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Drug repositioning offers the significant advantage of greatly reducing the cost and time of drug discovery by identifying new therapeutic indications for existing drugs. In particular, computational approaches using networks in drug repositioning have attracted attention for inferring potential associations between drugs and diseases efficiently based on the network connectivity. In this article, we proposed a network-based drug repositioning method to construct a drug–gene–disease tensor by integrating drug–disease, drug–gene, and disease–gene associations and predict drug–gene–disease triple associations through tensor decomposition. The proposed method, which ensembles generalized tensor decomposition (GTD) and multi-layer perceptron (MLP), models drug–gene–disease associations through GTD and learns the features of drugs, genes, and diseases through MLP, providing more flexibility and non-linearity than conventional tensor decomposition. We experimented with drug–gene–disease association prediction using two distinct networks created by chemical structures and ATC codes as drug features. Moreover, we leveraged drug, gene, and disease latent vectors obtained from the predicted triple associations to predict drug–disease, drug–gene, and disease–gene pairwise associations. Our experimental results revealed that the proposed ensemble method was superior for triple association prediction. The ensemble model achieved an AUC of 0.96 in predicting triple associations for new drugs, resulting in an approximately 7% improvement over the performance of existing models. It also showed competitive accuracy for pairwise association prediction compared with previous methods. This study demonstrated that incorporating genetic information leads to notable advancements in drug repositioning. [ABSTRACT FROM AUTHOR]

Drug repositioning, which involves the identification of new therapeutic indications for approved drugs, considerably reduces the time and cost of developing new drugs. Recent computational drug repositioning methods use heterogeneous networks to identify drug–disease associations. This review reveals existing network-based approaches for predicting drug–disease associations in three major categories: graph mining, matrix factorization or completion, and deep learning. We selected eleven methods from the three categories to compare their predictive performances. The experiment was conducted using two uniform datasets on the drug and disease sides, separately. We constructed heterogeneous networks using drug–drug similarities based on chemical structures and ATC codes, ontology-based disease–disease similarities, and drug–disease associations. An improved evaluation metric was used to reflect data imbalance as positive associations are typically sparse. The prediction results demonstrated that methods in the graph mining and matrix factorization or completion categories performed well in the overall assessment. Furthermore, prediction on the drug side had higher accuracy than on the disease side. Selecting and integrating informative drug features in drug–drug similarity measurement are crucial for improving disease-side prediction. [ABSTRACT FROM AUTHOR]

Frontotemporal dementia (FTD) is a primary cause of dementia encompassing a broad range of clinical phenotypes and cellular pathologies. Genetic discoveries in FTD have largely been driven by linkage studies in well-documented extended families, explaining most of the patients with a known pathogenic mutation. In the context of complex diseases, it is hypothesized that mutations with reduced penetrance or a combination of low-effect size variants with environmental factors drive disease. Furthermore, these genes are likely to be part of the interaction networks of known FTD genes, contributing to converging cellular processes. In this review, we examine gene discovery approaches in FTD and introduce network biology concepts as tools to assist gene identification studies in genetically complex disease. Most genetic findings in frontotemporal dementia (FTD) result from family-based linkage studies, while the discovery and replication rate of cohort-based gene identification studies are relatively low. Protein products of genes causing the same or related diseases are organized in modules, with more frequent interactions with each other than expected by random chance and contribute to converging cellular pathways. Several algorithms have been developed to identify disease modules to prioritize genetic findings in the context of complex diseases. In this review we discuss classical gene identification strategies and introduce network biology concepts through protein interaction networks as tools to assist gene identification studies in frontotemporal dementia. [ABSTRACT FROM AUTHOR]

