Fernando Martín-Sánchez

Methodological Review: Data integration and genomic medicine

Genomic medicine aims to revolutionize health care by applying our growing understanding of the molecular basis of disease. Research in this arena is data intensive, which means data sets are large and highly heterogeneous. To create knowledge from data, researchers must integrate these large and diverse data sets. This presents daunting informatic challenges such as representation of data that is suitable for computational inference (knowledge representation), and linking heterogeneous data sets (data integration). Fortunately, many of these challenges can be classified as data integration problems, and technologies exist in the area of data integration that may be applied to these challenges. In this paper, we discuss the opportunities of genomic medicine as well as identify the informatics challenges in this domain. We also review concepts and methodologies in the field of data integration. These data integration concepts and methodologies are then aligned with informatics challenges in genomic medicine and presented as potential solutions. We conclude this paper with challenges still not addressed in genomic medicine and gaps that remain in data integration research to facilitate genomic medicine.

Health outcomes and related effects of using social media in chronic disease management

Whilst the future for social media in chronic disease management appears to be optimistic, there is limited concrete evidence indicating whether and how social media use significantly improves patient outcomes. This review examines the health outcomes and related effects of using social media, while also exploring the unique affordances underpinning these effects. Few studies have investigated social medias potential in chronic disease, but those we found indicate impact on health status and other effects are positive, with none indicating adverse events. Benefits have been reported for psychosocial management via the ability to foster support and share information; however, there is less evidence of benefits for physical condition management. We found that studies covered a very limited range of social media platforms and that there is an ongoing propensity towards reporting investigations of earlier social platforms, such as online support groups (OSG), discussion forums and message boards. Finally, it is hypothesized that for social media to form a more meaningful part of effective chronic disease management, interventions need to be tailored to the individualized needs of sufferers. The particular affordances of social media that appear salient in this regard from analysis of the literature include: identity, flexibility, structure, narration and adaptation. This review suggests further research of high methodological quality is required to investigate the affordances of social media and how these can best serve chronic disease sufferers. Evidence-based practice (EBP) using social media may then be considered.

Host adaptive immunity deficiency in severe pandemic influenza

IntroductionPandemic A/H1N1/2009 influenza causes severe lower respiratory complications in rare cases. The association between host immune responses and clinical outcome in severe cases is unknown.MethodsWe utilized gene expression, cytokine profiles and generation of antibody responses following hospitalization in 19 critically ill patients with primary pandemic A/H1N1/2009 influenza pneumonia for identifying host immune responses associated with clinical outcome. Ingenuity pathway analysis 8.5 (IPA) (Ingenuity Systems, Redwood City, CA) was used to select, annotate and visualize genes by function and pathway (gene ontology). IPA analysis identified those canonical pathways differentially expressed (P < 0.05) between comparison groups. Hierarchical clustering of those genes differentially expressed between groups by IPA analysis was performed using BRB-Array Tools v.3.8.1.ResultsThe majority of patients were characterized by the presence of comorbidities and the absence of immunosuppressive conditions. pH1N1 specific antibody production was observed around day 9 from disease onset and defined an early period of innate immune response and a late period of adaptive immune response to the virus. The most severe patients (n = 12) showed persistence of viral secretion. Seven of the most severe patients died. During the late phase, the most severe patient group had impaired expression of a number of genes participating in adaptive immune responses when compared to less severe patients. These genes were involved in antigen presentation, B-cell development, T-helper cell differentiation, CD28, granzyme B signaling, apoptosis and protein ubiquitination. Patients with the poorest outcomes were characterized by proinflammatory hypercytokinemia, along with elevated levels of immunosuppressory cytokines (interleukin (IL)-10 and IL-1ra) in serum.ConclusionsOur findings suggest an impaired development of adaptive immunity in the most severe cases of pandemic influenza, leading to an unremitting cycle of viral replication and innate cytokine-chemokine release. Interruption of this deleterious cycle may improve disease outcome.

