Early Diagnosis Of Alzheimer's Disease Research Articles

Analysis of Speech Features in Alzheimer’s Disease with Machine Learning: A Case-Control Study

Background: Changes in the speech and language of patients with Alzheimer’s disease (AD) have been reported. Using machine learning to characterize these irregularities may contribute to the early, non-invasive diagnosis of AD. Methods: We conducted cognitive function assessments, including the Mini-Mental State Examination, with 83 patients with AD and 75 healthy elderly participants, and recorded pre- and post-assessment conversations to evaluate participants’ speech. We analyzed the characteristics of the spectrum, intensity, fundamental frequency, and minute temporal variation (∆) of the intensity and fundamental frequency of the speech and compared them between patients with AD and healthy participants. Additionally, we evaluated the performance of the speech features that differed between the two groups as single explanatory variables. Results: We found significant differences in almost all elements of the speech spectrum between the two groups. Regarding the intensity, we found significant differences in all the factors except for the standard deviation between the two groups. In the performance evaluation, the areas under the curve revealed by logistic regression analysis were higher for the center of gravity (0.908 ± 0.036), mean skewness (0.904 ± 0.023), kurtosis (0.932 ± 0.023), and standard deviation (0.977 ± 0.012) of the spectra. Conclusions: This study used machine learning to reveal speech features of patients diagnosed with AD in comparison with healthy elderly people. Significant differences were found between the two groups in all components of the spectrum, paving the way for early non-invasive diagnosis of AD in the future.

Early Alzheimer's disease diagnosis via handwriting with self-attention mechanisms.

The neurodegenerative diseases like Alzheimer's disease (AD) can result in progressive decline in both cognitive functions and motor skills, which have critical need for accurate early diagnosis. However, current diagnosis approaches primarily rely on timely clinical magnetic resonance imaging (MRI) scans, which impede widely application for potential patients. Leveraging handwriting as a diagnostic tool offers significant potential for identifying AD in its early stages. This study aims to develop an efficient, rapid, and accurate method for early diagnosis of AD by utilizing handwriting analysis, a promising avenue due to its association with compromised motor skills in neurodegenerative diseases. We propose a novel methodology that leverages self-attention mechanisms for the early diagnosis of AD. Our approach integrates data from 25 distinct handwriting tasks available in the DARWIN (Diagnosis AlzheimeR WIth haNdwriting) dataset. The Self-Attention model achieved an accuracy of 94.3% and an F1-score of 94.5%, outperforming other state-of-the-art models, including traditional machine learning and deep learning approaches. Specially, the Self-Attention model surpassed the previous best model, the convolutional neural networks, by approximately 4% in both accuracy and F1-score. Additionally, the model demonstrated superior precision (94.7%), sensitivity (94.5%), and specificity (94.1%), indicating high reliability and excellent identification of true positive and true negative cases, which is crucial in medical diagnostics. Handwriting analysis, powered by self-attention mechanisms, offers significant potential as a diagnostic tool for identifying AD in its early stages, providing an effective alternative to traditional MRI-based diagnosis.

Spectral graph convolutional neural network for Alzheimer's disease diagnosis and multi-disease categorization from functional brain changes in magnetic resonance images

Alzheimer's disease (AD) is a progressive neurological disorder characterized by the gradual deterioration of cognitive functions, leading to dementia and significantly impacting the quality of life for millions of people worldwide. Early and accurate diagnosis is crucial for the effective management and treatment of this debilitating condition. This study introduces a novel framework based on Spectral Graph Convolutional Neural Networks (SGCNN) for diagnosing AD and categorizing multiple diseases through the analysis of functional changes in brain structures captured via magnetic resonance imaging (MRI). To assess the effectiveness of our approach, we systematically analyze structural modifications to the SGCNN model through comprehensive ablation studies. The performance of various Convolutional Neural Networks (CNNs) is also evaluated, including SGCNN variants, Base CNN, Lean CNN, and Deep CNN. We begin with the original SGCNN model, which serves as our baseline and achieves a commendable classification accuracy of 93%. In our investigation, we perform two distinct ablation studies on the SGCNN model to examine how specific structural changes impact its performance. The results reveal that Ablation Model 1 significantly enhances accuracy, achieving an impressive 95%, while Ablation Model 2 maintains the baseline accuracy of 93%. Additionally, the Base CNN model demonstrates strong performance with a classification accuracy of 93%, whereas both the Lean CNN and Deep CNN models achieve 94% accuracy, indicating their competitive capabilities. To validate the models' effectiveness, we utilize multiple evaluation metrics, including accuracy, precision, recall, and F1-score, ensuring a thorough assessment of their performance. Our findings underscore that Ablation Model 1 (SGCNN Model 1) delivers the highest predictive accuracy among the tested models, highlighting its potential as a robust approach for Alzheimer's image classification. Ultimately, this research aims to facilitate early diagnosis and treatment of AD, contributing to improved patient outcomes and advancing the field of neurodegenerative disease diagnosis.

