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Abstract In the evolving landscape of data science and computational biology, Causal Networks (CNs) have emerged as a robust framework for modelling causal relationships among elements of complex systems derived from experimental data. CNs can efficiently model causal relationships emerging in a single system while comparing multiple systems, allowing to understand rewiring in different cells, tissues, and physiological states with a deeper perspective. Despite the existence of network models, namely differential networks, that have been used to compare coexpression and correlation structures, causality needs to be introduced in differential analysis to robustly provide direction to the edges of such networks, in order to better understand the flows of information, and also to better intervene in their functioning, for example for agricultural or pharmacological purposes. Resolved to reach this ambitious goal, we introduce Differential Causal Networks (DCNs), a novel framework that represents differences between two existing CNs. A DCN is obtained from experimental data by comparing two CNs, and it is a power tool for highlighting differences in causal relations. After a careful definition and design of DCNs, we test our algorithm to model possible differential causal relationships between genes responsible for the onset of type 2 diabetes mellitus-related pathologies considering patients’ sex at the tissue level. DCNs allowed us to shed light on causal differences between sexes across nine tissues. We also compare differences among three possible definitions of DCNs to highlight similarities and differences of biological importance. Code, Data and Supplementary Information are available at https://github.com/hguzzi/DifferentialCausalNetworks .

Abstract Patient triage is crucial in emergency departments, ensuring timely and appropriate care based on correctly evaluating the emergency grade of patient conditions. Triage methods are generally performed by human operator based on her own experience and information that are gathered from the patient management process. Thus, it is a process that can generate errors in emergency-level associations. Recently, Traditional triage methods heavily rely on human decisions, which can be subjective and prone to errors. A growing interest has recently been focused on leveraging artificial intelligence (AI) to develop algorithms to maximize information gathering and minimize errors in patient triage processing. We define and implement an AI-based module to manage patients’ emergency code assignments in emergency departments. It uses historical data from the emergency department to train the medical decision-making process. Data containing relevant patient information, such as vital signs, symptoms, and medical history, accurately classify patients into triage categories. Experimental results demonstrate that the proposed algorithm achieved high accuracy outperforming traditional triage methods. By using the proposed method, we claim that healthcare professionals can predict severity index to guide patient management processing and resource allocation.

Abstract Background Evidence has shown that women with type 2 diabetes (T2DM) have a higher excess risk for cardiovascular disease (CVD) than men with T2DM. Subjects with either T2DM or prediabetes exhibit myocardial insulin resistance, but it is still unsettled whether sex-related differences in myocardial insulin resistance occur in diabetic and prediabetic subjects. Methods We aimed to evaluate sex-related differences in myocardial glucose metabolic rate (MRGlu), assessed using dynamic PET with 18F-FDG combined with euglycemic-hyperinsulinemic clamp, in subjects with normal glucose tolerance (NGT; n = 20), prediabetes (n = 11), and T2DM (n = 26). Results Women with prediabetes or T2DM exhibited greater relative differences in myocardial MRGlu than men with prediabetes or T2DM when compared with their NGT counterparts. As compared with women with NGT, those with prediabetes exhibited an age-adjusted 35% lower myocardial MRGlu value (P = 0.04) and women with T2DM a 74% lower value (P = 0.006), respectively. Conversely, as compared with men with NGT, men with T2DM exhibited a 40% lower myocardial MRGlu value (P = 0.004), while no significant difference was observed between men with NGT and prediabetes. The statistical test for interaction between sex and glucose tolerance on myocardial MRGlu (P

