Molecular dynamics simulations through GPU video games technologies

Bioinformatics is the scientific field that focuses on the application of computer technology to the management of biological information. Over the years, bioinformatics applications have been used to store, process and integrate biological and genetic information, using a wide range of methodologies. One of the most de novo techniques used to understand the physical movements of atoms and molecules is molecular dynamics (MD). MD is an in silico method to simulate the physical motions of atoms and molecules under certain conditions. This has become a state strategic technique and now plays a key role in many areas of exact sciences, such as chemistry, biology, physics and medicine. Due to their complexity, MD calculations could require enormous amounts of computer memory and time and therefore their execution has been a big problem. Despite the huge computational cost, molecular dynamics have been implemented using traditional computers with a central memory unit (CPU). A graphics processing unit (GPU) computing technology was first designed with the goal to improve video games, by rapidly creating and displaying images in a frame buffer such as screens. The hybrid GPU-CPU implementation, combined with parallel computing is a novel technology to perform a wide range of calculations. GPUs have been proposed and used to accelerate many scientific computations including MD simulations. Herein, we describe the new methodologies developed initially as video games and how they are now applied in MD simulations

Effectiveness of myAirCoach: A mHealth Self-Management System in Asthma

Background: Self-management programs have beneficial effects on asthma control, but their implementation in clinical practice is poor. Mobile health (mHealth) could play an important role in enhancing self-management. Objective: To assess the clinical effectiveness and technology acceptance of myAirCoach-supported self-management on top of usual care in patients with asthma using inhalation medication. Methods: Patients were recruited in 2 separate studies. The myAirCoach system consisted of an inhaler adapter, an indoor air-quality monitor, a physical activity tracker, a portable spirometer, a fraction exhaled nitric oxide device, and an app. The primary outcome was asthma control; secondary outcomes were exacerbations, quality of life, and technology acceptance. In study 1, 30 participants were randomized to either usual care or myAirCoach support for 3 to 6 months; in study 2, 12 participants were provided with the myAirCoach system in a 3-month before-after study. Results: In study 1, asthma control improved in the intervention group compared with controls (Asthma Control Questionnaire difference, 0.70; P = .006). A total of 6 exacerbations occurred in the intervention group compared with 12 in the control group (hazard ratio, 0.31; P = .06). Asthma-related quality of life improved (mini Asthma-related Quality of Life Questionnaire difference, 0.53; P = .04), but forced expiratory volume in 1 second was unchanged. In study 2, asthma control improved by 0.86 compared with baseline (P = .007) and quality of life by 0.16 (P = .64). Participants reported positive attitudes toward the system. Discussion: Using the myAirCoach support system improves asthma control and quality of life, with a reduction in severe asthma exacerbations. Well-validated mHealth technologies should therefore be further studied

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

The B cell receptor immunoglobulin (BcR IG) gene repertoires of marginal zone (MZ) lymphoproliferations were analyzed in order to obtain insight into their ontogenetic relationships. Our cohort included cases with MZ lymphomas (n=488) i.e. splenic (SMZL), nodal (NMZL) and extranodal (ENMZL) as well as provisional entities (n=76) according to the World Health Organization classification. The most striking IG gene repertoire skewing was observed in SMZL. However, restrictions were also identified in all other MZ lymphomas studied, particularly ENMZL, with significantly different IG gene distributions depending on the primary site of involvement. Cross-entity comparisons of the MZ IG sequence dataset with a large dataset of IG sequences (MZ-related or not; n=65,837) revealed four major clusters of cases sharing homologous ('public') heavy variable complementarity-determining region 3. These clusters included rearrangements from SMZL, ENMZL (gastric, salivary gland, ocular adnexa), chronic lymphocytic leukemia but also rheumatoid factors and non-malignant spleen MZ cells. In conclusion, different MZ lymphomas display biased immunogenetic signatures indicating distinct antigen exposure histories. The existence of rare public stereotypes raises the intriguing possibility that common, pathogen-triggered, immune-mediated mechanisms, may result in diverse B lymphoproliferations due to targeting versatile progenitor B cells and/or operating in particular microenvironments.This work was supported in part by H2020 “AEGLE, An analytics framework for integrated and personalized healthcare services in Europe”, by the European Union (EU); H2020 No. 692298 project “MEDGENET, Medical Genomics and Epigenomics Network” by the EU; grant AZV 15-30015A from the Ministry of Health of the Czech Republic, and the project CEITEC2020 LQ1601 from the Ministry of Education, Youth, and Sports of the Czech Republic; Bloodwise Research Grant (15019); the Swedish Cancer Society, the Swedish Research Council, the Knut and Alice Wallenberg Foundation, Karolinska Institutet, Stockholm, the Lion’s Cancer Research Foundation, Uppsala, the Marcus Borgström Foundation and Selander’s Foundation, Uppsala

