

The Cerebrospinal Fluid Free-Glycans Hex1 and HexNAc1Hex1Neu5Ac1 as Potential Biomarkers of Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common neurodegenerative disorder, affecting a growing number of elderly people. In order to improve the early and differential diagnosis of AD, better biomarkers are needed. Glycosylation is a protein post-translational modification that is modulated in the course of many diseases, including neurodegeneration. Aiming to improve AD diagnosis and differential diagnosis through glycan analytics methods, we report the glycoprotein glycome of cerebrospinal fluid (CSF) isolated from a total study cohort of 262 subjects. The study cohort consisted of patients with AD, healthy controls and patients suffering from other types of dementia. CSF free-glycans were also isolated and analyzed in this study, and the results reported for the first time the presence of 19 free glycans in this body fluid. The free-glycans consisted of complete or truncated N-/O-glycans as well as free monosaccharides. The free-glycans Hex1 and HexNAc1Hex1Neu5Ac1 were able to discriminate AD from controls and from patients suffering from other types of dementia. Regarding CSF N-glycosylation, high proportions of high-mannose, biantennary bisecting core-fucosylated N-glycans were found, whereby only about 20% of the N-glycans were sialylated. O-Glycans and free-glycan fragments were less sialylated in AD patients than in controls. To conclude, this comprehensive study revealed for the first time the biomarker potential of free glycans for the differential diagnosis of AD.

Development of Improved Weighed Quantum Lion Optimization with Smooth Support Vector Machine for Alzheimer’s Disease

Accurate diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) was identified on an early stage is essential in the healthcare industry to stop degeneration. The Smooth Support Vector Machine (SSVM) model, Principal Component Analysis (PCA), feature extraction, and Magnetic Resonance Imaging (MRI) image prepossessing are the components for the diagnosis of AD is proposed in this research at early stage. To assist in the classifier's training, we proposed a novel Improved Weighed Quantum Lion Optimization (IWQLO). The SSVM parameters are specifically proposed to be optimized using a new Switching delayed Lion Optimization (SLO) algorithm. The IWQLO-SSVM approach was effectively used to classify AD and MCI utilizing MRI scans of the Alzheimer's disease Neuroimaging Initiative (ADNI) database and Outcome and Assessment Information Set (OASIS) database. For six example scenarios, the classification accuracy of our proposed method is acceptable. Testing show that the proposed approach improves the performance measures such as accuracy, precision, specificity, sensitivity and recall for detecting the early stage AD diagnosis.

Analysis of EEG characteristics in Alzheimer’s disease

Alzheimer’s disease (AD) brings a heavy burden to society and families because of the high misdiagnosis rate, prevalence rate, and disability rate. At present, the treatment of AD still faces great challenges. Early intervention can effectively delay the disease progression and improve the burden of symptom care. However, the key to early intervention is accurately identifying and assessing the disease. The examination is difficult to be widely used because of its high invasive cost. There is an urgent need for a simple, convenient, and non-invasive diagnosis of AD. Studies have shown that the brain number of AD patients is slower than that of healthy people, and the complexity of EEG is disturbed. Clinical practice has proved that the specificity and sensitivity of α, β, δ, θ absolute power and α/θ absolute power ratio, especially θ absolute power and α/θ absolute power ratio in the model, are important electrophysiological indexes for AD diagnosis EEG. Non-invasive and economical electrophysiological techniques can characterize and identify the EEG characteristics of AD patients from different angles. They are relatively simple to implement, and patients have a high degree of cooperation. Therefore, EEG, as a diagnostic method of AD, can improve the accuracy of differential diagnosis and early detection of AD and quantify the severity of the disease. It has the advantage of non-invasive convenience and price, which is of great significance for early diagnosis of AD.