Methodological ReviewHealth outcomes and related effects of using social media in chronic disease management: A literature review and analysis of affordances☆

Whilst the future for social media in chronic disease management appears to be optimistic, there is limited concrete evidence indicating whether and how social media use significantly improves patient outcomes. This review examines the health outcomes and related effects of using social media, while also exploring the unique affordances underpinning these effects. Few studies have investigated social medias potential in chronic disease, but those we found indicate impact on health status and other effects are positive, with none indicating adverse events. Benefits have been reported for psychosocial management via the ability to foster support and share information; however, there is less evidence of benefits for physical condition management. We found that studies covered a very limited range of social media platforms and that there is an ongoing propensity towards reporting investigations of earlier social platforms, such as online support groups (OSG), discussion forums and message boards. Finally, it is hypothesized that for social media to form a more meaningful part of effective chronic disease management, interventions need to be tailored to the individualized needs of sufferers. The particular affordances of social media that appear salient in this regard from analysis of the literature include: identity, flexibility, structure, narration and adaptation. This review suggests further research of high methodological quality is required to investigate the affordances of social media and how these can best serve chronic disease sufferers. Evidence-based practice (EBP) using social media may then be considered.

Biological and Medical Data Analysis

Medical Databases and Information Systems.- Application of Three-Level Handprinted Documents Recognition in Medical Information Systems.- Data Management and Visualization Issues in a Fully Digital Echocardiography Laboratory.- A Framework Based on Web Services and Grid Technologies for Medical Image Registration.- Biomedical Image Processing Integration Through INBIOMED: A Web Services-Based Platform.- The Ontological Lens: Zooming in and out from Genomic to Clinical Level.- Data Analysis and Image Processing.- Dynamics of Vertebral Column Observed by Stereovision and Recurrent Neural Network Model.- Endocardial Tracking in Contrast Echocardiography Using Optical Flow.- Unfolding of Virtual Endoscopy Using Ray-Template.- Knowledge Discovery and Data Mining.- Integration of Genetic and Medical Information Through a Web Crawler System.- Vertical Integration of Bioinformatics Tools and Information Processing on Analysis Outcome.- A Grid Infrastructure for Text Mining of Full Text Articles and Creation of a Knowledge Base of Gene Relations.- Prediction of the Performance of Human Liver Cell Bioreactors by Donor Organ Data.- A Bioinformatic Approach to Epigenetic Susceptibility in Non-disjunctional Diseases.- Foreseeing Promising Bio-medical Findings for Effective Applications of Data Mining.- Statistical Methods and Tools for Biomedical Data Analysis.- Hybridizing Sparse Component Analysis with Genetic Algorithms for Blind Source Separation.- Hardware Approach to the Artificial Hand Control Algorithm Realization.- Improving the Therapeutic Performance of a Medical Bayesian Network Using Noisy Threshold Models.- SVM Detection of Premature Ectopic Excitations Based on Modified PCA.- Decision Support Systems.- A Text Corpora-Based Estimation of the Familiarity of Health Terminology.- On Sample Size and Classification Accuracy: A Performance Comparison.- Influenza Forecast: Comparison of Case-Based Reasoning and Statistical Methods.- Tumor Classification from Gene Expression Data: A Coding-Based Multiclass Learning Approach.- Boosted Decision Trees for Diagnosis Type of Hypertension.- Markov Chains Pattern Recognition Approach Applied to the Medical Diagnosis Tasks.- Computer-Aided Sequential Diagnosis Using Fuzzy Relations - Comparative Analysis of Methods.- Collaborative Systems in Biomedical Informatics.- Service Oriented Architecture for Biomedical Collaborative Research.- Simultaneous Scheduling of Replication and Computation for Bioinformatic Applications on the Grid.- The INFOBIOMED Network of Excellence: Developments for Facilitating Training and Mobility.- Bioinformatics: Computational Models.- Using Treemaps to Visualize Phylogenetic Trees.- An Ontological Approach to Represent Molecular Structure Information.- Focal Activity in Simulated LQT2 Models at Rapid Ventricular Pacing: Analysis of Cardiac Electrical Activity Using Grid-Based Computation.- Bioinformatics: Structural Analysis.- Extracting Molecular Diversity Between Populations Through Sequence Alignments.- Detection of Hydrophobic Clusters in Molecular Dynamics Protein Unfolding Simulations Using Association Rules.- Protein Secondary Structure Classifiers Fusion Using OWA.- Efficient Computation of Fitness Function by Pruning in Hydrophobic-Hydrophilic Model.- Evaluation of Fuzzy Measures in Profile Hidden Markov Models for Protein Sequences.- Bioinformatics: Microarray Data Analysis.- Relevance, Redundancy and Differential Prioritization in Feature Selection for Multiclass Gene Expression Data.- Gene Selection and Classification of Human Lymphoma from Microarray Data.- Microarray Data Analysis and Management in Colorectal Cancer.