Καινοτόμες ΤΠΕ εφαρμογές σε χρόνιες παθήσεις στο πλαίσιο της ιατρικής ακρίβειας

Health care has seen massive data growth over the last several years, with some reports estimating that health care data generation increases by 48\% annually. The totality of data related to patient healthcare and well-being comprise “big data” in the healthcare industry. Big data scientists in healthcare take advantage of the data explosion to discover associations and understand patterns within the health data as well as to extract insights regarding the potential improvement of precision medicine. Precision medicine is "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person." Based on this approach, healthcare professionals and healthcare regulators are able to predict more accurately treatment and prevention strategies that will work in groups of people for particular diseases. The advent of precision medicine demands, besides the detailed patient clinical profiles, data of different types such as biological, sensor data, physiological and environmental. The term “precision medicine” has become very popular, fuelled by scientific as well as political perspectives. Thus, the primary aim of precision medicine is to link individuals with the best possible treatment in the hope of improving clinical outcomes, and ultimately, patient health and quality of life. Data understanding holds the promise of supporting a wide range of functions in medicine and healthcare, including among other clinical decision support, disease surveillance, and population health management. Hence, the objectives of this Ph.D. thesis are: (1) to demonstrate the implementation of ICT methodologies built on the big healthcare data-driven research, (2) to describe their application and evaluation in a wide range of Chronic Diseases, such as Chronic Lymphocytic Leukaemia, Alzheimer’s Disease, Parkinson’s Disease, Sjogren’s Syndrome, Asthma, COPD and Chronic Kidney Disease and (3) to demonstrate the system scalability based on the architecture of microservices.Biomedical applications described here are included in the process of designing an eHealth ecosystem towards precision medicine of Chronic Diseases. The implementation and use of these novel approaches offer the opportunity to combine diverse datasets, including data from the electronic health record, with emerging big data sources, such as real-time sensor data, continuous patient monitoring, and laboratory results. The approach of this dissertation can enable cost-effective and scalable initiatives in precision medicine. Different healthcare organizations could take advantage of the technical advances in order to provide comprehensive access for computational health care and precision medicine research.Η υγειονομική περίθαλψη παρουσίασε τεράστια αύξηση στην παραγωγή δεδομένων τα τελευταία χρόνια, ενώ μερικές εκθέσεις εκτιμούν ότι θα αυξάνεται κατά 48\% ετησίως. Το σύνολο των δεδομένων που σχετίζονται με την υγειονομική περίθαλψη και την ευημερία των ασθενών αποτελούν τα «μεγάλα δεδομένα» υγείας. Οι επιστήμονες του τομέα επωφελούνται από την έκρηξη δεδομένων για να ανακαλύψουν συσχετισμούς και να κατανοήσουν τα πρότυπα στα δεδομένα υγείας, καθώς και για να εξαγάγουν ιδέες σχετικά με τη δυνητική βελτίωση της ιατρικής ακρίβειας. Η ιατρική ακρίβειας είναι μια αναδυόμενη προσέγγιση για τη θεραπεία και πρόληψη ασθενειών που λαμβάνει υπόψη την ατομική μεταβλητότητα στα γονίδια, το περιβάλλον και τον τρόπο ζωής για κάθε άτομο. Με βάση αυτή την προσέγγιση, οι επαγγελματίες του τομέα της υγείας και οι ρυθμιστές της υγειονομικής περίθαλψης είναι σε θέση να προβλέψουν με μεγαλύτερη ακρίβεια τις στρατηγικές αντιμετώπισης και πρόληψης, καθώς και τις ομάδες ατόμων για συγκεκριμένες θεραπείες. Η ιατρική ακριβείας απαιτεί, εκτός από τα λεπτομερή κλινικά χαρακτηριστικά του ασθενούς, δεδομένα διαφορετικών τύπων, όπως βιολογικά δεδομένα αισθητήρων, φυσιολογικά και περιβαλλοντικά στοιχεία. Ο όρος "ιατρική ακρίβειας" έχει γίνει πολύ δημοφιλής, τροφοδοτούμενος από επιστημονικές και πολιτικές προοπτικές. Έτσι, ο πρωταρχικός στόχος της ιατρικής ακρίβειας είναι η σύνδεση των ατόμων με την καλύτερη δυνατή θεραπεία, με την ελπίδα βελτίωσης των κλινικών αποτελεσμάτων και, τελικά, της υγείας των ασθενών και της ποιότητας ζωής. Η κατανόηση των δεδομένων είναι ικανή να υποστηρίξει ένα ευρύ φάσμα λειτουργιών, συμπεριλαμβανομένης μεταξύ άλλων της υποστήριξης των κλινικών αποφάσεων, της συνεχούς παρακολούθησης των ασθενειών και της διαχείρισης της υγείας του πληθυσμού. Ως εκ τούτου, οι στόχοι αυτής της διδακτορικής διατριβής είναι: α) η επίδειξη της εφαρμογής των ΤΠΕ μεθοδολογιών μέσα απο τη χρήση μεγάλων δεδομένων υγείας, β) η περιγραφή της εφαρμογής και αξιολόγησής τους σε ένα ευρύ φάσμα χρόνιων ασθενειών, όπως η χρόνια λεμφοκυτταρική λευχαιμία , Ασθένεια Alzheimer, νόσο του Πάρκινσον, σύνδρομο Sjogren, άσθμα, ΧΑΠ και χρόνια νεφρική νόσο και γ) να παρουσιάσει την επεκτασιμότητα του συστήματος μέσω της αρχιτεκτονική των μικρο-υπηρεσιών. Οι βιοϊατρικές εφαρμογές που περιγράφονται στην παρούσα διατριβή συμπεριλαμβάνονται στη διαδικασία σχεδιασμού ενός οικοσυστήματος ηλεκτρονικής υγείας για ιατρική ακρίβειας των χρόνιων παθήσεων. Η εφαρμογή και η χρήση αυτών των καινοτόμων προσεγγίσεων προσφέρει τη δυνατότητα συνδυασμού διαφορετικών συνόλων δεδομένων, συμπεριλαμβανομένων δεδομένων από το ηλεκτρονικό αρχείο υγείας, με αναδυόμενες μεγάλες πηγές δεδομένων, όπως δεδομένα αισθητήρων, δεδομένα από συνεχή παρακολούθηση ασθενών έσω εφαρμογών ψηφιακής υγείας και εργαστηριακά αποτελέσματα. Η προσέγγιση αυτής της διατριβής μπορεί να προσφέρει οικονομικά αποδοτικές και κλιμακούμενες πρωτοβουλίες στην ιατρική ακρίβειας. Διαφορετικές δομές υγειονομικής περίθαλψης θα μπορούσαν να επωφεληθούν από την τεχνική πρόοδο, προκειμένου να παρέχουν ολοκληρωμένη πρόσβαση για την υπολογιστική υγειονομική περίθαλψη και την έρευνα ιατρικής ακρίβειας

