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.

Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer's Disease.

The treatment of complex and multifactorial diseases constitutes a big challenge in day-to-day clinical practice. As many parameters influence clinical phenotypes, accurate diagnosis and prompt therapeutic management is often difficult. Significant research and investment focuses on state-of-the-art genomic and metagenomic analyses in the burgeoning field of Precision (or Personalized) Medicine with genome-wide-association-studies (GWAS) helping in this direction by linking patient genotypes at specific polymorphic sites (single-nucleotide polymorphisms, SNPs) to the specific phenotype. The generation of polygenic risk scores (PRSs) is a relatively novel statistical method that associates the collective genotypes at many of a person's SNPs to a trait or disease. As GWAS sample sizes increase, PRSs may become a powerful tool for prevention, early diagnosis and treatment. However, the complexity and multidimensionality of genetic and environmental contributions to phenotypes continue to pose significant challenges for the clinical, broad-scale use of PRSs. To improve the value of PRS measures, we propose a novel pipeline which might better utilize GWAS results and improve the utility of PRS when applied to Alzheimer's Disease (AD), as a paradigm of multifactorial disease with existing large GWAS datasets that have not yet achieved significant clinical impact. We propose a refined approach for the construction of AD PRS improved by (1), taking into consideration the genetic loci where the SNPs are located, (2) evaluating the post-translational impact of SNPs on coding and non-coding regions by focusing on overlap with open chromatin data and SNPs that are expression quantitative trait loci (QTLs), and (3) scoring and annotating the severity of the associated clinical phenotype into the PRS. Open chromatin and eQTL data need to be carefully selected based on tissue/cell type of origin (e.g., brain, excitatory neurons). Applying such filters to traditional PRS on GWAS studies of complex diseases like AD, can produce a set of SNPs weighted according to our algorithm and a more useful PRS. Our proposed methodology may pave the way for new applications of genomic machine and deep learning pipelines to GWAS datasets in an effort to identify novel clinically useful genetic biomarkers for complex diseases like AD.

The Importance of Diagnostic and Prognostic Biomarker Identification and Classification Towards Understanding ALS Pathogenesis.

ALS is increasingly perceived as a multisystem neurodegenerative disorder, and the identification of a panel of biomarkers that accurately reflect features of pathology is a priority, not only for diagnostic purposes but also for prognostic or predictive applications [1]. Thus, as a multisystemic disease, it is likely that a panel of biomarkers will be needed to fully capture the features of ALS pathology. Taking also into consideration the fact that its causes remain unknown to their majority, it remains a complex disease driven by a combination of several systemic parameters [2]. Beyond the monogenic causes, representing the 15% of the ALS cases, which list up to 30 gene mutations with the most frequent being C9orf72, SOD1, FUS, and TARDBP/TDP43 [3-5], much research is being done to identify and associate possible causes for the 80% of ALS cases (sALS and fALS combined) which at the moment are not explained by a known mutation [2, 4]. ALS sporadic cases are related to a multigenic component, and/or involve epigenetic modification, and/or result from DNA damage, environmental risk factors, behavioural factors, oxidative stress or viral infections leading to cellular dysfunctions [4, 6-10]. ALS diagnosis is lengthy and there is a typical diagnostic delay of 9-15 months from onset to diagnostic confirmation based on clinical and electrophysiological criteria as well as the exclusion of diseases with similar behaviour to ALS. Three major exclusion criteria are involved in the diagnosis process: the Revised El Escorial Criteria (REEC), the Airlie House Criteria (AHC) and the Awaji Criteria [11, 12]. Taking into consideration that the average survival is 2-4 years, it makes it urgent for the researchers to improve diagnostic speed and accuracy for ALS [13, 14]. In the absence of a reliable diagnostic test for ALS, biomarkers are a strong weapon not only for its diagnosis but also for understanding the pathomechanism as well as a basis for the development of therapeutics. Recent global research has accepted the fact that biomarkers will facilitate the combination of therapeutics with diagnostics and will thus play an important role in the development of personalized medicine [15]. This paper proposes a combination of diagnostic and prognostic biomarkers to meet the scope of global research. Thus, biomarkers specific to ALS pathology need to be identified towards the development of a reliable fast diagnostic test, while at the same time prognostic biomarkers should also be identified to monitor the status of the pathology as various candidates may serve both purposes. Finally, since different sub-cohorts of ALS patients respond differently to treatments [16], the identification of ALS biomarkers will contribute to a better understanding of the disease pathogenesis and permit targeted drug development and patient stratification leading to more efficient clinical trials.