Drug repositioning offers the significant advantage of greatly reducing the cost and time of drug discovery by identifying new therapeutic indications for existing drugs. In particular, computational approaches using networks in drug repositioning have attracted attention for inferring potential associations between drugs and diseases efficiently based on the network connectivity. In this article, we proposed a network-based drug repositioning method to construct a drug–gene–disease tensor by integrating drug–disease, drug–gene, and disease–gene associations and predict drug–gene–disease triple associations through tensor decomposition. The proposed method, which ensembles generalized tensor decomposition (GTD) and multi-layer perceptron (MLP), models drug–gene–disease associations through GTD and learns the features of drugs, genes, and diseases through MLP, providing more flexibility and non-linearity than conventional tensor decomposition. We experimented with drug–gene–disease association prediction using two distinct networks created by chemical structures and ATC codes as drug features. Moreover, we leveraged drug, gene, and disease latent vectors obtained from the predicted triple associations to predict drug–disease, drug–gene, and disease–gene pairwise associations. Our experimental results revealed that the proposed ensemble method was superior for triple association prediction. The ensemble model achieved an AUC of 0.96 in predicting triple associations for new drugs, resulting in an approximately 7% improvement over the performance of existing models. It also showed competitive accuracy for pairwise association prediction compared with previous methods. This study demonstrated that incorporating genetic information leads to notable advancements in drug repositioning.

Drug repositioning, which involves the identification of new therapeutic indications for approved drugs, considerably reduces the time and cost of developing new drugs. Recent computational drug repositioning methods use heterogeneous networks to identify drug–disease associations. This review reveals existing network-based approaches for predicting drug–disease associations in three major categories: graph mining, matrix factorization or completion, and deep learning. We selected eleven methods from the three categories to compare their predictive performances. The experiment was conducted using two uniform datasets on the drug and disease sides, separately. We constructed heterogeneous networks using drug–drug similarities based on chemical structures and ATC codes, ontology-based disease–disease similarities, and drug–disease associations. An improved evaluation metric was used to reflect data imbalance as positive associations are typically sparse. The prediction results demonstrated that methods in the graph mining and matrix factorization or completion categories performed well in the overall assessment. Furthermore, prediction on the drug side had higher accuracy than on the disease side. Selecting and integrating informative drug features in drug–drug similarity measurement are crucial for improving disease-side prediction.

Background: The German research networks DescribePSP and ProPSP prospectively collect comprehensive clinical data, imaging data and biomaterials of patients with a clinical diagnosis of progressive supranuclear palsy. Progressive supranuclear palsy is a rare, adult-onset, neurodegenerative disease with striking clinical heterogeneity. Since now, prospective natural history data are largely lacking. Clinical research into treatment strategies has been limited due to delay in clinical diagnosis and lack of natural history data on distinct clinical phenotypes.Methods: The DescribePSP network is organized by the German Center for Neurodegenerative Diseases. DescribePSP is embedded in a larger network with parallel cohorts of other neurodegenerative diseases and healthy controls. The DescribePSP network is directly linked to other Describe cohorts with other primary diagnoses of the neurodegenerative and vascular disease spectrums and also to an autopsy program for clinico-pathological correlation. The ProPSP network is organized by the German Parkinson and Movement Disorders Society. Both networks follow the same core protocol for patient recruitment and collection of data, imaging and biomaterials. Both networks host a web-based data registry and a central biorepository. Inclusion/exclusion criteria follow the 2017 Movement Disorder Society criteria for the clinical diagnosis of progressive supranuclear palsy.Results: Both networks started recruitment of patients by the end of 2015. As of November 2020, N = 354 and 269 patients were recruited into the DescribePSP and the ProPSP studies, respectively, and N = 131 and 87 patients received at least one follow-up visit.Conclusions: The DescribePSP and ProPSP networks are ideal resources for comprehensive natural history data of PSP, including imaging data and biological samples. In contrast to previous natural history studies, DescribePSP and ProPSP include not only patients with Richardson's syndrome, but also variant PSP phenotypes as well as patients at very early disease stages, before a diagnosis of possible or probable PSP can be made. This will allow for identification and evaluation of early biomarkers for diagnosis, prognosis, and progression.