Automated shape-based clustering of 3D immunoglobulin protein structures in chronic lymphocytic leukemia

Background: Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D structure analysis has been utilised in order to discover indications of antigenic pressure. The latter has been mostly based on the 3D models of the clonotypic B cell receptor immunoglobulin (BcR IG) amino acid sequences. Therefore, their accuracy is directly dependent on the quality of the model construction algorithms and the specific methods used to compare the ensuing models. Thus far, reliable and robust methods that can group the IG 3D models based on their structural characteristics are missing. Results: Here we propose a novel method for clustering a set of proteins based on their 3D structure focusing on 3D structures of BcR IG from a large series of patients with CLL. The method combines techniques from the areas of bioinformatics, 3D object recognition and machine learning. The clustering procedure is based on the extraction of 3D descriptors, encoding various properties of the local and global geometrical structure of the proteins. The descriptors are extracted from aligned pairs of proteins. A combination of individual 3D descriptors is also used as an additional method. The comparison of the automatically generated clusters to manual annotation by experts shows an increased accuracy when using the 3D descriptors compared to plain bioinformatics-based comparison. The accuracy is increased even more when using the combination of 3D descriptors. Conclusions: The experimental results verify that the use of 3D descriptors commonly used for 3D object recognition can be effectively applied to distinguishing structural differences of proteins. The proposed approach can be applied to provide hints for the existence of structural groups in a large set of unannotated BcR IG protein files in both CLL and, by logical extension, other contexts where it is relevant to characterize BcR IG structural similarity. The method does not present any limitations in application and can be extended to other types of proteins