Colon cancer molecular subtypes identified by expression profiling and associated to stroma, mucinous type and different clinical behavior

BackgroundColon cancer patients with the same stage show diverse clinical behavior dueto tumor heterogeneity. We aimed to discover distinct classes of tumorsbased on microarray expression patterns, to analyze whether the molecularclassification correlated with the histopathological stages or otherclinical parameters and to study differences in the survival.MethodsHierarchical clustering was performed for class discovery in 88 colon tumors(stages I to IV). Pathways analysis and correlations between clinicalparameters and our classification were analyzed. Tumor subtypes werevalidated using an external set of 78 patients. A 167 gene signatureassociated to the main subtype was generated using the 3-Nearest-Neighbormethod. Coincidences with other prognostic predictors were assesed.ResultsHierarchical clustering identified four robust tumor subtypes withbiologically and clinically distinct behavior. Stromal components(p < 0.001), nuclear β-catenin (p = 0.021),mucinous histology (p = 0.001), microsatellite-instability(p = 0.039) and BRAF mutations (p < 0.001) wereassociated to this classification but it was independent of Dukes stages(p = 0.646). Molecular subtypes were established from stage I.High-stroma-subtype showed increased levels of genes and altered pathwaysdistinctive of tumour-associated-stroma and components of the extracellularmatrix in contrast to Low-stroma-subtype. Mucinous-subtype was reflected bythe increased expression of trefoil factors and mucins as well as by ahigher proportion of MSI and BRAF mutations. Tumor subtypes werevalidated using an external set of 78 patients. A 167 gene signatureassociated to the Low-stroma-subtype distinguished low risk patients fromhigh risk patients in the external cohort (Dukes B andC:HR = 8.56(2.53-29.01); Dukes B,C andD:HR = 1.87(1.07-3.25)). Eight different reported survival genesignatures segregated our tumors into two groups the Low-stroma-subtype andthe other tumor subtypes.ConclusionsWe have identified novel molecular subtypes in colon cancer with distinctbiological and clinical behavior that are established from the initiation ofthe tumor. Tumor microenvironment is important for the classification andfor the malignant power of the tumor. Differential gene sets and biologicalpathways characterize each tumor subtype reflecting underlying mechanisms ofcarcinogenesis that may be used for the selection of targeted therapeuticprocedures. This classification may contribute to an improvement in themanagement of the patients with CRC and to a more comprehensiveprognosis.

Nanoinformatics and DNA-based computing: catalyzing nanomedicine.