Background: The German research networks DescribePSP and ProPSP prospectively collect comprehensive clinical data, imaging data and biomaterials of patients with a clinical diagnosis of progressive supranuclear palsy. Progressive supranuclear palsy is a rare, adult-onset, neurodegenerative disease with striking clinical heterogeneity. Since now, prospective natural history data are largely lacking. Clinical research into treatment strategies has been limited due to delay in clinical diagnosis and lack of natural history data on distinct clinical phenotypes. Methods: The DescribePSP network is organized by the German Center for Neurodegenerative Diseases. DescribePSP is embedded in a larger network with parallel cohorts of other neurodegenerative diseases and healthy controls. The DescribePSP network is directly linked to other Describe cohorts with other primary diagnoses of the neurodegenerative and vascular disease spectrums and also to an autopsy program for clinico-pathological correlation. The ProPSP network is organized by the German Parkinson and Movement Disorders Society. Both networks follow the same core protocol for patient recruitment and collection of data, imaging and biomaterials. Both networks host a web-based data registry and a central biorepository. Inclusion/exclusion criteria follow the 2017 Movement Disorder Society criteria for the clinical diagnosis of progressive supranuclear palsy. Results: Both networks started recruitment of patients by the end of 2015. As of November 2020, N = 354 and 269 patients were recruited into the DescribePSP and the ProPSP studies, respectively, and N = 131 and 87 patients received at least one follow-up visit. Conclusions: The DescribePSP and ProPSP networks are ideal resources for comprehensive natural history data of PSP, including imaging data and biological samples. In contrast to previous natural history studies, DescribePSP and ProPSP include not only patients with Richardson's syndrome, but also variant PSP phenotypes as well as patients at very early disease stages, before a diagnosis of possible or probable PSP can be made. This will allow for identification and evaluation of early biomarkers for diagnosis, prognosis, and progression. [ABSTRACT FROM AUTHOR]

Abstract The fundamental understanding of altered complex molecular interactions in a diseased condition is the key to its cure. The overall functioning of these molecules is kind of jugglers play in the cell orchestra and to anticipate these relationships among the molecules is one of the greatest challenges in modern biology and medicine. Network science turned out to be providing a successful and simple platform to understand complex interactions among healthy and diseased tissues. Furthermore, much information about the structure and dynamics of a network is concealed in the eigenvalues of its adjacency matrix. In this review, we illustrate rapid advancements in the field of network science in combination with spectral graph theory that enables us to uncover the complexities of various diseases. Interpretations laid by network science approach have solicited insights into molecular relationships and have reported novel drug targets and biomarkers in various complex diseases.

Abstract Tools from network science can be utilized to study relations between diseases. Different studies focus on different types of inter-disease linkages. One of them is the comorbidity patterns derived from large-scale longitudinal data of hospital discharge records. Researchers seek to describe comorbidity relations as a network to characterize pathways of disease progressions and to predict future risks. The first step in such studies is the construction of the network itself, which subsequent analyses rest upon. There are different ways to build such a network. In this paper, we provide an overview of several existing statistical approaches in network science applicable to weighted directed networks. We discuss the differences between the null models that these models assume and their applications. We apply these methods to the inpatient data of approximately one million people, spanning approximately 17 years, pertaining to the Montreal Census Metropolitan Area. We discuss the differences in the structure of the networks built by different methods, and different features of the comorbidity relations that they extract. We also present several example applications of these methods.

