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Krokidis MG, Pucha KA, Mustapic M, Exarchos TP, Vlamos P, Kapogiannis D. Lipidomic Analysis of Plasma Extracellular Vesicles Derived from Alzheimer's Disease Patients. Cells 2024; 13:702. [PMID: 38667317 PMCID: PMC11049154 DOI: 10.3390/cells13080702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/31/2024] [Accepted: 04/14/2024] [Indexed: 04/28/2024] Open
Abstract
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.
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Affiliation(s)
- Marios G. Krokidis
- Laboratory of Bioinformatics and Human Electrophysiology, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (T.P.E.); (P.V.)
| | - Krishna A. Pucha
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health (NIA/NIH), Baltimore, MD 21224, USA; (K.A.P.); (M.M.)
| | - Maja Mustapic
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health (NIA/NIH), Baltimore, MD 21224, USA; (K.A.P.); (M.M.)
| | - Themis P. Exarchos
- Laboratory of Bioinformatics and Human Electrophysiology, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (T.P.E.); (P.V.)
| | - Panagiotis Vlamos
- Laboratory of Bioinformatics and Human Electrophysiology, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (T.P.E.); (P.V.)
| | - Dimitrios Kapogiannis
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health (NIA/NIH), Baltimore, MD 21224, USA; (K.A.P.); (M.M.)
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Pezoulas VC, Kalatzis F, Exarchos TP, Goules A, Tzioufas AG, Fotiadis DI. FHBF: Federated hybrid boosted forests with dropout rates for supervised learning tasks across highly imbalanced clinical datasets. Patterns (N Y) 2024; 5:100893. [PMID: 38264722 PMCID: PMC10801222 DOI: 10.1016/j.patter.2023.100893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 11/03/2023] [Accepted: 11/10/2023] [Indexed: 01/25/2024]
Abstract
Although several studies have deployed gradient boosting trees (GBT) as a robust classifier for federated learning tasks (federated GBT [FGBT]), even with dropout rates (federated gradient boosting trees with dropout rate [FDART]), none of them have investigated the overfitting effects of FGBT across heterogeneous and highly imbalanced datasets within federated environments nor the effect of dropouts in the loss function. In this work, we present the federated hybrid boosted forests (FHBF) algorithm, which incorporates a hybrid weight update approach to overcome ill-posed problems that arise from overfitting effects during the training across highly imbalanced datasets in the cloud. Eight case studies were conducted to stress the performance of FHBF against existing algorithms toward the development of robust AI models for lymphoma development across 18 European federated databases. Our results highlight the robustness of FHBF, yielding an average loss of 0.527 compared with FGBT (0.611) and FDART (0.584) with increased classification performance (0.938 sensitivity, 0.732 specificity).
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Affiliation(s)
- Vasileios C. Pezoulas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
| | - Fanis Kalatzis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
| | - Themis P. Exarchos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
- Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Andreas Goules
- Department of Pathophysiology, Faculty of Medicine, National and Kapodistrian University of Athens (NKUA), 15772 Athens, Greece
| | - Athanasios G. Tzioufas
- Department of Pathophysiology, Faculty of Medicine, National and Kapodistrian University of Athens (NKUA), 15772 Athens, Greece
| | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
- Biomedical Research Institute, FORTH, 45110 Ioannina, Greece
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Krokidis MG, Dimitrakopoulos GN, Vrahatis AG, Exarchos TP, Vlamos P. Challenges and limitations in computational prediction of protein misfolding in neurodegenerative diseases. Front Comput Neurosci 2024; 17:1323182. [PMID: 38250244 PMCID: PMC10796696 DOI: 10.3389/fncom.2023.1323182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/19/2023] [Indexed: 01/23/2024] Open
Affiliation(s)
| | | | | | | | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
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4
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Krokidis MG, Vrahatis AG, Lazaros K, Skolariki K, Exarchos TP, Vlamos P. Machine Learning Analysis of Alzheimer's Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions. Curr Issues Mol Biol 2023; 45:8652-8669. [PMID: 37998721 PMCID: PMC10670182 DOI: 10.3390/cimb45110544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 10/15/2023] [Accepted: 10/26/2023] [Indexed: 11/25/2023] Open
Abstract
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.
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Affiliation(s)
- Marios G. Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (A.G.V.); (K.L.); (K.S.); (T.P.E.); (P.V.)
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5
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Efraimidis E, Krokidis MG, Exarchos TP, Lazar T, Vlamos P. In Silico Structural Analysis Exploring Conformational Folding of Protein Variants in Alzheimer's Disease. Int J Mol Sci 2023; 24:13543. [PMID: 37686347 PMCID: PMC10487466 DOI: 10.3390/ijms241713543] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/26/2023] [Accepted: 08/30/2023] [Indexed: 09/10/2023] Open
Abstract
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.
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Affiliation(s)
- Evangelos Efraimidis
- Bioinformatics and Neuroinformatics MSc Program, Hellenic Open University, 26335 Patras, Greece;
| | - Marios G. Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (T.P.E.)
| | - Themis P. Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (T.P.E.)
| | - Tamas Lazar
- VIB–VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie (VIB), B1050 Brussels, Belgium;
- Structural Biology Brussels, Department of Bioengineering, Vrije Universiteit Brussel, B1050 Brussels, Belgium
| | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (T.P.E.)
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Skolariki K, Vrahatis AG, Krokidis MG, Exarchos TP, Vlamos P. Assessing and Modelling of Post-Traumatic Stress Disorder Using Molecular and Functional Biomarkers. Biology (Basel) 2023; 12:1050. [PMID: 37626936 PMCID: PMC10451531 DOI: 10.3390/biology12081050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 07/03/2023] [Accepted: 07/24/2023] [Indexed: 08/27/2023]
Abstract
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.
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Affiliation(s)
| | | | - Marios G. Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (K.S.); (A.G.V.); (T.P.E.); (P.V.)
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Tsakanikas VD, Siogkas PK, Potsika VT, Sakellarios AI, Pleouras DS, Kigka VI, Exarchos TP, Koncar IB, Fotiadis DI. TAXINOMISIS: A cloud - based platform for risk profiling and patient specific management of the carotid artery disease. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-4. [PMID: 38083155 DOI: 10.1109/embc40787.2023.10340947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Carotid Artery Disease is a complex multi-disciplinary medical condition causing strokes and several other disfunctions to individuals. Within this work, a cloud - based platform is proposed for clinicians and medical doctors that provides a comprehensive risk assessment tool for carotid artery disease. It includes three modeling levels: baseline data-driven risk assessment, blood flow simulations and plaque progression modeling. The proposed models, which have been validated through a wide set of studies within the TAXINOMISIS project, are delivered to the end users through an easy-to-use cloud platform. The architecture and the deployment of this platform includes interfaces for handling the electronic patient record, the 3D arterial reconstruction, blood flow simulations and risk assessment reporting. TAXINOMISIS, compared with both similar software approaches and with the current clinical workflow, assists clinicians to treat patients more effectively and more accurately by providing innovative and validated tools.Clinical Relevance - Asymptomatic carotid artery disease is a prevalent condition that affects a significant portion of the population, leading to an increased risk of stroke and other cardiovascular events. Early detection and appropriate treatment of this condition can significantly reduce the risk of adverse outcomes and improve patient outcomes. The development of a software tool to assist clinicians in the assessment and management of asymptomatic patients with carotid artery disease is therefore of great clinical relevance. By providing a comprehensive and reliable assessment of the disease and its risk factors, this tool will enable clinicians to make informed decisions regarding patient management and treatment. The impact of this tool on patient outcomes and the reduction of healthcare costs will be of great importance to both patients and the healthcare system.