Abstract Proteomic-based analysis is used to identify biomarkers in blood samples and tissues. Data produced by devices such as mass spectrometry requires platforms to identify and quantify proteins (or peptides). Clinical information can be related to mass spectrometry data to identify diseases at an early stage. Machine learning techniques can be used to support physicians and biologists in studying and classifying pathologies. We present the application of machine learning techniques to define a pipeline aimed at studying and classifying proteomics data enriched using clinical information. The pipeline allows users to relate established blood biomarkers with clinical parameters and proteomics data. The proposed pipeline entails three main phases: (i) feature selection, (ii) models training, and (iii) models ensembling. We report the experience of applying such a pipeline to prostate-related diseases. Models have been trained on several biological datasets. We report experimental results about two datasets that result from the integration of clinical and mass spectrometry-based data in the contexts of serum and urine analysis. The pipeline receives input data from blood analytes, tissue samples, proteomic analysis, and urine biomarkers. It then trains different models for feature selection, classification and voting. The presented pipeline has been applied on two datasets obtained in a 2 years research project which aimed to extract hidden information from mass spectrometry, serum, and urine samples from hundreds of patients. We report results on analyzing prostate datasets serum with 143 samples, including 79 PCa and 84 BPH patients, and an urine dataset with 121 samples, including 67 PCa and 54 BPH patients. As results pipeline allowed to identify interesting peptides in the two datasets, 6 for the first one and 2 for the second one. The best model for both serum (AUC=0.87, Accuracy=0.83, F1=0.81, Sensitivity=0.84, Specificity=0.81) and urine (AUC=0.88, Accuracy=0.83, F1=0.83, Sensitivity=0.85, Specificity=0.80) datasets showed good predictive performances. We made the pipeline code available on GitHub and we are confident that it will be successfully adopted in similar clinical setups.

Long non-coding RNAs (lncRNAs) have gained prominence due to their involvement in various cellular processes, but their specific roles remain elusive. Dysregulation of lncRNAs has been implicated in the pathogenesis of several diseases. In this study, we aimed to shed light on the role of lncRNAs in individuals infected with human immunodeficiency virus type 1 (HIV-1) by examining their changes in the expression patterns related to the initiation of antiretroviral therapy (ART) during acute or chronic phases of infection, compared to healthy controls. We found 316 differentially expressed (DE) lncRNAs in patients receiving long-term ART, shedding light on their potential roles. We also observed interactions between these DE lncRNAs and specific microRNAs (miRNAs). Some of these miRNAs, such as hsa-miR-574-5p, hsa-miR-765, hsa-miR-6165, hsa-miR-1207-5p, and hsa-miR-378i, are associated with cancer progression or suppression, while others, including hsa-miR-328-5p, hsa-miR-4753-3p, and MiR-664, play roles in immune system regulation. Furthermore, our study revealed substantial enrichment in distinct Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, such as axon guidance, PI3K-Akt signaling, and MAPK signaling pathways. Although our results indicate possible molecular processes impacted by the discovered lncRNAs, we cannot explicitly establish causality or specific connections between lncRNAs and genes in these pathways, fostering more specific studies. Furthermore, Gene Ontology (GO) analysis highlighted terms such as cytoskeletal protein binding, ion channel function, synaptic processes, neuron projection, and the somatodendritic compartment, underscoring the relevance of lncRNAs in these cellular components within the context of HIV-1 infection and ART treatment. In conclusion, our study emphasizes the need for further exploration of lncRNAs as potential biomarkers and therapeutic targets in HIV-1-infected patients, with a particular focus on CD4+ T cells. Understanding the functions of lncRNAs in these contexts may pave the way for novel treatment strategies and improved patient outcomes, aligning with the broader goals of our research.

Abstract Biocontrol agents are safe and effective methods for controlling plant disease pathogens, such as Fusarium solani, which causes dry wilt, and Pectobacterium spp., responsible for potato soft rot disease. Discovering agents that can effectively control both fungal and bacterial pathogens in potatoes has always presented a challenge. Biological controls were investigated using 500 bacterial strains isolated from rhizospheric microbial communities, along with two promising biocontrol strains: Pseudomonas (T17-4 and VUPf5). Bacillus velezensis (Q12 and US1) and Pseudomonas chlororaphis VUPf5 exhibited the highest inhibition of fungal growth and pathogenicity in both laboratory (48%, 48%, 38%) and greenhouse (100%, 85%, 90%) settings. Q12 demonstrated better control against bacterial pathogens in vivo (approximately 50%). Whole-genome sequencing of Q12 and US1 revealed a genome size of approximately 4.1 Mb. Q12 had 4413 gene IDs and 4300 coding sequences, while US1 had 4369 gene IDs and 4255 coding sequences. Q12 exhibited a higher number of genes classified under functional subcategories related to stress response, cell wall, capsule, levansucrase synthesis, and polysaccharide metabolism. Both Q12 and US1 contained eleven secondary metabolite gene clusters as identified by the antiSMASH and RAST servers. Notably, Q12 possessed the antibacterial locillomycin and iturin A gene clusters, which were absent in US1. This genetic information suggests that Q12 may have a more pronounced control over bacterial pathogens compared to US1. Metabolic profiling of the superior strains, as determined by LC/MS/MS, validated our genetic findings. The investigated strains produced compounds such as iturin A, bacillomycin D, surfactin, fengycin, phenazine derivatives, etc. These compounds reduced spore production and caused deformation of the hyphae in F. solani. In contrast, B. velezensis UR1, which lacked the production of surfactin, fengycin, and iturin, did not affect these structures and failed to inhibit the growth of any pathogens. Our findings suggest that locillomycin and iturin A may contribute to the enhanced control of bacterial pectolytic rot by Q12.