Omnidirectional improvement of mitochondrial health in Alzheimer's disease by multi-targeting engineered activated neutrophil exosomes

Alzheimer's disease (AD) is one kind of devasting neurodegenerative disorders affecting over 50 million people worldwide. Multi-targeted therapy has emerged as a new treatment for diagnosing and alleviating the pathogenesis process of AD; however, the current strategy is limited by its unsatisfactory efficiency. In our study, engineered activated neutrophil-derived exosomes (MP@Cur-MExo) were developed to improve the mitochondrial function in neurons by targeting and alleviating Aβ-induced neurotoxicity. MP@Cur-MExo are exosomes derived from IL-8-stimulated neutrophils decorated with mitochondria targeting ligand and Aβ targeted ligand modified SPION. Engineered exosomes can be cleaved by matrix metallopeptidase-2, which is overexpressed in the AD brain. Consequently, the released SPION and Curcumin-loaded engineered exosomes collaboratively protected neuron cells against Aβ-induced mitochondrial deficiency. In addition, MP@Cur-MExo effectively accumulated in the inflamed region of AD brain at an early stage, allowing early diagnosis of AD through bimodal (MRI/IVIS) imaging. Importantly, in a mouse model at an early stage of AD, intravenously injected MP@Cur-MExo restored mitochondrial function and reduced Aβ-induced mitochondrial damage, thereby attenuating AD progression. In conclusion, our designed engineered exosomes demonstrated that omnidirectional improvement of mitochondrial function can serve as a novel and practical approach for the diagnosis and treatment of neurodegenerative diseases. This study also reveals a promising therapeutic agent for impeding AD progression for future clinical applications.

Future Therapeutic Strategies for Alzheimer’s Disease: Focus on Behavioral and Psychological Symptoms

Alzheimer’s disease (AD) is a progressive, degenerative brain disorder that impairs memory and thinking skills, leading to significant economic and humanistic burdens. It is associated with various neuropsychiatric symptoms (NPS) such as anxiety, agitation, depression, aggression, apathy, and psychosis. NPSs are common in patients with AD, affecting up to 97% of individuals diagnosed with AD. The severity of NPS is linked to disease progression and cognitive decline. NPS in Alzheimer’s disease leads to increased morbidity, mortality, caregiver burden, earlier nursing home placement, and higher healthcare costs. Despite their significant impact, clinical research on NPS in AD is limited. In clinical settings, accurately distinguishing and diagnosing NPS related to AD remains a challenge. Additionally, conventional treatments for NPS in AD are often ineffective, highlighting the need for new therapies that target these specific symptoms. Understanding these comorbidities can aid in early diagnosis and better management of AD. In this review, we provide a summary of the various neurological and psychiatric symptoms (NPS) associated with AD and new candidates under development for the treatment of NPS based on their therapeutic targets and mechanisms. On top of the conventional NPS studied so far, this review adds recent advancements in the understanding of social functional impairment in AD. This review also provides information that can contribute to the advancement of studies and translational research in this field by emphasizing therapeutic targets and mechanisms of action focused on AD-related NPS rather than conventional mechanisms targeted in AD drug development. Above all, considering the relative lack of research in this new field despite the importance of clinical, medical, and translational research, it may increase interest in NPS in AD, its pathophysiological mechanisms, and potential therapeutic candidates such as molecules with antioxidant potential.