Genetic Pathways Involved in the Pathogenesis of Parkinson's Disease.

Parkinson's disease (PD) is the second most common neurodegenerative disease. PD pathogenesis includes both genetic and environmental factors. Previous studies have linked the disease with several genes such as Parkin, SNCA, PINK1 and HTRA2. BiNGO software utilizes GO annotations in order to detect over-represented genes in terms of biological processes, cellular components and molecular functions. Three databases were utilized for this study (Ensembl, DisGeNET and UniProt). Data processing provided 110 genes associated with PD for further analysis. The aim of this study was to identify genes associated with PD and perform a functional enrichment analysis. Cytoscape and BiNGO software analysis presented several new genes that could play a potential role in pathogenesis of the disease. Future steps include additional research in order to establish the exact mechanism of action of these genes and pathways on PD.

Mobile Application for Monitoring and Preventing Cognitive Decline Through Lifestyle Intervention.

Diagnosing and preventing Alzheimer's disease is a complex task, partly due to being characterized by a lengthy asymptomatic stage. In order to tackle this, most preclinical studies are multidimensional in nature and largely focus on prevention through lifestyle interventions, such as improving nutrition and introducing physical as well as cognitive exercise. With the widespread use of mobile smart devices today, mobile health applications can help inform high-risk individuals at a low cost, while also aiding in the prevention of cognitive decline through constant virtual coaching services that contribute to lifestyle interventions. Under this light, a mobile application is developed in the context of this paper that provides risk assessment of individuals, daily monitoring of factors that have been found to help prevent cognitive impairment, and individually tailored guidance based on the individual's progress. The developed application is also capable of reassessing users' risk to track their progress, while also providing these services in an intuitive and user-friendly manner, which could enable the future development of more accurate models through the collected data.

Development of a Protein Biochip Platform for Parkinson's Disease.

Parkinson's disease (PD) is a complex neurodegenerative disorder, characterized by severe motor symptoms which lead to progressive weakness of motor function caused by prominent loss of dopamine-secreting neurons within the substantia nigra. Compelling neuropathological evidence reveals the accumulation of insoluble protein aggregates, such as α-synuclein and tau, which are important hallmarks of the disease. Protein biochips have great potential to be powerful tools for clinical diagnostics, whereas novel sensing methods implementing biosensors for protein quantification in body fluids are highly required. Herein, the development of a device using a thin film of conductive polymer acid-doped polyaniline that can detect specific biomolecules is examined. The polymer is shown to change conductivity in the presence of proteins, so this direct chemical to electric transduction can be used to quantify concentration alterations. The fabrication of such a device is proposed, so that it can be implemented in rapid screening tests as part of an integrated holistic point-of-care diagnostics model that brings together a multidisciplinary healthcare team of PD experts.

Bioinformatics Approaches for Parkinson's Disease in Clinical Practice: Data-Driven Biomarkers and Pharmacological Treatment.