Acquired immunodeficiency syndrome (AIDS) has transitioned from a progressive, fatal disease to a chronic, manageable disease thanks to better defining of antiretroviral therapy, contributing to increased life expectancy. In parallel, a growing number of subjects without clinical signs of disease but living with chronic HIV infection (also indicated as PLWHs, i.e., People Living With HIV) are experiencing early cardiovascular disease, and the risk increases with age. However, a progressive increase in the prevalence of multiple comorbidity diseases has been reported as these patients age, including cardiovascular disease (CVD). Cardiovascular mortality can be related to viral infection, a progressive reduction in response to antiretroviral therapy, chronic inflammation, and lifestyle. Cardiovascular ageing represents a relevant issue in the management of HIV-infected patients. Although the exact pathophysiological mechanism that leads PLWHs to develop cardiovascular disease is not entirely understood, there is substantial evidence that they accumulate age-related conditions earlier than the general population. Furthermore, since the proportion of PLWHs growing older than 50 years has progressively increased, this results in a complex interaction between disease-related pathophysiology and the exposition of a growing burden of cardiovascular risk factors. We performed a study to relate the effect ageing gas on genes associated with HIV and cardiovascular diseases. We performed a systematic review of the genes most frequently associated with ageing in HIV-infected subjects, followed by a bioinformatic analysis to explore the biological impact of the ageing-related genes.

Abstract Background Some phenotypical changes may be related to changes in the associations among genes. The set of such associations is referred to as gene interaction (or association) networks. An association network represents the set of associations among genes in a given condition. Given two experimental conditions, Differential network analysis (DNA) algorithms analyse these differences by deriving a novel network representing the differences. Such algorithms receive as input experimental gene-expression data of two different conditions (e.g. healthy vs. diseased), then they derive experimental networks of associations among genes and, finally, they analyse differences among networks using statistical approaches. We explore the possibility to study possible rewiring due to sex factors, differently from classical approaches. Methods We apply DNA methods to evidence possible sex based differences on genes responsible for comorbidities of type 2 diabetes mellitus. Results Our analysis evidences the presence of differential networks in tissues that may explain the difference in the insurgence of comorbidities between males and females. Conclusion Main contributions of this work are (1) the definition of a novel framework of analysis able to shed light on the differences between males and females; (2) the identification of differential networks related to diabetes comorbidities.

Abstract The study of the relationship between type 2 diabetes mellitus (T2DM) disease and other pathologies (comorbidities), together with patient age variation, poses a challenge for medical research. There is evidence that patients affected by T2DM are more likely to develop comorbidities as they grow older. Variation of gene expression can be correlated to changes in T2DM comorbidities insurgence and progression. Understanding gene expression changes requires the analysis of large heterogeneous data at different scales as well as the integration of different data sources into network medicine models. Hence, we designed a framework to shed light on uncertainties related to age effects and comorbidity by integrating existing data sources with novel algorithms. The framework is based on integrating and analysing existing data sources under the hypothesis that changes in the basal expression of genes may be responsible for the higher prevalence of comorbidities in older patients. Using the proposed framework, we selected genes related to comorbidities from existing databases, and then analysed their expression with age at the tissues level. We found a set of genes that changes significantly in certain specific tissues over time. We also reconstructed the associated protein interaction networks and the related pathways for each tissue. Using this mechanistic framework, we detected interesting pathways related to T2DM whose genes change their expression with age. We also found many pathways related to insulin regulation and brain activities, which can be used to develop specific therapies. To the best of our knowledge, this is the first study that analyses such genes at the tissue level together with age variations.

