Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

Emerging role and translational potential of small extracellular vesicles in neuroscience

Small extracellular vesicles (sEV) are endogenous lipid-bound membrane vesicles secreted by both prokaryotic and eukaryotic cells into the extracellular environment, performs several biological functions such as cell-cell communication, transfer of proteins, mRNA, and ncRNA to target cells in distant sites. Due to their role in molecular pathogenesis and its potential to deliver biological cargo to target cells, it has become a prominent area of interest in recent research in the field of Neuroscience. However, their role in neurological disorders, like neurodegenerative diseases is more complex and still unaddressed. Thus, this review focuses on the role of sEV in neurodegenerative and neurodevelopmental diseases, including their biogenesis, classification, and pathogenesis, with translational advantages and limitations in the area of neurobiology.

Effects of a dual intervention (motor and virtual reality-based cognitive) on cognition in patients with mild cognitive impairment: a single-blind, randomized controlled trial

BACKGROUND

The increase in cases of mild cognitive impairment (MCI) underlines the urgency of finding effective methods to slow its progression. Given the limited effectiveness of current pharmacological options to prevent or treat the early stages of this deterioration, non-pharmacological alternatives are especially relevant.

OBJECTIVE

To assess the effectiveness of a cognitive-motor intervention based on immersive virtual reality (VR) that simulates an activity of daily living (ADL) on cognitive functions and its impact on depression and the ability to perform such activities in patients with MCI.

METHODS

Thirty-four older adults (men, women) with MCI were randomized to the experimental group (n = 17; 75.41 ± 5.76) or control (n = 17; 77.35 ± 6.75) group. Both groups received motor training, through aerobic, balance and resistance activities in group. Subsequently, the experimental group received cognitive training based on VR, while the control group received traditional cognitive training. Cognitive functions, depression, and the ability to perform activities of daily living (ADLs) were assessed using the Spanish versions of the Montreal Cognitive Assessment (MoCA-S), the Short Geriatric Depression Scale (SGDS-S), and the of Instrumental Activities of Daily Living (IADL-S) before and after 6-week intervention (a total of twelve 40-minutes sessions).

RESULTS

Between groups comparison did not reveal significant differences in either cognitive function or geriatric depression. The intragroup effect of cognitive function and geriatric depression was significant in both groups (p < 0.001), with large effect sizes. There was no statistically significant improvement in any of the groups when evaluating their performance in ADLs (control, p = 0.28; experimental, p = 0.46) as expected. The completion rate in the experimental group was higher (82.35%) compared to the control group (70.59%). Likewise, participants in the experimental group reached a higher level of difficulty in the application and needed less time to complete the task at each level.

CONCLUSIONS

The application of a dual intervention, through motor training prior to a cognitive task based on Immersive VR was shown to be a beneficial non-pharmacological strategy to improve cognitive functions and reduce depression in patients with MCI. Similarly, the control group benefited from such dual intervention with statistically significant improvements.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06313931; https://clinicaltrials.gov/study/NCT06313931 .

Secondary Bioactive Metabolites from Foods of Plant Origin as Theravention Agents against Neurodegenerative Disorders

Neurodegenerative disorders (NDDs) such as Alzheimer's (AD) and Parkinson's (PD) are on the rise, robbing people of their memories and independence. While risk factors such as age and genetics play an important role, exciting studies suggest that a diet rich in foods from plant origin may offer a line of defense. These kinds of foods, namely fruits and vegetables, are packed with a plethora of powerful bioactive secondary metabolites (SBMs), including terpenoids, polyphenols, glucosinolates, phytosterols and capsaicinoids, which exhibit a wide range of biological activities including antioxidant, antidiabetic, antihypertensive, anti-Alzheimer's, antiproliferative, and antimicrobial properties, associated with preventive effects in the development of chronic diseases mediated by oxidative stress such as type 2 diabetes mellitus, respiratory diseases, cancer, cardiovascular diseases, and NDDs. This review explores the potential of SBMs as theravention agents (metabolites with therapeutic and preventive action) against NDDs. By understanding the science behind plant-based prevention, we may be able to develop new strategies to promote brain health and prevent the rise in NDDs. The proposed review stands out by emphasizing the integration of multiple SBMs in plant-based foods and their potential in preventing NDDs. Previous research has often focused on individual compounds or specific foods, but this review aims to present a comprehensive fingerprint of how a diet rich in various SBMs can synergistically contribute to brain health. The risk factors related to NDD development and the diagnostic process, in addition to some examples of food-related products and medicinal plants that significantly reduce the inhibition of acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and β-site amyloid precursor protein (APP) cleaving enzyme 1 (BACE1), are highlighted.