Health is an adaptive state unique to each person. This subjective state must be distinguished from the objective state of disease. The experience of health and illness (or poor health) can occur both in the absence and presence of objective disease. Given that the subjective experience of health, as well as the finding of objective disease in the community, follow a Pareto distribution, the following questions arise: What are the processes that allow the emergence of four observable states—(1) subjective health in the absence of objective disease, (2) subjective health in the presence of objective disease, (3) illness in the absence of objective disease, and (4) illness in the presence of objective disease? If we consider each individual as a unique biological system, these four health states must emerge from physiological network structures and personal behaviors. The underlying physiological mechanisms primarily arise from the dynamics of external environmental and internal patho/physiological stimuli, which activate regulatory systems including the hypothalamic-pituitary-adrenal axis and autonomic nervous system. Together with other systems, they enable feedback interactions between all of the person's system domains and impact on his system's entropy. These interactions affect individual behaviors, emotional, and cognitive responses, as well as molecular, cellular, and organ system level functions. This paper explores the hypothesis that health is an emergent state that arises from hierarchical network interactions between a person's external environment and internal physiology. As a result, the concept of health synthesizes available qualitative and quantitative evidence of interdependencies and constraints that indicate its top-down and bottom-up causative mechanisms. Thus, to provide effective care, we must use strategies that combine person-centeredness with the scientific approaches that address the molecular network physiology, which together underpin health and disease. Moreover, we propose that good health can also be promoted by strengthening resilience and self-efficacy at the personal and social level, and via cohesion at the population level. Understanding health as a state that is both individualized and that emerges from multi-scale interdependencies between microlevel physiological mechanisms of health and disease and macrolevel societal domains may provide the basis for a new public discourse for health service and health system redesign.

Connectivity in an aquatic setting is determined by a combination of hydrodynamic circulation and the biology of the organisms driving linkages. These complex processes can be simulated in coupled biological-physical models. The physical model refers to an underlying circulation model defined by spatially-explicit nodes, often incorporating a particle-tracking model. The particles can then be given biological parameters or behaviors (such as maturity and/or survivability rates, diel vertical migrations, avoidance, or seeking behaviors). The output of the bio-physical models can then be used to quantify connectivity among the nodes emitting and/or receiving the particles. Here we propose a method that makes use of kernel density estimation (KDE) on the output of a particle-tracking model, to quantify the infection or infestation pressure (IP) that each node causes on the surrounding area. Because IP is the product of both exposure time and the concentration of infectious agent particles, using KDE (which also combine elements of time and space), more accurately captures IP. This method is especially useful for those interested in infectious agent networks, a situation where IP is a superior measure of connectivity than the probability of particles from each node reaching other nodes. Here we illustrate the method by modeling the connectivity of salmon farms via sea lice larvae in the Broughton Archipelago, British Columbia, Canada. Analysis revealed evidence of two sub-networks of farms connected via a single farm, and evidence that the highest IP from a given emitting farm was often tens of kilometers or more away from that farm. We also classified farms as net emitters, receivers, or balanced, based on their structural role within the network. By better understanding how these salmon farms are connected to each other via their sea lice larvae, we can effectively focus management efforts to minimize the spread of sea lice between farms, advise on future site locations and coordinated treatment efforts, and minimize any impact of farms on juvenile wild salmon. The method has wide applicability for any system where capturing infectious agent networks can provide useful guidance for management or preventative planning decisions.

In this survey paper, we will present a number of core algorithmic questions concerning several transitive reduction problems on network that have applications in network synthesis and analysis involving cellular processes. Our starting point will be the so-called minimum equivalent digraph problem, a classic computational problem in combinatorial algorithms. We will subsequently consider a few non-trivial extensions or generalizations of this problem motivated by applications in systems biology. We will then discuss the applications of these algorithmic methodologies in the context of three major biological research questions: synthesizing and simplifying signal transduction networks, analyzing disease networks, and measuring redundancy of biological networks.