Parkinson's disease is a gradually progressive neurodegenerative disorder characterized by a selective loss of dopaminergic neurons in the midbrain area called the substantia nigra pars compacta and cytoplasmic alpha-synuclein-rich inclusions termed Lewy bodies. The etiology and pathogenesis remain incompletely understood. The development of reliable biomarkers for the early and accurate diagnosis, including biochemical, genetic, clinical, and neuroimaging markers, is crucial for unraveling the pathogenic processes of the disease as well as patients' progress surveillance. High-throughput technologies and system biology methodologies can support the identification of potent molecular fingerprints together with the establishment of dynamic network biomarkers. Emphasis is given on multi-omics datasets and dysregulated pathways associated with differentially expressed transcripts, modified protein motifs, and altered metabolic profiles. Although there is no therapy that terminates the neurodegenerative process and dopamine replacement strategy with L-DOPA represents the most effective treatment, numerous therapeutic protocols such as dopamine receptor agonists, MAO-B inhibitors, and cholinesterase inhibitors represent candidate treatments providing at the same time valuable network-based approaches to drug repositioning. Computational methodologies and bioinformatics platforms for visualization, clustering, and validating of molecular and clinical datasets provide important insights into diagnostic processing and therapeutic pipeline.

Correction to: Particular Biomolecular Processes as Computing Paradigms.

In the original version of this book, Chapter 20 was inadvertently published without carrying the corrections provided by the author. This has now been rectified in this revised version of the book.

A Hypothesis of Circulating MicroRNAs' Implication in High Incidence of Atrial Fibrillation and Other Electrocardiographic Abnormalities in Cancer Patients.

MicroRNAs are short non-coding RNA molecules that control posttranscriptional gene expression and are present in tissues cells but also circulate in biological fluids in various forms (exosome, connected with proteins, apoptotic bodies, etc.). The roles that circulated extracellular serum microRNAs possess in cancer development, like in the delivery from a recipient cell to distant tissues and the repression of host genes resulting in the impairment of critical functions, are still undetermined. Disturbances, such as the higher incidence of atrial fibrillation in cancer patients, could be analyzed in the frame of suppressive action of circulated microRNAs in genes that control cardiac conduction in atrium. More precisely, mir-21 overexpression in tissues promotes atrium fibrosis and impairs conductibility. A possible hypothesis is that the high levels of circulating microRNA in cancer may exert the same effect. Further experiments are necessary to corroborate the hypothesis.

Exhaled Breath Condensate (EBC): Is It a Viable Source of Biomarkers for Lung Diseases?

The exhaled breath condensate is a source of biomarkers with many advantages and benefits compared to other traditional sampling techniques in respiratory medicine. It is a biological product that is formed by cooling the exhaled air via its guidance through a condenser. It is characterized as a cocktail of volatile and non-volatile compounds with water being the predominant constituent. Its composition presents a non-uniformed structure as the volatile and the non-volatile compounds vary in type and ratio. All these compounds originate from the whole respiratory tract. Some of them fulfil the criteria to be characterized as biomarkers since there is a similarity between the content of the exhaled breath condensate and the respiratory tract lining fluid. In addition, the potential biomarkers of the exhaled breath condensate and those from other biological fluids are equivalent.Advantages and Disadvantages Its place in the respiratory medicine as a matrix of biomarkers relies on its various strengths. Some of them are very important and make it exceptional regarding its application, such as its totally non-invasive character and its usage in all ages, while others present a more potential action regarding its purpose such as the categorization of respiratory diseases. However, there are limitations in its application due to the lack of standardization of its conduct which can be minimized by following the official recommendations. Additional studies are needed to develop said standardization.Aim The aim of this paper is to present a brief and comprehensive picture of the sampling technique of the exhaled breath condensate, as well as the criteria to make it a preferred choice as a source of biomarkers.

Buccal Mucosa Biomarkers in Alzheimer's Disease.

Alzheimer's disease (AD) precipitation in the elderly population increases the need for sensitive biomarkers that can be applied to large population screening. Buccal cells can be obtained easily, noninvasively, and contain many proteins related to cerebral processes. Hence, they offer an ideal candidate for AD biomarker discovery. The purpose of this study is to provide an overview of the current research landscape covering both clinical and methodological issues. A brief summary is given on related laboratory techniques to ascertain protein concentration changes due to AD. At the end, we describe a protocol designed in our laboratory for disease early diagnosis.

Amyotrophic Lateral Sclerosis: Current Status in Diagnostic Biomarkers.