Exploratory Tau PET/CT with [11C]PBB3 in Patients with Suspected Alzheimer's Disease and Frontotemporal Lobar Degeneration: A Pilot Study on Correlation with PET Imaging and Cerebrospinal Fluid Biomarkers

Accurately diagnosing Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) is challenging due to overlapping symptoms and limitations of current imaging methods. This study investigates the use of [11C]PBB3 PET/CT imaging to visualize tau pathology and improve diagnostic accuracy. Given diagnostic challenges with symptoms and conventional imaging, [11C]PBB3 PET/CT's potential to enhance accuracy was investigated by correlating tau pathology with cerebrospinal fluid (CSF) biomarkers, positron emission tomography (PET), computed tomography (CT), amyloid-beta, and Mini-Mental State Examination (MMSE). We conducted [11C]PBB3 PET/CT imaging on 24 patients with suspected AD or FTLD, alongside [11C]PiB PET/CT (13 patients) and [18F]FDG PET/CT (15 patients). Visual and quantitative assessments of [11C]PBB3 uptake using standardized uptake value ratios (SUV-Rs) and correlation analyses with clinical assessments were performed. The scans revealed distinct tau accumulation patterns; 13 patients had no or faint uptake (PBB3-negative) and 11 had moderate to pronounced uptake (PBB3-positive). Significant inverse correlations were found between [11C]PBB3 SUV-Rs and MMSE scores, but not with CSF-tau or CSF-amyloid-beta levels. Here, we show that [11C]PBB3 PET/CT imaging can reveal distinct tau accumulation patterns and correlate these with cognitive impairment in neurodegenerative diseases. Our study demonstrates the potential of [11C]PBB3-PET imaging for visualizing tau pathology and assessing disease severity, offering a promising tool for enhancing diagnostic accuracy in AD and FTLD. Further research is essential to validate these findings and refine the use of tau-specific PET imaging in clinical practice, ultimately improving patient care and treatment outcomes.

From Skin and Gut to the Brain: The Infectious Journey of the Human Commensal Fungus Malassezia and Its Neurological Consequences

The human mycobiome encompasses diverse communities of fungal organisms residing within the body and has emerged as a critical player in shaping health and disease. While extensive research has focused on the skin and gut mycobiome, recent investigations have pointed toward the potential role of fungal organisms in neurological disorders. Among those fungal organisms, the presence of the commensal fungus Malassezia in the brain has created curiosity because of its commensal nature and primary association with the human skin and gut. This budding yeast is responsible for several diseases, such as Seborrheic dermatitis, Atopic dermatitis, Pityriasis versicolor, Malassezia folliculitis, dandruff, and others. However recent findings surprisingly show the presence of Malassezia DNA in the brain and have been linked to diseases like Alzheimer's disease, Parkinson's disease, Multiple sclerosis, and Amyotrophic lateral sclerosis. The exact role of Malassezia in these disorders is unknown, but its ability to infect human cells, travel through the bloodstream, cross the blood-brain barrier, and reside along with the lipid-rich neuronal cells are potential mechanisms responsible for pathogenesis. This also includes the induction of pro-inflammatory cytokines, disruption of the blood-brain barrier, gut-microbe interaction, and accumulation of metabolic changes in the brain environment. In this review, we discuss these key findings from studies linking Malassezia to neurological disorders, emphasizing the complex and multifaceted nature of these cases. Furthermore, we discuss potential mechanisms through which Malassezia might contribute to the development of neurological conditions. Future investigations will open up new avenues for our understanding of the fungal gut-brain axis and how it influences human behavior. Collaborative research efforts among microbiologists, neuroscientists, immunologists, and clinicians hold promise for unraveling the enigmatic connections between human commensal Malassezia and neurological disorders.

