Lipidomic Analysis of Plasma Extracellular Vesicles Derived from Alzheimer's Disease Patients.

Analysis of blood-based indicators of brain health could provide an understanding of early disease mechanisms and pinpoint possible intervention strategies. By examining lipid profiles in extracellular vesicles (EVs), secreted particles from all cells, including astrocytes and neurons, and circulating in clinical samples, important insights regarding the brain's composition can be gained. Herein, a targeted lipidomic analysis was carried out in EVs derived from plasma samples after removal of lipoproteins from individuals with Alzheimer's disease (AD) and healthy controls. Differences were observed for selected lipid species of glycerolipids (GLs), glycerophospholipids (GPLs), lysophospholipids (LPLs) and sphingolipids (SLs) across three distinct EV subpopulations (all-cell origin, derived by immunocapture of CD9, CD81 and CD63; neuronal origin, derived by immunocapture of L1CAM; and astrocytic origin, derived by immunocapture of GLAST). The findings provide new insights into the lipid composition of EVs isolated from plasma samples regarding specific lipid families (MG, DG, Cer, PA, PC, PE, PI, LPI, LPE, LPC), as well as differences between AD and control individuals. This study emphasizes the crucial role of plasma EV lipidomics analysis as a comprehensive approach for identifying biomarkers and biological targets in AD and related disorders, facilitating early diagnosis and potentially informing novel interventions.

The RODI mHealth app Insight: Machine-Learning-Driven Identification of Digital Indicators for Neurodegenerative Disorder Detection.

Neurocognitive Disorders (NCDs) pose a significant global health concern, and early detection is crucial for optimizing therapeutic outcomes. In parallel, mobile health apps (mHealth apps) have emerged as a promising avenue for assisting individuals with cognitive deficits. Under this perspective, we pioneered the development of the RODI mHealth app, a unique method for detecting aligned with the criteria for NCDs using a series of brief tasks. Utilizing the RODI app, we conducted a study from July to October 2022 involving 182 individuals with NCDs and healthy participants. The study aimed to assess performance differences between healthy older adults and NCD patients, identify significant performance disparities during the initial administration of the RODI app, and determine critical features for outcome prediction. Subsequently, the results underwent machine learning processes to unveil underlying patterns associated with NCDs. We prioritize the tasks within RODI based on their alignment with the criteria for NCDs, thus acting as key digital indicators for the disorder. We achieve this by employing an ensemble strategy that leverages the feature importance mechanism from three contemporary classification algorithms. Our analysis revealed that tasks related to visual working memory were the most significant in distinguishing between healthy individuals and those with an NCD. On the other hand, processes involving mental calculations, executive working memory, and recall were less influential in the detection process. Our study serves as a blueprint for future mHealth apps, offering a guide for enhancing the detection of digital indicators for disorders and related conditions.

Machine Learning Analysis of Alzheimer's Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions.

Advancements in molecular biology have revolutionized our understanding of complex diseases, with Alzheimer's disease being a prime example. Single-cell sequencing, currently the most suitable technology, facilitates profoundly detailed disease analysis at the cellular level. Prior research has established that the pathology of Alzheimer's disease varies across different brain regions and cell types. In parallel, only machine learning has the capacity to address the myriad challenges presented by such studies, where the integration of large-scale data and numerous experiments is required to extract meaningful knowledge. Our methodology utilizes single-cell RNA sequencing data from healthy and Alzheimer's disease (AD) samples, focused on the cortex and hippocampus regions in mice. We designed three distinct case studies and implemented an ensemble feature selection approach through machine learning, also performing an analysis of distinct age-related datasets to unravel age-specific effects, showing differential gene expression patterns within each condition. Important evidence was reported, such as enrichment in central nervous system development and regulation of oligodendrocyte differentiation between the hippocampus and cortex of 6-month-old AD mice as well as regulation of epinephrine secretion and dendritic spine morphogenesis in 15-month-old AD mice. Our outcomes from all three of our case studies illustrate the capacity of machine learning strategies when applied to single-cell data, revealing critical insights into Alzheimer's disease.

In Silico Structural Analysis Exploring Conformational Folding of Protein Variants in Alzheimer's Disease.

