An ICT infrastructure to integrate clinical and molecular data in oncology research

<p>Abstract</p> <p>Background</p> <p>The ONCO-i2b2 platform is a bioinformatics tool designed to integrate clinical and research data and support translational research in oncology. It is implemented by the University of Pavia and the IRCCS Fondazione Maugeri hospital (FSM), and grounded on the software developed by the Informatics for Integrating Biology and the Bedside (i2b2) research center. I2b2 has delivered an open source suite based on a data warehouse, which is efficiently interrogated to find sets of interesting patients through a query tool interface.</p> <p>Methods</p> <p>Onco-i2b2 integrates data coming from multiple sources and allows the users to jointly query them. I2b2 data are then stored in a data warehouse, where facts are hierarchically structured as ontologies. Onco-i2b2 gathers data from the FSM pathology unit (PU) database and from the hospital biobank and merges them with the clinical information from the hospital information system.</p> <p>Our main effort was to provide a robust integrated research environment, giving a particular emphasis to the integration process and facing different challenges, consecutively listed: biospecimen samples privacy and anonymization; synchronization of the biobank database with the i2b2 data warehouse through a series of Extract, Transform, Load (ETL) operations; development and integration of a Natural Language Processing (NLP) module, to retrieve coded information, such as SNOMED terms and malignant tumors (TNM) classifications, and clinical tests results from unstructured medical records. Furthermore, we have developed an internal SNOMED ontology rested on the NCBO BioPortal web services.</p> <p>Results</p> <p>Onco-i2b2 manages data of more than 6,500 patients with breast cancer diagnosis collected between 2001 and 2011 (over 390 of them have at least one biological sample in the cancer biobank), more than 47,000 visits and 96,000 observations over 960 medical concepts.</p> <p>Conclusions</p> <p>Onco-i2b2 is a concrete example of how integrated Information and Communication Technology architecture can be implemented to support translational research. The next steps of our project will involve the extension of its capabilities by implementing new plug-in devoted to bioinformatics data analysis as well as a temporal query module.</p

Met-activating genetically improved chimeric factor-1 promotes angiogenesis and hypertrophy in adult myogenesis

BACKGROUND: Myogenic progenitor cells (activated satellite cells) are able to express both HGF and its receptor cMet. After muscle injury, HGF-Met stimulation promotes activation and primary division of satellite cells. MAGIC-F1 (Met-Activating Genetically Improved Chimeric Factor-1) is an engineered protein that contains two human Met-binding domains that promotes muscle hypertrophy. MAGIC-F1 protects myogenic precursors against apoptosis and increases their fusion ability enhancing muscle differentiation. Hemizygous and homozygous Magic-F1 transgenic mice displayed constitutive muscle hypertrophy. METHODS: Here we describe microarray analysis on Magic-F1 myogenic progenitor cells showing an altered gene signatures on muscular hypertrophy and angiogenesis compared to wild-type cells. In addition, we performed a functional analysis on Magic-F1+/+ transgenic mice versus controls using treadmill test. RESULTS: We demonstrated that Magic-F1+/+ mice display an increase in muscle mass and cross-sectional area leading to an improvement in running performance. Moreover, the presence of MAGIC-F1 affected positively the vascular network, increasing the vessel number in fast twitch fibers. Finally, the gene expression profile analysis of Magic-F1+/+ satellite cells evidenced transcriptomic changes in genes involved in the control of muscle growth, development and vascularisation. CONCLUSION: We showed that MAGIC -F1-induced muscle hypertrophy affects positively vascular network, increasing vessel number in fast twitch fibers. This was due to unique features of mammalian skeletal muscle and its remarkable ability to adapt promptly to different physiological demands by modulating the gene expression profile in myogenic progenitors

PATIENT VOICES, a project for the integration of the systematic assessment of patient reported outcomes and experiences within a comprehensive cancer center: a protocol for a mixed method feasibility study