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Pezoulas VC, Exarchos TP, Tachos NS, Goules A, Tzioufas AG, Fotiadis DI. Boosting the performance of MALT lymphoma classification in patients with primary Sjögren's Syndrome through data augmentation: a case study. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-4. [PMID: 38083761 DOI: 10.1109/embc40787.2023.10340802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Sjögren's Syndrome (SS) patients with mucosa associated lymphoid tissue lymphomas (MALTLs) and diffuse large B-cell lymphomas (DLBCLs) have 10-year survival rates of 80% and 40%, respectively. This highlights the unique biologic burden of the two histologic forms, as well as, the need for early detection and thorough monitoring of these patients. The lack of MALTL patients and the fact that most studies are single cohort and combine patients with different lymphoma subtypes narrow the understanding of MALTL progression. Here, we propose a data augmentation pipeline that utilizes an advanced synthetic data generator which is trained on a Pan European data hub with primary SS (pSS) patients to yield a high-quality synthetic data pool. The latter is used for the development of an enhanced MALTL classification model. Four scenarios were defined to assess the reliability of augmentation. Our results revealed an overall improvement in the accuracy, sensitivity, specificity, and AUC by 7%, 6.3%, 9%, and 6.3%, respectively. This is the first case study that utilizes data augmentation to reflect the progression of MALTL in pSS.
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9
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Vrahatis AG, Skolariki K, Krokidis MG, Lazaros K, Exarchos TP, Vlamos P. Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning. Sensors (Basel) 2023; 23:s23094184. [PMID: 37177386 PMCID: PMC10180573 DOI: 10.3390/s23094184] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/19/2023] [Accepted: 04/19/2023] [Indexed: 05/15/2023]
Abstract
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.
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Affiliation(s)
- Aristidis G Vrahatis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Konstantina Skolariki
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Marios G Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Konstantinos Lazaros
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Themis P Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
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Dimakopoulos GA, Vrahatis AG, Exarchos TP, Ntanasi E, Yannakoulia M, Kosmidis MH, Dardiotis E, Hadjigeorgiou G, Sakka P, Scarmeas N, Vlamos P. Application of Machine Learning Techniques in the HELIAD Study Data for the Development of Diagnostic Models in MCI and Dementia. Adv Exp Med Biol 2023; 1424:187-192. [PMID: 37486493 DOI: 10.1007/978-3-031-31982-2_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
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.
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Affiliation(s)
- George A Dimakopoulos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Aristidis G Vrahatis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Themis P Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Eva Ntanasi
- Department of Nutrition and Diatetics, Harokopio University, Athens, Greece
| | - Mary Yannakoulia
- Department of Nutrition and Diatetics, Harokopio University, Athens, Greece
| | - Mary H Kosmidis
- Lab of Cognitive Neuroscience, School of Psychology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | | | - Paraskevi Sakka
- Athens Association of Alzheimer's Disease and Related Disorders, Athens, Greece
| | - Nikolaos Scarmeas
- 1st Department of Neurology, Aiginition Hospital, National and Kapodistrian University of Athens, Medical School, Athens, Greece
- Department of Neurology, Columbia University, New York, NY, USA
| | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.
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Exarchos TP, Whelan R, Tarnanas I. Dynamic Reconfiguration of Dominant Intrinsic Coupling Modes in Elderly at Prodromal Alzheimer's Disease Risk. Adv Exp Med Biol 2023; 1424:1-22. [PMID: 37486474 DOI: 10.1007/978-3-031-31982-2_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
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 β1 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, β1} 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.
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Affiliation(s)
| | - Robert Whelan
- Trinity College Institute of Neurosciences, Trinity College, Dublin, Ireland
| | - Ioannis Tarnanas
- Altoida Inc, Houston, TX, USA
- Global Brain Health Institute, Trinity College, Dublin, Ireland
- University of California, San Francisco, CA, USA
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Avramouli A, Krokidis MG, Exarchos TP, Vlamos P. Protein Structure Prediction for Disease-Related Insertions/Deletions in Presenilin 1 Gene. Adv Exp Med Biol 2023; 1423:31-40. [PMID: 37525031 DOI: 10.1007/978-3-031-31978-5_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
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.
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Affiliation(s)
- Antigoni Avramouli
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Marios G Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Themis P Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.
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Papikinos T, Krokidis MG, Vrahatis A, Vlamos P, Exarchos TP. Signature-Based Computational Drug Repurposing for Amyotrophic Lateral Sclerosis. Adv Exp Med Biol 2023; 1424:201-211. [PMID: 37486495 DOI: 10.1007/978-3-031-31982-2_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
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.
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Affiliation(s)
- Thomas Papikinos
- Department of Informatics, Bioinformatics and Human Electrophysiology Laboratory, Ionian University, Corfu, Greece.
| | - Marios G Krokidis
- Department of Informatics, Bioinformatics and Human Electrophysiology Laboratory, Ionian University, Corfu, Greece
| | - Aris Vrahatis
- Department of Informatics, Bioinformatics and Human Electrophysiology Laboratory, Ionian University, Corfu, Greece
| | - Panagiotis Vlamos
- Department of Informatics, Bioinformatics and Human Electrophysiology Laboratory, Ionian University, Corfu, Greece
| | - Themis P Exarchos
- Department of Informatics, Bioinformatics and Human Electrophysiology Laboratory, Ionian University, Corfu, Greece
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Krokidis MG, Exarchos TP, Vrahatis AG, Tzouvelekis C, Drakoulis D, Papavassileiou F, Vlamos P. A Sensor-Based Platform for Early-Stage Parkinson's Disease Monitoring. Adv Exp Med Biol 2023; 1424:23-29. [PMID: 37486475 DOI: 10.1007/978-3-031-31982-2_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
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.
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Affiliation(s)
- Marios G Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.
| | - Themis P Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Aristidis G Vrahatis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Christos Tzouvelekis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | | | | | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.
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15
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Papageorgiou L, Kalospyrou E, Papakonstantinou E, Diakou I, Pierouli K, Dragoumani K, Bacopoulou F, Chrousos GP, Exarchos TP, Vlamos P, Eliopoulos E, Vlachakis D. 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. Adv Exp Med Biol 2023; 1424:97-115. [PMID: 37486484 DOI: 10.1007/978-3-031-31982-2_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
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.