Proteins sensing of plasma-derived extracellular vesicles in Alzheimer’s disease based on primer exchange reaction amplification strategy

Neuron-derived extracellular vesicles (EVs) in blood offer unique cellular and molecular information related to the onset and progression of Alzheimer’s disease (AD), and they are increasingly being investigated as sources of biomarkers. However, sensitively detecting AD-related EVs markers in blood remains a challenge. In this study, we employed a magnetic separation-assisted primer exchange reaction (MSPER) amplification strategy to achieve highly sensitive and convenient detection of CD63 and Aβ42 proteins on EVs (CD63+/Aβ42+ EVs). Specifically, CD63-expressing EVs are enriched using CD63 antibody-functionalized immune-magnetic beads (anti-CD63@IMBs). Subsequently, Aβ42 and CD63 aptamer are introduced to efficiently recognize CD63 and Aβ42 proteins on the EVs. Iterative primer exchange reaction (PER) concatemers, which create additional binding sites for fluorescence imagers, are then used to produce an amplified fluorescence signal. With this method, we achieved a limit of detection (LOD) of 7.2 × 103 particles/mL for sensitive detection of CD63+/Aβ42+ EVs. More importantly, this MSPER amplification strategy allows us to distinguish AD model mice from healthy controls with high accuracy. This method may provide an alternative for the early diagnosis and monitoring of AD through the detection of CD63+/Aβ42+ EVs.

Redox Oxygen Species-Responsive Nanotheranostics with Dual-Channel Fluorescent Turn-On for Early Diagnosis and Targeted Therapy of Alzheimer's Disease.

Current diagnosis and treatment strategies mainly focus on the pathologies of the mid-to-late stage of AD (Alzheimer's disease), with clinical outcomes that are far from ideal. Herein, we developed the ROS (reactive oxygen species)-responsive brain neuronal targeting nanotheranostic platforms that possess the dual-channel fluorescent "turn-on" properties and release drugs in AD neurons in response to ROS, thereby simultaneously facilitating the diagnosis and therapy of early AD. Through the modification of acetylcholine receptor targeting RVG29 peptide, the nanotheranostics penetrated BBB and accumulated into diseased neurons in an intact form, consequently maximizing the diagnostic and therapeutic performance. The anti-oxidative drug baicalein conjugated onto the surface of nanotheranostics via ROS-cleavable boronate ester linkage rapidly released for ROS scavenging, while the encapsulated fluorophores turned on their fluorescence for AD diagnosis upon microenvironment stimuli. This nanotheranostic strategy exhibited highly sensitivity with a ROS detection limit of up to 100µm and accurately early detection of ROS in 3×Tg AD mice at 6 months of age in vivo. In addition, it could also rescue memory defects, scavenge oxidative stress, attenuate neuroinflammation and enhance neuroprotective effect in 3×Tg AD mice. This work opens up a promising and smart strategy for early diagnosis and therapy in neurodegenerative disease.

Implantable Intraocular Fluorescence Sensor for Visualized Monitoring of Alzheimer's Disease

AbstractWith an increase in the older individuals, the global incidence of Alzheimer's disease (AD) is increasing. Early diagnosis of AD necessitates long‐term monitoring; however, conventional diagnostic methods such as positron emission tomography (PET) and magnetic resonance imaging (MRI) are unsuitable for frequent use because of infrastructure limitations. The eye, connected to the brain via the optic nerve, offers a potential window for monitoring neurodegenerative diseases. This study presents a novel system for continuous intraocular monitoring using a fluorescent aptamer‐conjugated intraocular lens (FAIOL). FAIOL emits fluorescent signals in the presence of beta‐secretase‐1 (BACE1), an enzyme associated with amyloid beta 1–42 production. These signals can be analyzed using mobile phone applications. In vitro, experiments demonstrates that FAIOL responded to BACE1 by detecting low concentrations of the target molecule over extended periods. Ex vivo tests using porcine eyeballs shows an increase in the fluorescence signal intensity by more than 8% within 5 h in the presence of BACE1. The integrated image analysis program reduces sensor noise and provides numerical signal values, facilitating a straightforward interpretation. FAIOL holds significant potential as an innovative platform for the early diagnosis and long‐term monitoring of AD and promises improved patient outcomes through timely intervention and ongoing surveillance.