MicroRNA biomarkers as next-generation diagnostic tools for neurodegenerative diseases: a comprehensive review

Neurodegenerative diseases (NDs) are characterized by abnormalities within neurons of the brain or spinal cord that gradually lose function, eventually leading to cell death. Upon examination of affected tissue, pathological changes reveal a loss of synapses, misfolded proteins, and activation of immune cells-all indicative of disease progression-before severe clinical symptoms become apparent. Early detection of NDs is crucial for potentially administering targeted medications that may delay disease advancement. Given their complex pathophysiological features and diverse clinical symptoms, there is a pressing need for sensitive and effective diagnostic methods for NDs. Biomarkers such as microRNAs (miRNAs) have been identified as potential tools for detecting these diseases. We explore the pivotal role of miRNAs in the context of NDs, focusing on Alzheimer's disease, Parkinson's disease, Multiple sclerosis, Huntington's disease, and Amyotrophic Lateral Sclerosis. The review delves into the intricate relationship between aging and NDs, highlighting structural and functional alterations in the aging brain and their implications for disease development. It elucidates how miRNAs and RNA-binding proteins are implicated in the pathogenesis of NDs and underscores the importance of investigating their expression and function in aging. Significantly, miRNAs exert substantial influence on post-translational modifications (PTMs), impacting not just the nervous system but a wide array of tissues and cell types as well. Specific miRNAs have been found to target proteins involved in ubiquitination or de-ubiquitination processes, which play a significant role in regulating protein function and stability. We discuss the link between miRNA, PTM, and NDs. Additionally, the review discusses the significance of miRNAs as biomarkers for early disease detection, offering insights into diagnostic strategies.

Unraveling the Epigenetic Landscape: Insights into Parkinson's Disease, Amyotrophic Lateral Sclerosis, and Multiple Sclerosis

Parkinson's disease (PD), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS) are examples of neurodegenerative movement disorders (NMDs), which are defined by a gradual loss of motor function that is frequently accompanied by cognitive decline. Although genetic abnormalities have long been acknowledged as significant factors, new research indicates that epigenetic alterations are crucial for the initiation and development of disease. This review delves into the complex interactions that exist between the pathophysiology of NMDs and epigenetic mechanisms such DNA methylation, histone modifications, and non-coding RNAs. Here, we examine how these epigenetic changes could affect protein aggregation, neuroinflammation, and gene expression patterns, thereby influencing the viability and functionality of neurons. Through the clarification of the epigenetic terrain underpinning neurodegenerative movement disorders, this review seeks to enhance comprehension of the underlying mechanisms of the illness and augment the creation of innovative therapeutic strategies.

Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics

Neurodegenerative conditions significantly impact patient quality of life. Many conditions do not have a cure, but with appropriate and timely treatment the advance of the disease could be diminished. However, many patients only seek a diagnosis once the condition progresses to a point at which the quality of life is significantly impacted. Effective non-invasive and readily accessible methods for early diagnosis can considerably enhance the quality of life of patients affected by neurodegenerative conditions. This work explores the potential of convolutional neural networks (CNNs) for patient gain freezing associated with Parkinson's disease. Sensor data collected from wearable gyroscopes located at the sole of the patient's shoe record walking patterns. These patterns are further analyzed using convolutional networks to accurately detect abnormal walking patterns. The suggested method is assessed on a public real-world dataset collected from parents affected by Parkinson's as well as individuals from a control group. To improve the accuracy of the classification, an altered variant of the recent crayfish optimization algorithm is introduced and compared to contemporary optimization metaheuristics. Our findings reveal that the modified algorithm (MSCHO) significantly outperforms other methods in accuracy, demonstrated by low error rates and high Cohen's Kappa, precision, sensitivity, and F1-measures across three datasets. These results suggest the potential of CNNs, combined with advanced optimization techniques, for early, non-invasive diagnosis of neurodegenerative conditions, offering a path to improve patient quality of life.