Synaptic vesicle glycoprotein 2 A in serum is an ideal biomarker for early diagnosis of Alzheimer’s disease

BackgroundPrevious studies have demonstrated that early intervention was the best plan to inhibit the progression of Alzheimer’s disease (AD), which relied on the discovery of early diagnostic biomarkers. In this study, synaptic vesicle glycoprotein 2 A (SV2A) was examined to improve the early diagnostic efficiency in AD.MethodsIn this study, biomarker testing was performed through the single-molecule array (Simoa). A total of 121 subjects including cognitively unimpaired controls, amnestic mild cognitive impairment (aMCI), AD and other types of dementia underwent cerebrospinal fluid (CSF) SV2A testing; 430 subjects including health controls, aMCI, AD and other types of dementia underwent serum SV2A, glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL) and p-tau217 testing; 92 subjects including aMCI and AD underwent both CSF SV2A and serum SV2A testing; 115 cognitively unimpaired subjects including APOE ε4 carriers and APOE ε4 non-carriers were tested for serum SV2A, GFAP, NfL and p-tau217. Then, the efficacy of SV2A for the early diagnosis of AD and its ability to identify those at high risk of AD from a cognitively unimpaired population were further analyzed.ResultsBoth CSF and serum SV2A significantly and positively correlated with cognitive performance in patients with AD, and their levels gradually decreased with the progression of AD. Serum SV2A demonstrated excellent diagnostic efficacy for aMCI, with a sensitivity of 97.8%, which was significantly higher than those of NfL, GFAP, and p-tau217. The SV2A-positive rates ranged from 92.86 to 100% in aMCI cases that were negative for the above three biomarkers. Importantly, of all the biomarkers tested, serum SV2A had the highest positivity rate (81.82%) in individuals at risk for AD.ConclusionsSerum SV2A was demonstrated to be a novel and ideal biomarker for the early diagnosis of AD, which can effectively distinguish those at high risk of AD in cognitively unimpaired populations.

Beta-amyloid plaque deposits and increased tau protein in the early diagnosis of alzheimer’s disease: the importance of physical activity in the prevention and evolution of the patient

In recent years, it has become clear that the β-amyloid (Aβ) component of senile plaques may be the key molecule in the pathology of Alzheimer's disease (AD). However, the origin and site of Aβ's neurotoxic action are still the subject of controversy. The precursor of the β-amyloid peptide is the β-amyloid precursor protein, which is predominantly neuronal. The hypothesis is that intraneuronal dysregulation of Amyloid Precursor Protein (APP) leads to the accumulation of Aβ peptides in intracellular compartments. This accumulation impairs the trafficking of APP, which initiates a cascade of pathological changes and causes the degeneration of pyramidal neurons. The increased secretion of Aβ as a function of stressed neurons and degenerated neuron remnants provides seeds for extracellular Aβ aggregates, which induce secondary degenerative events involving neighboring cells such as neurons, astroglia and macrophages/microglia. The benefits of physical exercise to reduce low-grade inflammation and improve cognitive function have become a growing field of interest. Epidemiological research has shown that a sedentary lifestyle intensifies the processes of disability and dependence, and also increases the incidence of chronic diseases. Physical exercise has been inversely associated with high levels of different inflammatory markers. It plays a neurotrophic role, capable of promoting a reduction in the accumulation of β-amyloid peptide and the hyperphosphorylation of tau protein, being an important alternative pathway for reducing the degenerative process, which does not result in the release of pro-inflammatory factors, as well as helping to reduce the levels of pro-inflammatory cytokines and improve peripheral concentrations.

Biofluid biomarkers for Alzheimer's disease.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disease, with a complex pathogenesis and an irreversible course. Therefore, the early diagnosis of AD is particularly important for the intervention, prevention, and treatment of the disease. Based on the different pathophysiological mechanisms of AD, the research progress of biofluid biomarkers are classified and reviewed. In the end, the challenges and perspectives of future research are proposed.

Neuroinflammation increases oxygen extraction in a mouse model of Alzheimer’s disease