Background: Disease-disease similarities can be investigated from multiple perspectives. Identifying similar diseases based on the underlying biomolecular interactions can be especially useful, because it may shed light on the common causes of the diseases and therefore may provide clues for possible treatments. Here we introduce DeCoaD, a web-based program that uses a novel method to assign pair-wise similarity scores, called correlations, to genetic diseases. Findings: DeCoaD uses a random walk to model the low of information in a network within which nodes are either diseases or proteins and links signify either protein-protein interactions or disease-protein associations. For each protein node, the total number of visits by the random walker is called the weight of that node. Using a disease as both the starting and the terminating points of the random walks, a corresponding vector, whose elements are the weights associated with the proteins, can be constructed. The similarity between two diseases is deined as the cosine of the angle between their associated vectors. For a user-specified disease, DeCoaD outputs a list of similar diseases (with their corresponding correlations), and a graphical representation of the disease families that they belong to. Based on a probabilistic clustering algorithm, DeCoaD also outputs the clusters that the disease of interest is a member of, and the corresponding probabilities. The program also provides an interface to run enrichment analysis for the given disease or for any of the clusters that contains it. Conclusions: DeCoaD uses a novel algorithm to suggest non-trivial similarities between diseases with known gene associations, and also clusters the diseases based on their similarity scores. DeCoaD is available at http://www.ncbi. nlm.nih.gov/CBBresearch/Yu/mn/DeCoaD/. [ABSTRACT FROM AUTHOR]

Objective Computational drug repositioning can lead to a considerable reduction in cost and time in any drug development process. Recent approaches have addressed the network-based nature of biological information for performing complex prioritization tasks. In this work, we propose a new methodology based on heterogeneous network prioritization that can aid researchers in the drug repositioning process. Methods We have developed DrugNet, a new methodology for drug–disease and disease-drug prioritization. Our approach is based on a network-based prioritization method called ProphNet which has recently been developed by the authors. ProphNet is able to integrate data from complex networks involving a wide range of types of elements and interactions. In this work, we built a network of interconnected drugs, proteins and diseases and applied DrugNet to different types of tests for drug repositioning. Results We tested the performance of our approach on different validation tests, including cross validation and tests based on real clinical trials. DrugNet achieved a mean AUC value of 0.9552 ± 0.0015 in 5-fold cross validation tests, and a mean AUC value of 0.8364 for tests based on recent clinical trials (phases 0–4) not present in our data. These results suggest that DrugNet could be very useful for discovering new drug uses. We also studied specific cases of particular interest, proving the benefits of heterogeneous data integration in this problem. Conclusions Our methodology suggests that new drugs can be repositioned by generating ranked lists of drugs based on a given disease query or vice versa. Our study shows that the simultaneous integration of information about diseases, drugs and targets can lead to a significant improvement in drug repositioning tasks. DrugNet is available as a web tool from http://genome2.ugr.es/drugnet/ (accessed 23.09.14). Matlab source code is also available on the website. [ABSTRACT FROM AUTHOR]

In this survey paper, we will present a number of core algorithmic questions concerning several transitive reduction problems on network that have applications in network synthesis and analysis involving cellular processes. Our starting point will be the so-called minimum equivalent digraph problem, a classic computational problem in combinatorial algorithms. We will subsequently consider a few non-trivial extensions or generalizations of this problem motivated by applications in systems biology. We will then discuss the applications of these algorithmic methodologies in the context of three major biological research questions: synthesizing and simplifying signal transduction networks, analyzing disease networks, and measuring redundancy of biological networks. [ABSTRACT FROM AUTHOR]

Background: Biopsies taken from individual tumours exhibit extensive differences in their cellular composition due to the inherent heterogeneity of cancers and vagaries of sample collection. As a result genes expressed in specific cell types, or associated with certain biological processes are detected at widely variable levels across samples in transcriptomic analyses. This heterogeneity also means that the level of expression of genes expressed specifically in a given cell type or process, will vary in line with the number of those cells within samples or activity of the pathway, and will therefore be correlated in their expression. Results: Using a novel 3D network-based approach we have analysed six large human cancer microarray datasets derived from more than 1,000 individuals. Based upon this analysis, and without needing to isolate the individual cells, we have defined a broad spectrum of cell-type and pathway-specific gene signatures present in cancer expression data which were also found to be largely conserved in a number of independent datasets. Conclusions: The conserved signature of the tumour-associated macrophage is shown to be largely-independent of tumour cell type. All stromal cell signatures have some degree of correlation with each other, since they must all be inversely correlated with the tumour component. However, viewed in the context of established tumours, the interactions between stromal components appear to be multifactorial given the level of one component e.g. vasculature, does not correlate tightly with another, such as the macrophage. [ABSTRACT FROM AUTHOR]