Innovation at the Intersection: Emerging Translational Research in Neurology and Psychiatry

Translational research in neurological and psychiatric diseases is a rapidly advancing field that promises to redefine our approach to these complex conditions [...].

Advancing the Frontier: Neuroimaging Techniques in the Early Detection and Management of Neurodegenerative Diseases

Alzheimer's and Parkinson's diseases are among the most prevalent neurodegenerative conditions affecting aging populations globally, presenting significant challenges in early diagnosis and management. This narrative review explores the pivotal role of advanced neuroimaging techniques in detecting and managing these diseases at early stages, potentially slowing their progression through timely interventions. Recent advancements in MRI, such as ultra-high-field systems and functional MRI, have enhanced the sensitivity for detecting subtle structural and functional changes. Additionally, the development of novel amyloid-beta tracers and other emerging modalities like optical imaging and transcranial ultrasonography have improved the diagnostic accuracy and capability of existing methods. This review highlights the clinical applications of these technologies in Alzheimer's and Parkinson's diseases, where they have shown improved diagnostic performance, enabling earlier intervention and better prognostic outcomes. Moreover, the integration of artificial intelligence (AI) and longitudinal research is emerging as a promising enhancement to refine early detection strategies further. However, this review also addresses the technical, ethical, and accessibility challenges in the field, advocating for the more extensive use of advanced imaging technologies to overcome these barriers. Finally, we emphasize the need for a holistic approach that incorporates both neurological and psychiatric perspectives, which is crucial for optimizing patient care and outcomes in the management of neurodegenerative diseases.

Blood-brain barrier biomarkers

The blood-brain barrier (BBB) is a dynamic interface that regulates the exchange of molecules and cells between the brain parenchyma and the peripheral blood. The BBB is mainly composed of endothelial cells, astrocytes and pericytes. The integrity of this structure is essential for maintaining brain and spinal cord homeostasis and protection from injury or disease. However, in various neurological disorders, such as traumatic brain injury, Alzheimer's disease, and multiple sclerosis, the BBB can become compromised thus allowing passage of molecules and cells in and out of the central nervous system parenchyma. These agents, however, can serve as biomarkers of BBB permeability and neuronal damage, and provide valuable information for diagnosis, prognosis and treatment. Herein, we provide an overview of the BBB and changes due to aging, and summarize current knowledge on biomarkers of BBB disruption and neurodegeneration, including permeability, cellular, molecular and imaging biomarkers. We also discuss the challenges and opportunities for developing a biomarker toolkit that can reliably assess the BBB in physiologic and pathophysiologic states.

Essential Oil Molecules Can Break the Loop of Oxidative Stress in Neurodegenerative Diseases

Essential oils (EOs) are mixtures of volatile compounds, extracted from aromatic plants, with multiple activities including antioxidant and anti-inflammatory ones. EOs are complex mixtures easy to find on the market and with low costs. In this mini narrative review, we have collected the results of in vitro and in vivo studies, which tested these EOs on validated models of neurodegeneration and in particular of the two main neurodegenerative diseases (NDs) that afflict humans: Alzheimer's and Parkinson's. Since EO compositions can vary greatly, depending on the environmental conditions, plant cultivar, and extraction methods, we focused our attention to studies involving single EO molecules, and in particular those that have demonstrated the ability to cross the blood-brain barrier. These single EO molecules, alone or in defined mixtures, could be interesting new therapies to prevent or slow down oxidative and inflammatory processes which are common mechanisms that contribute to neuronal death in all NDs.