BACKGROUND: Listening to "patient voices" in terms of symptoms, emotional status and experiences with care, is crucial for patient empowerment in clinical practice. Despite convincing evidence that routine patient reported outcomes and experience measurements (PRMs) with rapid feed-back to oncologists can improve symptom control, patient well-being and cost effectiveness, PRMs are not commonly used in cancer care, due to barriers at various level. Part of these barriers may be overcome through electronic PRMs collection (ePRMs) integrated with the electronic medical record (EMR). The PATIENT VOICES initiative is aimed at achieving a stepwise integration of ePRMs assessment into routine cancer care. The feasibility project presented here is aimed at assessing the knowledge, use and attitudes toward PRMs in a comprehensive cancer centre; developing and assessing feasibility of a flexible system for ePRM assessment; identifying barriers to and developing strategies for implementation and integration of ePRMs clinical practice. METHODS: The project has been organized into four phases: a) pre-development; b) software development and piloting; c) feasibility assessment; d) post-development. A convergent mixed method design, based on concurrent quantitative and qualitative data collection will be applied. A web-survey on health care providers (HCPs), qualitative studies on patients and HCPs (semi-structured interviews and focus groups) as well as longitudinal and cross-sectional quantitative studies will be carried out. The quantitative studies will enroll 600 patients: 200 attending out-patient clinics (physical symptom assessement), 200 attending inpatient wards (psychological distress assessment) and 200 patients followed by multidisciplinary teams (patient experience with care assessment). The Edmonton symptom assessment scale, the Distress Thermometer, and a tool adapted from existing patient reported experience with cancer care questionnaires, will be used in quantitative studies. A multi-disciplinary stakeholder team including researchers, clinicians, health informatics professionals, health system administrators and patients will be involved in the development of potentially effective implementation strategies in the post development phase. DISCUSSION: The documentation of potential advantages and implementation barriers achieved within this feasibility project, will serve as a starting point for future and more focused interventions aimed at achieving effective ePRMs routine assessment in cancer care. TRIAL REGISTRATION: ClinicalTrials.gov ( NCT03968718 ) May 30th, 2019

P132 Uncovering blood biomarkers of Inflammatory Bowel Diseases by Raman spectroscopy and FAP dosage: toward a noninvasive triage of patients in first care diagnostic

Abstract Background Currently, a major point of concern in the management of Inflammatory Bowel Diseases (IBD) is the absence of accurate and specific circulating biomarkers able to drive diagnosis in a timely and noninvasive manner. Aim of the present study was to explore blood biomarkers of IBD by coupling the targeted detection of circulating fibroblast activation protein (FAP), a recognized valuable marker of bowel lesion in IBD, and Raman spectroscopy (RS), a quick and label-free metabolomic technique that provides a real-time biochemical characterization of plasma samples without any previously known target. Methods Blood samples were collected from over 140 patients with IBD and 170 control subjects matched for gender and age. Isolated plasma was analysed by enzyme-linked immunosorbent assay for quantitative detection of circulating form of FAP. RS was performed on dry droplets of plasma, with the aim to decipher specific fingerprint of IBD in peripheral blood. A predictive model was built on FAP and Raman data separately, to determine specificity, sensitivity and accuracy of the two approaches in patients classification. Supervised multivariate model was applied on a subset of 203 patients to discriminate IBD and control subjects based on combined datasets. Results FAP levels were reduced in patients with IBD as compared to controls (p&lt;0.0001). The sensitivity and specificity of FAP were 70% and 84% based on the optimal cutoff (57.6 ng mL-1, AUC=0.78). Raman spectra of IBD plasma revealed significant differences in peaks corresponding to carotenoids, proteins with β-sheet secondary structure, lipids and aromatic amino-acids. A machine learning model was applied on a subset of patients reaching an accuracy of 85% in classifying IBD and control subjects. No statistically significant differences were observed so far between the discriminative performance of the sole RS or the combination of RS and FAP. Conclusion RS and FAP dosage enable new discoveries in the biological fingerprint of IBD plasma and provide novel candidate biomarkers of IBD. Our preliminary results strongly suggest that novel blood-based approaches could represent a fast noninvasive way to triage patents with suspected IBD in first care diagnostic, to be applied prior to further specific evaluation

A framework for selecting deep learning hyper-parameters

Recent research has found that deep learning architectures show significant improvements over traditional shallow algorithms when mining high dimensional datasets. When the choice of algorithm employed, hyper-parameter setting, number of hidden layers and nodes within a layer are combined, the identification of an optimal configuration can be a lengthy process. Our work provides a framework for building deep learning architectures via a stepwise approach, together with an evaluation methodology to quickly identify poorly performing architectural configurations. Using a dataset with high dimensionality, we illustrate how different architectures perform and how one algorithm configuration can provide input for fine-tuning more complex models

