Enlightening the advancements in electrochemical bioanalysis for the diagnosis of Alzheimer's disease and other neurodegenerative disorders

Development of a selective electrochemical microsensor based on molecularly imprinted polydopamine/ZIF-67/laser-induced graphene for point-of-care determination of 3-nitrotyrosine

3-nitrotyrosine (3-NT) is a biomarker closely associated with the early diagnosis of oxidative stress-related disorders. The development of an accurate, cost-effective, point-of-care 3-NT sensor holds significant importance for self-monitoring and clinical treatment. In this study, a selective, sensitive, and portable molecularly imprinted electrochemical sensor was developed. ZIF-67 with strong adsorption capacity was facilely modified on an electrochemically active laser-induced graphene (LIG) substrate (formed ZIF-67/LIG). Subsequently, biocompatible dopamine was chosen as the functional monomer, and interference-free ʟ-tyrosine was used as the dummy template to create molecularly imprinted polydopamine (MIPDA) on the ZIF-67/LIG, endowing the sensor with selectivity. The morphologies, electrochemical properties, and detection performance of the sensor were comprehensively investigated using scanning electron microscopy, cyclic voltammetry, electrochemical impedance spectroscopy, and differential pulse voltammetry. To achieve the best performance, several parameters were optimized, including the number of polymerization cycles (15), elution time (60 min), incubation time (7 min), and pH of the buffer solution (6). The turnaround time for this sensor is 10 min. Benefiting from the alliance of MIPDA, ZIF-67, and LIG, the sensor exhibited excellent sensitivity with a detection limit of 6.71 nM, and distinguished selectivity against 11 interfering substances. To enable convenient clinical diagnosis, a customized electrochemical microsensor with MIPDA/ZIF-67/LIG was designed, showcasing excellent reliability and convenience in detecting biological samples without pretreatment. The proposed microsensor will not only facilitate clinical diagnosis and improve patient care, but also provide inspiration for the development of other portable and accurate electrochemical biosensors.

Improving Alzheimer's Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA

The examination of Alzheimer's disease (AD) using adaptive machine learning algorithms has unveiled promising findings. However, achieving substantial credibility in medical contexts necessitates a combination of notable accuracy, minimal processing time, and universality across diverse populations. Therefore, we have formulated a hybrid methodology in this study to classify AD by employing a brain MRI image dataset. We incorporated an averaging filter during preprocessing in the initial stage to reduce extraneous details. Subsequently, a combined strategy was utilized, involving principal component analysis (PCA) in conjunction with stepwise linear discriminant analysis (SWLDA), followed by an artificial neural network (ANN). SWLDA employs a combination of forward and backward recursion methods to choose a restricted set of features. The forward recursion identifies the most interconnected features based on partial Z-test values. Conversely, the backward recursion method eliminates the least correlated features from the same feature space. After the extraction and selection of features, an optimized artificial neural network (ANN) was utilized to differentiate the various classes of AD. To demonstrate the significance of this hybrid approach, we utilized publicly available brain MRI datasets using a 10-fold cross-validation strategy. The proposed method excelled over existing state-of-the-art systems, attaining weighted average recognition rates of 99.35% and 96.66%, respectively, across all the datasets.

Ranuncoside's attenuation of scopolamine-induced memory impairment in mice via Nrf2 and NF-ĸB signaling

Scopolamine is a well-known pharmacological agent responsible for causing memory impairment in animals, as well as oxidative stress and neuroinflammation inducer which lead to the development of Alzheimer disease. Although a cure for Alzheimer's disease is unavailable. Ranuncoside, a metabolite obtained from a medicinal plant has demonstrated antioxidant and anti-inflammatory properties in vitro, making it a promising treatment with potential anti-Alzheimer disease properties. However, as ranuncoside has not been evaluated for its antioxidant and anti-neuroinflammatory properties in any in vivo model, our study aimed to evaluate its neurotherapeutic efficacy against scopolamine-induced memory impairment in adult male albino mice. Mice were randomly divided into four experimental groups. Mice of group I was injected with saline, group II was injected with scopolamine (1 mg/kg/day) for 3 weeks. After receiving a daily injection of scopolamine for 1 week, the mice of group III were injected with ranuncoside (10 mg/kg) every other day for 2 weeks along with scopolamine daily and group IV were injected with ranuncoside on 5th alternate days. Behavioral tests (i.e., Morris water maze and Y-maze) were performed to determine the memory-enhancing effect of ranuncoside against scopolamine's memory deleterious effect. Western blot analysis was also performed to further elucidate the anti-neuroinflammatory and antioxidant effects of ranuncoside against scopolamine-induced neuroinflammation and oxidative stress. Our results showed memory-enhancing, anti-neuroinflammatory effect, and antioxidant effects of ranuncoside against scopolamine by increasing the expression of the endogenous antioxidant system (i.e., Nrf2 and HO-1), followed by blocking neuroinflammatory markers such as NF-κB, COX-2, and TNF-α. The results also revealed that ranuncoside possesses hypoglycemic and hypolipidemic effects against scopolamine-induced hyperglycemia and hyperlipidemia in mice as well as scopolamine's hyperglycemic effect. In conclusion, our findings suggest that ranuncoside could be a potential agent for the management of Alzheimer's disease, hyperglycemia, and hyperlipidemia.