Morphometric and Nanomechanical Screening of Peripheral Blood Cells with Atomic Force Microscopy for Label-Free Assessment of Alzheimer's Disease, Parkinson's Disease, and Amyotrophic Lateral Sclerosis

Neurodegenerative disorders (NDDs) are complex, multifactorial disorders with significant social and economic impact in today's society. NDDs are predicted to become the second-most common cause of death in the next few decades due to an increase in life expectancy but also to a lack of early diagnosis and mainly symptomatic treatment. Despite recent advances in diagnostic and therapeutic methods, there are yet no reliable biomarkers identifying the complex pathways contributing to these pathologies. The development of new approaches for early diagnosis and new therapies, together with the identification of non-invasive and more cost-effective diagnostic biomarkers, is one of the main trends in NDD biomedical research. Here we summarize data on peripheral biomarkers, biofluids (cerebrospinal fluid and blood plasma), and peripheral blood cells (platelets (PLTs) and red blood cells (RBCs)), reported so far for the three most common NDDs-Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). PLTs and RBCs, beyond their primary physiological functions, are increasingly recognized as valuable sources of biomarkers for NDDs. Special attention is given to the morphological and nanomechanical signatures of PLTs and RBCs as biophysical markers for the three pathologies. Modifications of the surface nanostructure and morphometric and nanomechanical signatures of PLTs and RBCs from patients with AD, PD, and ALS have been revealed by atomic force microscopy (AFM). AFM is currently experiencing rapid and widespread adoption in biomedicine and clinical medicine, in particular for early diagnostics of various medical conditions. AFM is a unique instrument without an analog, allowing the generation of three-dimensional cell images with extremely high spatial resolution at near-atomic scale, which are complemented by insights into the mechanical properties of cells and subcellular structures. Data demonstrate that AFM can distinguish between the three pathologies and the normal, healthy state. The specific PLT and RBC signatures can serve as biomarkers in combination with the currently used diagnostic tools. We highlight the strong correlation of the morphological and nanomechanical signatures between RBCs and PLTs in PD, ALS, and AD.

Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders

Alzheimer's disease (AD) is the most common form of neurodegenerative disorder. The prodromal phase of AD is mild cognitive impairment (MCI). The capacity to predict the transitional phase from MCI to AD represents a challenge for the scientific community. The adoption of artificial intelligence (AI) is useful for diagnostic, predictive analysis starting from the clinical epidemiology of neurodegenerative disorders. We propose a Machine Learning Model (MLM) where the algorithms were trained on a set of neuropsychological, neurophysiological, and clinical data to predict the diagnosis of cognitive decline in both MCI and AD patients.

METHODS

We built a dataset with clinical and neuropsychological data of 4848 patients, of which 2156 had a diagnosis of AD, and 2684 of MCI, for the Machine Learning Model, and 60 patients were enrolled for the test dataset. We trained an ML algorithm using RoboMate software based on the training dataset, and then calculated its accuracy using the test dataset.

RESULTS

The Receiver Operating Characteristic (ROC) analysis revealed that diagnostic accuracy was 86%, with an appropriate cutoff value of 1.5; sensitivity was 72%; and specificity reached a value of 91% for clinical data prediction with MMSE.

CONCLUSION

This method may support clinicians to provide a second opinion concerning high prognostic power in the progression of cognitive impairment. The MLM used in this study is based on big data that were confirmed in enrolled patients and given a credibility about the presence of determinant risk factors also supported by a cognitive test score.

A comprehensive review of protein misfolding disorders, underlying mechanism, clinical diagnosis, and therapeutic strategies

INTRODUCTION

Proteins are the most common biological macromolecules in living system and are building blocks of life. They are extremely dynamic in structure and functions. Due to several modifications, proteins undergo misfolding, leading to aggregation and thereby developing neurodegenerative and systemic diseases. Understanding the pathology of these diseases and the techniques used to diagnose them is therefore crucial for their effective management . There are several techniques, currently being in use to diagnose them and those will be discussed in this review.

AIM/OBJECTIVES

Current review aims to discuss an overview of protein aggregation and the underlying mechanisms linked to neurodegeneration and systemic diseases. Also, the review highlights protein misfolding disorders, their clinical diagnosis, and treatment strategies.