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Affiliation(s)
- Louis Papageorgiou
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Efstathia Kalospyrou
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Eleni Papakonstantinou
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Io Diakou
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Katerina Pierouli
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Konstantina Dragoumani
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Flora Bacopoulou
- University Research Institute of Maternal and Child Health & Precision Medicine, National and Kapodistrian University of Athens, "Aghia Sophia" Children's Hospital, Athens, Greece
| | - George P Chrousos
- University Research Institute of Maternal and Child Health & Precision Medicine, National and Kapodistrian University of Athens, "Aghia Sophia" Children's Hospital, Athens, Greece
| | - Themis P Exarchos
- Department of Informatics, Bioinformatics & Human Electrophysiology Laboratory, Ionian University, Corfu, Greece
| | - Panagiotis Vlamos
- Department of Informatics, Bioinformatics & Human Electrophysiology Laboratory, Ionian University, Corfu, Greece
| | - Elias Eliopoulos
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Dimitrios Vlachakis
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece.
- University Research Institute of Maternal and Child Health & Precision Medicine, National and Kapodistrian University of Athens, "Aghia Sophia" Children's Hospital, Athens, Greece.
- Division of Endocrinology and Metabolism, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece.
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16
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Papageorgiou L, Mangana E, Papakonstantinou E, Diakou I, Pierouli K, Dragoumani K, Bacopoulou F, Chrousos GP, Exarchos TP, Vlamos P, Eliopoulos E, Vlachakis D. An Updated Evolutionary and Structural Study of TBK1 Reveals Highly Conserved Motifs as Potential Pharmacological Targets in Neurodegenerative Diseases. Adv Exp Med Biol 2023; 1423:41-57. [PMID: 37525032 DOI: 10.1007/978-3-031-31978-5_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
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-β, 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.
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Affiliation(s)
- Louis Papageorgiou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Eleni Mangana
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Eleni Papakonstantinou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Io Diakou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Katerina Pierouli
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Konstantina Dragoumani
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Flora Bacopoulou
- University Research Institute of Maternal and Child Health & Precision Medicine, National and Kapodistrian University of Athens, "Aghia Sophia" Children's Hospital, Athens, Greece
| | - George P Chrousos
- University Research Institute of Maternal and Child Health & Precision Medicine, National and Kapodistrian University of Athens, "Aghia Sophia" Children's Hospital, Athens, Greece
| | - Themis P Exarchos
- Bioinformatics & Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Panagiotis Vlamos
- Bioinformatics & Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Elias Eliopoulos
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Dimitrios Vlachakis
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece.
- University Research Institute of Maternal and Child Health & Precision Medicine, National and Kapodistrian University of Athens, "Aghia Sophia" Children's Hospital, Athens, Greece.
- Division of Endocrinology and Metabolism, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece.
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17
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Sagiadinou M, Vlamos P, Exarchos TP, Vlachakis D, Kostopoulou C. Improving Patient-Centered Dementia Screening for General, Multicultural Population and Persons with Disabilities from Primary Care Professionals with a Web-Based App. Adv Exp Med Biol 2023; 1424:265-272. [PMID: 37486503 DOI: 10.1007/978-3-031-31982-2_30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
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.
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18
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Skolariki K, Exarchos TP, Vlamos P. Computational Models for Biomarker Discovery. Adv Exp Med Biol 2023; 1424:289-295. [PMID: 37486506 DOI: 10.1007/978-3-031-31982-2_33] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
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.
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Mantellos G, Exarchos TP, Dimitrakopoulos GN, Vlamos P, Papastamatiou N, Karaiskos K, Minos P, Alexandridis T, Axiotopoulos S, Tsakiridis D, Avramoudis V, Vasiliadis A, Stagakis S. Integrating Wearable Sensors and Machine Learning for the Detection of Critical Events in Industry Workers. Adv Exp Med Biol 2023; 1424:213-222. [PMID: 37486496 DOI: 10.1007/978-3-031-31982-2_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
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.
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Affiliation(s)
- George Mantellos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Themis P Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.
| | - Georgios N Dimitrakopoulos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
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20
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Krokidis MG, Exarchos TP, Avramouli A, Vrahatis AG, Vlamos P. Computational and Functional Insights of Protein Misfolding in Neurodegeneration. Adv Exp Med Biol 2023; 1423:201-206. [PMID: 37525045 DOI: 10.1007/978-3-031-31978-5_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
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.
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Affiliation(s)
- Marios G Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.
| | - Themis P Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Antigoni Avramouli
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Aristidis G Vrahatis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
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21
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Grammenos G, Exarchos TP. Pressure Prediction on Mechanical Ventilation Control Using Bidirectional Long-Short Term Memory Neural Networks. Adv Exp Med Biol 2023; 1424:31-40. [PMID: 37486476 DOI: 10.1007/978-3-031-31982-2_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
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%.
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22
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Chalkioti M, Exarchos TP. A Mobile Application for Supporting and Monitoring Elderly Population to Perform the Interventions of the FINGER Study. Adv Exp Med Biol 2023; 1424:167-173. [PMID: 37486491 DOI: 10.1007/978-3-031-31982-2_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
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.
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Affiliation(s)
- Maria Chalkioti
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Themis P Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.
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23
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Avramouli A, Krokidis MG, Exarchos TP, Vlamos P. In Silico Structural Analysis Predicting the Pathogenicity of PLP1 Mutations in Multiple Sclerosis. Brain Sci 2022; 13:brainsci13010042. [PMID: 36672024 PMCID: PMC9856082 DOI: 10.3390/brainsci13010042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
The X chromosome gene PLP1 encodes myelin proteolipid protein (PLP), the most prevalent protein in the myelin sheath surrounding the central nervous system. X-linked dysmyelinating disorders such as Pelizaeus-Merzbacher disease (PMD) or spastic paraplegia type 2 (SPG2) are typically caused by point mutations in PLP1. Nevertheless, numerous case reports have shown individuals with PLP1 missense point mutations which also presented clinical symptoms and indications that were consistent with the diagnostic criteria of multiple sclerosis (MS), a disabling disease of the brain and spinal cord with no current cure. Computational structural biology methods were used to assess the impact of these mutations on the stability and flexibility of PLP structure in order to determine the role of PLP1 mutations in MS pathogenicity. The analysis showed that most of the variants can alter the functionality of the protein structure such as R137W variants which results in loss of helix and H140Y which alters the ordered protein interface. In silico genomic methods were also performed to predict the significance of these mutations associated with impairments in protein functionality and could suggest a better definition for therapeutic strategies and clinical application in MS patients.