Electrochemical Immunosensors Developed for Amyloid-Beta and Tau Proteins, Leading Biomarkers of Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurological disease and a serious cause of dementia, which constitutes a threat to human health. The clinical evidence has found that extracellular amyloid-beta peptides (Aβ), phosphorylated tau (p-tau), and intracellular tau proteins, which are derived from the amyloid precursor protein (APP), are the leading biomarkers for accurate and early diagnosis of AD due to their central role in disease pathology, their correlation with disease progression, their diagnostic value, and their implications for therapeutic interventions. Their detection and monitoring contribute significantly to understanding AD and advancing clinical care. Available diagnostic techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are mainly used to validate AD diagnosis. However, these methods are expensive, yield results that are difficult to interpret, and have common side effects such as headaches, nausea, and vomiting. Therefore, researchers have focused on developing cost-effective, portable, and point-of-care alternative diagnostic devices to detect specific biomarkers in cerebrospinal fluid (CSF) and other biofluids. In this review, we summarized the recent progress in developing electrochemical immunosensors for detecting AD biomarkers (Aβ and p-tau protein) and their subtypes (AβO, Aβ_(1-40), Aβ_(1-42), t-tau, cleaved-tau (c-tau), p-tau₁₈₁, p-tau₂₃₁, p-tau₃₈₁, and p-tau₄₄₁). We also evaluated the key characteristics and electrochemical performance of developed immunosensing platforms, including signal interfaces, nanomaterials or other signal amplifiers, biofunctionalization methods, and even primary electrochemical sensing performances (i.e., sensitivity, linear detection range, the limit of detection (LOD), and clinical application).

Classification of Alzheimer's Disease Based on Deep Learning of Brain Structural and Metabolic Data

To improve the diagnosis and classification of Alzheimer's disease (AD), a modeling method is proposed based on the combining magnetic resonance images (MRI) brain structural data with metabolite levels of the frontal and parietal regions. First, multi-atlas brain segmentation technology based on T1-weighted images and edited magnetic resonance spectroscopy (MRS) were used to extract data of 279 brain regions and levels of 12 metabolites from regions of interest (ROIs) in the frontal and parietal regions. The t-test combined with false discovery rate (FDR) correction was used to reduce the dimensionality in the data, and MRI structural data of 54 brain regions and levels of 4 metabolites that obviously correlated with AD were screened out. Lastly, the stacked auto-encoder neural network (SAE) was used to classify AD and healthy controls (HCs), which judged the effect of classification method by fivefold cross validation. The results indicated that the mean accuracy of the five experimental model increased from 96 to 100%, the AUC value increased from 0.97 to 1, specificity increased from 90 to 100%, and F1 value increased from 0.97 to 1. Comparing the effect of each metabolite on model performance revealed that the gamma-aminobutyric acid (GABA) + levels in the parietal region resulted in the most significant improvement in model performance, with the accuracy rate increasing from 96 to 98%, the AUC value increased from 0.97 to 0.99 and the specificity increasing from 90 to 95%. Moreover, the GABA + levels in the parietal region was significantly correlated with Mini Mental State Examination (MMSE) scores of patients with AD (r = 0.627), and the F statistics were largest (F = 25.538), which supports the hypothesis that dysfunctional GABAergic system play an important role in the pathogenesis of AD. Overall, our findings support that a comprehensive method that combines MRI structural and metabolic data of brain regions can improve model classification efficiency of AD.