BackgroundNeuroinflammation, impaired metabolism, and hypoperfusion are fundamental pathological hallmarks of early Alzheimer’s disease (AD). Numerous studies have asserted a close association between neuroinflammation and disrupted cerebral energetics. During AD progression and other neurodegenerative disorders, a persistent state of chronic neuroinflammation reportedly exacerbates cytotoxicity and potentiates neuronal death. Here, we assessed the impact of a neuroinflammatory challenge on metabolic demand and microvascular hemodynamics in the somatosensory cortex of an AD mouse model.MethodsWe utilized in vivo 2-photon microscopy and the phosphorescent oxygen sensor Oxyphor 2P to measure partial pressure of oxygen (pO2) and capillary red blood cell flux in cortical microvessels of awake mice. Intravascular pO2 and capillary RBC flux measurements were performed in 8-month-old APPswe/PS1dE9 mice and wildtype littermates on days 0, 7, and 14 of a 14-day period of lipopolysaccharide-induced neuroinflammation.ResultsBefore the induced inflammatory challenge, AD mice demonstrated reduced metabolic demand but similar capillary red blood cell flux as their wild type counterparts. Neuroinflammation provoked significant reductions in cerebral intravascular oxygen levels and elevated oxygen extraction in both animal groups, without significantly altering red blood cell flux in capillaries.ConclusionsThis study provides evidence that neuroinflammation alters cerebral oxygen demand at the early stages of AD without substantially altering vascular oxygen supply. The results will guide our understanding of neuroinflammation’s influence on neuroimaging biomarkers for early AD diagnosis.

334 Identification of novel plasma protein biomarkers for diagnosis and prediction of Alzheimer’s disease in African Americans

OBJECTIVES/GOALS: To identify novel panel of plasma protein biomarkers to improve prediction and diagnosis of Alzheimer’s disease (AD) for African Americans (AA), who are at greater risk of developing AD compared to non-Hispanic White individuals but are underrepresented in AD research. METHODS/STUDY POPULATION: Pre-existing plasma samples from 460 AA individuals with clinical diagnoses of AD, cognitively unimpaired (CU), mild cognitive impairment (MCI), or dementia with Lewy bodies (DLB) will undergo untargeted proteomics using the SomaScan assay, where modified single stranded DNA aptamers bind to protein targets which are quantified by DNA microarray. Protein expression levels will be compared between diagnostic groups to identify differentially expressed proteins. Additional clinical, genetic, and lifestyle factors will be compared with protein expression when available. Proteins of interest, identified by differential protein expression analysis results, will be included in receiver operating characteristic analyses to identify the optimal set of proteins for diagnostic classification. RESULTS/ANTICIPATED RESULTS: A pilot experiment utilizing plasma from 40 individuals identified multiple differentially expressed proteins (DEPs) between AD and non-AD groups. Eight proteins were nominated from the differential protein analysis into a receiver operating characteristic (ROC) analysis based on pvalue and previous implication in AD genome wide association studies. Proteins involved in microglial activation, neuronal adhesion, cell proliferation, and innate immunity were nominated. The ROC model achieved 100% classification accuracy of AD and CU groups using age, sex, and the eight nominated proteins. It is expected that there will be more significant associations when utilizing the full cohort of 460 AA and that DEPs between AD, CU, MCI, and DLB will be identified. DISCUSSION/SIGNIFICANCE: The nomination of a novel panel of plasma biomarkers developed from an AA cohort will directly serve the AA community by improving access to an early and accurate diagnosis of AD. Access to improved prediction and diagnosis will likely improve disease management, thus improving patient outcomes and decreasing burden on families and caregivers.

Memory impairment in Amyloidβ-status Alzheimer's disease is associated with a reduction in CA1 and dentate gyrus volume: In vivo MRI at 7T

IntroductionIn Alzheimer's disease (AD), early diagnosis facilitates treatment options and leads to beneficial outcomes for patients, their carers and the healthcare system. The neuropsychological battery of the Uniform Data Set (UDSNB3.0) assesses cognition in ageing and dementia, by measuring scores across different cognitive domains such as attention, memory, processing speed, executive function and language. However, its neuroanatomical correlates have not been investigated using 7 Tesla MRI (7T MRI). MethodsWe used 7T MRI to investigate the correlations between hippocampal subfield volumes and the UDSNB3.0 in 24 individuals with Amyloidβ-status AD and 18 age-matched controls, with respective age ranges of 60 (42–76) and 62 (52–79) years. AD participants with a Medial Temporal Atrophy scale of higher than 2 on 3T MRI were excluded from the study. ResultsA significant difference in the entire hippocampal volume was observed in the AD group compared to healthy controls (HC), primarily influenced by CA1, the largest hippocampal subfield. Notably, no significant difference in whole brain volume between the groups implied that hippocampal volume loss was not merely reflective of overall brain atrophy. UDSNB3.0 cognitive scores showed significant differences between AD and HC, particularly in Memory, Language, and Visuospatial domains. The volume of the Dentate Gyrus (DG) showed a significant association with the Memory and Executive domain scores in AD patients as assessed by the UDSNB3.0.. The data also suggested a non-significant trend for CA1 volume associated with UDSNB3.0 Memory, Executive, and Language domain scores in AD. In a reassessment focusing on hippocampal subfields and MoCA memory subdomains in AD, associations were observed between the DG and Cued, Uncued, and Recognition Memory subscores, whereas CA1 and Tail showed associations only with Cued memory. DiscussionThis study reveals differences in the hippocampal volumes measured using 7T MRI, between individuals with early symptomatic AD compared with healthy controls. This highlights the potential of 7T MRI as a valuable tool for early AD diagnosis and the real-time monitoring of AD progression and treatment efficacy. ClinicalTrials.govID NCT04992975 (Clinicaltrial.gov 2023)