FMRI-based Alzheimer’s disease detection via functional connectivity analysis: a systematic review

Alzheimer’s disease is a common brain disorder affecting many people worldwide. It is the primary cause of dementia and memory loss. The early diagnosis of Alzheimer’s disease is essential to provide timely care to AD patients and prevent the development of symptoms of this disease. Various non-invasive techniques can be utilized to diagnose Alzheimer’s in its early stages. These techniques include functional magnetic resonance imaging, electroencephalography, positron emission tomography, and diffusion tensor imaging. They are mainly used to explore functional and structural connectivity of human brains. Functional connectivity is essential for understanding the co-activation of certain brain regions co-activation. This systematic review scrutinizes various works of Alzheimer’s disease detection by analyzing the learning from functional connectivity of fMRI datasets that were published between 2018 and 2024. This work investigates the whole learning pipeline including data analysis, standard preprocessing phases of fMRI, feature computation, extraction and selection, and the various machine learning and deep learning algorithms that are used to predict the occurrence of Alzheimer’s disease. Ultimately, the paper analyzed results on AD and highlighted future research directions in medical imaging. There is a need for an efficient and accurate way to detect AD to overcome the problems faced by patients in the early stages.

Development and assessment of algorithms for predicting brain amyloid positivity in a population without dementia

BackgroundThe accumulation of amyloid-β (Aβ) peptide in the brain is a hallmark of Alzheimer’s disease (AD), occurring years before symptom onset. Current methods for quantifying in vivo amyloid load involve invasive or costly procedures, limiting accessibility. Early detection of amyloid positivity in non-demented individuals is crucial for aiding early AD diagnosis and for initiating anti-amyloid immunotherapies at early stages. This study aimed to develop and validate predictive models to identify brain amyloid positivity in non-demented patients, using routinely collected clinical data.MethodsPredictive models for amyloid positivity were developed using data from 853 non-demented participants in the MEMENTO cohort. Amyloid levels were measured potentially repeatedly during study course through Positron Emision Tomography or CerebroSpinal Fluid analysis.The probability of amyloid positivity was modelled using mixed-effects logistic regression. Predictors included demographic information, cognitive assessments, visual brain MRI evaluations of hippocampal atrophy and lobar microbleeds, AD-related blood biomarkers (Aβ42/40 and P-tau181), and ApoE4 status. Models were subjected to internal cross-validation and external validation using data from the Amsterdam Dementia Cohort. Performance also was evaluated in a subsample that met the main criteria of the Appropriate Use Recommendations (AUR) for lecanemab.ResultsThe most effective model incorporated demographic data, cognitive assessments, ApoE status, and AD-related blood biomarkers, achieving AUCs of 0.82 [95%CI 0.81-0.82] in MEMENTO sample and 0.90 [95%CI 0.86-0.94] in the external validation sample. This model significantly outperformed a reference model based solely on demographic and cognitive data, with an AUC difference in MEMENTO of 0.10 [95%CI 0.10-0.11]. A similar model without ApoE genotype achieved comparable discriminatory performance. MRI markers did not improve model performance. Performances in AUR of lecanemab subsample were comparable.ConclusionA predictive model integrating demographic, cognitive, and blood biomarker data offers a promising method to help identify amyloid status in non-demented patients. ApoE genotype and brain MRI data were not necessary for strong discriminatory ability, suggesting that ApoE genotyping may be deferred when assessing the risk-benefit ratio of immunotherapies in amyloid-positive patients who desire treatment. The integration of this model into clinical practice could reduce the need for lumbar puncture or PET examinations to confirm amyloid status.

Optical Coherence Tomography Image Analysis for Detection of Alzheimer’s Disease: A Comprehensive Structured Review