Ontologies are used to support access to a multitude of databases that cover domains relevant information. Heterogeneity and different semantics can be accessed by using structured texts and descriptions in a hierarchical concept definition. We are interested in Life Sciences (LS) related ontologies including components taken from molecular biology, bioinformatics, physics, chemistry, medicine and other related areas. An Ontology comprises: (i) term connections, (ii) the identification of core concepts, (iii) data management, (iv) knowledge classification and integration to collect key information. An ontology may be very useful in navigating through LS terms. This paper explores some available biomedical ontologies and frameworks. It describes the most common ontology development environments (ODE): Protégé, Topbraid Composer, Ontostudio, Fluent Editor, VocBench, Swoop and Obo-edit, to create ontologies from textual scientific resources for LS plans. It also compares ontology methodologies in terms of Usability, Scalability, Stability, Integration, Documentation and Originality.

Abstract The structure of proteins impacts directly on the function they perform. Mutations in the primary sequence can provoke structural changes with consequent modification of functional properties. SARS-CoV-2 proteins have been extensively studied during the pandemic. This wide dataset, related to sequence and structure, has enabled joint sequence-structure analysis. In this work, we focus on the SARS-CoV-2 S (Spike) protein and the relations between sequence mutations and structure variations, in order to shed light on the structural changes stemming from the position of mutated amino acid residues in three different SARS-CoV-2 strains. We propose the use of protein contact network (PCN) formalism to: (i) obtain a global metric space and compare various molecular entities, (ii) give a structural explanation of the observed phenotype, and (iii) provide context dependent descriptors of single mutations. PCNs have been used to compare sequence and structure of the Alpha, Delta, and Omicron SARS-CoV-2 variants, and we found that omicron has a unique mutational pattern leading to different structural consequences from mutations of other strains. The non-random distribution of changes in network centrality along the chain has allowed to shed light on the structural (and functional) consequences of mutations.

Abstract Background Alterations in myocardial mechano-energetic efficiency (MEEi), which represents the capability of the left ventricles to convert the chemical energy obtained by oxidative metabolism into mechanical work, have been associated with cardiovascular disease. Although whole-body insulin resistance has been related to impaired myocardial MEEi, it is unknown the relationship between cardiac insulin resistance and MEEi. Aim of this study was to evaluate the relationship between insulin-stimulated myocardial glucose metabolic rate (MrGlu) and myocardial MEEi in subjects having different degrees of glucose tolerance. Methods We evaluated insulin-stimulated myocardial MrGlu using cardiac dynamic positron emission tomography (PET) with 18F-Fluorodeoxyglucose (18F-FDG) combined with euglycemic-hyperinsulinemic clamp, and myocardial MEEi in 57 individuals without history of coronary heart disease having different degrees of glucose tolerance. The subjects were stratified into tertiles according to their myocardial MrGlu values. Results After adjusting for age, gender and BMI, subjects in I tertile showed a decrease in myocardial MEEi (0.31 ± 0.05 vs 0.42 ± 0.14 ml/s*g, P = 0.02), and an increase in myocardial oxygen consumption (MVO2) (10,153 ± 1375 vs 7816 ± 1229 mmHg*bpm, P

Abstract Triage procedure is used in Emergency Departments (ED) to manage the patient's treatment and prioritise care access. This is a largely resource‐consuming phase and relevant to reduce risk and optimise resource management. Moreover, the presence of patients in the ED (both in treatment rooms and in waiting rooms after triage) may increase the patients' time of stay, thus creating problems for critical patients and for healthcare process management. Moreover, it has been proved that a large fraction of ED incoming patients do not require emergency treatments and might be treated in ambulatory or by family doctors. In such cases, the triage wastes resources and time. In addition, the decision of a low priority or no ED necessity is relevant considering that underestimating treatment necessity may cause errors in patient treatments. Improving triage related decisions is a relevant task. It has been shown that computational methods such as machine learning (ML) may support triage by providing better stratification of patients as well as better results in terms of outcome. We here present a literature review discussing some recent approaches to predict the severity of patients and in particular, we present recent approaches based on ML. We use PRISMA methodology to include works in our analysis. Finally, the future directions of research and open problems are highlighted.