Abstract: The discovery of novel disease biomarkers is a crucial challenge for translational bioinformatics. Demonstration of both their classification power and reproducibility across independent datasets are essential requirements to assess their potential clinical relevance. Small datasets and multiplicity of putative biomarker sets may explain lack of predictive reproducibility. Studies based on pathway-driven discovery approaches have suggested that, despite such discrepancies, the resulting putative biomarkers tend to be implicated in common biological processes. Investigations of this problem have been mainly focused on datasets derived from cancer research. We investigated the predictive andfunctional concordance of five methods for discovering putative biomarkers in four independently-generated datasets from the cardiovascular disease domain. A diversity of biosignatures was identified by the different methods. However, we found strong biological process concordance between them, especially in the case of methods based on gene set analysis. With a few exceptions, we observed lack of classification reproducibility using independent datasets. Partial overlaps between our putative sets of biomarkers and the primary studies exist. Despite the observed limitations, pathway-driven or gene set analysis can predict potentially novel biomarkers and can jointly point to biomedically-relevant underlying molecular mechanisms. [Copyright &y& Elsevier]

Genome research has entered the functional evaluation phase now and high-throughput (HT) methods provide an enormous amount of raw data for that purpose. However, functional verification still requires experimental regimens not suitable for application in a HT style, requiring an efficient discovery and selection process pinpointing biological mechanisms and processes for subsequent targeted verification. Regulatory networks and underlying molecular mechanisms can now be deduced through the interpretation of HT-data in the context of biologic knowledge. Computational models of promoter structures are suitable for genome-wide searches and a number of recent examples demonstrate their usefulness in prediction and selection of functional targets for experimental examples demonstrate their usefulness in prediction and selection of functional targets for experimental verification. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) can be used to infer genome intervals that are involved in genetic diseases. However, investigating a large number of putative mutations for GWAS is resource- and time-intensive. Network-based computational approaches are being used for efficient disease-gene association prediction. Network-based methods are based on the underlying assumption that the genes causing the same diseases are located close to each other in a molecular network, such as a protein-protein interaction (PPI) network. In this survey, we provide an overview of network-based disease-gene association prediction methods based on three categories: graph-theoretic algorithms, machine learning algorithms, and an integration of these two. We experimented with six selected methods to compare their prediction performance using a heterogeneous network constructed by combining a genome-wide weighted PPI network, an ontology-based disease network, and disease-gene associations. The experiment was conducted in two different settings according to the presence and absence of known disease-associated genes. The results revealed that HerGePred, an integrative method, outperformed in the presence of known disease-associated genes, whereas PRINCE, which adopted a network propagation algorithm, was the most competitive in the absence of known disease-associated genes. Overall, the results demonstrated that the integrative methods performed better than the methods using graph-theory only, and the methods using a heterogeneous network performed better than those using a homogeneous PPI network only.

Over a decade ago, a new discipline called network medicine emerged as an approach to understand human diseases from a network theory point-of-view. Disease networks proved to be an intuitive and powerful way to reveal hidden connections among apparently unconnected biomedical entities such as diseases, physiological processes, signaling pathways, and genes. One of the fields that has benefited most from this improvement is the identification of new opportunities for the use of old drugs, known as drug repurposing. The importance of drug repurposing lies in the high costs and the prolonged time from target selection to regulatory approval of traditional drug development. In this document we analyze the evolution of disease network concept during the last decade and apply a data science pipeline approach to evaluate their functional units. As a result of this analysis, we obtain a list of the most commonly used functional units and the challenges that remain to be solved. This information can be very valuable for the generation of new prediction models based on disease networks. [ABSTRACT FROM AUTHOR]

Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Health is an adaptive state unique to each person. This subjective state must be distinguished from the objective state of disease. The experience of health and illness (or poor health) can occur both in the absence and presence of objective disease. Given that the subjective experience of health, as well as the finding of objective disease in the community, follow a Pareto distribution, the following questions arise: What are the processes that allow the emergence of four observable states-(1) subjective health in the absence of objective disease, (2) subjective health in the presence of objective disease, (3) illness in the absence of objective disease, and (4) illness in the presence of objective disease? If we consider each individual as a unique biological system, these four health states must emerge from physiological network structures and personal behaviors. The underlying physiological mechanisms primarily arise from the dynamics of external environmental and internal patho/physiological stimuli, which activate regulatory systems including the hypothalamic-pituitary-adrenal axis and autonomic nervous system. Together with other systems, they enable feedback interactions between all of the person's system domains and impact on his system's entropy. These interactions affect individual behaviors, emotional, and cognitive responses, as well as molecular, cellular, and organ system level functions. This paper explores the hypothesis that health is an emergent state that arises from hierarchical network interactions between a person's external environment and internal physiology. As a result, the concept of health synthesizes available qualitative and quantitative evidence of interdependencies and constraints that indicate its top-down and bottom-up causative mechanisms. Thus, to provide effective care, we must use strategies that combine person-centeredness with the scientific approaches that address the molecular network physiology, which together underpin health and disease. Moreover, we propose that good health can also be promoted by strengthening resilience and self-efficacy at the personal and social level, and via cohesion at the population level. Understanding health as a state that is both individualized and that emerges from multi-scale interdependencies between microlevel physiological mechanisms of health and disease and macrolevel societal domains may provide the basis for a new public discourse for health service and health system redesign.

Connectivity in an aquatic setting is determined by a combination of hydrodynamic circulation and the biology of the organisms driving linkages. These complex processes can be simulated in coupled biological-physical models. The physical model refers to an underlying circulation model defined by spatially-explicit nodes, often incorporating a particle-tracking model. The particles can then be given biological parameters or behaviors (such as maturity and/or survivability rates, diel vertical migrations, avoidance, or seeking behaviors). The output of the bio-physical models can then be used to quantify connectivity among the nodes emitting and/or receiving the particles. Here we propose a method that makes use of kernel density estimation (KDE) on the output of a particle-tracking model, to quantify the infection or infestation pressure (IP) that each node causes on the surrounding area. Because IP is the product of both exposure time and the concentration of infectious agent particles, using KDE (which also combine elements of time and space), more accurately captures IP. This method is especially useful for those interested in infectious agent networks, a situation where IP is a superior measure of connectivity than the probability of particles from each node reaching other nodes. Here we illustrate the method by modeling the connectivity of salmon farms via sea lice larvae in the Broughton Archipelago, British Columbia, Canada. Analysis revealed evidence of two sub-networks of farms connected via a single farm, and evidence that the highest IP from a given emitting farm was often tens of kilometers or more away from that farm. We also classified farms as net emitters, receivers, or balanced, based on their structural role within the network. By better understanding how these salmon farms are connected to each other via their sea lice larvae, we can effectively focus management efforts to minimize the spread of sea lice between farms, advise on future site locations and coordinated treatment efforts, and minimize any impact of farms on juvenile wild salmon. The method has wide applicability for any system where capturing infectious agent networks can provide useful guidance for management or preventative planning decisions.

