PyMulSim: a method for computing node similarities between multilayer networks via graph isomorphism networks.

BACKGROUND: In bioinformatics, interactions are modelled as networks, based on graph models. Generally, these support a single-layer structure which incorporates a specific entity (i.e., node) and only one type of link (i.e., edge). However, real-world biological systems consisting of biological objects belonging to heterogeneous entities, and these operate and influence each other in multiple contexts, simultaneously. Usually, node similarities are investigated to assess the relatedness between biological objects in a network of interest, and node embeddings are widely used for studying novel interaction from a topological point of view. About that, the state-of-the-art presents several methods for evaluating the node similarity inside a given network, but methodologies able to evaluate similarities between pairs of nodes belonging to different networks are missing. The latter are crucial for studies that relate different biological networks, e.g., for Network Alignment or to evaluate the possible evolution of the interactions of a little-known network on the basis of a well-known one. Existing methods are ineffective in evaluating nodes outside their structure, even more so in the context of multilayer networks, in which the topic still exploits approaches adapted from static networks. In this paper, we presented pyMulSim, a novel method for computing the pairwise similarities between nodes belonging to different multilayer networks. It uses a Graph Isomorphism Network (GIN) for the representative learning of node features, that uses for processing the embeddings and computing the similarities between the pairs of nodes of different multilayer networks. RESULTS: Our experimentation investigated the performance of our method. Results show that our method effectively evaluates the similarities between the biological objects of a source multilayer network to a target one, based on the analysis of the node embeddings. Results have been also assessed for different noise levels, also through statistical significance analyses properly performed for this purpose. CONCLUSIONS: PyMulSim is a novel method for computing the pairwise similarities between nodes belonging to different multilayer networks, by using a GIN for learning node embeddings. It has been evaluated both in terms of performance and validity, reporting a high degree of reliability.

Multilayer network alignment based on topological assessment via embeddings.

BACKGROUND: Network graphs allow modelling the real world objects in terms of interactions. In a multilayer network, the interactions are distributed over layers (i.e., intralayer and interlayer edges). Network alignment (NA) is a methodology that allows mapping nodes between two or multiple given networks, by preserving topologically similar regions. For instance, NA can be applied to transfer knowledge from one biological species to another. In this paper, we present DANTEml, a software tool for the Pairwise Global NA (PGNA) of multilayer networks, based on topological assessment. It builds its own similarity matrix by processing the node embeddings computed from two multilayer networks of interest, to evaluate their topological similarities. The proposed solution can be used via a user-friendly command line interface, also having a built-in guided mode (step-by-step) for defining input parameters. RESULTS: We investigated the performance of DANTEml based on (i) performance evaluation on synthetic multilayer networks, (ii) statistical assessment of the resulting alignments, and (iii) alignment of real multilayer networks. DANTEml over performed a method that does not consider the distribution of nodes and edges over multiple layers by 1193.62%, and a method for temporal NA by 25.88%; we also performed the statistical assessment, which corroborates the significance of its own node mappings. In addition, we tested the proposed solution by using a real multilayer network in presence of several levels of noise, in accordance with the same outcome pursued for the NA on our dataset of synthetic networks. In this case, the improvement is even more evident: +4008.75% and +111.72%, compared to a method that does not consider the distribution of nodes and edges over multiple layers and a method for temporal NA, respectively. CONCLUSIONS: DANTEml is a software tool for the PGNA of multilayer networks based on topological assessment, that is able to provide effective alignments both on synthetic and real multi layer networks, of which node mappings can be validated statistically. Our experimentation reported a high degree of reliability and effectiveness for the proposed solution.

Aligning Cross-Species Interactomes for Studying Complex and Chronic Diseases.