Accurate protein structure prediction using computational methods remains a challenge in molecular biology. Recent advances in AI-powered algorithms provide a transformative effect in solving this problem. Even though AlphaFold's performance has improved since its release, there are still limitations that apply to its efficacy. In this study, a selection of proteins related to the pathology of Alzheimer's disease was modeled, with Presenilin-1 (PSN1) and its mutated variants in the foreground. Their structural predictions were evaluated using the ColabFold implementation of AlphaFold, which utilizes MMseqs2 for the creation of multiple sequence alignments (MSAs). A higher number of recycles than the one used in the AlphaFold DB was selected, and no templates were used. In addition, prediction by RoseTTAFold was also applied to address how structures from the two deep learning frameworks match reality. The resulting conformations were compared with the corresponding experimental structures, providing potential insights into the predictive ability of this approach in this particular group of proteins. Furthermore, a comprehensive examination was performed on features such as predicted regions of disorder and the potential effect of mutations on PSN1. Our findings consist of highly accurate superpositions with little or no deviation from experimentally determined domain-level models.

Methods for cell-type annotation on scRNA-seq data: A recent overview.

The evolution of single-cell technology is ongoing, continually generating massive amounts of data that reveal many mysteries surrounding intricate diseases. However, their drawbacks continue to constrain us. Among these, annotating cell types in single-cell gene expressions pose a substantial challenge, despite the myriad of tools at our disposal. The rapid growth in data, resources, and tools has consequently brought about significant alterations in this area over the years. In our study, we spotlight all note-worthy cell type annotation techniques developed over the past four years. We provide an overview of the latest trends in this field, showcasing the most advanced methods in taxonomy. Our research underscores the demand for additional tools that incorporate a biological context and also predicts that the rising trend of graph neural network approaches will likely lead this research field in the coming years.

Assessing and Modelling of Post-Traumatic Stress Disorder Using Molecular and Functional Biomarkers.

Post-traumatic stress disorder (PTSD) is a complex psychological disorder that develops following exposure to traumatic events. PTSD is influenced by catalytic factors such as dysregulated hypothalamic-pituitary-adrenal (HPA) axis, neurotransmitter imbalances, and oxidative stress. Genetic variations may act as important catalysts, impacting neurochemical signaling, synaptic plasticity, and stress response systems. Understanding the intricate gene networks and their interactions is vital for comprehending the underlying mechanisms of PTSD. Focusing on the catalytic factors of PTSD is essential because they provide valuable insights into the underlying mechanisms of the disorder. By understanding these factors and their interplay, researchers may uncover potential targets for interventions and therapies, leading to more effective and personalized treatments for individuals with PTSD. The aforementioned gene networks, composed of specific genes associated with the disorder, provide a comprehensive view of the molecular pathways and regulatory mechanisms involved in PTSD. Through this study valuable insights into the disorder's underlying mechanisms and opening avenues for effective treatments, personalized interventions, and the development of biomarkers for early detection and monitoring are provided.

Setting Up a Bio-AFM to Study Protein Misfolding in Neurodegenerative Diseases.

The clinical pathology of neurodegenerative diseases suggests that earlier onset and progression are related to the accumulation of protein aggregates due to misfolding. A prominent way to extract useful information regarding single-molecule studies of protein misfolding at the nanoscale is by capturing the unbinding molecular forces through forced mechanical tension generated and monitored by an atomic force microscopy-based single-molecule force spectroscopy (AFM-SMFS). This AFM-driven process results in an amount of data in the form of force versus molecular extension plots (force-distance curves), the statistical analysis of which can provide insights into the underlying energy landscape and assess a number of characteristic elastic and kinetic molecular parameters of the investigated sample. This chapter outlines the setup of a bio-AFM-based SMFS technique for single-molecule probing. The infrastructure used as a reference for this presentation is the Bruker ForceRobot300.

Protein Structure Prediction for Disease-Related Insertions/Deletions in Presenilin 1 Gene.

More than 450 mutations, some of which have unknown toxicity, have been reported in the presenilin 1 gene, which is the most common cause of Alzheimer's disease (AD) with an early onset. PSEN1 mutations are thought to be responsible for approximately 80% of cases of monogenic AD, which are characterized by complete penetrance and an early age of onset. It is still unknown exactly how mutations in the presenilin 1 gene can cause dementia and neurodegeneration; however, both conditions have been linked to these changes. In this chapter, well-known computational analysis servers and accessible databases such as Uniprot, iTASSER, and PDBeFold are examined for their ability to predict the functional domains of mutant proteins and quantify the effect that these mutations have on the three-dimensional structure of the protein.

An Updated Evolutionary and Structural Study of TBK1 Reveals Highly Conserved Motifs as Potential Pharmacological Targets in Neurodegenerative Diseases.