The SARS-CoV-2 virus, which is a major threat to human health, has undergone many mutations during the replication process due to errors in the replication steps and modifications in the structure of viral proteins. The XBB variant was identified for the first time in Singapore in the fall of 2022. It was then detected in other countries, including the United States, Canada, and the United Kingdom. We study the impact of sequence changes on spike protein structure on the subvariants of XBB, with particular attention to the velocity of variant diffusion and virus activity with respect to its diffusion. We examine the structural and functional distinctions of the variants in three different conformations: (i) spike glycoprotein in complex with ACE2 (1-up state), (ii) spike glycoprotein (closed-1 state), and (iii) S protein (open-1 state). We also estimate the affinity binding between the spike protein and ACE2. The market binding affinity observed in specific variants raises questions about the efficacy of current vaccines in preparing the immune system for virus variant recognition. This work may be useful in devising strategies to manage the ongoing COVID-19 pandemic. To stay ahead of the virus evolution, further research and surveillance should be carried out to adjust public health measures accordingly.

Rehabilitation is a complex set of interventions involving the assessment, management, and treatment of injuries. It aims to support and facilitate an individual’s recovery process by restoring a physiological function, e.g., limb movement, compromised by physical impairments, injuries or diseases to a condition as close to normal as possible. Innovative devices and solutions make the rehabilitation process of patients easier during their daily activities. Devices support physicians and physiotherapists in monitoring and measuring patients’ physical improvements during rehabilitation. In this context, we report the design and implementation of a low-cost rehabilitation system, which is a programmable device designed to support tele-rehabilitation of the upper limbs. The proposed system includes a mechanism to acquire and analyze data and signals related to rehabilitation processes.

Vaccination has been the most effective way to control the outbreak of the COVID-19 pandemic. The numbers and types of vaccines have reached considerable proportions, even if the question of vaccine procedures and frequency still needs to be resolved. We have come to learn the necessity of defining vaccination distribution strategies with regard to COVID-19 that could be used for any future pandemics of similar gravity. In fact, vaccine monitoring implies the existence of a strategy that should be measurable in terms of input and output, based on a mathematical model, including death rates, the spread of infections, symptoms, hospitalization, and so on. This paper addresses the issue of vaccine diffusion and strategies for monitoring the pandemic. It provides a description of the importance and take up of vaccines and the links between procedures and the containment of COVID-19 variants, as well as the long-term effects. Finally, the paper focuses on the global scenario in a world undergoing profound social and political change, with particular attention on current and future health provision. This contribution would represent an example of vaccination experiences, which can be useful in other pandemic or epidemiological contexts.

Abstract Background Representations of the relationships among data using networks are widely used in several research fields such as computational biology, medical informatics and social network mining. Recently, complex networks have been introduced to better capture the insights of the modelled scenarios. Among others, dual networks (DNs) consist of mapping information as pairs of networks containing the same set of nodes but with different edges: one, called physical network, has unweighted edges, while the other, called conceptual network, has weighted edges. Results We focus on DNs and we propose a tool to find common subgraphs (aka communities) in DNs with particular properties. The tool, called Dual-Network-Analyser, is based on the identification of communities that induce optimal modular subgraphs in the conceptual network and connected subgraphs in the physical one. It includes the Louvain algorithm applied to the considered case. The Dual-Network-Analyser can be used to study DNs, to find common modular communities. We report results on using the tool to identify communities on synthetic DNs as well as real cases in social networks and biological data. Conclusion The proposed method has been tested by using synthetic and biological networks. Results demonstrate that it is well able to detect meaningful information from DNs.

Scalise et al. examine the mRNAome and miRNAome of cardiomyocytes differentiated from murine adult cardiac stem cells (CSCs). Their results show that the differentiation process follows a trajectory of miRNA/mRNA expression that resembles that of adult cardiomyocytes.

Chronic heart failure (CHF) is a prevalent and multifactorial condition associated with a significant burden of morbidity and mortality. Despite progress in its clinical management, the projected increase in CHF prevalence due to population ageing, increased cardiovascular risk burdens, and advancing diagnostic and therapeutic options have led to a growing burden on healthcare systems and public budgets worldwide. In this context, artificial intelligence (AI) holds promise in assisting clinical decision-making, especially in analysing raw image data and electrocardiogram recordings. This article provides an overview of the current gaps and needs in CHF research and clinical management and the current and under-development AI-powered tools that may address these gaps and needs.