Neurodegenerative diseases (NDs) are a group of complex disorders characterized by the progressive degeneration and dysfunction of neurons in the central nervous system. NDs encompass many conditions, including Alzheimer's disease and Parkinson's disease. Alzheimer's disease (AD) is a complex disease affecting almost forty million people worldwide. AD is characterized by a progressive decline of cognitive functions related to the loss of connections between nerve cells caused by the prevalence of extracellular Aß plaques and intracellular neurofibrillary tangles plaques. Parkinson's disease (PD) is a neurodegenerative disorder that primarily affects the movement of an individual. The exact cause of Parkinson's disease is not fully understood, but it is believed to involve a combination of genetic and environmental factors. Some cases of PD are linked to mutations in the LRRK2, PARKIN and other genes, which are associated with familial forms of the disease. Different research studies have applied the Protein Protein Interaction (PPI) networks to understand different aspects of disease progression. For instance, Caenorhabditis elegans is widely used as a model organism for the study of AD due to roughly 38% of its genes having a human ortholog. This study's goal consists of comparing PPI network of C. elegans and human by applying computational techniques, widely used for the analysis of PPI networks between species, such as Local Network Alignment (LNA). For this aim, we used L-HetNetAligner algorithm to build a local alignment among two PPI networks, i.e., C. elegans and human PPI networks associated with AD and PD built-in silicon. The results show that L-HetNetAligner can find local alignments representing functionally related subregions. In conclusion, since local alignment enables the extraction of functionally related modules, the method can be used to study complex disease progression.

Identifying Candidate Gene-Disease Associations via Graph Neural Networks.

Real-world objects are usually defined in terms of their own relationships or connections. A graph (or network) naturally expresses this model though nodes and edges. In biology, depending on what the nodes and edges represent, we may classify several types of networks, gene-disease associations (GDAs) included. In this paper, we presented a solution based on a graph neural network (GNN) for the identification of candidate GDAs. We trained our model with an initial set of well-known and curated inter- and intra-relationships between genes and diseases. It was based on graph convolutions, making use of multiple convolutional layers and a point-wise non-linearity function following each layer. The embeddings were computed for the input network built on a set of GDAs to map each node into a vector of real numbers in a multidimensional space. Results showed an AUC of 95% for training, validation, and testing, that in the real case translated into a positive response for 93% of the Top-15 (highest dot product) candidate GDAs identified by our solution. The experimentation was conducted on the DisGeNET dataset, while the DiseaseGene Association Miner (DG-AssocMiner) dataset by Stanford's BioSNAP was also processed for performance evaluation only.

A Method Based on Temporal Embedding for the Pairwise Alignment of Dynamic Networks.

In network analysis, real-world systems may be represented via graph models, where nodes and edges represent the set of biological objects (e.g., genes, proteins, molecules) and their interactions, respectively. This representative knowledge-graph model may also consider the dynamics involved in the evolution of the network (i.e., dynamic networks), in addition to a classic static representation (i.e., static networks). Bioinformatics solutions for network analysis allow knowledge extraction from the features related to a single network of interest or by comparing networks of different species. For instance, we may align a network related to a well known species to a more complex one in order to find a match able to support new hypotheses or studies. Therefore, the network alignment is crucial for transferring the knowledge between species, usually from simplest (e.g., rat) to more complex (e.g., human). Methods: In this paper, we present Dynamic Network Alignment based on Temporal Embedding (DANTE), a novel method for pairwise alignment of dynamic networks that applies the temporal embedding to investigate the topological similarities between the two input dynamic networks. The main idea of DANTE is to consider the evolution of interactions and the changes in network topology. Briefly, the proposed solution builds a similarity matrix by integrating the tensors computed via the embedding process and, subsequently, it aligns the pairs of nodes by performing its own iterative maximization function. Results: The performed experiments have reported promising results in terms of precision and accuracy, as well as good robustness as the number of nodes and time points increases. The proposed solution showed an optimal trade-off between sensitivity and specificity on the alignments produced on several noisy versions of the dynamic yeast network, by improving by ∼18.8% (with a maximum of 20.6%) the Area Under the Receiver Operating Characteristic (ROC) Curve (i.e., AUC or AUROC), compared to two well known methods: DYNAMAGNA++ and DYNAWAVE. From the point of view of quality, DANTE outperformed these by ∼91% as nodes increase and by ∼75% as the number of time points increases. Furthermore, a ∼23.73% improvement in terms of node correctness was reported with our solution on real dynamic networks.

Liver injury associated with high value of D-dimer plasmatic level in COVID-19 patients.