Five decades of research and practical application of computers in biomedicine has given rise to the discipline of medical informatics, which has made many advances in genomic and translational medicine possible. Developments in nanotechnology are opening up the prospects for nanomedicine and regenerative medicine where informatics and DNA computing can become the catalysts enabling health care applications at sub-molecular or atomic scales. Although nanomedicine promises a new exciting frontier for clinical practice and biomedical research, issues involving cost-effectiveness studies, clinical trials and toxicity assays, drug delivery methods, and the implementation of new personalized therapies still remain challenging. Nanoinformatics can accelerate the introduction of nano-related research and applications into clinical practice, leading to an area that could be called “translational nanoinformatics.” At the same time, DNA and RNA computing presents an entirely novel paradigm for computation. Nanoinformatics and DNA-based computing are together likely to completely change the way we model and process information in biomedicine and impact the emerging field of nanomedicine most strongly. In this article, we review work in nanoinformatics and DNA (and RNA)-based computing, including applications in nanopediatrics. We analyze their scientific foundations, current research and projects, envisioned applications and potential problems that might arise from them.

A systematic review of types and efficacy of online interventions for cancer patients.

OBJECTIVE This review examines the evidence-based literature surrounding the use of online resources for adult cancer patients. The focus is online resources that connect patients with their healthcare clinician and with supportive and educational resources, their efficacy and the outcome measures used to assess them. METHODS The following databases were systematically searched for relevant literature: MEDLINE, PsychINFO, Cochrane Central Register of Controlled Trials, CINAHL, Inspec and Computers and Applied Science. Included were studies conducted in an outpatient setting, and reporting a measurable, clinically relevant outcome. Fourteen studies satisfied the inclusion criteria. RESULTS The efficacy of online interventions was varied, with some demonstrating positive effects on quality of life and related measures, and two demonstrating poorer outcomes for intervention participants. The majority of interventions reported mixed results. Included interventions were too heterogeneous for meta-analysis. CONCLUSIONS The overall benefit of online interventions for cancer patients is unclear. Although there is a plethora of interventions reported without analysis, current interventions demonstrate mixed efficacy of limited duration when rigorously evaluated. PRACTICE IMPLICATIONS The efficacy of on-line interventions for cancer patients is unclear. All on-line interventions should be developed using the available evidence-base and rigorously evaluated to expand our understanding of this area.

An artificial neural network improves the non-invasive diagnosis of significant fibrosis in HIV/HCV coinfected patients

OBJECTIVE To develop an artificial neural network to predict significant fibrosis (F≥2) (ANN-SF) in HIV/Hepatitis C (HCV) coinfected patients using clinical data derived from peripheral blood. METHODS Patients were randomly divided into an estimation group (217 cases) used to generate the ANN and a test group (145 cases) used to confirm its power to predict F≥2. Liver fibrosis was estimated according to the METAVIR score. RESULTS The values of the area under the receiver operating characteristic curve (AUC-ROC) of the ANN-SF were 0.868 in the estimation set and 0.846 in the test set. In the estimation set, with a cut-off value of <0.35 to predict the absence of F≥2, the sensitivity (Se), specificity (Sp), and positive (PPV) and negative predictive values (NPV) were 94.1%, 41.8%, 66.3% and 85.4% respectively. Furthermore, with a cut-off value of >0.75 to predict the presence of F≥2, the ANN-SF provided Se, Sp, PPV and NPV of 53.8%, 94.9%, 92.8% and 62.8% respectively. In the test set, with a cut-off value of <0.35 to predict the absence of F≥2, the Se, Sp, PPV and NPV were 91.8%, 51.7%, 72.9% and 81.6% respectively. Furthermore, with a cut-off value of >0.75 to predict the presence of F≥2, the ANN-SF provided Se, Sp, PPV and NPV of 43.5%, 96.7%, 94.9% and 54.7% respectively. CONCLUSION The ANN-SF accurately predicted significant fibrosis and outperformed other simple non-invasive indices for HIV/HCV coinfected patients. Our data suggest that ANN may be a helpful tool for guiding therapeutic decisions in clinical practice concerning HIV/HCV coinfection.

Medical Informatics and Bioinformatics: A Bibliometric Study

This paper reports on an analysis of the bioinformatics and medical informatics literature with the objective to identify upcoming trends that are shared among both research fields to derive benefits from potential collaborative initiatives for their future. Our results present the main characteristics of the two fields and show that these domains are still relatively separated