Optical Coherence Tomography (OCT) has emerged as a promising non-invasive imaging modality for the early detection of Alzheimer’s Disease (AD). This systematic literature review aims to consolidate current research on OCT image analysis for AD detection, addressing the growing need for early and accurate diagnostic tools. Despite the advances in neuroimaging, early diagnosis of AD remains challenging due to its asymptomatic nature in initial stages and the invasiveness of traditional methods. To achieve this, we conducted an extensive search of related articles from reputable databases (Scopus and Web of Science), focusing on studies published between 2022-2024. The flow of study was based on PRISMA framework. The database found (n = 29) final primary data. This review was divided into three themes, (1) retinal and ocular biomarkers for AD, (2) optical coherence tomography angiography (OCTA) and imaging techniques, and (3) machine learning and computational approaches for Alzheimer’s disease diagnosis. Key findings include the enlargement of the periarteriole capillary-free zone and changes in retinal nerve fibre layer thickness as potential AD biomarkers. Based on the review, the implementation of image analysis for OCT images have shown substantial potential for AD detection. By evaluating the past studies, gaps for the current research were discovered including the need for larger, more diverse cohorts and longitudinal studies to validate these biomarkers. In summary, detection of AD is possible through thorough OCT image analysis, but further research could be suggested to enhance its clinical applicability and reliability.

Highly sensitive β-amyloid1–42 oligomer aptamer-based assay via dual cyclic amplifications of three-way catalytic hairpin assembly and palindrome polymerase reaction

β-amyloid1–42 oligomer (AβO1–42), a neurotoxic protein, is considered the key factor in the progression of Alzheimer's disease (AD). And, high sensitivity detection of AβO1–42 is of considerable importance for the early diagnosis and deterioration prevention of AD. We present here an ultrasensitive fluorescent AβO1–42 detection approach based on aptamer recognition probes and a new dual cyclic signal amplification strategy by combining three-way catalytic hairpin assembly (3W-CHA) with palindromic sequence-based polymerase amplification (PBPA). Once the aptamers bind to target AβO1–42 molecules, ssDNA sequences are released to initiate 3W-CHA between three hairpins to unfold the signal hairpins and to form Y-shaped DNA structures (Y-DNAs). Subsequently, the assistance ssDNAs bind the Y-DNAs to expose the palindromic sequences to yield Y-DNA dimers, which lead to cyclic PBPA reaction with presence of polymerase to further unfold more signal hairpins for yielding considerably magnified fluorescence recovery, enabling sensitive assay of AβO1–42 down to 2.4 pM within a dynamic range of 5 pM∼50 nM. Furthermore, this strategy has been validated for interrogation of low AβO1–42 levels in artificial cerebrospinal fluid (CSF), suggesting its promising potential for application in early diagnosis and prevention of AD.

Early diagnosis of Alzheimer's Disease based on multi-attention mechanism.

Alzheimer's Disease is a neurodegenerative disorder, and one of its common and prominent early symptoms is language impairment. Therefore, early diagnosis of Alzheimer's Disease through speech and text information is of significant importance. However, the multimodal data is often complex and inconsistent, which leads to inadequate feature extraction. To address the problem, We propose a model for early diagnosis of Alzheimer's Disease based on multimodal attention(EDAMM). Specifically, we first evaluate and select three optimal feature extraction methods, Wav2Vec2.0, TF-IDF and Word2Vec, to extract acoustic and linguistic features. Next, by leveraging self-attention mechanism and cross-modal attention mechanisms, we generate fused features to enhance and capture the inter-modal correlation information. Finally, we concatenate the multimodal features into a composite feature vector and employ a Neural Network(NN) classifier to diagnose Alzheimer's Disease. To evaluate EDAMM, we perform experiments on two public datasets, i.e., NCMMSC2021 and ADReSSo. The results show that EDAMM improves the performance of Alzheimer's Disease diagnosis over state-of-the-art baseline approaches on both datasets.

Exploring the Association between Pro-Inflammation and the Early Diagnosis of Alzheimer’s Disease in Buccal Cells Using Immunocytochemistry and Machine Learning Techniques

The progressive aging of the global population and the high impact of neurodegenerative diseases, such as Alzheimer’s disease (AD), underscore the urgent need for innovative diagnostic and therapeutic strategies. AD, the most prevalent neurodegenerative disorder among the elderly, is expected to affect 75 million people in developing countries by 2030. Despite extensive research, the precise etiology of AD remains elusive due to its heterogeneity and complexity. The key pathological features of AD, including amyloid-beta plaques and hyperphosphorylated tau protein, are established years before clinical symptoms appear. Recent studies highlight the pivotal role of neuroinflammation in AD pathogenesis, with the chronic activation of the brain’s immune system contributing to the disease’s progression. Pro-inflammatory cytokines, such as TNF-α, IL-1β, and IL-6, are elevated in AD and mild cognitive impairment (MCI) patients, suggesting a strong link between peripheral inflammation and CNS degeneration. There is a pressing need for minimally invasive, cost-effective diagnostic methods. Buccal mucosa cells and saliva, which share an embryological origin with the CNS, show promise for AD diagnosis and prognosis. This study integrates cellular observations with advanced data processing and machine learning to identify significant biomarkers and patterns, aiming to enhance the early diagnosis and prevention strategies for AD.

