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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.

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.

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.

Abstract The discovery of the role of non-coding RNAs (ncRNAs) in the onset and progression of malignancies is a promising frontier of cancer genetics. It is clear that ncRNAs are candidates for therapeutic intervention, since they may act as biomarkers or key regulators of cancer gene network. Recently, profiling and sequencing of ncRNAs disclosed deep deregulation in human cancers mostly due to aberrant mechanisms of ncRNAs biogenesis, such as amplification, deletion, abnormal epigenetic or transcriptional regulation. Although dysregulated ncRNAs may promote hallmarks of cancer as oncogenes or antagonize them as tumor suppressors, the mechanisms behind these events remain to be clarified. The development of new bioinformatic tools as well as novel molecular technologies is a challenging opportunity to disclose the role of the “dark matter” of the genome. In this review, we focus on currently available platforms, computational analyses and experimental strategies to investigate ncRNAs in cancer. We highlight the differences among experimental approaches aimed to dissect miRNAs and lncRNAs, which are the most studied ncRNAs. These two classes indeed need different investigation taking into account their intrinsic characteristics, such as length, structures and also the interacting molecules. Finally, we discuss the relevance of ncRNAs in clinical practice by considering promises and challenges behind the bench to bedside translation.

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.

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.