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.

Pressure Prediction on Mechanical Ventilation Control Using Bidirectional Long-Short Term Memory Neural Networks.

Life support systems are playing a critical role on keeping a patient alive when admitted in ICU bed. One of the most popular life support system is Mechanical Ventilation which helps a patient to breath when breathing is inadequate to maintain life. Despite its important role during ICU admission, the technology for Mechanical Ventilation hasn't change a lot for several years. In this paper, we developed a model using artificial neural networks, in an attempt to make ventilators more intelligent and personalized to each patient's needs. We used artificial data to train a deep learning model that predicts the correct pressure to be applied on patient's lungs every timepoint within a breath cycle. Our model was evaluated using cross-validation and achieved a Mean Absolute Error of 0.19 and a Mean Absolute Percentage Error of 2%.

A Mobile Application for Supporting and Monitoring Elderly Population to Perform the Interventions of the FINGER Study.

Alzheimer's disease is a progressive disease that is caused by the destruction of brain neurons. It seems it affects a large group of the world's population that is estimated around 47 million and is expected to triple by 2050. Slowly but surely, the patient's condition is deteriorating, due to the increase of symptom severity, rendering him/her in need of special care. A great percentage of these cases can be attributed to some common modifiable risk factors such as hypertension, obesity, a lack of exercise, alcohol misuse, smoking, unhealthy diet, and a low level of education. The Finnish Geriatric Intervention Study (FINGER Study) proves that some interventions focused on the abovementioned risk factors of the individual's daily life can contribute to delay the occurrence of Alzheimer's disease. Concurrently, the rapid development of smart devices encourages the use of health applications that provide guiding tools and suggestions based on the user's status. The outcome of this paper is the development of a mobile application, to implement and monitor the interventions proposed by the FINGER Study. Based on the user's profile, it offers the ability to evaluate the likelihood of cognitive decline, monitor the process, and help delay the disease's occurrence.

Dynamic Reconfiguration of Dominant Intrinsic Coupling Modes in Elderly at Prodromal Alzheimer's Disease Risk.

Large-scale human brain networks interact across both spatial and temporal scales. Especially for electro- and magnetoencephalography (EEG/MEG), there are many evidences that there is a synergy of different subnetworks that oscillate on a dominant frequency within a quasi-stable brain temporal frame. Intrinsic cortical-level integration reflects the reorganization of functional brain networks that support a compensation mechanism for cognitive decline. Here, a computerized intervention integrating different functions of the medial temporal lobes, namely, object-level and scene-level representations, was conducted. One hundred fifty-eight patients with mild cognitive impairment underwent 90 min of training per day over 10 weeks. An active control (AC) group of 50 subjects was exposed to documentaries, and a passive control group of 55 subjects did not engage in any activity. Following a dynamic functional source connectivity analysis, the dynamic reconfiguration of intra- and cross-frequency coupling mechanisms before and after the intervention was revealed. After the neuropsychological and resting state electroencephalography evaluation, the ratio of inter versus intra-frequency coupling modes and also the contribution of ß1 frequency was higher for the target group compared to its pre-intervention period. These frequency-dependent contributions were linked to neuropsychological estimates that were improved due to intervention. Additionally, the time-delays of the cortical interactions were improved in {δ, θ, α2, ß1} compared to the pre-intervention period. Finally, dynamic networks of the target group further improved their efficiency over the total cost of the network. This is the first study that revealed a dynamic reconfiguration of intrinsic coupling modes and an improvement of time-delays due to a target intervention protocol.

Computational Models for Biomarker Discovery.

Alzheimer's disease (AD) is a prevalent and debilitating neurodegenerative disorder characterized by progressive cognitive decline. Early diagnosis and accurate prediction of disease progression are critical for developing effective therapeutic interventions. In recent years, computational models have emerged as powerful tools for biomarker discovery and disease prediction in Alzheimer's and other neurodegenerative diseases. This paper explores the use of computational models, particularly machine learning techniques, in analyzing large volumes of data and identifying patterns related to disease progression. The significance of early diagnosis, the challenges in classifying patients at the mild cognitive impairment (MCI) stage, and the potential of computational models to improve diagnostic accuracy are examined. Furthermore, the importance of incorporating diverse biomarkers, including genetic, molecular, and neuroimaging indicators, to enhance the predictive capabilities of these models is highlighted. The paper also presents case studies on the application of computational models in simulating disease progression, analyzing neurodegenerative cascades, and predicting the future development of Alzheimer's. Overall, computational models for biomarker discovery offer promising opportunities to advance our understanding of Alzheimer's disease, facilitate early diagnosis, and guide the development of targeted therapeutic strategies.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning.

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.

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.

A Sensor-Based Perspective in Early-Stage Parkinson's Disease: Current State and the Need for Machine Learning Processes.