Boosting the sensitivity for tau protein detection with a bifunctional nanoenzyme signal amplifier

The trace amounts of tau protein in the blood are considered a significant biomarker in the early diagnosis of Alzheimer's disease. Herein, an electrochemical biosensor has been developed that utilizes a bifunctional nanoenzyme signal amplifier (Nanosa), which incorporates hybridization chain reaction (HCR) amplification and peroxide-mimicking nanoenzymes enhancement to enable ultrasensitive detection of tau. The Nanosa utilizes a peroxide-active MoS2/Au substrate on which DNA aptamer and hairpin probes are fixed as signal amplifiers capable of triggering HCR. The Fc molecules were initially aggregated at the electrode interface through HCR amplification, and were tested by adding an electrolyte solution containing hydrogen peroxide (H2O2). Under the catalysis of a peroxide nanoenzyme, the electrochemical signal of Fc molecules was further enhanced. The bifunctional Nanosa can not only achieve the pre-enrichment of Fc molecules at the electrode interface but also catalyze the enhancement of its electrochemical signal. The well-designed and optimized poly adenine tail not only as an electrode coupling agent but also as an excellent antifouling coating. The ingenious design of the Nanosa-based biosensing strategy enables the ultrasensitive analysis of tau with a broad linear range from 0.1 pg/mL to 100 ng/mL and a lower LOD of 33.4 fg/mL. Meanwhile, the outstanding analytical performance of this Nanosa-based biosensing strategy enables its successful application in determining tau levels in real plasma samples, and showed good agreement with results obtained using a commercial ELISA kit.

Ultrasensitive Aptasensing Platform for the Detection of β-Amyloid-42 Peptide Based on MOF Containing Bimetallic Porphyrin Graphene Oxide and Gold Nanoparticles.

The prompt detection of diseases hinges on the accessibility and the capability to identify relevant biomarkers. The integration of aptamers and the incorporation of nanomaterials into signal transducers have not only expedited but also enhanced the development of nanoaptasensors, enabling heightened sensitivity and selectivity. Here, the bimetallic nickel-cobalt-porphyrin metal-organic framework ((Ni + Cu)TPyP MOF) is regarded as an electron mediator, immobilization platform for an Alzheimer aptamer and to increase the electrochemical signal for the detection of the main biomarker of Alzheimer's disease (AD), amyloid β (Aβ-42). Furthermore, the ((Ni + Cu)TPyP MOF) was combined with reduced graphene oxide (rGO) and gold nanoparticles (AuNPs), on a gold electrode (GE) to provide an efficient interface for immobilizing aptamer strands. Concurrently, the incorporation of rGO and AuNPs imparts enhanced electrical conductivity and efficacious catalytic activity, establishing them as adept electrochemical indicators. Owing to the superior excellent electrical conductivity of rGO and AuNPs, coupled with the presence of ample mesoporous channels and numerous Ni and Cu metal sites within (Ni + Cu)TPyP MOF, this nanostructure with abundant functional groups is proficient in immobilizing a substantial quantity of aptamer. These interactions are achieved through robust π-π stacking and electrostatic interactions, alongside the high affinity between the thiol group of the aptamer and AuNPs concurrently. The as-prepared ternary (Au@(Ni + Cu)TPyP MOF/rGO) nanostructure electrode exhibited an enhancement in its electrochemically active surface area of about 7 times, compared with the bare electrode and the Aβ-42 redox process is highly accelerated, so the peak currents are significantly higher than those obtained with bare GE substrate. Under the optimized conditions, the designed aptasensor had the quantitative detection of Aβ-42 with a low detection limit of 48.6 fg mL-1 within the linear range of 0.05 pg mL-1 to 5 ng mL-1 by differential pulse voltammetry (DPV), accompanied by precise reproducibility, satisfactory stability (95.6% of the initial activity after 10 days), and minimal impact of interfering agents. Recorded results in human blood plasma demonstrated the high efficacy of porphyrin MOF system sensing even in the clinical matrix. The great performance of this aptasensor indicates that our new design of Au@(Ni + Cu)TPyP MOF/rGO nanostructure provides more opportunities for the detection of chemical signals in early diagnosis of Alzheimer's disease.