TANK-binding kinase 1 protein (TBK1) is a kinase that belongs to the IκB (IKK) family. TBK1, also known as T2K, FTDALS4, NAK, IIAE8, and NF-κB, is responsible for the phosphorylation of the amino acid residues, serine and threonine. This enzyme is involved in various key biological processes, including interferon activation and production, homeostasis, cell growth, autophagy, insulin production, and the regulation of TNF-α, IFN-ß, and IL-6. Mutations in the TBK1 gene alter the protein's normal function and may lead to an array of pathological conditions, including disorders of the central nervous system. The present study sought to elucidate the role of the TBK1 protein in amyotrophic lateral sclerosis (ALS), a human neurodegenerative disorder. A broad evolutionary and phylogenetic analysis of TBK1 was performed across numerous organisms to distinguish conserved regions important for the protein's function. Subsequently, mutations and SNPs were explored, and their potential effect on the enzyme's function was investigated. These analytical steps, in combination with the study of the secondary, tertiary, and quaternary structure of TBK1, enabled the identification of conserved motifs, which can function as novel pharmacological targets and inform therapeutic strategies for amyotrophic lateral sclerosis.

Computational and Functional Insights of Protein Misfolding in Neurodegeneration.

Protein folding is the process by which a polypeptide chain self-assembles into the correct three-dimensional structure, so that it ends up in the biologically active, native state. Under conditions of proteotoxic stress, mutations, or cellular aging, proteins can begin to aggregate into non-native structures such as ordered amyloid fibrils and plaques. Many neurodegenerative diseases involve the misfolding and aggregation of specific proteins into abnormal, toxic species. Experimental approaches including crystallography and AFM (atomic force microscopy)-based force spectroscopy are used to exploit the folding and structural characterization of protein molecules. At the same time, computational techniques through molecular dynamics, fold recognition, and structure prediction are widely applied in this direction. Benchmarking analysis for combining and comparing computational methodologies with functional studies can decisively unravel robust interactions between the side groups of the amino acid sequence and monitor alterations in intrinsic protein dynamics with high precision as well as adequately determine potent conformations of the folded patterns formed in the polypeptide structure.

Human Breast Milk Omics and Neurodevelopment.

Breast milk is the ideal food for the premature and mature babies and has undoubtedly immediate and ultimate benefits. Among other things, it protects against infections, reduces the risk of necrotizing enterocolitis and retinopathy of the premature babies, improves neurodevelopmental outcome, and reduces the risk of obesity and metabolic syndrome later in life. In the present study, breast milk will be studied with all the available omics technologies. More specifically, functional genomics, comparative genomics, transcriptomics, sequencing, proteomics, and metabolomics will be performed. The above results and this multidimensional information will be coordinated under the framework of a holistic approach of systems biology and bioinformatic analysis. Important IncRNAs and protein molecules will be validated as candidate biomarkers in exosomes of a larger group of breast milk and blood/serum samples. Validated ncRNAs/proteins will be analyzed in exudates of breast milk and bovine, goat, and sheep milk to explore new ways to improve milk synthesis. Expression of ncRNAs, unlike mRNAs, is a direct indicator of their functional presence. The information to be generated in this study will be analyzed by mining and data combining techniques and algorithms. After defining breast milk molecular fingerprinting, an attempt will be made to enhance the commercial product. The benefits of breast milk are attributed to its various components, including nutrients, hormones, growth factors, immune cells, antibodies, cytokines, antimicrobial peptides, and extracellular vesicles.

A Sensor-Based Platform for Early-Stage Parkinson's Disease Monitoring.

Biosensing platforms have gained much attention in clinical practice screening thousands of samples simultaneously for the accurate detection of important markers in various diseases for diagnostic and prognostic purposes. Herein, a framework for the design of an innovative methodological approach combined with data processing and appropriate software in order to implement a complete diagnostic system for Parkinson's disease exploitation is presented. The integrated platform consists of biochemical and peripheral sensor platforms for measuring biological and biometric parameters of examinees, a central collection and management unit along with a server for storing data, and a decision support system for patient's state assessment regarding the occurrence of the disease. The suggested perspective is oriented on data processing and experimental implementation and can provide a powerful holistic evaluation of personalized monitoring of patients or individuals at high risk of manifestation of the disease.

Spiking Neural Networks and Mathematical Models.

Neural networks are applied in various scientific fields such as medicine, engineering, pharmacology, etc. Investigating operations of neural networks refers to estimating the relationship among single neurons and their contributions to the network as well. Hence, studying a single neuron is an essential process to solve complex brain problems. Mathematical models that simulate neurons and the way they transmit information are proven to be an indispensable tool for neuroscientists. Constructing appropriate mathematical models to simulate information transmission of a biological neural network is a challenge for researchers, as in the real world, identical neurons in terms of their electrophysiological characteristics in different brain regions do not contribute in the same way to information transmission within a neural network due to the intrinsic characteristics. This review highlights four mathematical, single-compartment models: Hodgkin-Huxley, Izhikevich, Leaky Integrate, and Fire and Morris-Lecar, and discusses comparison among them in terms of their biological plausibility, computational complexity, and applications, according to modern literature.