METHODOLOGY

Literature related to neurodegenerative and systemic diseases was explored through PubMed, Google Scholar, Scopus, and Medline databases. The keywords used for literature survey and analysis are protein aggregation, neurodegenerative disorders, Alzheimer's disease, Parkinson's disease, systemic diseases, protein aggregation mechanisms, etc. DISCUSSION /CONCLUSION: This review summarises the pathogenesis of neurodegenerative and systemic disorders caused by protein misfolding and aggregation. The clinical diagnosis and therapeutic strategies adopted for the management of these diseases are also discussed to aid in a better understanding of protein misfolding disorders. Many significant concerns about the role, characteristics, and consequences of protein aggregates in neurodegenerative and systemic diseases are not clearly understood to date. Regardless of technological advancements, there are still great difficulties in the management and cure of these diseases. Therefore, for better understanding, diagnosis, and treatment of neurodegenerative and systemic diseases, more studies to identify novel drugs that may aid in their treatment and management are required.

The emerging role of copper in depression

Copper (Cu) is an essential trace element in the brain and serves as an important cofactor for numerous enzymes involved in a wide range of biochemical processes including neurobehavioral, mitochondrial respiration, and antioxidant effects. Recent studies have demonstrated that copper dyshomeostasis is tightly associated with the development of depression by inducing oxidative stress and inflammatory responses. However, these findings have remained controversial so far. Cumulative studies have shown a positive association, while some other studies showed no association and even a negative association between serum/plasma copper level and depression. Based on these conflicted results, the association was speculated to be due to the clinical features of the population, stages of the disease, severity of copper excess, and types of specimens detected in these studies. In addition, there was an inverse association between dietary copper intake and depression. Furthermore, increasing copper intake could influence dietary zinc and iron intake to prevent and treat depression. Thus, copper supplementation may be a good measure to manage depression. This review provided a deeper understanding of the potential applicability of copper in the prevention and treatment of depression.

Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA)

Mild cognitive impairment (MCI) is often considered a precursor to Alzheimer's disease (AD) and early diagnosis may help improve treatment effectiveness. To identify accurate MCI biomarkers, researchers have utilized various neuroscience techniques, with electroencephalography (EEG) being a popular choice due to its low cost and better temporal resolution. In this scoping review, we analyzed 2310 peer-reviewed articles on EEG and MCI between 2012 and 2022 to track the research progress in this field. Our data analysis involved co-occurrence analysis using VOSviewer and a Patterns, Advances, Gaps, Evidence of Practice, and Research Recommendations (PAGER) framework. We found that event-related potentials (ERP), EEG, epilepsy, quantitative EEG (QEEG), and EEG-based machine learning were the primary research themes. The study showed that ERP/EEG, QEEG, and EEG-based machine learning frameworks provide high-accuracy detection of seizure and MCI. These findings identify the main research themes in EEG and MCI and suggest promising avenues for future research in this field.

Syndrome Pattern Recognition Method Using Sensed Patient Data for Neurodegenerative Disease Progression Identification

Neurodegenerative diseases are a group of conditions that involve the progressive loss of function of neurons in the brain and spinal cord. These conditions can result in a wide range of symptoms, such as difficulty with movement, speech, and cognition. The causes of neurodegenerative diseases are poorly understood, but many factors are believed to contribute to the development of these conditions. The most important risk factors include ageing, genetics, abnormal medical conditions, toxins, and environmental exposures. A slow decline in visible cognitive functions characterises the progression of these diseases. If left unattended or unnoticed, disease progression can result in serious issues such as the cessation of motor function or even paralysis. Therefore, early recognition of neurodegenerative diseases is becoming increasingly important in modern healthcare. Many sophisticated artificial intelligence technologies are incorporated into modern healthcare systems for the early recognition of these diseases. This research article introduces a Syndrome-dependent Pattern Recognition Method for the early detection and progression monitoring of neurodegenerative diseases. The proposed method determines the variance between normal and abnormal intrinsic neural connectivity data. The observed data is combined with previous and healthy function examination data to identify the variance. In this combined analysis, deep recurrent learning is exploited by tuning the analysis layer based on variance suppressed by identifying normal and abnormal patterns in the combined analysis. This variance from different patterns is recurrently used to train the learning model for maximising of recognition accuracy. The proposed method achieves 16.77% high accuracy, 10.55% high precision, and 7.69% high pattern verification. It reduces the variance and verification time by 12.08% and 12.02%, respectively.