Browsing Institut Politècnic d’Innovació i Recerca en Sostenibilitat (INSPIRES) by Author "Alves, Rui"
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- ItemOpen AccessComputer-assisted initial diagnosis of rare diseases(PeerJ, 2016) Alves, Rui; Piñol, Marc; Vilaplana Mayoral, Jordi; Teixidó Torrelles, Ivan; Cruz, Joaquim; Comas, Jorge; Vilaprinyo Terré, Ester; Sorribas Tello, Albert; Solsona Tehàs, FrancescIntroduction. Most documented rare diseases have genetic origin. Because of their low individual frequency, an initial diagnosis based on phenotypic symptoms is not always easy, as practitioners might never have been exposed to patients suffering from the relevant disease. It is thus important to develop tools that facilitate symptom-based initial diagnosis of rare diseases by clinicians. In this work we aimed at developing a computational approach to aid in that initial diagnosis. We also aimed at implementing this approach in a user friendly web prototype. We call this tool Rare Disease Discovery. Finally, we also aimed at testing the performance of the prototype. Methods. Rare Disease Discovery uses the publicly available ORPHANET data set of association between rare diseases and their symptoms to automatically predict the most likely rare diseases based on a patient’s symptoms. We apply the method to retrospectively diagnose a cohort of 187 rare disease patients with confirmed diagnosis. Subsequently we test the precision, sensitivity, and global performance of the system under different scenarios by running large scale Monte Carlo simulations. All settings account for situations where absent and/or unrelated symptoms are considered in the diagnosis. Results. We find that this expert system has high diagnostic precision (≥80%) and sensitivity (≥99%), and is robust to both absent and unrelated symptoms. Discussion. The Rare Disease Discovery prediction engine appears to provide a fast and robust method for initial assisted differential diagnosis of rare diseases. We coupled this engine with a user-friendly web interface and it can be freely accessed at http://disease-discovery.udl.cat/. The code and most current database for the whole project can be downloaded from https://github.com/Wrrzag/DiseaseDiscovery/tree/no_classifiers.
- ItemOpen AccessDatabase constraints applied to metabolic pathway reconstruction tools(Hindawi Publishing Corporation, 2014) Vilaplana Mayoral, Jordi; Solsona Tehàs, Francesc; Teixidó Torrelles, Ivan; Usié Chimenos, Anabel; Karathia, Hiren; Alves, Rui; Mateo Fornés, JordiOur group developed two biological applications, Biblio-MetReS and Homol-MetReS, accessing the same database of organisms with annotated genes. Biblio-MetReS is a data-mining application that facilitates the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the process(es) of interest and their function. It also enables the sets of proteins involved in the process(es) in different organisms to be compared directly. The efficiency of these biological applications is directly related to the design of the shared database.We classified and analyzed the different kinds of access to the database. Based on this study, we tried to adjust and tune the configurable parameters of the database server to reach the best performance of the communication data link to/from the database system. Different database technologies were analyzed.We started the study with a public relational SQL database, MySQL.Then, the same database was implemented by a MapReduce-based database named HBase. The results indicated that the standard configuration of MySQL gives an acceptable performance for low or medium size databases. Nevertheless, tuning database parameters can greatly improve the performance and lead to very competitive runtimes.
- ItemRestrictedIdentification of line-specific strategies for improving carotenoid production in synthetic maize through data-driven mathematical modeling(Wiley online library, 2016) Comas, Jorge; Benfeitas, Rui; Vilaprinyo Terré, Ester; Sorribas Tello, Albert; Solsona Tehàs, Francesc; Farré Martinez, Gemma; Berman Quintana, Judit; Zorrilla López, Uxue; Capell Capell, Teresa; Sandmann, Gerhard; Zhu, Changfu; Christou, Paul; Alves, RuiPlant synthetic biology is still in its infancy. However, synthetic biology approaches have been used to manipulate and improve the nutritional and health value of staple food crops such as rice, potato and maize. With current technologies, production yields of the synthetic nutrients are a result of trial and error, and systematic rational strategies to optimize those yields are still lacking. Here, we present a workflow that combines gene expression and quantitative metabolomics with mathematical modeling to identify strategies for increasing production yields of nutritionally important carotenoids in the seed endosperm synthesized through alternative biosynthetic pathways in synthetic lines of white maize, which is normally devoid of carotenoids. Quantitative metabolomics and gene expression data are used to create and fit parameters of mathematical models that are specific to four independent maize lines. Sensitivity analysis and simulation of each model is used to predict which gene activities should be further engineered in order to increase production yields for carotenoid accumulation in each line. Some of these predictions (e.g. increasing Zmlycb/Gllycb will increase accumulated β-carotenes) are valid across the four maize lines and consistent with experimental observations in other systems. Other predictions are line specific. The workflow is adaptable to any other biological system for which appropriate quantitative information is available. Furthermore, we validate some of the predictions using experimental data from additional synthetic maize lines for which no models were developed.