Ultrasensitive and Multiple Biomarker Discrimination for Alzheimer's Disease via Plasmonic & Microfluidic Sensing Technologies.

As the population ages, the worldwide prevalence of Alzheimer's disease (AD) as the most common dementia in the elderly is increasing dramatically. However, a long-term challenge is to achieve rapid and accurate early diagnosis of AD by detecting hallmarks such as amyloid beta (Aβ42). Here, a multi-channel microfluidic-based plasmonic fiber-optic biosensing platform is established for simultaneous detection and differentiation of multiple AD biomarkers. The platform is based on a gold-coated, highly-tilted fiber Bragg grating (TFBG) and a custom-developed microfluidics. TFBG excites a high-density, narrow-cladding-mode spectral comb that overlaps with the broad absorption of surface plasmons for high-precision interrogation, enabling ultrasensitive monitoring of analytes. In situ detection and in-parallel discrimination of different forms of Aβ42 in cerebrospinal fluid (CSF) are successfully demonstrated with a detection of limit in the range of ≈30-170 pg mL-1, which is one order of magnitude below the clinical cut-off level in AD onset, providing high detection sensitivity for early diagnosis of AD. The integration of the TFBG sensor with multi-channel microfluidics enables simultaneous detection of multiple biomarkers using sub-µL sample volumes, as well as combining initial binding rate and real-time response time to differentiate between multiple biomarkers in terms of binding kinetics. With the advantages of multi-parameter, low consumption, and highly sensitive detection, the sensor represents an urgently needed potentials for large-scale diagnosis of diseases at earlystage.

Prediction Of Mild Cognitive Impairment to Alzheimer’s Disease Conversion Via Machine Learning

Alzheimer’s Disease (AD) is a progressive neurodegenerative condition characterized by deterioration of cognitive functions. Although there does not exist a cure for AD, early diagnosis and intervention during stages of Mild Cognitive Impairment (MCI) can be incredibly beneficial in slowing its development. However, predicting the conversion from MCI to AD remains a challenging task. This paper aims to provide an overview of the current research on predicting MCI to AD conversion, with a focus on machine learning methodologies to aid feature extraction and classification. Through a literature review, we aim to offer insights into the latest state-of-the-art techniques to predict MCI to AD conversion as well as prevailing trends in this field including deep learning, transfer learning, contrastive learning, and graph neural networks. We conclude from our study that utilizing AD/HC classes for feature extraction is a promising approach for generating discriminative features for stable MCI and progressive MCI classification. In addition, employing multi-modality models is instrumental in attaining a robust validation framework for the early diagnosis of AD.

Multifunctional Liposomes Targeting Amyloid-β Oligomers for Early Diagnosis and Therapy of Alzheimer's Disease.

Early detection and treatment are crucial for Alzheimer's disease (AD) management. Current diagnostic and therapeutic methods focus on late-stage amyloid fibrils and plaques, overlooking toxic soluble amyloid β oligomers (AβOs) accumulating early in AD. A multifunctional liposome-based platform is designed for early diagnosis and therapy of AD, leveraging a novel self-assembled cyclic d,l-α-peptide (CP-2) that selectively targets AβOs. Biocompatible CP-2 conjugated liposomes (CP-2-LPs) effectively disrupt Aβ aggregation and mitigate Aβ-mediated toxicity in human neuroblastoma cells. In transgenic Caenorhabditis elegans AD models, CP-2-LPs significantly outperformed free CP-2 by improving cognitive and behavioral functions, extending lifespan, and reducing toxic AβO levels. Intravenous injection of fluorescently labeled CP-2-LPs reveals effective blood-brain barrier penetration, with significantly higher brain fluorescence in transgenic mice than WT, enabling precise diagnosis. These findings underscore CP-2-LPs as a valuable tool for early detection and targeted therapy in AD.