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Krokidis MG, Dimitrakopoulos GN, Vrahatis AG, Tzouvelekis C, Drakoulis D, Papavassileiou F, Exarchos TP, Vlamos P. A Sensor-Based Perspective in Early-Stage Parkinson's Disease: Current State and the Need for Machine Learning Processes. Sensors (Basel) 2022; 22:409. [PMID: 35062370 PMCID: PMC8777583 DOI: 10.3390/s22020409] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/02/2021] [Accepted: 01/04/2022] [Indexed: 02/04/2023]
Abstract
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.
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Affiliation(s)
- Marios G. Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | - Georgios N. Dimitrakopoulos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | - Aristidis G. Vrahatis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | - Christos Tzouvelekis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | | | | | - Themis P. Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | - Panayiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
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25
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Goules AV, Exarchos TP, Fotiadis DI, Tzioufas A. The clinical and technical impact of the HarmonicSS project. Clin Exp Rheumatol 2021; 39 Suppl 133:17-19. [DOI: 10.55563/clinexprheumatol/u7knfy] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 07/09/2021] [Indexed: 11/13/2022]
Affiliation(s)
- Andreas V. Goules
- Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, Greece.
| | | | - Dimitris I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, Greece
| | - Athanasios Tzioufas
- Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, Greece
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26
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Chatzis LG, Pezoulas V, Voulgari PV, Baldini C, Exarchos TP, Fotiadis DI, Mavragani CP, Skopouli FN, Moutsopoulos HM, Tzioufas AG, Goules AV. Combined seronegativity in Sjögren's syndrome. Clin Exp Rheumatol 2021; 39 Suppl 133:80-84. [PMID: 34665703 DOI: 10.55563/clinexprheumatol/47a4kr] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 09/06/2021] [Indexed: 11/13/2022]
Abstract
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.
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Affiliation(s)
- Loukas G Chatzis
- Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, and Institute for Autoimmune, Systemic and Neurological Diseases, Athens, Greece
| | - Vasilis Pezoulas
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, Greece
| | - Paraskevi V Voulgari
- Rheumatology Clinic, Department of Internal Medicine, Medical School, University of Ioannina, Greece
| | - Chiara Baldini
- Rheumatology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Italy
| | - Themis P Exarchos
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, Greece
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, and Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, Ioannina, Greece
| | - Clio P Mavragani
- Department of Physiology, Medical School, National and Kapodistrian University of Athens, Greece
| | - Fotini N Skopouli
- Department of Nutrition and Clinical Dietetics, Harokopio University of Athens, and Department of Medicine and Clinical Immunology, Euroclinic of Athens, Greece
| | - Haralampos M Moutsopoulos
- Institute for Autoimmune, Systemic and Neurological Diseases, Athens, and Athens Academy of Athens, Chair Medical Sciences/Immunology, Greece
| | - Athanasios G Tzioufas
- Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, and Institute for Autoimmune, Systemic and Neurological Diseases, Athens, Greece
| | - Andreas V Goules
- Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, and Institute for Autoimmune, Systemic and Neurological Diseases, Athens, Greece.
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Pezoulas VC, Exarchos TP, Tzioufas AG, Fotiadis DI. Multiple additive regression trees with hybrid loss for classification tasks across heterogeneous clinical data in distributed environments: a case study. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:1670-1673. [PMID: 34891606 DOI: 10.1109/embc46164.2021.9629912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Multiple additive regression trees (MART) have been widely used in the literature for various classification tasks. However, the overfitting effects of MART across heterogeneous and highly imbalanced big data structures within distributed environments has not yet been investigated. In this work, we utilize distributed MART with hybrid loss to resolve overfitting effects during the training of disease classification models in a case study with 10 heterogeneous and distributed clinical datasets. Lexical and semantic analysis methods were utilized to match heterogeneous terminologies with 80% overlap. Data augmentation was used to resolve class imbalance yielding virtual data with goodness of fit 0.01 and correlation difference 0.02. Our results highlight the favorable performance of the proposed distributed MART on the augmented data with an average increase by 7.3% in the accuracy, 6.8% in sensitivity, 10.4% in specificity, for a specific loss function topology.
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Pezoulas VC, Kalatzis F, Exarchos TP, Chatzis L, Gandolfo S, Goules A, De Vita S, Tzioufas AG, Fotiadis DI. A federated AI strategy for the classification of patients with Mucosa Associated Lymphoma Tissue (MALT) lymphoma across multiple harmonized cohorts. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:1666-1669. [PMID: 34891605 DOI: 10.1109/embc46164.2021.9630014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Mucosa Associated Lymphoma Tissue (MALT) type is an extremely rare type of lymphoma which occurs in less than 3% of patients with primary Sjögren's Syndrome (pSS). No reported studies so far have been able to investigate risk factors for MALT development across multiple cohort databases with sufficient statistical power. Here, we present a generalized, federated AI (artificial intelligence) strategy which enables the training of AI algorithms across multiple harmonized databases. A case study is conducted towards the development of MALT classification models across 17 databases on pSS. Advanced AI algorithms were developed, including federated Multinomial Naïve Bayes (FMNB), federated gradient boosting trees (FGBT), FGBT with dropouts (FDART), and the federated Multilayer Perceptron (FMLP). The FDART with dropout rate 0.3 achieved the best performance with sensitivity 0.812, and specificity 0.829, yielding 8 biomarkers as prominent for MALT development.
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Pezoulas VC, Hazapis O, Lagopati N, Exarchos TP, Goules AV, Tzioufas AG, Fotiadis DI, Stratis IG, Yannacopoulos AN, Gorgoulis VG. Machine Learning Approaches on High Throughput NGS Data to Unveil Mechanisms of Function in Biology and Disease. Cancer Genomics Proteomics 2021; 18:605-626. [PMID: 34479914 PMCID: PMC8441762 DOI: 10.21873/cgp.20284] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 07/21/2021] [Accepted: 08/03/2021] [Indexed: 12/13/2022] Open
Abstract
In this review, the fundamental basis of machine learning (ML) and data mining (DM) are summarized together with the techniques for distilling knowledge from state-of-the-art omics experiments. This includes an introduction to the basic mathematical principles of unsupervised/supervised learning methods, dimensionality reduction techniques, deep neural networks architectures and the applications of these in bioinformatics. Several case studies under evaluation mainly involve next generation sequencing (NGS) experiments, like deciphering gene expression from total and single cell (scRNA-seq) analysis; for the latter, a description of all recent artificial intelligence (AI) methods for the investigation of cell sub-types, biomarkers and imputation techniques are described. Other areas of interest where various ML schemes have been investigated are for providing information regarding transcription factors (TF) binding sites, chromatin organization patterns and RNA binding proteins (RBPs), while analyses on RNA sequence and structure as well as 3D dimensional protein structure predictions with the use of ML are described. Furthermore, we summarize the recent methods of using ML in clinical oncology, when taking into consideration the current omics data with pharmacogenomics to determine personalized treatments. With this review we wish to provide the scientific community with a thorough investigation of main novel ML applications which take into consideration the latest achievements in genomics, thus, unraveling the fundamental mechanisms of biology towards the understanding and cure of diseases.