In-situ nanozyme catalytic amplification coupled with a universal antibody orientation strategy based electrochemical immunosensor for AD-related biomarker

An in-situ nanozyme signal tag combined with a DNA-mediated universal antibody-oriented strategy was proposed to establish a high-performance immunosensing platform for Alzheimer's disease (AD)-related biomarker detection. Briefly, a Zr-based metal-organic framework (MOF) with peroxidase (POD)-like activity was synthesized to encapsulating the electroactive molecule methylene blue (MB), and subsequently modified with a layer of gold nanoparticles on its surface. This led to the creation of double POD-like activity nanozymes surrounding the MB molecule to form a nanozyme signal tag. A large number of hydroxyl radicals were generated by the nanozyme signal tag with the help of H2O2, which catalyzed MB molecules in situ to achieve efficient signal amplification. Subsequently, a DNA-aptamer-mediated universal antibody-oriented strategy was proposed to enhance the binding efficiency for the antigen (target). Meanwhile, a poly adenine was incorporated at the end of the aptamer, facilitating binding to the gold electrode and providing anti-fouling properties due to the hydrophilicity of the phosphate group. Under optimal conditions, this platform was successfully employed for highly sensitive detection of AD-associated tau protein and BACE1, achieving limits of detection with concentrations of 3.34 fg/mL and 1.67 fg/mL, respectively. It is worth mentioning that in the tau immunosensing mode, 20 clinical samples from volunteers of varying ages were analyzed, revealing significantly higher tau expression levels in the blood samples of elderly volunteers compared to young volunteers. This suggests that the developed strategy holds great promise for early AD diagnosis.

3D Multimodal Fusion Network with Disease-induced Joint Learning for Early Alzheimer's Disease Diagnosis.

Multimodal neuroimaging provides complementary information critical for accurate early diagnosis of Alzheimer's disease (AD). However, the inherent variability between multimodal neuroimages hinders the effective fusion of multimodal features. Moreover, achieving reliable and interpretable diagnoses in the field of multimodal fusion remains challenging. To address them, we propose a novel multimodal diagnosis network based on multi-fusion and disease-induced learning (MDL-Net) to enhance early AD diagnosis by efficiently fusing multimodal data. Specifically, MDL-Net proposes a multi-fusion joint learning (MJL) module, which effectively fuses multimodal features and enhances the feature representation from global, local, and latent learning perspectives. MJL consists of three modules, global-aware learning (GAL), local-aware learning (LAL), and outer latent-space learning (LSL) modules. GAL via a self-adaptive Transformer (SAT) learns the global relationships among the modalities. LAL constructs local-aware convolution to learn the local associations. LSL module introduces latent information through outer product operation to further enhance feature representation. MDL-Net integrates the disease-induced region-aware learning (DRL) module via gradient weight to enhance interpretability, which iteratively learns weight matrices to identify AD-related brain regions. We conduct the extensive experiments on public datasets and the results confirm the superiority of our proposed method. Our code will be available at: https://github.com/qzf0320/MDL-Net.

Clinical Implementation of Amyloid PET: Latest Findings and Practical Approaches in the Diagnosis and Treatment of Alzheimer's Disease

Amyloid PET plays a crucial role in the early diagnosis of Alzheimer's disease and the determination of the feasibility of disease-modifying therapies. It offers several advantages, including high sensitivity and specificity, minimal invasiveness, and the ability to provide spatial evaluation, all of which contribute to the optimization of dementia care. However, proper use and interpretation of the results require a thorough understanding of their limitations. Although careful consideration is necessary when using scans on asymptomatic individuals, clinical applications could broaden if preemptive treatments and high-precision individual risk assessments for the preclinical stage are developed.