Investigation of Whole and Glandular Saliva as a Biomarker for Alzheimer’s Disease Diagnosis

Salivary Aβ40, Aβ42, t-tau, and p-tau 181 are commonly employed in Alzheimer’s disease (AD) investigations. However, the collection method of these biomarkers can affect their levels. To assess the impact of saliva collection methods on biomarkers in this study, 15 healthy people were employed in the morning with six saliva collection methods. The chosen methods were then applied in 30 AD patients and 30 non-AD controls. The levels of salivary biomarkers were calculated by a specific enzyme-linked immunosorbent assay. The receiver operating characteristic was utilized to assess salivary biomarkers in AD patients. The results demonstrated that the highest levels of salivary Aβ40, Aβ42, t-tau, and p-tau were in different saliva collection methods. The correlations between different saliva biomarkers in the same collection method were different. Salivary Aβ40, Aβ42, t-tau, and p-tau had no significant association. Salivary Aβ42 was higher in AD than in non-AD controls. However, p-tau/t-tau and Aβ42/Aβ40 had some relevance. The area under the curve for four biomarkers combined in AD diagnosis was 92.11%. An alternate saliva collection method (e.g., USS in Aβ40, UPS in Aβ42, t-tau, SSS in p-tau 181) was demonstrated in this study. Moreover, combining numerous biomarkers improves AD diagnosis.

Alzheimer's disease diagnosis based on detection of autoantibodies against Aβ using Aβ40 peptide in liposomes

BACKGROUND

Alzheimer's disease (AD) is the most common form of dementia and affect more than 50 million people worldwide. Thus, there is a high demand by non-invasive methods for an early diagnosis. This work explores the AD diagnostic using the amyloid beta 1-40 (Aβ40) peptide encapsulated into dipalmitoyl phosphatidyl glycerol (DPPG) liposomes and immobilized on polyethylene imine previously deposited on screen-printed carbon electrodes to detect autoantibodies against Aβ40, a potential biomarker found in plasma samples.

METHODS

The immunosensor assembly was accompanied by atomic force microscopy (AFM) images that showed globular aggregates from 20 to 200 nm corresponding liposomes and by cyclic voltammetry (CV) through increase of the voltammogram area each material deposited. After building the immunosensor, when it was exposed to antibody anti-Aβ40, there was an increase in film roughness of approximately 9 nm, indicating the formation of the immunocomplex.

RESULTS

In the detection by CV, the presence of specific antibody, in the range of 0.1 to 10 μg/ml, resulted in an increase in the voltammograms area and current in 0.45 V reaching 3.2 µA.V and 5.7 μA, respectively, in comparison with the control system, which remained almost unchanged from 0.1 μg/ml. In patient samples, both cerebrospinal fluid (CSF) and plasma, was possible separated among positive and negative samples for AD using CV profile and area, with a difference of 0.1 μA.V from the upper error bar of healthy samples for CSF sample and 0.6 μA.V for plasma sample.

CONCLUSIONS

These results showed the feasibility of the method employed for the non-invasive diagnostic of Alzheimer's disease detecting natural autoantibodies that circulate in plasma through a simple and easy-to-interpret method.

Salivary biomarkers of neurodegenerative and demyelinating diseases and biosensors for their detection

Salivary analysis is gaining increasing interest as a novel and promising field of research for the diagnosis of neurodegenerative and demyelinating diseases related to aging. The collection of saliva offers several advantages, being noninvasive, stress-free, and repeatable. Moreover, the detection of biomarkers directly in saliva could allow an early diagnosis of the disease, leading to timely treatments. The aim of this manuscript is to highlight the most relevant researchers' findings relatively to salivary biomarkers of neurodegenerative and demyelinating diseases, and to describe innovative and advanced biosensing strategies for the detection of salivary biomarkers. This review is focused on five relevant aging-related neurodegenerative disorders (Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis, Huntington's disease, Multiple Sclerosis) and the salivary biomarkers most commonly associated with them. Advanced biosensors enabling molecular diagnostics for the detection of salivary biomarkers are presented, in order to stimulate future research in this direction and pave the way for their clinical application.

Electrochemical biosensors for biometal-protein interactions in neurodegenerative diseases

Electrochemical biosensors have been adopted into a wide range of applications in the study of biometal-protein interactions in neurodegenerative diseases. Transition metals such as zinc, copper, and iron that are significant to biological functions have been shown to have strong implications in the progressive neural degeneration in Alzheimer's disease (AD), Parkinson's disease (PD), and prion protein diseases. This review presents a summative examination of the progress made in the design, fabrication, and applications of electrochemical biosensors in recent literature at understanding the metal-protein interactions in neurodegenerative diseases. The focus will be drawn on disease-causing biomarkers such as amyloid-β (Aβ) and tau proteins for AD, α-synuclein (α-syn) for PD, and prion proteins (PrP). Topics such as the use of electrochemical biosensing in monitoring biometal-induced conformational changes, elucidation of complexation motifs, production of reactive oxygen species (ROS) as well as the influence on downstream biomolecular interactions will be discussed. Major results and important concepts presented in these studies will be summarized in the hope to spark inspiration for the next generation of electrochemical sensors.