BACKGROUND: The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), causal agent of the coronavirus disease (COVID-19), has infected millions of people worldwide. Currently, the scientific community debates on the direct viral responsibility of liver damage or whether the observed changes are secondary manifestations of systemic inflammation triggered by COVID-19. The hepatic involvement is associated with worse clinical outcomes and higher risk of COVID-19 related morbidity and mortality. Furthermore, SARS-CoV-2 infection may predispose patients to thrombotic disease due to excessive inflammation, platelet activation, and endothelial dysfunction. METHODS: In this paper, we reported a cross-sectional analysis of five patients affected by a severe form of COVID-19, who died between April 11 and May 1, 2020. Each patient has been subjected to a medico-legal autopsy in which both gross and histological liver changes were evaluated, as well as the correlation with the related coagulation profile. RESULTS: In three cases of our cohort, the thromboembolism was recognized as cause of death. Furthermore, a significant statistical difference between D-dimer values at hospital admission and death among enrolled patients (P=0.033), was evaluated. No patient has recorded a pre-existing liver disease. CONCLUSIONS: Our results support the evidence that hepatic damage in subjects with severe form of COVID-19 is related to the changes in coagulative and fibrinolytic pathways. Hence, the evaluation of D-dimer blood levels may be useful in clinical practice to predict the involvement of the liver and the prognosis of these patients. This data highlights the fundamental role of coagulation balance in subjects with advanced form of COVID-19.

Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with Rt Estimation.

On 31 December 2019, a cluster of pneumonia cases of unknown etiology was reported in Wuhan (China). The cases were declared to be Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO). COVID-19 has been defined as SARS Coronavirus 2 (SARS-CoV-2). Some countries, e.g., Italy, France, and the United Kingdom (UK), have been subjected to frequent restrictions for preventing the spread of infection, contrary to other ones, e.g., the United States of America (USA) and Sweden. The restrictions afflicted the evolution of trends with several perturbations that destabilized its normal evolution. Globally, Rt has been used to estimate time-varying reproduction numbers during epidemics. Methods: This paper presents a solution based on Deep Learning (DL) for the analysis and forecasting of epidemic trends in new positive cases of SARS-CoV-2 (COVID-19). It combined a neural network (NN) and an Rt estimation by adjusting the data produced by the output layer of the NN on the related Rt estimation. Results: Tests were performed on datasets related to the following countries: Italy, the USA, France, the UK, and Sweden. Positive case registration was retrieved between 24 February 2020 and 11 January 2022. Tests performed on the Italian dataset showed that our solution reduced the Mean Absolute Percentage Error (MAPE) by 28.44%, 39.36%, 22.96%, 17.93%, 28.10%, and 24.50% compared to other ones with the same configuration but that were based on the LSTM, GRU, RNN, ARIMA (1,0,3), and ARIMA (7,2,4) models, or an NN without applying the Rt as a corrective index. It also reduced MAPE by 17.93%, the Mean Absolute Error (MAE) by 34.37%, and the Root Mean Square Error (RMSE) by 43.76% compared to the same model without the adjustment performed by the Rt. Furthermore, it allowed an average MAPE reduction of 5.37%, 63.10%, 17.84%, and 14.91% on the datasets related to the USA, France, the UK, and Sweden, respectively.

Clinical Patterns of Primary Biliary Cholangitis: Comparison Between Two European Case Series.

BACKGROUND: Primary biliary cholangitis (PBC) is a chronic autoimmune cholestatic liver disease characterized by progressive destruction of the intrahepatic bile ducts, followed by fibrous substitution of the ducts and potential evolution in cirrhosis. The geographical disparity in the prevalence of PBC suggests a possible role of environmental factors in developing the disease. We analyzed two groups of patients with different geographical prevalence. METHODS: This study concerned the analysis of 14 Caucasian patients in two groups: ten patients enrolled in the Digestive Diseases Unit, University of Catanzaro (Italy), and four patients enrolled in the Department of Hepatology, University Hospital Kràlovskè Vinohrady of Prague (Czech Republic). The statistical analysis was performed using the software IBM SPSS (v. 20, Windows). RESULTS: The Italian group showed a statistically significant difference in the total bilirubin values at diagnosis and during the last control (0.74±0.267 vs. 0.56±0.246; p-value: 0.013). Moreover, the comparison between the two groups showed a statistically significant difference in the serum albumin values at the time of the last control (4.6±0.231 vs. 4.15±0.532; p-value: 0.048). CONCLUSION: Our data indicate an effective difference in the onset and clinical presentation between our two groups. More epidemiologic, prospective, and multicenter research projects are warranted to advance PBC knowledge in Europe.

