Federated architecture for biomedical data integration

The last decades have been characterized by a continuous adoption of IT solutions in the healthcare sector, which resulted in the proliferation of tremendous amounts of data over heterogeneous systems. Distinct data types are currently generated, manipulated, and stored, in the several institutions where patients are treated. The data sharing and an integrated access to this information will allow extracting relevant knowledge that can lead to better diagnostics and treatments. This thesis proposes new integration models for gathering information and extracting knowledge from multiple and heterogeneous biomedical sources. The scenario complexity led us to split the integration problem according to the data type and to the usage specificity. The first contribution is a cloud-based architecture for exchanging medical imaging services. It offers a simplified registration mechanism for providers and services, promotes remote data access, and facilitates the integration of distributed data sources. Moreover, it is compliant with international standards, ensuring the platform interoperability with current medical imaging devices. The second proposal is a sensor-based architecture for integration of electronic health records. It follows a federated integration model and aims to provide a scalable solution to search and retrieve data from multiple information systems. The last contribution is an open architecture for gathering patient-level data from disperse and heterogeneous databases. All the proposed solutions were deployed and validated in real world use cases.

Revisiting molecular diagnostics of Streptococcus pneumoniae

Streptococcus pneumoniae is a human pathobiont that colonizes the nasopharynx. S. pneumoniae is responsible for causing non-invasive and invasive disease such as otitis, pneumonia, meningitis, and sepsis, being a leading cause of infectious diseases worldwide. Due to similarities with closely related species sharing the same niche, it may be a challenge to correctly distinguish S. pneumoniae from its relatives when using only non-culture based methods such as real time PCR (qPCR). In 2007, a molecular method targeting the major autolysin (lytA) of S. pneumoniae by a qPCR assay was proposed by Carvalho and collaborators to identify pneumococcus. Since then, this method has been widely used worldwide. In 2013, the gene encoding for the ABC iron transporter lipoprotein PiaA, was proposed by Trzcinzki and collaborators to be used in parallel with the lytA qPCR assay. However, the presence of lytA gene homologues has been described in closely related species such as S. pseudopneumoniae and S. mitis and the presence of piaA gene is not ubiquitous between S. pneumoniae. The hyaluronate lyase gene (hylA) has been described to be ubiquitous in S. pneumoniae. This gene has not been used so far as a target for the identification of S. pneumoniae. The aims of our study were to evaluate the specificity, sensitivity, positive predicted value (PPV) and negative predicted value (NPV) of the lytA and piaA qPCR methods; design and implement a new assay targeting the hylA gene and evaluate the same parameters above described; analyze the assays independently and the possible combinations to access what is the best approach using qPCR to identify S. pneumoniae. A total of 278 previously characterized strains were tested: 61 S. pseudopneumoniae, 37 Viridans group strains, 30 type strains from other streptococcal species and 150 S. pneumoniae strains. The collection included both carriage and disease isolates. By Mulilocus Sequence Analysis (MLSA) we confirmed that strains of S. pseudopneumoniae could be misidentified as S. pneumoniae when lytA qPCR assay is used. The results showed that as a single target, lytA had the best combination of specificity, sensitivity, PPV and NPV being, 98.5%, 100.0%, 98.7% and 100.0% respectively. The combination of targets with the best values of specificity, sensibility, PPV and NPV were lytA and piaA, with 100.0%, 93.3%, 97.9% and 92.6%, respectively. Nonetheless by MLSA we confirmed that strains of S. pseudopneumoniae could be misidentified as S. pneumoniae and some capsulated (23F, 6B and 11A) and non-capsulated S. pneumoniae were not Identified using this assay. The hylA gene as a single target had the lowest PPV. Nonetheless it was capable to correctly identify all S. pneumoniae.