Biosensors-on-Chip: An Up-to-Date Review

Generally, biosensors are designed to translate physical, chemical, or biological events into measurable signals, thus offering qualitative and/or quantitative information regarding the target analytes. While the biosensor field has received considerable scientific interest, integrating this technology with microfluidics could further bring significant improvements in terms of sensitivity and specificity, resolution, automation, throughput, reproducibility, reliability, and accuracy. In this manner, biosensors-on-chip (BoC) could represent the bridging gap between diagnostics in central laboratories and diagnostics at the patient bedside, bringing substantial advancements in point-of-care (PoC) diagnostic applications. In this context, the aim of this manuscript is to provide an up-to-date overview of BoC system development and their most recent application towards the diagnosis of cancer, infectious diseases, and neurodegenerative disorders.

Body Fluid Biomarkers for Alzheimer's Disease-An Up-To-Date Overview

Neurodegeneration is a highly complex process which is associated with a variety of molecular mechanisms related to ageing. Among neurodegenerative disorders, Alzheimer's disease (AD) is the most common, affecting more than 45 million individuals. The underlying mechanisms involve amyloid plaques and neurofibrillary tangles (NFTs) deposition, which will subsequently lead to oxidative stress, chronic neuroinflammation, neuron dysfunction, and neurodegeneration. The current diagnosis methods are still limited in regard to the possibility of the accurate and early detection of the diseases. Therefore, research has shifted towards the identification of novel biomarkers and matrices as biomarker sources, beyond amyloid-β and tau protein levels within the cerebrospinal fluid (CSF), that could improve AD diagnosis. In this context, the aim of this paper is to provide an overview of both conventional and novel biomarkers for AD found within body fluids, including CSF, blood, saliva, urine, tears, and olfactory fluids.

Potential of Microfluidics and Lab-on-Chip Platforms to Improve Understanding of "prion-like" Protein Assembly and Behavior

Human aging is accompanied by a relevant increase in age-associated chronic pathologies, including neurodegenerative and metabolic diseases. The appearance and evolution of numerous neurodegenerative diseases is paralleled by the appearance of intracellular and extracellular accumulation of misfolded proteins in affected brains. In addition, recent evidence suggests that most of these amyloid proteins can behave and propagate among neural cells similarly to infective prions. In order to improve understanding of the seeding and spreading processes of these "prion-like" amyloids, microfluidics and 3D lab-on-chip approaches have been developed as highly valuable tools. These techniques allow us to monitor changes in cellular and molecular processes responsible for amyloid seeding and cell spreading and their parallel effects in neural physiology. Their compatibility with new optical and biochemical techniques and their relative availability have increased interest in them and in their use in numerous laboratories. In addition, recent advances in stem cell research in combination with microfluidic platforms have opened new humanized in vitro models for myriad neurodegenerative diseases affecting different cellular targets of the vascular, muscular, and nervous systems, and glial cells. These new platforms help reduce the use of animal experimentation. They are more reproducible and represent a potential alternative to classical approaches to understanding neurodegeneration. In this review, we summarize recent progress in neurobiological research in "prion-like" protein using microfluidic and 3D lab-on-chip approaches. These approaches are driven by various fields, including chemistry, biochemistry, and cell biology, and they serve to facilitate the development of more precise human brain models for basic mechanistic studies of cell-to-cell interactions and drug discovery.

Electrochemical Biosensors Based on Nanomaterials for Early Detection of Alzheimer's Disease

Alzheimer's disease (AD) is an untreatable neurodegenerative disease that initially manifests as difficulty to remember recent events and gradually progresses to cognitive impairment. The incidence of AD is growing yearly as life expectancy increases, thus early detection is essential to ensure a better quality of life for diagnosed patients. To reach that purpose, electrochemical biosensing has emerged as a cost-effective alternative to traditional diagnostic techniques, due to its high sensitivity and selectivity. Of special relevance is the incorporation of nanomaterials in biosensors, as they contribute to enhance electron transfer while promoting the immobilization of biological recognition elements. Moreover, nanomaterials have also been employed as labels, due to their unique electroactive and electrocatalytic properties. The aim of this review is to add value in the advances achieved in the detection of AD biomarkers, the strategies followed for the incorporation of nanomaterials and its effect in biosensors performance.