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Affiliation(s)
- Vasileios C Pezoulas
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, Ioannina, Greece
| | - Orsalia Hazapis
- Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Nefeli Lagopati
- Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
- Biomedical Research Foundation of the Academy of Athens, Athens, Greece
| | - Themis P Exarchos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
- Department of Informatics, Ionian University, Corfu, Greece
| | - Andreas V Goules
- Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Athanasios G Tzioufas
- Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, Ioannina, Greece
| | - Ioannis G Stratis
- Department of Mathematics, National and Kapodistrian University of Athens, Athens, Greece
| | - Athanasios N Yannacopoulos
- Department of Statistics, and Stochastic Modelling and Applications Laboratory, Athens University of Economics and Business (AUEB), Athens, Greece;
| | - Vassilis G Gorgoulis
- Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece;
- Biomedical Research Foundation of the Academy of Athens, Athens, Greece
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, Manchester Cancer Research Centre, NIHR Manchester Biomedical Research Centre, University of Manchester, Manchester, U.K
- Center for New Biotechnologies and Precision Medicine, Medical School, National and Kapodistrian University of Athens, Athens, Greece
- Faculty of Health and Medical Sciences, University of Surrey, Surrey, U.K
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Chatzis L, Goules AV, Pezoulas V, Baldini C, Gandolfo S, Skopouli FN, Exarchos TP, Kapsogeorgou EK, Donati V, Voulgari PV, Mavragani CP, Gorgoulis V, De Vita S, Fotiadis D, Voulgarelis M, Moutsopoulos HM, Tzioufas AG. A biomarker for lymphoma development in Sjogren's syndrome: Salivary gland focus score. J Autoimmun 2021; 121:102648. [PMID: 34029875 DOI: 10.1016/j.jaut.2021.102648] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/27/2021] [Accepted: 04/28/2021] [Indexed: 12/19/2022]
Abstract
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.
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Affiliation(s)
- Loukas Chatzis
- Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Andreas V Goules
- Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Vasilis Pezoulas
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, Ioannina, Greece
| | - Chiara Baldini
- Rheumatology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Italy
| | - Saviana Gandolfo
- Rheumatology Clinic, Department of Medical area, University of Udine, Udine, Italy
| | - Fotini N Skopouli
- Department of Nutrition and Clinical Dietetics, Harokopio University of Athens, Athens, Greece; Department of Medicine and Clinical Immunology, Euroclinic of Athens, Athens, Greece
| | - Themis P Exarchos
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, Ioannina, Greece
| | - Efstathia K Kapsogeorgou
- Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Valentina Donati
- Unit of Pathological Anatomy 2, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Paraskevi V Voulgari
- Rheumatology Clinic, Department of Internal Medicine, Medical School, University of Ioannina, Ioannina, Greece
| | - Clio P Mavragani
- Department of Physiology, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Vasilis Gorgoulis
- Laboratory of Histology-Embryology Molecular Carcinogenesis Group Medical School National and Kapodistrian University of Athens, Athens, Greece
| | - Salvatore De Vita
- Rheumatology Clinic, Department of Medical area, University of Udine, Udine, Italy
| | - Dimitrios Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, Ioannina, Greece; Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, Ioannina, Greece
| | - Michalis Voulgarelis
- Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Haralampos M Moutsopoulos
- Athens Academy of Athens, Chair Medical Sciences/Immunology, Greece; Institute for Autoimmune Systemic and Neurological Diseases, Greece
| | - Athanasios G Tzioufas
- Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece.
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Krokidis MG, Exarchos TP, Vlamos P. Data-driven biomarker analysis using computational omics approaches to assess neurodegenerative disease progression. Math Biosci Eng 2021; 18:1813-1832. [PMID: 33757212 DOI: 10.3934/mbe.2021094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The complexity of biological systems suggests that current definitions of molecular dysfunctions are essential distinctions of a complex phenotype. This is well seen in neurodegenerative diseases (ND), such as Alzheimer's disease (AD) and Parkinson's disease (PD), multi-factorial pathologies characterized by high heterogeneity. These challenges make it necessary to understand the effectiveness of candidate biomarkers for early diagnosis, as well as to obtain a comprehensive mapping of how selective treatment alters the progression of the disorder. A large number of computational methods have been developed to explain network-based approaches by integrating individual components for modeling a complex system. In this review, high-throughput omics methodologies are presented for the identification of potent biomarkers associated with AD and PD pathogenesis as well as for monitoring the response of dysfunctional molecular pathways incorporating multilevel clinical information. In addition, principles for efficient data analysis pipelines are being discussed that can help address current limitations during the experimental process by increasing the reproducibility of benchmarking studies.
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Affiliation(s)
- Marios G Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Greece
| | - Themis P Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Greece
| | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Greece
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Alvanou AG, Stylidou A, Exarchos TP. Web-Based Decision Support System for Coronary Heart Disease Diagnosis. GeNeDis 2020 2021; 1338:31-38. [DOI: 10.1007/978-3-030-78775-2_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Siogkas PK, Kalykakis GE, Anagnostopoulos CD, Exarchos TP. Fluid Dynamics–Derived Parameters in Coronary Vessels. GeNeDis 2020 2021; 1337:291-297. [DOI: 10.1007/978-3-030-78771-4_32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Vergis S, Bezas K, Exarchos TP. Decision Support System for Breast Cancer Detection Using Biomarker Indicators. GeNeDis 2020 2021; 1338:13-19. [DOI: 10.1007/978-3-030-78775-2_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Siogkas PK, Kalykakis G, Anagnostopoulos CD, Exarchos TP. The effect of the degree and location of coronary stenosis on the hemodynamic status of a coronary vessel. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020:2671-2674. [PMID: 33018556 DOI: 10.1109/embc44109.2020.9175302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The ongoing advances in the field of cardiovascular modelling during the past years have allowed for the creation of accurate three-dimensional models of the major coronary arteries. The aforementioned 3D models can accurately mimic the human coronary vasculature if they are combined with sophisticated computational fluid dynamics algorithms and shed light to non-trivial issues that concern the clinicians. One of these issues is to define whether a coronary lesion is more dangerous to present with ischemia if it is at a proximal or a distal part of the vessel. In this work, we aim to investigate the aforementioned issue by reconstructing in 3D a coronary arterial model from a healthy subject using Computed Tomography Coronary Angiography images and by editing it to create eight diseased arterial models that contain one or two lesions of different severities. After carrying out the appropriate blood flow simulations using the finite element method, we observed that the distal lesions are more dangerous than the proximal ones in terms of hemodynamic significance. Moreover, the distal severe stenosis (i.e. 70% diameter reduction) present with the highest peak Wall Shear Stress (WSS) values in comparison to the proximal ones.