Changing demographics and an increasing burden of multiple chronic comorbidities in Western countries dictate refocusing of heart failure (HF) services from acute in-hospital care to better support the long inter-critical out-of- hospital phases of HF. In Italy, as well as in other countries, needs of the HF population are not adequately addressed by current HF outpatient services, as documented by differences in age, gender, comorbidities and recommended therapies between patients discharged for acute hospitalized HF and those followed-up at HF clinics. The Italian Working Group on Heart Failure has drafted a guidance document for the organisation of a national HF care network. Aims of the document are to describe tasks and requirements of the different health system points of contact for HF patients, and to define how diagnosis, management and care processes should be documented and shared among health-care professionals. The document classifies HF outpatient clinics in three groups: (i) community HF clinics, devoted to management of stable patients in strict liaison with primary care, periodic re-evaluation of emerging clinical needs and prompt treatment of impending destabilizations, (ii) hospital HF clinics, that target both new onset and chronic HF patients for diagnostic assessment, treatment planning and early post-discharge follow-up. They act as main referral for general internal medicine units and community clinics, and (iii) advanced HF clinics, directed at patients with severe disease or persistent clinical instability, candidates to advanced treatment options such as heart transplant or mechanical circulatory support. Those different types of HF clinics are integrated in a dedicated network for management of HF patients on a regional basis, according to geographic features. By sharing predefined protocols and communication systems, these HF networks integrate multi-professional providers to ensure continuity of care and patient empowerment. In conclusion, This guidance document details roles and interactions of cardiology specialists, so as to best exploit the added value of their input in the care of HF patients and is intended to promote a more efficient and effective organization of HF services.

The multifactorial origin of most chronic disorders of the brain, including schizophrenia, has been well accepted. Consequently, pharmacotherapy would require multi-targeted strategies. This contrasts to the majority of drug therapies used until now, addressing more or less specifically only one target molecule. Nevertheless, quite some searches for multiple molecular targets specific for mental disorders have been undertaken. For example, genome-wide association studies have been conducted to discover new target genes of disease. Unfortunately, these attempts have not fulfilled the great hopes they have started with. Polypharmacology and network pharmacology approaches of drug treatment endeavor to abandon the one-drug one-target thinking. To this end, most approaches set out to investigate network topologies searching for modules, endowed with "important" nodes, such as "hubs" or "bottlenecks", encompassing features of disease networks, and being useful as tentative targets of drug therapies. This kind of research appears to be very promising. However, blocking or inhibiting "important" targets may easily result in destruction of network integrity. Therefore, it is suggested here to study functions of nodes with lower centrality for more subtle impact on network behavior. Targeting multiple nodes with low impact on network integrity by drugs with multiple activities ("dirty drugs") or by several drugs, simultaneously, avoids to disrupt network integrity and may reset deviant dynamics of disease. Natural products typically display multi target functions and therefore could help to identify useful biological targets. Hence, future efforts should consider to combine drug-target networks with target-disease networks using mathematical (graph theoretical) tools, which could help to develop new therapeutic strategies in long-term psychiatric disorders.

Objective: (1) To evaluate a state-of-the-art natural language processing (NLP)-based approach to automatically de-identify a large set of diverse clinical notes. (2) To measure the impact of de-identification on the performance of information extraction algorithms on the de-identified documents. Material and methods A cross-sectional study that included 3503 stratified, randomly selected clinical notes (over 22 note types) from five million documents produced at one of the largest US pediatric hospitals. Sensitivity, precision, F value of two automated de-identification systems for removing all 18 HIPAA-defined protected health information elements were computed. Performance was assessed against a manually generated ‘gold standard’. Statistical significance was tested. The automated de-identification performance was also compared with that of two humans on a 10% subsample of the gold standard. The effect of de-identification on the performance of subsequent medication extraction was measured. Results: The gold standard included 30 815 protected health information elements and more than one million tokens. The most accurate NLP method had 91.92% sensitivity (R) and 95.08% precision (P) overall. The performance of the system was indistinguishable from that of human annotators (annotators' performance was 92.15%(R)/93.95%(P) and 94.55%(R)/88.45%(P) overall while the best system obtained 92.91%(R)/95.73%(P) on same text). The impact of automated de-identification was minimal on the utility of the narrative notes for subsequent information extraction as measured by the sensitivity and precision of medication name extraction. Discussion and conclusion NLP-based de-identification shows excellent performance that rivals the performance of human annotators. Furthermore, unlike manual de-identification, the automated approach scales up to millions of documents quickly and inexpensively.