A New Strategy for Ultrasensitive Detection Based on Target microRNA-Triggered Rolling Circle Amplification in the Early Diagnosis of Alzheimer's Disease.

There is an urgent need to accurately quantify microRNA (miRNA)-based Alzheimer's disease (AD) biomarkers, which have emerged as promising diagnostic biomarkers. In this study, we present a rapid and universal approach to establishing a target miRNA-triggered rolling circle amplification (RCA) detection strategy, which achieves ultrasensitive detection of several targets, including miR-let7a-5p, miR-34a-5p, miR-206-3p, miR-9-5p, miR-132-3p, miR-146a-5p, and miR-21-5p. Herein, the padlock probe contains three repeated signal strand binding regions and a target miRNA-specific region. The target miRNA-specific region captures miRNA, and then the padlock probe is circularized with the addition of T4 DNA ligase. Subsequently, an RCA reaction is triggered, and RCA products containing multiple signal strand binding regions are generated to trap abundant fluorescein-labeled signal strands. The addition of exonuclease III (Exo III) causes signal strand digestion and leads to RCA product recycling and liberation of fluorescein. Ultimately, graphene oxide (GO) does not absorb the liberated fluorescein because of poor mutual interaction. This method exhibited high specificity, sensitivity, repeatability, and stability toward let-7a, with a detection limit of 19.35 fM and a linear range of 50 fM to 5 nM. Moreover, it showed excellent applicability for recovering miRNAs in normal human serum. Our strategy was applied to detect miRNAs in the plasma of APP/PS1 mice, demonstrating its potential in the diagnosis of miRNA-associated disease and biochemical research.

Unraveling Alzheimer’s Disease Classification with Deep Learning: A Comprehensive Review

Abstract: Alzheimer's disease (AD) is a neurological disease that progresses over time and is the primary global cause of dementia. Timely and accurate diagnosis is essential for therapeutic and interventional success. Conventional approaches, like clinical evaluations and neuroimaging procedures, are not always able to detect the illness at an early stage. This paper provides an extensive overview of current developments in using deep learning methods for AD early diagnosis and Identification. Numerous approaches have been put up, utilizing diverse forms of data such as MRI scans, eye-tracking actions, and text data that is clinically significant. Convolutional neural networks (CNNs), deep neural networks (DNNs), and long short-term memory networks (LSTMs) are just a few of the deep learning designs used by these models, which highlight how flexible and adaptive these methods are. Using cutting-edge methods to extract discriminative features from the data, feature extraction and selection have become essential components of these models. Additionally, a number of studies have concentrated on improving model performance through the use of methods such as depth-wise convolutional procedures, ensemble learning approaches, and particle swarm optimization for hyperparameter tweaking. Even with high accuracy rates attained, there is still opportunity for development, particularly with regard to managing data scarcity and model interpretability. This survey indicates possible routes for future research and offers insightful information about the state of deep learning applications in AD categorization.

Brain age prediction via cross-stratified ensemble learning

As an important biomarker of neural aging, the brain age reflects the integrity and health of the human brain. Accurate prediction of brain age could help to understand the underlying mechanism of neural aging. In this study, a cross-stratified ensemble learning algorithm with staking strategy was proposed to obtain brain age and the derived predicted age difference (PAD) using T1-weighted magnetic resonance imaging (MRI) data. The approach was characterized as by implementing two modules: one was three base learners of 3D-DenseNet, 3D-ResNeXt, 3D-Inception-v4; another was 14 secondary learners of liner regressions. To evaluate performance, our method was compared with single base learners, regular ensemble learning algorithms, and state-of-the-art (SOTA) methods. The results demonstrated that our proposed model outperformed others models, with three metrics of mean absolute error (MAE), root mean-squared error (RMSE), and coefficient of determination (R2) of 2.9405 years, 3.9458 years, and 0.9597, respectively. Furthermore, there existed significant differences in PAD among the three groups of normal control (NC), mild cognitive impairment (MCI) and Alzheimer's disease (AD), with an increased trend across NC, MCI, and AD. It was concluded that the proposed algorithm could be effectively used in computing brain aging and PAD, and offering potential for early diagnosis and assessment of normal brain aging and AD.