Metabolic syndrome is a condition characterized by a clustering of metabolic abnormalities associated with an increased risk of type 2 diabetes and cardiovascular disease. An impaired insulin-stimulated myocardial glucose metabolism has been shown to be a risk factor for the development of cardiovascular disease in patients with type 2 diabetes. Whether cardiac insulin resistance occurs in subjects with metabolic syndrome remains uncertain. To investigate this issue, we evaluated myocardial glucose metabolic rate using cardiac dynamic 18F-FDG-PET combined with euglycemic-hyperinsulinemic clamp in three groups: a group of normal glucose tolerant individuals without metabolic syndrome (n = 10), a group of individuals with type 2 diabetes and metabolic syndrome (n = 19), and a group of subjects with type 2 diabetes without metabolic syndrome (n = 6). After adjusting for age and gender, individuals with type 2 diabetes and metabolic syndrome exhibited a significant reduction in insulin-stimulated myocardial glucose metabolic rate (10.5 ± 9.04 μmol/min/100 g) as compared with both control subjects (32.9 ± 9.7 μmol/min/100 g; P < 0.0001) and subjects with type 2 diabetes without metabolic syndrome (25.15 ± 4.92 μmol/min/100 g; P = 0.01). Conversely, as compared with control subjects (13.01 ± 8.53 mg/min x Kg FFM), both diabetic individuals with metabolic syndrome (3.06 ± 1.7 mg/min × Kg FFM, P = 0.008) and those without metabolic syndrome (2.91 ± 1.54 mg/min × Kg FFM, P = 0.01) exhibited a significant reduction in whole-body insulin-stimulated glucose disposal, while no difference was observed between the 2 groups of subjects with type 2 diabetes with or without metabolic syndrome. Univariate correlations showed that myocardial glucose metabolism was positively correlated with insulin-stimulated glucose disposal (r = 0.488, P = 0.003), and negatively correlated with the presence of metabolic syndrome (r = −0.743, P < 0.0001) and with its individual components. In conclusion, our data suggest that an impaired myocardial glucose metabolism may represent an early cardio-metabolic defect in individuals with the coexistence of type 2 diabetes and metabolic syndrome, regardless of whole-body insulin resistance.

Motivation: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease, 2019; COVID-19) is associated with adverse outcomes in patients. It has been observed that lethality seems to be related to the age of patients. While ageing has been extensively demonstrated to be accompanied by some modifications at the gene expression level, a possible link with COVID-19 manifestation still need to be investigated at the molecular level.Objectives: This study aims to shed out light on a possible link between the increased COVID-19 lethality and the molecular changes that occur in elderly people.Methods: We considered public datasets of ageing-related genes and their expression at the tissue level. We selected human proteins interacting with viral ones that are known to be related to the ageing process. Finally, we investigated changes in the expression level of coding genes at the tissue, gender and age level.Results: We observed a significant intersection between some SARS-CoV-2 interactors and ageing-related genes, suggesting that those genes are particularly affected by COVID-19 infection. Our analysis evidenced that virus infection particularly involves ageing molecular mechanisms centred around proteins EEF2, NPM1, HMGA1, HMGA2, APEX1, CHEK1, PRKDC, and GPX4. We found that HMGA1 and NPM1 have different expressions in the lung of males, while HMGA1, APEX1, CHEK1, EEF2, and NPM1 present changes in expression in males due to ageing effects.Conclusion: Our study generated a mechanistic framework to clarify the correlation between COVID-19 incidence in elderly patients and molecular mechanisms of ageing. We also provide testable hypotheses for future investigation and pharmacological solutions tailored to specific age ranges.

Networks-based models have been used to represent and analyse datasets in many fields such as computational biology, medical informatics and social networks. Nevertheless, it has been recently shown that, in their standard form, they are unable to capture some aspects of the investigated scenarios. Thus, more complex and enriched models, such as heterogeneous networks or dual networks, have been proposed. We focus on the latter model, which consists of a pair of networks having the same nodes but different edges. In dual networks, one network, called physical, has unweighted edges representing binary associations among nodes. The other is an edge-weighted one where weights represent the strength of the associations among nodes. Dual networks capture in a single model some aspects that cannot be described by using a standard model. Dual networks can be used, for instance, to capture a co-authorships network, where physical network represents co-authors. In contrast, the conceptual network is used to model topics sharing among a couple of authors by means of edge connections. This allows capturing similar interests among authors even though they are not co-authors. We propose an innovative algorithm to find the Densest Connected Subgraph (DCS) in dual networks. DCS is the largest density subgraph in the conceptual network, which is also connected in the physical network. A DCS represents a set of highly similar nodes. Moreover, since DCS is a computationally hard problem, we propose novel heuristics to solve it. We tested the proposed algorithm on social, biological, and co-authorship networks. Results demonstrate that our approach is efficient and is able to extract meaningful information from dual networks.