Treatment with phosphatidylcholine of patients with nonalcoholic fatty liver disease: a prospective pilot study.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is characterized by a complex clinical picture that includes nonalcoholic steatohepatitis and cirrhosis. In the last decades, several studies have shown that NAFLD increases the risk of cardiovascular diseases. In a prospective pilot study, the benefit of treatment with phosphatidylcholine in NAFLD patients has been assessed. METHODS: Thirty patients with NAFLD were enrolled. All received treatment with phosphatidylcholine (Essentiale® Forte N; Sanofi, Paris, France) 300 mg capsules, administered 2 at time orally, 3 times a day with meals for three months. The clinical and laboratory parameters before and after treatment were compared. RESULTS: After the administration of Essentiale® Forte N (Sanofi) the level of alanine aminotransferase (ALT) decreased by 59.6% (P<0.05) and that of aspartate transaminase (AST) decreased by 75.4% (P<0.05). Moreover, after treatment, an increase in antioxidant enzymes superoxide dismutase by 48% (P<0.05) and glutathione peroxidase by 48.1% (P<0.05) was observed. CONCLUSIONS: The results of the study indicate that treatment with Essentiale® Forte N (Sanofi) for 3 months was associated with a significant decrease in transaminase levels, in the activity of lipid peroxidation markers and with an increase in the level of antioxidant enzymes.

Evaluation of serum calprotectin as novel biomarker in psoriatic patients: a prospective pilot study.

BACKGROUND: Psoriasis is a chronic immune-mediated inflammatory disease characterized by erythematous plaques that can extend along the entire skin surface. In the latest years, it has been shown that serum calprotectin correlated strongly with several inflammatory biomarkers. Since high levels of calprotectin have been found in psoriatic lesions, it is of paramount importance to investigate the role of serum calprotectin as a possible novel diagnostic marker of psoriasis. Aim of our prospective pilot study was to assess the level of serum calprotectin in psoriatic patients. METHODS: Between January 2018 and July 2019, 45 subjects were enrolled at the Dermatology Unit of Magna Graecia University of Catanzaro, Italy. Thirty-two of them were psoriatic patients and 13 healthy controls. Psoriasis severity was assessed by the Psoriasis Area Severity Index. RESULTS: A statistically significant difference between the two groups (P=0.01) was found in terms of body mass index, higher among patients than in controls. By performing the Student's t-test for unpaired data, serum calprotectin resulted significantly higher (P=0.033) among psoriatic patients than in controls. Furthermore, performing the receiver operator characteristic curve analysis, serum calprotectin showed a significant area under the curve, implying its possible role in finding psoriatic patients. Our study aimed to evaluate the serum levels of calprotectin in a group of psoriatic patients and in a control group. The results showed that serum calprotectin levels were significantly higher in the patient group than in the control group. This result confirms the observations present in the literature. CONCLUSIONS: In this pilot study psoriatic patients had a significant high level of serum calprotectin than healthy subjects, and this biomarker had high accuracy in identifying patients. Further studies, with larger sample size will need to confirm our data.

Deep Neck Infections: Decisional Algorithm for Patients with Multiple Spaces Involvement.

BACKGROUND: Deep Neck Infections (DNIs) spread along fascial planes and involve neck spaces. Recently, their incidence has decreased due to the introduction of antibiotics; nevertheless, complications related to DNIs are often life-threatening. OBJECTIVE: The purpose of this article is focused on the identification of predisposing factors of these complications, as well as on the development of a reliable therapeutic algorithm. METHODS: Sixty patients with DNIs were enrolled from 2006 to 2019 for a retrospective study. The exclusion criteria for the present study were cellulitis, small abscesses responding to empiric or specific antibiotic therapy, or involvement of only one deep neck space. During the analysis, the following parameters of interest have been evaluated: gender, age, site of origin, pathways of spread, comorbidities, clinical features, bacteriology data, type of surgical approach required, complications, duration of hospitalization and mortality rate. On admission, microbial swab analysis was performed. RESULTS: Diabetes Mellitus (DM), Chronic Obstructive Pulmonary Disease (COPD), iron deficiency anemia and the involvement of multiple spaces have been associated with a significantly higher risk of developing complications. Most of our patients had polymicrobial infections. All patients underwent surgical drainage. The complication rate had occurred in 56.6% of patients, while death in 18.3%. CONCLUSION: DNIs represent a medical and surgical emergency with potentially serious complications; thus, avoidance of diagnostic delay is mandatory. Our preliminary data suggest the importance of evaluating the extent of infections because the involvement of multiple spaces requires timely surgery due to the higher risk of complications and mortality.

SARS-CoV-2 Spread Dynamics in Italy: The Calabria Experience.