Multi-proteomic analyses of 5xFAD mice reveal new molecular signatures of early-stage Alzheimer's disease.

An early diagnosis of Alzheimer's disease is crucial as treatment efficacy is limited to the early stages. However, the current diagnostic methods are limited to mid or later stages of disease development owing to the limitations of clinical examinations and amyloid plaque imaging. Therefore, this study aimed to identify molecular signatures including blood plasma extracellular vesicle biomarker proteins associated with Alzheimer's disease to aid early-stage diagnosis. The hippocampus, cortex, and blood plasma extracellular vesicles of 3- and 6-month-old 5xFAD mice were analyzed using quantitative proteomics. Subsequent bioinformatics and biochemical analyses were performed to compare the molecular signatures between wild type and 5xFAD mice across different brain regions and age groups to elucidate disease pathology. There was a unique signature of significantly altered proteins in the hippocampal and cortical proteomes of 3- and 6-month-old mice. The plasma extracellular vesicle proteomes exhibited distinct informatic features compared with the other proteomes. Furthermore, the regulation of several canonical pathways (including phosphatidylinositol 3-kinase/protein kinase B signaling) differed between the hippocampus and cortex. Twelve potential biomarkers for the detection of early-stage Alzheimer's disease were identified and validated using plasma extracellular vesicles from stage-divided patients. Finally, integrin α-IIb, creatine kinase M-type, filamin C, glutamine γ-glutamyltransferase 2, and lysosomal α-mannosidase were selected as distinguishing biomarkers for healthy individuals and early-stage Alzheimer's disease patients using machine learning modeling with approximately 79% accuracy. Our study identified novel early-stage molecular signatures associated with the progression of Alzheimer's disease, thereby providing novel insights into its pathogenesis.

Regulation of triazine-COF and CoAl-LDH substrates in composites for high-performance neurofilament-light chains electrochemical immunosensors

Early diagnosis of Alzheimer’s disease (AD) can be achieved through the detection of neurofilament light chain (NfL) biomarker, which can slow down the progress of AD and improve quality of life for patients. In this paper, polyvalent Tb-metal organic frameworks (Tb-MOF) and CoAl-layered double hydroxides (CoAl-LDH) are directly acceded to the synthesis system of triazine-covalent organic frameworks (triazine-COF) using one-pot synthesis to obtain the COF-based composite (COF/MOF-LDH). Similarly, the composite of LDH-based (LDH/MOF-COF) is acquired by integrating Tb-MOF and triazine-COF into the synthesis system of CoAl-LDH via hydrothermal procedure. COF/MOF-LDH and LDH/MOF-COF are used as electrode modification materials to construct label-free electrochemical immunosensors for anchoring and detecting NfL. Under optimal conditions for detecting NfL, both modified sensors exhibit high degree of selectivity, stability and reproducibility. However, the COF/MOF-LDH sensor displays a wider linear concentration range (0.05 pg/mL - 100 ng/mL) with lower limit detection of 0.017 pg/mL (S/N=3), and high sensitivity of 130.8 μA·ng−1·mL·cm−2, attributing to large conjugation system of triazine-COF used for substrate modulation and the immobilisation of the anti-NfL through strong π-π* affect and hydrogen bonding. Likely, the incorporation of polyvalent Tb-MOF and CoAl-LDH in COF/MOF-LDH considerably enhance sensitivity via improving active sites and response signals. Furthermore, the morphology, pore size and specific surface area differences of the composites are a reflection of the results of the substrate material control strategy. Especially, the N2 adsorption/desorption tests indicate the larger mesoporous structure of COF/MOF-LDH which facilitate the electron transfer and anti-NfL immobilization. Taken together, the immunosensor based on COF/MOF-LDH shows excellent detection performance which provides a pathway for early screening of AD.