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Tsakanikas VD, Siogkas PK, Mantzaris MD, Potsika VT, Kigka VI, Exarchos TP, Koncar IB, Jovanovic M, Vujcic A, Ducic S, Pelisek J, Fotiadis DI. A deep learning oriented method for automated 3D reconstruction of carotid arterial trees from MR imaging. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020:2408-2411. [PMID: 33018492 DOI: 10.1109/embc44109.2020.9176532] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The scope of this paper is to present a new carotid vessel segmentation algorithm implementing the U-net based convolutional neural network architecture. With carotid atherosclerosis being the major cause of stroke in Europe, new methods that can provide more accurate image segmentation of the carotid arterial tree and plaque tissue can help improve early diagnosis, prevention and treatment of carotid disease. Herein, we present a novel methodology combining the U-net model and morphological active contours in an iterative framework that accurately segments the carotid lumen and outer wall. The method automatically produces a 3D meshed model of the carotid bifurcation and smaller branches, using multispectral MR image series obtained from two clinical centres of the TAXINOMISIS study. As indicated by a validation study, the algorithm succeeds high accuracy (99.1% for lumen area and 92.6% for the perimeter) for lumen segmentation. The proposed algorithm will be used in the TAXINOMISIS study to obtain more accurate 3D vessel models for improved computational fluid dynamics simulations and the development of models of atherosclerotic plaque progression.
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Pezoulas VC, Exarchos TP, Tzioufas AG, De Vita S, Fotiadis DI. Predicting lymphoma outcomes and risk factors in patients with primary Sjögren's Syndrome using gradient boosting tree ensembles. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2019:2165-2168. [PMID: 31946330 DOI: 10.1109/embc.2019.8857557] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Primary Sjogren's Syndrome (pSS) is a chronic autoimmune disease followed by exocrine gland dysfunction, where it has been long stated that 5% of pSS patients are prone to lymphoma development. In this work, we use clinical data from 449 pSS patients to develop a first, rule-based, supervised learning model that can be used to predict lymphoma outcomes, as well as, identify prominent features for lymphoma prediction in pSS patients. Towards this direction, the gradient boosting method combined with regression tree ensembles is used to derive a rule-based, decision model for lymphoma prediction. Our results reveal an average accuracy 87.1% and area under the curve score 88%, highlighting the importance of the C4 value, the rheumatoid factor and the lymphadenopathy factor as prominent lymphoma predictors, among others.
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Pezoulas VC, Kourou KD, Kalatzis F, Exarchos TP, Zampeli E, Gandolfo S, Goules A, Baldini C, Skopouli F, De Vita S, Tzioufas AG, Fotiadis DI. Overcoming the Barriers That Obscure the Interlinking and Analysis of Clinical Data Through Harmonization and Incremental Learning. IEEE Open J Eng Med Biol 2020; 1:83-90. [PMID: 35402941 PMCID: PMC8940202 DOI: 10.1109/ojemb.2020.2981258] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 02/23/2020] [Accepted: 03/09/2020] [Indexed: 11/22/2022] Open
Abstract
Goal: To present a framework for data sharing, curation, harmonization and federated data analytics to solve open issues in healthcare, such as, the development of robust disease prediction models. Methods: Data curation is applied to remove data inconsistencies. Lexical and semantic matching methods are used to align the structure of the heterogeneous, curated cohort data along with incremental learning algorithms including class imbalance handling and hyperparameter optimization to enable the development of disease prediction models. Results: The applicability of the framework is demonstrated in a case study of primary Sjögren's Syndrome, yielding harmonized data with increased quality and more than 85% agreement, along with lymphoma prediction models with more than 80% sensitivity and specificity. Conclusions: The framework provides data quality, harmonization and analytics workflows that can enhance the statistical power of heterogeneous clinical data and enables the development of robust models for disease prediction.
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Affiliation(s)
- Vasileios C Pezoulas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and EngineeringUniversity of Ioannina GR45110 Ioannina Greece
| | - Konstantina D Kourou
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and EngineeringUniversity of Ioannina GR45110 Ioannina Greece
- Department of Biological Applications and TechnologyUniversity of Ioannina GR45110 Ioannina Greece
| | - Fanis Kalatzis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and EngineeringUniversity of Ioannina GR45110 Ioannina Greece
| | - Themis P Exarchos
- Department of InformaticsIonian University GR49100 Corfu Greece
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and EngineeringUniversity of Ioannina GR45100 Ioannina Greece
| | - Evi Zampeli
- Institute for Systemic Autoimmune and Neurological Diseases GR11743 Athens Greece
| | - Saviana Gandolfo
- Clinic of Rheumatology, Department of Medical and Biological SciencesUdine University IT33100 Udine Italy
| | - Andreas Goules
- Department of Pathophysiology, School of MedicineUniversity of Athens GR15772 Athens Greece
| | - Chiara Baldini
- Department of Clinical and Experimental MedicineUniversity of Pisa Pisa IT56126 Italy
| | - Fotini Skopouli
- Department of Internal Medicine and Clinical ImmunologyEuroclinic Hospital GR11521 Athens Greece
| | - Salvatore De Vita
- Clinic of Rheumatology, Department of Medical and Biological SciencesUdine University IT33100 Udine Italy
| | - Athanasios G Tzioufas
- Department of Pathophysiology, School of MedicineUniversity of Athens GR15772 Athens Greece
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and EngineeringUniversity of Ioannina GR45110 Ioannina Greece
- Department of Biomedical ResearchFORTH-IMBB GR45110 Ioannina Greece
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Pezoulas VC, Kourou KD, Kalatzis F, Exarchos TP, Venetsanopoulou AI, Zampeli E, Gandolfo S, Skopouli FN, De Vita S, Tzioufas AG, Fotiadis DI. Enhancing medical data quality through data curation: a case study in primary Sjögren's syndrome. Clin Exp Rheumatol 2019; 37 Suppl 118:90-96. [PMID: 31287405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 06/18/2019] [Indexed: 06/09/2023]
Abstract
OBJECTIVES To address the need for automatically assessing the quality of clinical data in terms of accuracy, relevance, conformity, and completeness, through the concise development and application of an automated method which is able to automatically detect problematic fields and match clinical terms under a specific domain. METHODS The proposed methodology involves the automated construction of three diagnostic reports that summarise valuable information regarding the types and ranges of each term in the dataset, along with the detected outliers, inconsistencies, and missing values, followed by a set of clinically relevant terms based on a reference model which serves as a set of terms which describes the domain knowledge of a disease of interest. RESULTS A case study was conducted using anonymised data from 250 patients who were diagnosed with primary Sjögren's syndrome (pSS), yielding reliable outcomes that were highlighted for clinical evaluation. Our method was able to successfully identify 28 features with detected outliers, and unknown data types, as well as, identify outliers, missing values, similar terms, and inconsistencies within the dataset. The data standardisation method was able to match 76 out of 85 (89.41%) pSS-related terms according to a standard pSS reference model which has been introduced by the clinicians. CONCLUSIONS Our results confirm the clinical value of the data curation method towards the improvement of the dataset quality through the precise identification of outliers, missing values, inconsistencies, and similar terms, as well as, through the automated detection of pSS-related relevant terms towards data standardisation.