Amyotrophic lateral sclerosis (ALS) is a rare, neurodegenerative disease that affects the human motor system. ALS is a highly heterogeneous disease, depending on several causative factors. The heterogeneity of the disease is also reflected in the variation of the symptoms in ALS patients. The worldwide annual incidence of ALS is about 2.08 per 100,000 with uniform rates in Caucasian populations and lower rates in African, Asian, and Hispanic populations, while the number of individuals with ALS is expected to grow significantly between 2015 and 2040 with an estimated increase of 69% (Chio et al. 2013a; Arthur et al. 2016).

Alzheimer's Disease: The Role of Mutations in Protein Folding.

Misfolded proteins result when a protein follows the wrong folding pathway. Accumulation of misfolded proteins can cause disorders, known as amyloid diseases. Unfortunately, some of them are very common. The most prevalent one is Alzheimer's disease. Alzheimer's disease is a neurodegenerative disorder and the commonest form of dementia. The current study aims to assess the impact of somatic mutations in PSEN1 gene. The said mutations are the most common cause of familial Alzheimer's disease. As protein functionality can be affected by mutations, the study of possible alterations in the tertiary structure of proteins may reveal new insights related to the relationship between mutations and protein functions. To examine the effect of mutations, the primary structures and their related mutations were retrieved from public databases. Each structure (mutated and unmutated) was predicted based on effective structure prediction methodologies. A benchmarking of the structural predictive tools was accomplished. Comparative analyses of mutated and unmutated proteins were performed based on classic bioinformatics methods (TM-Score, RMSD, etc.) as well as on established shape-based descriptors retrieved from object recognition methodologies. Unsupervised methodologies were applied to the structures, in order to identify groups of mutation with similar mutational impact. Our results provide an essential knowledge toward protein's functionality in structure-based drug design.

A Systems Biology Approach for the Identification of Active Molecular Pathways During the Progression of Alzheimer's Disease.

MOTIVATION: In the last years, systems-level network-based approaches have gained ground in the research field of systems biology. These approaches are based on the analysis of high-throughput sequencing studies, which are rapidly increasing year by year. Nowadays, the single-cell RNA-sequencing, an optimized next-generation sequencing (NGS) technology that offers a better understanding of the function of an individual cell in the context of its microenvironment, prevails. RESULTS: Toward this direction, a method is developed in which active molecular subpathways are recorded during the time evolution of the disease under study. This method operates for expression profiling by high-throughput sequencing data. Its capability is based on capturing the temporal changes of local gene communities that form a disease-perturbed subpathway. The aforementioned methods are applied to real data from a recent study that uses single-cell RNA-sequencing data related with the progression of neurodegeneration. More specific, microglia cells were isolated from the hippocampus of a mouse model with Alzheimer's disease-like phenotypes and severe neurodegeneration and of control mice at multiple time points during progression of neurodegeneration. Our analysis offers a different view for neurodegeneration progression under the perspective of systems biology. CONCLUSION: Our approach into the molecular perspective using a temporal tracking of active pathways in neurodegeneration at single-cell resolution may offer new insights for designing new efficient strategies to treat Alzheimer's and other neurodegenerative diseases.

Modeling the Critical Activation of Chaperone Machinery in Protein Folding.

Protein homeostasis is a dynamic network that plays a pivotal role in systems' maintenance within a cell. This quality control system involves a number of mechanisms regarding the process of protein folding. Chaperones play a critical role in the folding, refolding, and unfolding of proteins. Aggregation of misfolded proteins is a common characteristic of neurodegenerative diseases. Chaperones act in a variety of pathways in this critical interplay between protein homeostasis network and misfolded protein's load. Moreover, ER stress-induced cell death mechanisms (such as the unfolded protein response) are activated as a response. Therefore, there is a critical balance in the accumulation of misfolded proteins and ER stress response mechanisms which can lead to cell death. Our focus is in understanding the different mechanisms that govern ER stress signaling in health and disease in order to represent the regulation of protein homeostasis and balance of protein synthesis and degradation in the ER. Our proposed model describes, using hybrid modeling, the function of chaperones' machinery for protein folding.