DRDs and Brain-Derived Neurotrophic Factor Share a Common Therapeutic Ground: A Novel Bioinformatic Approach Sheds New Light Toward Pharmacological Treatment of Cognitive and Behavioral Disorders.

Cognitive and behavioral disorders are subgroups of mental health disorders. Both cognitive and behavioral disorders can occur in people of different ages, genders, and social backgrounds, and they can cause serious physical, mental, or social problems. The risk factors for these diseases are numerous, with a range from genetic and epigenetic factors to physical factors. In most cases, the appearance of such a disorder in an individual is a combination of his genetic profile and environmental stimuli. To date, researchers have not been able to identify the specific causes of these disorders, and as such, there is urgent need for innovative study approaches. The aim of the present study was to identify the genetic factors which seem to be more directly responsible for the occurrence of a cognitive and/or behavioral disorder. More specifically, through bioinformatics tools and software as well as analytical methods such as systemic data and text mining, semantic analysis, and scoring functions, we extracted the most relevant single nucleotide polymorphisms (SNPs) and genes connected to these disorders. All the extracted SNPs were filtered, annotated, classified, and evaluated in order to create the "genomic grammar" of these diseases. The identified SNPs guided the search for top suspected genetic factors, dopamine receptors D and neurotrophic factor BDNF, for which regulatory networks were built. The identification of the "genomic grammar" and underlying factors connected to cognitive and behavioral disorders can aid in the successful disease profiling and the establishment of novel pharmacological targets and provide the basis for personalized medicine, which takes into account the patient's genetic background as well as epigenetic factors.

Using Biomarkers for Cognitive Enhancement and Evaluation in Mobile Applications.

Clinicians are increasingly using biomarkers to diagnose and monitor cognitive conditions such as mild cognitive impairment, Alzheimer's disease, and dementia. Biomarkers are classified into two main categories based on their clinical goal: disease-associated biomarkers and drug-related biomarkers. In the case of disease-associated biomarkers, neuroimaging biomarkers are used to predict and validate Alzheimer's disease at any of its stages including mild cognitive impairment. The use of mobile and wearable devices to collect data about a person's daily activities and behaviors has led to the emergence of a new type of biomarker known as digital biomarkers. This type of data provides a digital reflection of a person's function in the context of everyday life and can be used to monitor and track changes in an individual's health and behaviors over time. The use of biomarkers in mobile applications for cognitive enhancement and evaluation can provide valuable insights into an individual's cognitive health and can help to optimize treatment and prevention strategies.

Application of Machine Learning Techniques in the HELIAD Study Data for the Development of Diagnostic Models in MCI and Dementia.

The increase in the population's life expectancy leads to an increase in the incidence of dementia and, therefore, in diseases such as Alzheimer's. Towards this direction, the HELIAD1 study is the first large-scale epidemiological study aimed at assessing epidemiological data on dementia, mild mental decline, and other neuropsychiatric disorders associated with old age. This is a huge study with several computational challenges, most of which can be addressed by machine learning processes. The objectives of this study were to detect patterns in the HELIAD clinical data that classify with high accuracy various levels of cognitive impairment by training ML algorithms and hence apply derived model on future clinical data to predict with the same accuracy the class variable. We propose a machine learning method based on RUSBoost classifier to identify a critical subset of biomarkers that classify accurately between neurological patients with mild cognitive impairment (MCI) or dementia of the Alzheimer's type (DAT) and the cognitively healthy control (CHC) group. In this study we used a highly skewed (imbalanced) dataset with most observations (majority class) belonging to the CHC group. The method proposed predicts accurately the clinical diagnosis label and effectively classifies the neurological patients from the CHC class. In particular, the classification accuracy (actual vs predicted) for the three classes of the clinical diagnosis was 97%, 78%, and 91% for control, MCI, and dementia class, respectively.