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Affiliation(s)
- Vasileios C Pezoulas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Greece
| | - Konstantina D Kourou
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, and Department of Biological Applications and Technology, University of Ioannina, Greece
| | - Fanis Kalatzis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Greece
| | - Themis P Exarchos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, and Department of Informatics, Ionian University, Corfu, Greece
| | | | - Evi Zampeli
- Institute for Systemic Autoimmune and Neurological Diseases, Athens, Greece
| | - Saviana Gandolfo
- Clinic of Rheumatology, Department of Medical and Biological Sciences, University of Udine, Italy
| | - Fotini N Skopouli
- Department of Internal Medicine and Clinical Immunology, Euroclinic Hospital, Athens, Greece
| | - Salvatore De Vita
- Clinic of Rheumatology, Department of Medical and Biological Sciences, University of Udine, Italy
| | | | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, and Department of Biomedical Research, FORTH-IMBB, Ioannina, Greece.
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Seghieri C, Lupi E, Exarchos TP, Ferro F, Tzioufas AG, Baldini C. Variation in primary Sjögren's syndrome care among European countries. Clin Exp Rheumatol 2019; 37 Suppl 118:27-28. [PMID: 31464679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 07/11/2019] [Indexed: 06/10/2023]
Affiliation(s)
- Chiara Seghieri
- Institute of Management and Department EMbeDS, Scuola Superiore Sant'Anna, Pisa, Italy.
| | - Enrico Lupi
- Institute of Management and Department EMbeDS, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Themis P Exarchos
- Unit of Medical Technology and IntelligentInformation Systems, University of Ioannina, Greece
| | - Francesco Ferro
- Rheumatology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Italy
| | - Athanasios G Tzioufas
- Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, Greece
| | - Chiara Baldini
- Rheumatology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Italy
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Goules AV, Exarchos TP, Pezoulas VC, Kourou KD, Venetsanopoulou AI, De Vita S, Fotiadis DI, Tzioufas AG. Sjögren's syndrome towards precision medicine: the challenge of harmonisation and integration of cohorts. Clin Exp Rheumatol 2019; 37 Suppl 118:175-184. [PMID: 31464663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 06/18/2019] [Indexed: 06/10/2023]
Abstract
Primary Sjögren's syndrome (pSS) is a chronic, systemic autoimmune disease with diverse clinical picture and outcome. The disease affects primarily middle-aged females and involves the exocrine glands leading to dry mouth and eyes. When the disease extends beyond the exocrine glands (systemic form), certain extraglandular manifestations involving liver, kidney, lungs, peripheral nervous system and the skin may occur. Primary SS is considered the crossroad between autoimmunity and lymphoproliferation, since approximately 5% of patients develop NHL associated lymphomas. As with every chronic disease with complex aetiopathogenesis and clinical heterogeneity, pSS has certain unmet needs that have to be addressed: a) classification and stratification of patients; b) understanding the distinct pathogenetic mechanisms and clinical phenotypes; c) defining and interpreting the real needs of patients regarding the contemporary diagnostic and therapeutic approaches; d) physician and patients' training regarding the wide spectrum of the disease; e) creating common policies across European countries to evaluate and manage SS patients. To achieve these goals, an intense effort is being currently undertaken by the HarmonicSS consortium in order to harmonise and integrate the largest European cohorts of pSS patients. In this review, we present an overview of our perception and vision, as well as new issues arising from this project such as harmonisation protocols and procedures, data sharing principles and various ethical and legal issues originating from these approaches.
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Affiliation(s)
- Andreas V Goules
- Pathophysiology Department, Athens School of Medicine, National and Kapodistrian University of Athens, Greece
| | - Themis P Exarchos
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, Greece
| | - Vasilis C Pezoulas
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, Greece
| | - Konstadina D Kourou
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, Greece
| | - Aliki I Venetsanopoulou
- Pathophysiology Department, Athens School of Medicine, National and Kapodistrian University of Athens, Greece
| | - Salvatore De Vita
- Rheumatology Clinic, DSMB, AOU Santa Maria della Misericordia, University of Udine, Italy
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, Greece
| | - Athanasios G Tzioufas
- Pathophysiology Department, Athens School of Medicine, National and Kapodistrian University of Athens, Greece.
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Siogkas PK, Papafaklis MI, Lakkas L, Exarchos TP, Karmpaliotis D, Ali ZA, Pelosi G, Parodi O, Katsouras CS, Fotiadis DI, Michalis LK. Virtual Functional Assessment of Coronary Stenoses Using Intravascular Ultrasound Imaging: A Proof-of-Concept Pilot Study. Heart Lung Circ 2019; 28:e33-e36. [PMID: 29895487 DOI: 10.1016/j.hlc.2018.02.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 12/19/2017] [Accepted: 02/11/2018] [Indexed: 11/27/2022]
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Pezoulas VC, Kourou KD, Kalatzis F, Exarchos TP, Venetsanopoulou A, Zampeli E, Gandolfo S, Skopouli F, De Vita S, Tzioufas AG, Fotiadis DI. Medical data quality assessment: On the development of an automated framework for medical data curation. Comput Biol Med 2019; 107:270-283. [PMID: 30878889 DOI: 10.1016/j.compbiomed.2019.03.001] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 03/01/2019] [Accepted: 03/02/2019] [Indexed: 01/25/2023]
Abstract
Data quality assessment has gained attention in the recent years since more and more companies and medical centers are highlighting the importance of an automated framework to effectively manage the quality of their big data. Data cleaning, also known as data curation, lies in the heart of the data quality assessment and is a key aspect prior to the development of any data analytics services. In this work, we present the objectives, functionalities and methodological advances of an automated framework for data curation from a medical perspective. The steps towards the development of a system for data quality assessment are first described along with multidisciplinary data quality measures. A three-layer architecture which realizes these steps is then presented. Emphasis is given on the detection and tracking of inconsistencies, missing values, outliers, and similarities, as well as, on data standardization to finally enable data harmonization. A case study is conducted in order to demonstrate the applicability and reliability of the proposed framework on two well-established cohorts with clinical data related to the primary Sjögren's Syndrome (pSS). Our results confirm the validity of the proposed framework towards the automated and fast identification of outliers, inconsistencies, and highly-correlated and duplicated terms, as well as, the successful matching of more than 85% of the pSS-related medical terms in both cohorts, yielding more accurate, relevant, and consistent clinical data.