Detecting Common Pathways and Key Molecules of Neurodegenerative Diseases from the Topology of Molecular Networks.

MotivationNeurodegenerative diseases (NDs), including amyotrophic lateral sclerosis, Parkinson's disease, Alzheimer's disease, and Huntington's disease, occur as a result of neurodegenerative processes. Thus, it has been increasingly appreciated that many neurodegenerative conditions overlap at multiple levels. However, traditional clinicopathological correlation approaches to better classify a disease have met with limited success. Discovering this overlap offers hope for therapeutic advances that could ameliorate many ND simultaneously. In parallel, in the last decade, systems biology approaches have become a reliable choice in complex disease analysis for gaining more delicate biological insights and have enabled the comprehension of the higher order functions of the biological systems.ResultsToward this orientation, we developed a systems biology approach for the identification of common links and pathways of ND, based on well-established and novel topological and functional measures. For this purpose, a molecular pathway network was constructed, using molecular interactions and relations of four main neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease). Our analysis captured the overlapped subregions forming molecular subpathways fully enriched in these four NDs. Also, it exported molecules that act as bridges, hubs, and key players for neurodegeneration concerning either their topology or their functional role.ConclusionUnderstanding these common links and central topologies under the perspective of systems biology and network theory and greater insights are provided to uncover the complex neurodegeneration processes.

Undergraduate Students' Brain Activity in Visual and Textual Programming.

Eight computer science students, novice programmers, who were in the first semester of their studies, participated in a field study in order to explore potential differences in their brain activity during programming with a visual versus a textual programming language. The students were asked to develop two specific programs in both programming languages (a total of four tasks). Measurements of cerebral activity were performed by the electroencephalography (EEG) imaging method. According to data analysis, it appears that the type of programming language did not affect the students' brain activity.

Diagnosing asthma and chronic obstructive pulmonary disease with machine learning.

This study examines the clinical decision support systems in healthcare, in particular about the prevention, diagnosis and treatment of respiratory diseases, such as Asthma and chronic obstructive pulmonary disease. The empirical pulmonology study of a representative sample (n = 132) attempts to identify the major factors that contribute to the diagnosis of these diseases. Machine learning results show that in chronic obstructive pulmonary disease's case, Random Forest classifier outperforms other techniques with 97.7 per cent precision, while the most prominent attributes for diagnosis are smoking, forced expiratory volume 1, age and forced vital capacity. In asthma's case, the best precision, 80.3 per cent, is achieved again with the Random Forest classifier, while the most prominent attribute is MEF2575.

Disease-biased and shared characteristics of the immunoglobulin gene repertoires in marginal zone B cell lymphoproliferations.

The B cell receptor immunoglobulin (Ig) gene repertoires of marginal zone (MZ) lymphoproliferations were analyzed in order to obtain insight into their ontogenetic relationships. Our cohort included cases with MZ lymphomas (n = 488), i.e. splenic (SMZL), nodal (NMZL) and extranodal (ENMZL), as well as provisional entities (n = 76), according to the WHO classification. The most striking Ig gene repertoire skewing was observed in SMZL. However, restrictions were also identified in all other MZ lymphomas studied, particularly ENMZL, with significantly different Ig gene distributions depending on the primary site of involvement. Cross-entity comparisons of the MZ Ig sequence dataset with a large dataset of Ig sequences (MZ-related or not; n = 65 837) revealed four major clusters of cases sharing homologous ('public') heavy variable complementarity-determining region 3. These clusters included rearrangements from SMZL, ENMZL (gastric, salivary gland, ocular adnexa), chronic lymphocytic leukemia, but also rheumatoid factors and non-malignant splenic MZ cells. In conclusion, different MZ lymphomas display biased immunogenetic signatures indicating distinct antigen exposure histories. The existence of rare public stereotypes raises the intriguing possibility that common, pathogen-triggered, immune-mediated mechanisms may result in diverse B lymphoproliferations due to targeting versatile progenitor B cells and/or operating in particular microenvironments. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.