A dashboard-based system for supporting diabetes care

[EN] Objective To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice. Methods The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers. Results The use of the decision support component in clinical activities produced a reduction in visit duration (P¿¿¿.01) and an increase in the number of screening exams for complications (P¿<¿.01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the system¿s capability of identifying and understanding the characteristics of patient subgroups treated at the center. Conclusion Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.This work was supported by the European Union in the Seventh Framework Programme, grant number 600914.Dagliati, A.; Sacchi, L.; Tibollo, V.; Cogni, G.; Teliti, M.; Martinez-Millana, A.; Traver Salcedo, V.... (2018). A dashboard-based system for supporting diabetes care. Journal of the American Medical Informatics Association. 25(5):538-547. https://doi.org/10.1093/jamia/ocx159S538547255Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H., & Tang, P. C. (2001). Clinical Decision Support Systems for the Practice of Evidence-based Medicine. Journal of the American Medical Informatics Association, 8(6), 527-534. doi:10.1136/jamia.2001.0080527Palmer, A. 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Association of FOXO3A locus with extreme longevity in the Southern Italian Centenarian Study

A number of potential candidate genes in a variety of biological pathways have been associated with longevity in model organisms. Many of these genes have human homologs and thus have the potential to provide insights into human longevity. Recently, several studies suggested that FOXO3A functions as a key bridge for various signaling pathways that influence aging and longevity. Interestingly, Willcox and colleagues identified several variants that displayed significant genotype-gender interaction in male human longevity. In particular, a nested case-control study was performed in an ethnic Japanese population in Hawaii, and five candidate longevity genes were chosen based on links to the insulin-insulin-like growth factor-1 (IGF-1) signaling pathway. In the Willcox study, the investigated genetic variations (rs2802292, rs2764264, and rs13217795) within the FOXO3A gene were significantly associated with longevity in male centenarians. We validated the association of FOXO3A polymorphisms with extreme longevity in males from the Southern Italian Centenarian Study. Particularly, rs2802288, a proxy of rs2802292, showed the best allelic association-minor allele frequency (MAF) = 0.49; p = 0.003; odds ratio (OR) = 1.51; 95% confidence interval (CI), 1.15-1.98). Furthermore, we undertook a meta-analysis to explore the significance of rs2802292 association with longevity by combining the association results of the current study and the findings coming from the Willcox et al. investigation. Our data point to a key role of FOXO3A in human longevity and confirm the feasibility of the identification of such genes with centenarian-controls studies. Moreover, we hypothesize the susceptibility to the longevity phenotype may well be the result of complex interactions involving genes and environmental factors but also gender

Democratized image analytics by visual programming through integration of deep models and small-scale machine learning

Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange (http://orange.biolab.si) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae

Systematic review of communication technologies to promote access and engagement of young people with diabetes into healthcare

Background: Research has investigated whether communication technologies (e.g. mobile telephony, forums, email) can be used to transfer digital information between healthcare professionals and young people who live with diabetes. The systematic review evaluates the effectiveness and impact of these technologies on communication. Methods: Nine electronic databases were searched. Technologies were described and a narrative synthesis of all studies was undertaken. Results: Of 20,925 publications identified, 19 met the inclusion criteria, with 18 technologies assessed. Five categories of communication technologies were identified: video-and tele-conferencing (n = 2); mobile telephony (n = 3); telephone support (n = 3); novel electronic communication devices for transferring clinical information (n = 10); and web-based discussion boards (n = 1). Ten studies showed a positive improvement in HbA1c following the intervention with four studies reporting detrimental increases in HbA1c levels. In fifteen studies communication technologies increased the frequency of contact between patient and healthcare professional. Findings were inconsistent of an association between improvements in HbA1c and increased contact. Limited evidence was available concerning behavioural and care coordination outcomes, although improvement in quality of life, patientcaregiver interaction, self-care and metabolic transmission were reported for some communication technologies. Conclusions: The breadth of study design and types of technologies reported make the magnitude of benefit and their effects on health difficult to determine. While communication technologies may increase the frequency of contact between patient and health care professional, it remains unclear whether this results in improved outcomes and is often the basis of the intervention itself. Further research is needed to explore the effectiveness and cost effectiveness of increasing the use of communication technologies between young people and healthcare professionals