Signature-Based Computational Drug Repurposing for Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a late-onset fatal neurodegenerative disease characterized by progressive loss of the upper and lower motor neurons. There are currently limited approved drugs for the disorder, and for this reason the strategy of repositioning already approved therapeutics could exhibit a successful outcome. Herein, we used CMAP and L1000CDS2 databases which include gene expression profiles datasets (genomic signatures) to identify potent compounds and classes of compounds which reverse disease's signature which could in turn reverse its phenotype. ALS signature was obtained by comparing gene expression of muscle biopsy specimens between diseased and healthy individuals. Statistical analysis was conducted to explore differentially transcripts in patients' samples. Then, the list of upregulated and downregulated genes was used to query both databases in order to determine molecules which downregulate the genes which are upregulated by ALS and vice versa. These compounds, based on their chemical structure along with known treatments, were clustered to reveal drugs with novel and potentially more effective mode of action with most of them predicted to affect pathways heavily involved in ALS. This evidence suggests that these compounds are strong candidates for moving to the next phase of the drug repurposing pipeline which is in vitro and in vivo experimental evaluation.

Integrating Wearable Sensors and Machine Learning for the Detection of Critical Events in Industry Workers.

The event where an industry worker experiences some sort of critical health problems on site, due to factors not strictly related to the job, poses a serious concern and is an issue of research. These events can be mitigated almost entirely if the workers' health is being monitored in real time by an occupational physician along with an artificial intelligence system that can foresee a health incident and act fast and efficiently. For this reason, we developed a framework of devices, systems, and algorithms which help the industry workers along with the industries to monitor such events and, if possible, minimize them. The aforementioned framework performs seamlessly and autonomously and creates a system where the health of the industry workers is being monitored in real time. In the proposed solution, the worker would wear a wrist sensor in the form of a smartwatch as well as a blood pressure device on the ear. These sensors can communicate directly with a cloud storage system to store sensor data, and then real-time data analysis can be performed. Subsequently, all results can be displayed in an interface operated by an occupational physician, and in case of a health issue event, the doctor and the worker will be notified.

A Comparison of the Various Methods for Selecting Features for Single-Cell RNA Sequencing Data in Alzheimer's Disease.

The high-throughput sequencing method known as RNA-Seq records the whole transcriptome of individual cells. Single-cell RNA sequencing, also known as scRNA-Seq, is widely utilized in the field of biomedical research and has resulted in the generation of huge quantities and types of data. The noise and artifacts that are present in the raw data require extensive cleaning before they can be used. When applied to applications for machine learning or pattern recognition, feature selection methods offer a method to reduce the amount of time spent on calculation while simultaneously improving predictions and offering a better knowledge of the data. The process of discovering biomarkers is analogous to feature selection methods used in machine learning and is especially helpful for applications in the medical field. An attempt is made by a feature selection algorithm to cut down on the total number of features by eliminating those that are unnecessary or redundant while retaining those that are the most helpful.We apply FS algorithms designed for scRNA-Seq to Alzheimer's disease, which is the most prevalent neurodegenerative disease in the western world and causes cognitive and behavioral impairment. AD is clinically and pathologically varied, and genetic studies imply a diversity of biological mechanisms and pathways. Over 20 new Alzheimer's disease susceptibility loci have been discovered through linkage, genome-wide association, and next-generation sequencing (Tosto G, Reitz C, Mol Cell Probes 30:397-403, 2016). In this study, we focus on the performance of three different approaches to marker gene selection methods and compare them using the support vector machine (SVM), k-nearest neighbors' algorithm (k-NN), and linear discriminant analysis (LDA), which are mainly supervised classification algorithms.

Improving Patient-Centered Dementia Screening for General, Multicultural Population and Persons with Disabilities from Primary Care Professionals with a Web-Based App.

BACKGROUND: Primary care serves as the first point of contact for people with dementia and is therefore a promising setting for screening, assessment, and initiation of specific treatment and care. According to literature, online applications can be effective by addressing different needs, such as screening, health counseling, and improving overall health status. AIM: Our goal was to propose a brief, inexpensive, noninvasive strategy for screening dementia to general, multicultural population and persons with disabilities, through a web-based app with a tailored multicomponent design. METHODS: We designed and developed a web-based application, which combines cognitive tests and biomarkers to assist primary care professionals screen dementia. We then conducted an implementation study to measure the usability of the app. Two groups of experts participated for the selection of the screening instruments, following the Delhi method. Then, 16 primary care professionals assessed the app to their patients (n = 132), and after they measured its usability with System Usability Scale. OUTCOMES: Two cognitive tools were integrated in the app, GPCOG and RUDAS, which are adequate for primary care settings and for screening multicultural and special needs population, without educational or language bias. Also, for assessing biomarkers, the CAIDE model was preferred, which resulted in individualized proposals, concerning the modifiable risk factors. Usability scored high for the majority of users. CONCLUSION: Utilization of the Dementia app could be incorporated into the routine practices of existing healthcare services and screening of multiple population for dementia.