INTRODUCTION: The first case of infection by SARS-CoV-2 (i.e., COVID-19) has been officially recorded by the Italian National Health Service on February 21st, 2020. Lombardy was the first Italian region to be affected by the pandemic. Subsequently, the entire Northern part of Italy recorded a high number of cases, while the South was hit following the migratory waves. On March 8th, the Italian Government has issued a decree that imposed a total lockdown, defining it as a state of isolation and restricting access in Lombardy and the other 14 provinces of Northern Italy. METHODS: We analyzed the virus trend in the period between February 24th and September 8th, 2020, focusing on Calabria, with regards to the following items: new positives, change of total positives, and total cases. Furthermore, we included other information, such as the incubation period, symptom resolution period, quarantine period. RESULTS: On March 27th, the epidemic curve spiked with 101 new positive cases validating the hypothesis that this abnormal event was related to the displacement of non-residents people, living in the Northern part of Italy, to the home regions in the South. The epidemic curve showed a decreasing trend in the period after lockdown, proving the effectiveness of this measure. From the end of the lockdown May 04th to September 8th, the registered trend was -94.51%. A negative growth rate indicates that the number of new positive cases is lower than the number of healed patients. CONCLUSION: This study describes the effectiveness of the Italian Government policy, particularly the role of lockdown, for the containment of SARS-CoV-2 contagion in Calabria, a region with a low SARS-CoV-2 infection rate within the registered period.

Anxiety and Gastrointestinal Symptoms Related to COVID-19 during Italian Lockdown.

The first case of infection by SARS-CoV-2 (i.e., COVID-19) was officially recorded by the Italian National Health Service on 21 February 2020. Respiratory tract manifestations are the most common symptoms, such as gastrointestinal symptoms (GISs) like nausea or sickness, diarrhea, and anorexia, and psychological effects may be reported in affected individuals. However, similar symptoms may be observed in healthy people as a consequence of an anxiety state. METHODS: We analyzed GISs and anxiety state during the COVID-19 lockdown period; from 9 March 2020 to 4 May 2020. A web-based survey consisting of 131 items was administered to 354 students affiliated with the School of Medicine of the University "Magna Graecia" of Catanzaro; Italy. A set of statistical analyses was performed to analyze the relationships among the answers to assess a correlation between the topics of interest. RESULTS: The statistical analysis showed that 54.0% of interviewed reported at least one GISs, 36.16% of which reported a positive history for familial GISs (FGISs). The 354 subjects included in our cohort may be stratified as follows: 25.99% GISs and FGISs, 27.97% GISs and no-FGISs, 10.17% no-GISs and FGISs, 35.87% no-GISs and no-FGISs. Results indicated an anxiety state for 48.9% of respondents, of which 64.74% also presented GISs. In addition, considered dietary habits, we detect the increased consumption of hypercaloric food, sweetened drinks, and alcoholic beverages. CONCLUSIONS: The increase of GISs during the lockdown period in a population of medical students, may be correlated to both dietary habits and anxiety state due to a concern for one's health.

ANCA: Alignment-Based Network Construction Algorithm.

Dynamic biological networks model changes in the network topology over time. However, often the topologies of these networks are not available at specific time points. Existing algorithms for studying dynamic networks often ignore this problem and focus only on the time points at which experimental data is available. In this paper, we develop a novel alignment based network construction algorithm, ANCA, that constructs the dynamic networks at the missing time points by exploiting the information from a reference dynamic network. Our experiments on synthetic and real networks demonstrate that ANCA predicts the missing target networks accurately, and scales to large-scale biological networks in practical time. Our analysis of an E. coli protein-protein interaction network shows that ANCA successfully identifies key temporal changes in the biological networks. Our analysis also suggests that by focusing on the topological differences in the network, our method can be used to find important genes and temporal functional changes in the biological networks.

Design and Implementation of a Telecardiology System for Mobile Devices.

This paper presents the design and implementation of a system for digital telecardiology on mobile devices called Remote Cardio Consultation (RCC). Using RCC may improve first intervention procedures in case of heart attack. In fact, it allows physicians to remotely consult ECG signals from a mobile device or smartphone by using a so-called app. The remote consultation is implemented by a server application collecting physician availability to answer upon client support requests. The app can be used by first intervention clinicians and allows reducing delays and decision errors in emergency interventions. Thus, best decision, certified and supported by cardiologists, can be obtained in case of heart attacks and first interventions even by base medical doctors able to produce and send an ECG. RCC tests have been performed, and the prototype is freely available as a service for testing.