Networks are widely used in bioinformatics and biomedicine to represent associations across a large class of biological entities. Network alignment refers to the set of approaches that aim to reveal similarities among networks. Local Network Alignment (LNA) algorithms find (relatively small) local regions of similarity between two or more networks. Such algorithms are in general based on a set of seed nodes that are used to build the alignment incrementally. A large fraction of LNA algorithms uses a set of vertices based on context information as seed nodes, even if this may cause a bias or a data-circularity problem. Moreover, using topology information to choose seed nodes improves overall alignment. Finally, similarities among nodes can be identified by network embedding methods (or representation learning). Given there are two networks, we propose to use network embedding to capture structural similarity among nodes, which can also be used to improve LNA effectiveness. We present an algorithm and experimental tests on real and syntactic graph data to find LNAs.

The Fractional Flow Reserve (FFR) and instantaneous wave-Free Ratio (iFR) have been proposed and clinically validated to measure the pressure gradient across coronary stenoses. They provide quantitative information on stenosis severity. Both are used in coronary revascularization procedures to measure intracoronary pressure giving quantitative information to evaluate coronary diseases during angiographic procedures. We designed and implemented a tool able to acquire and measure iFR and FFR supporting the physicians studying and treating patients in interventional cardiology laboratories. We designed an extensive case study to assess the performance of the tool in (i) acquiring pressure signals from blood pressure measurement systems; (ii) calculating FFR and iFR; and (iii) filtering out extra-beats signals during realtime signal analysis phases. The tool, named JLabChart, is available online. We tested it on two sets of data for a total of 600 cycles from 201 pressure measurements performed on 65 patients, from the Interventional Cardiology Unit of Magna Graecia University. The recognition of cardiac cycles and keypoint of the pressure curve was effective in 100% of cases for proximal (aortic) pressure and in 99.2% for distal pressure. The FFR calculated by JLabChart had an excellent correlation (Rp=0.960; p<0.001) with the FFR values obtained through the commercial systems. Similar results were obtained with iFR (Rp=0.998; p<0.001). Finally, the tool measurement results were compared with a commercial tool proving JLabChart’s efficiency with real cases. It was also compared with measurements performed on synthetic vessels and stenosis designed using the Comsol commercial tool. JLabChart is able to provide reliable measurements of FFR and iFR indexes used to support decisions on interventional procedures. It represents a valuable open source support system that can be used in an interventional cardiology laboratory.

Abstract Background Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. The atlas-based parcellations present some known limitations in cases of early brain development and abnormal anatomy. Consequently, it has been recently proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space, as a way to deal with the unknown correspondences of the parcels. Such process requires modeling of the brain using graph theory and the subsequent comparison of the structure of graphs. The latter step may be modeled as a network alignment (NA) problem. Results In this work, we first define the problem formally, then we test six existing state of the art of network aligners on diffusion MRI-derived brain networks. We compare the performances of algorithms by assessing six topological measures. We also evaluated the robustness of algorithms to alterations of the dataset. Conclusion The results confirm that NA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes. The analysis shows MAGNA++ is the best global alignment algorithm. The paper presented a new analysis methodology that uses network alignment for validating atlas-free parcellation brain connectomes. The methodology has been experimented on several brain datasets.

Multiple sclerosis (MS) is a chronic demyelinating autoimmune disease affecting the central nervous system. One of its manifestations concerns impaired speech, also known as dysarthria. In many cases, a proper speech evaluation can play an important role in the diagnosis of MS. The identification of abnormal voice patterns can provide valid support for a physician in the diagnosing and monitoring of this neurological disease. In this paper, we present a method for vocal signal analysis in patients affected by MS. The goal is to identify the dysarthria in MS patients to perform an early diagnosis of the disease and to monitor its progress. The proposed method provides the acquisition and analysis of vocal signals, aiming to perform feature extraction and to identify relevant patterns useful to impaired speech associated with MS. This method integrates two well-known methodologies, acoustic analysis and vowel metric methodology, to better define pathological compared to healthy voices. As a result, this method provides patterns that could be useful indicators for physicians in identifying patients affected by MS. Moreover, the proposed procedure could be a valid support in early diagnosis as well as in monitoring treatment success, thus improving a patient’s life quality.