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Affiliation(s)
- Vasileios C Pezoulas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece
| | - Konstantina D Kourou
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece; Department of Biological Applications and Technology, University of Ioannina, Ioannina, GR45110, Greece
| | - Fanis Kalatzis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece
| | - Themis P Exarchos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece; Department of Informatics, Ionian University, Corfu, GR49100, Greece
| | - Aliki Venetsanopoulou
- Department of Pathophysiology, School of Medicine, University of Athens, Athens, GR15772, Greece
| | - Evi Zampeli
- Institute for Systemic Autoimmune and Neurological Diseases, Athens, GR11743, Greece
| | - Saviana Gandolfo
- Clinic of Rheumatology, Department of Medical and Biological Sciences, Udine University, Udine, IT33100, Italy
| | - Fotini Skopouli
- Department of Internal Medicine and Clinical Immunology, Euroclinic Hospital, Athens, GR11521, Greece
| | - Salvatore De Vita
- Clinic of Rheumatology, Department of Medical and Biological Sciences, Udine University, Udine, IT33100, Italy
| | - Athanasios G Tzioufas
- Department of Pathophysiology, School of Medicine, University of Athens, Athens, GR15772, Greece
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece; Department of Biomedical Research, FORTH-IMBB, Ioannina, GR45110, Greece.
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Georga EI, Tachos NS, Sakellarios AI, Kigka VI, Exarchos TP, Pelosi G, Parodi O, Michalis LK, Fotiadis DI. Artificial Intelligence and Data Mining Methods for Cardiovascular Risk Prediction. ACTA ACUST UNITED AC 2019. [DOI: 10.1007/978-981-10-5092-3_14] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Kourou KD, Pezoulas VC, Georga EI, Exarchos TP, Tsanakas P, Tsiknakis M, Varvarigou T, De Vita S, Tzioufas A, Fotiadis DI. Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective. IEEE Rev Biomed Eng 2019; 12:303-318. [DOI: 10.1109/rbme.2018.2855055] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Pezoulas VC, Exarchos TP, Andronikou V, Varvarigou T, Tzioufas AG, De Vita S, Fotiadis DI. Towards the Establishment of a Biomedical Ontology for the Primary Sjögren's Syndrome. Annu Int Conf IEEE Eng Med Biol Soc 2018; 2018:4089-4092. [PMID: 30441255 DOI: 10.1109/embc.2018.8513349] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Primary Sjögren's Syndrome (pSS) has been characterized as a hypersensitivity reaction type II systemic autoimmune chronic disease causing exocrine gland dysfunction mainly affecting women near the menopausal age. pSS patients exhibit dryness of the main mucosal surfaces and are highly prone to lymphoma development. This paper presents a first biomedical ontology for pSS based on a reference model which was determined by pSS clinical experts. The ensuing ontology constitutes the fundamental basis for mapping pSS-related ontologies from international cohorts to a common ontology. The ontology mapping (i.e., schematic interlinking) procedure is, in fact, a preliminary step to harmonize heterogeneous medical data obtained from various cohorts.
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Athanasiou LS, Rigas GA, Sakellarios AI, Exarchos TP, Siogkas PK, Michalis LK, Parodi O, Vozzi F, Fotiadis DI. Three-dimensional reconstruction of coronary arteries and plaque morphology using CT angiography - comparison and registration using IVUS. Annu Int Conf IEEE Eng Med Biol Soc 2018; 2015:5638-41. [PMID: 26737571 DOI: 10.1109/embc.2015.7319671] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The aim of this study is to present a new method for three-dimensional (3D) reconstruction of coronary arteries and plaque morphology using Computed Tomography (CT) Angiography. The method is summarized in three steps. In the first step, image filters are applied to CT images and an initial estimation of the vessel borders is extracted. In the second step, the 3D centerline is extracted using the center of gravity of each rough artery border. Finally in the third step, the borders and the plaque are detected and placed onto the 3D centerline constructing a 3D surface. By using as gold standard the results of a recently presented Intravascular Ultrasound (IVUS) plaque characterization method, high correlation is observed for calcium objects detected by CT and IVUS. The correlation coefficients for objects' volume, surface area, length and angle are r=0.51, r=0.89, r=0.96 and r=0.93, respectively.
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Kigka VI, Rigas G, Sakellarios A, Siogkas P, Andrikos IO, Exarchos TP, Loggitsi D, Anagnostopoulos CD, Michalis LK, Neglia D, Pelosi G, Parodi O, Fotiadis DI. 3D reconstruction of coronary arteries and atherosclerotic plaques based on computed tomography angiography images. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.09.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Siogkas PK, Rigas G, Exarchos TP, Sakellarios AI, Papafaklis MI, Pelosi G, Parodi O, Michalis LK, Fotiadis DI. Computational estimation of the hemodynamic significance of coronary stenoses in arterial branches deriving from CCTA: A proof-of-concept study. Annu Int Conf IEEE Eng Med Biol Soc 2017; 2017:1348-1351. [PMID: 29060126 DOI: 10.1109/embc.2017.8037082] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The development of non-invasive methods for the accurate hemodynamic assessment of the coronary vasculature has become a non-trivial matter for the everyday clinical practice. Virtual Functional Assessment Index has already been suggested as a valid alternative to the invasively measured FFR but only on coronary arterial segments. In this work, we propose a novel method for the estimation of the severity of coronary lesions in arterial branches from CCTA derived images. Four left arterial branches were reconstructed in 3D using our in-house developed 3D reconstruction algorithm, and were subjected to computational blood flow simulations for the final calculation of the vFAI through the whole arterial branch. Strong correlation was found (r=0.82) between the two methods. A small relative error of 3.2% and a small trend of overestimation (0.023, SD=0.088) were also observed. All pathological cases presenting ischemia, were correctly discriminated by our method as hemodynamically significant lesions.
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Andrikos IO, Sakellarios AI, Siogkas PK, Rigas G, Exarchos TP, Athanasiou LS, Karanasos A, Toutouzas K, Tousoulis D, Michalis LK, Fotiadis DI. A novel hybrid approach for reconstruction of coronary bifurcations using angiography and OCT. Annu Int Conf IEEE Eng Med Biol Soc 2017; 2017:588-591. [PMID: 29059941 DOI: 10.1109/embc.2017.8036893] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The aim of this study is to present a new method for three-dimensional (3D) reconstruction of coronary bifurcations using biplane Coronary Angiographies and Optical Coherence Tomography (OCT) imaging. The method is based on a five step approach by improving a previous validated work in order to reconstruct coronary arterial bifurcations. In the first step the lumen borders are detected on the Frequency Domain (FD) OCT images. In the second step a semi-automated method is implemented on two angiographies for the extraction of the 2D bifurcation coronary artery centerline. In the third step the 3D path of the bifurcation artery is extracted based on a back projection algorithm. In the fourth step the lumen borders are placed onto the 3D catheter path. Finally, in the fifth step the intersection of the main and side branches produces the reconstructed model of the coronary bifurcation artery. Data from three patients are acquired for the validation of the proposed methodology and the results are compared against a reconstruction method using quantitative coronary angiography (QCA). The comparison between the two methods is achieved using morphological measures of the vessels as well as comparison of the wall shear stress (WSS) mean values.
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