Parkinson's disease (PD) is a progressive neurodegenerative disorder associated with dysfunction of dopaminergic neurons in the brain, lack of dopamine and the formation of abnormal Lewy body protein particles. PD is an idiopathic disease of the nervous system, characterized by motor and nonmotor manifestations without a discrete onset of symptoms until a substantial loss of neurons has already occurred, enabling early diagnosis very challenging. Sensor-based platforms have gained much attention in clinical practice screening various biological signals simultaneously and allowing researchers to quickly receive a huge number of biomarkers for diagnostic and prognostic purposes. The integration of machine learning into medical systems provides the potential for optimization of data collection, disease prediction through classification of symptoms and can strongly support data-driven clinical decisions. This work attempts to examine some of the facts and current situation of sensor-based approaches in PD diagnosis and discusses ensemble techniques using sensor-based data for developing machine learning models for personalized risk prediction. Additionally, a biosensing platform combined with clinical data processing and appropriate software is proposed in order to implement a complete diagnostic system for PD monitoring.

A biomarker for lymphoma development in Sjogren's syndrome: Salivary gland focus score.

The aim of this study is to explore the role of labial minor salivary gland (LMSG) focus score (FS) in stratifying Sjögren's Syndrome (SS) patients, lymphoma development prediction and to facilitate early lymphoma diagnosis. Ιn an integrated cohort of 1997 patients, 618 patients with FS ≥ 1 and at least one-year elapsing time interval from SS diagnosis to lymphoma diagnosis or last follow up were identified. Clinical, laboratory and serological features were recorded. A data driven logistic regression model was applied to identify independent lymphoma associated risk factors. Furthermore, a FS threshold maximizing the difference of time interval from SS until lymphoma diagnosis between high and low FS lymphoma subgroups was investigated, to develop a follow up strategy for early lymphoma diagnosis. Of the 618 patients, 560 were non-lymphoma SS patients while the other 58 had SS and lymphoma. FS, cryoglobulinemia and salivary gland enlargement (SGE) were proven to be independent lymphoma associated risk factors. Lymphoma patients with FS ≥ 4 had a statistically significant shorter time interval from SS to lymphoma diagnosis, compared to those with FS < 4 (4 vs 9 years, respectively, p = 0,008). SS patients with FS ≥ 4 had more frequently B cell originated manifestations and lymphoma, while in patients with FS < 4, autoimmune thyroiditis was more prevalent. In the latter group SGE was the only lymphoma independent risk factor. A second LMSG biopsy is patients with a FS ≥ 4, 4 years after SS diagnosis and in those with FS < 4 and a history of SGE, at 9-years, may contribute to an early lymphoma diagnosis. Based on our results we conclude that LMSG FS, evaluated at the time of SS diagnosis, is an independent lymphoma associated risk factor and may serve as a predictive biomarker for the early diagnosis of SS-associated lymphomas.

Combined seronegativity in Sjögren's syndrome.

OBJECTIVES: To describe the clinical spectrum of Sjögren's syndrome (SS) patients with combined seronegativity. METHODS: From a multicentre study population of consecutive SS patients fulfilling the 2016 ACR-EULAR classification criteria, patients with triple seronegativity [anti-Ro/SSA(-), anti-La/SSB(-), RF(-) and ANA(+)] and quadruple seronegativity [anti-Ro/SSA(-), anti-La/SSB(-), RF(-) and ANA(-)] were identified retrospectively. Both groups were matched in an 1:1 ratio with 2 distinct control SS groups: i) classic anti-Ro/SSA seropositive patients [SS(+)] and ii) classic anti-Ro/SSA seropositive patients with negative rheumatoid factor [SS(+)/RF(-)] to explore their effect on disease expression. Clinical, laboratory and, histologic features were compared. A comparison between triple and quadruple seronegative SS patients was also performed. REESULTS: One hundred thirty-five SS patients (8.6%) were identified as triple seronegative patients and 72 (4.5%) as quadruple. Triple seronegative patients had lower frequency of peripheral nervous involvement (0% vs. 7.2% p=0.002) compared to SS(+) controls and lower frequency of interstitial renal disease and higher prevalence of dry mouth than SS(+)/RF(-) controls. Quadruple seronegative patients presented less frequently with persistent lymphadenopathy (1.5% vs. 16.9 p=0.004) and lymphoma (0% vs. 9.8% p=0.006) compared to SS(+) controls and with lower prevalence of persistent lymphadenopathy (1.5% vs. 15.3% p=0.008) and higher frequency of dry eyes (98.6% vs. 87.5% p=0.01) and autoimmune thyroiditis (44.1% vs. 17.1% p=0.02) compared to SS(+)/RF(-) SS controls. Study groups comparative analysis revealed that triple seronegative patients had higher frequency of persistent lymphadenopathy and lymphoma, higher focus score and later age of SS diagnosis compared to quadruple seronegative patients. CONCLUSIONS: Combined seronegativity accounts for almost 9% of total SS population and is associated with a milder clinical phenotype, partly attributed to the absence of rheumatoid factor.

A DSS for Predicting Lymphoma in Primary Sjogren's Syndrome Patients.

Primary Sjogren's syndrome (pSS) is a chronic autoimmune disease followed by exocrine gland dysfunction. In this work, a web application was developed as a screening test based on a machine learning model that was trained on clinical data and is used to predict lymphoma outcomes in pSS patient. The results of the final model reveal a sensitivity of 100%, accuracy of 82%, and area under the curve of 98% and confirms the importance of C4 value, lymphadenopathy, and rheumatoid factor as prominent lymphoma predictors.