- ItemRestrictedS-PC: An e-treatment application for management of smoke-quitting patients(Elsevier, 2014) Vilaplana Mayoral, Jordi; Solsona Tehàs, Francesc; Abella i Pons, Francesc; Cuadrado, Josep; Alves, Rui; Mateo Fornés, JordiThe main objective ofthis paper is to present a new program thatfacilitates the management of people who want to quit smoking, implemented through an e-treatment software called S-PC (Smoker Patient Control). S-PC is a web-based application that manages groups of patients, provides a bidirectional communication through mobile text messages and e-mails between patients and clinicians and offers advice and control to keep track of the patients and their status. A total of 229 patients were enrolled in the study, randomly divided into two groups, although some variables were tested to ensure that there were no significant differences between the groups that could have an impact on the outcome of the treatment. There were no significant differences between the two groups regarding the ratio/number of males/females, tobacco dependence, co-oximetry, average cigarette consumption, current age and age when smoking started. The first group was made up of 104 patients (45.4% of the total) and followed a treatment that incorporated the S-PC tool, while the second one had 125 patients without the S-PC tool. S-PC was evaluated for its effectiveness at assisting the patients to give up smoking, and its effect on clinician time management. 74% of the S-PC group completed the treatment without relapses and remained abstinent three months after the completion of the treatment, understanding abstinence as being continuous (with no relapses allowed and co-oximetry below 1 ppm) from the day of stopping. In contrast only 45.6% of the No S-PC group completed the treatment without relapses and remained abstinent three months after completion of the treatment. The rate of admittance to the program has doubled in one year and patients went from having to wait for 3 months to be immediately admitted into the program.
- ItemOpen AccessTControl: A mobile app to follow up tobacco-quitting patients(Elsevier, 2017-04) Pifarré Montalà, Marc; Carrera Peruga, Adrián; Vilaplana Mayoral, Jordi; Cuadrado, Josep; Solsona, Sara; Abella i Pons, Francesc; Solsona Tehàs, Francesc; Alves, RuiBackground and Objective Tobacco smoking is a major risk factor for a wide range of respiratory and circulatory diseases in active and passive smokers. Well-designed campaigns are raising awareness to the problem and an increasing number of smokers seeks medical assistance to quit their habit. In this context, there is the need to develop mHealth Apps that assist and manage large smoke quitting programs in efficient and economic ways. Objectives Our main objective is to develop an efficient and free mHealth app that facilitates the management of, and assistance to, people who want to quit smoking. As secondary objectives, our research also aims at estimating the economic effect of deploying that App in the public health system. Methods Using JAVA and XML we develop and deploy a new free mHealth App for Android, called TControl (Tobacco-quitting Control). We deploy the App at the Tobacco Unit of the Santa Maria Hospital in Lleida and determine its stability by following the crashes of the App. We also use a survey to test usability of the app and differences in aptitude for using the App in a sample of 31 patients. Finally, we use mathematical models to estimate the economic effect of deploying TControl in the Catalan public health system. Results TControl keeps track of the smoke-quitting users, tracking their status, interpreting it, and offering advice and psychological support messages. The App also provides a bidirectional communication channel between patients and clinicians via mobile text messages. Additionally, registered patients have the option to interchange experiences with each other by chat. The App was found to be stable and to have high performances during startup and message sending. Our results suggest that age and gender have no statistically significant effect on patient aptitude for using TControl. Finally, we estimate that TControl could reduce costs for the Catalan public health system (CPHS) by up to € 400M in 10 years. Conclusions TControl is a stable and well behaved App, typically operating near optimal performance. It can be used independent of age and gender, and its wide implementation could decrease costs for the public health system.