How Patients Feel with Telemedicine Devices as an Enabling Factor for Personalised Medicine: A Preliminary Study.

Telemonitoring tools have become essential in today's healthcare, representing fundamental resources for chronic disease home management supporting early detection of clinical worsening with great reduction of hospitalization costs. Therefore the investigation of the patient compliance is a key enabling point. We aim to assess how patients with chronic coronary syndromes evaluate a telemonitoring device meant for ongoing health monitoring. Twenty-six patients used the device for a week and subsequently filled out a well-designed questionnaire. The survey questions were about the device's ease of use, satisfaction levels, perceived effectiveness, and its influence on the patients' healthcare experiences. This study emphasizes the significance of focusing on patient needs in telemedicine and the importance of addressing these concerns to improve telehealth interventions.

Machine learning pipeline to analyze clinical and proteomics data: experiences on a prostate cancer case.

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.

Sex-specific differences in myocardial glucose metabolic rate in non-diabetic, pre-diabetic and type 2 diabetic subjects.

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 < 0.0001) was significant suggesting a sex-specific association. CONCLUSIONS: Our data suggest that deterioration of glucose homeostasis in women is associated with a greater impairment in myocardial glucose metabolism as compared with men. The sex-specific myocardial insulin resistance could be an important factor responsible for the greater effect of T2DM on the excess risk of cardiovascular disease in women than in men.

The Omicron XBB.1 Variant and Its Descendants: Genomic Mutations, Rapid Dissemination and Notable Characteristics.

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.

Genome mining conformance to metabolite profile of Bacillus strains to control potato pathogens.

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.

Development of a predictive model to distinguish prostate cancer from benign prostatic hyperplasia by integrating serum glycoproteomics and clinical variables.

BACKGROUND: Prostate Cancer (PCa) represents the second leading cause of cancer-related death in men. Prostate-specific antigen (PSA) serum testing, currently used for PCa screening, lacks the necessary sensitivity and specificity. New non-invasive diagnostic tools able to discriminate tumoral from benign conditions and aggressive (AG-PCa) from indolent forms of PCa (NAG-PCa) are required to avoid unnecessary biopsies. METHODS: In this work, 32 formerly N-glycosylated peptides were quantified by PRM (parallel reaction monitoring) in 163 serum samples (79 from PCa patients and 84 from individuals affected by benign prostatic hyperplasia (BPH)) in two technical replicates. These potential biomarker candidates were prioritized through a multi-stage biomarker discovery pipeline articulated in: discovery, LC-PRM assay development and verification phases. Because of the well-established involvement of glycoproteins in cancer development and progression, the proteomic analysis was focused on glycoproteins enriched by TiO2 (titanium dioxide) strategy. RESULTS: Machine learning algorithms have been applied to the combined matrix comprising proteomic and clinical variables, resulting in a predictive model based on six proteomic variables (RNASE1, LAMP2, LUM, MASP1, NCAM1, GPLD1) and five clinical variables (prostate dimension, proPSA, free-PSA, total-PSA, free/total-PSA) able to distinguish PCa from BPH with an area under the Receiver Operating Characteristic (ROC) curve of 0.93. This model outperformed PSA alone which, on the same sample set, was able to discriminate PCa from BPH with an AUC of 0.79. To improve the clinical managing of PCa patients, an explorative small-scale analysis (79 samples) aimed at distinguishing AG-PCa from NAG-PCa was conducted. A predictor of PCa aggressiveness based on the combination of 7 proteomic variables (FCN3, LGALS3BP, AZU1, C6, LAMB1, CHL1, POSTN) and proPSA was developed (AUC of 0.69). CONCLUSIONS: To address the impelling need of more sensitive and specific serum diagnostic tests, a predictive model combining proteomic and clinical variables was developed. A preliminary evaluation to build a new tool able to discriminate aggressive presentations of PCa from tumors with benign behavior was exploited. This predictor displayed moderate performances, but no conclusions can be drawn due to the limited number of the sample cohort. Data are available via ProteomeXchange with identifier PXD035935.

Strategies and Trends in COVID-19 Vaccination Delivery: What We Learn and What We May Use for the Future.

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.

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.

A systems biology approach to pathogenesis of gastric cancer: gene network modeling and pathway analysis.

BACKGROUND: Gastric cancer (GC) ranks among the most common malignancies worldwide. This study aimed to find critical genes/pathways in GC pathogenesis. METHODS: Gene interactions were analyzed, and the protein-protein interaction network was drawn. Then enrichment analysis of the hub genes was performed and network cluster analysis and promoter analysis of the hub genes were done. Age/sex analysis was done on the identified genes. RESULTS: Eleven hub genes in GC were identified in the current study (ATP5A1, ATP5B, ATP5D, MT-ATP8, COX7A2, COX6C, ND4, ND6, NDUFS3, RPL8, and RPS16), mostly involved in mitochondrial functions. There was no report on the ATP5D, ND6, NDUFS3, RPL8, and RPS16 in GC. Our results showed that the most affected processes in GC are the metabolic processes, and the oxidative phosphorylation pathway was considerably enriched which showed the significance of mitochondria in GC pathogenesis. Most of the affected pathways in GC were also involved in neurodegenerative diseases. Promoter analysis showed that negative regulation of signal transduction might play an important role in GC pathogenesis. In the analysis of the basal expression pattern of the selected genes whose basal expression presented a change during the age, we found that a change in age may be an indicator of changes in disease insurgence and/or progression at different ages. CONCLUSIONS: These results might open up new insights into GC pathogenesis. The identified genes might be novel diagnostic/prognostic biomarkers or potential therapeutic targets for GC. This work, being based on bioinformatics analysis act as a hypothesis generator that requires further clinical validation.

SARS-CoV-2 protein structure and sequence mutations: Evolutionary analysis and effects on virus variants.

The structure and sequence of proteins strongly influence their biological functions. New models and algorithms can help researchers in understanding how the evolution of sequences and structures is related to changes in functions. Recently, studies of SARS-CoV-2 Spike (S) protein structures have been performed to predict binding receptors and infection activity in COVID-19, hence the scientific interest in the effects of virus mutations due to sequence, structure and vaccination arises. However, there is the need for models and tools to study the links between the evolution of S protein sequence, structure and functions, and virus transmissibility and the effects of vaccination. As studies on S protein have been generated a large amount of relevant information, we propose in this work to use Protein Contact Networks (PCNs) to relate protein structures with biological properties by means of network topology properties. Topological properties are used to compare the structural changes with sequence changes. We find that both node centrality and community extraction analysis can be used to relate protein stability and functionality with sequence mutations. Starting from this we compare structural evolution to sequence changes and study mutations from a temporal perspective focusing on virus variants. Finally by applying our model to the Omicron variant we report a timeline correlation between Omicron and the vaccination campaign.

Analysis of age-dependent gene-expression in human tissues for studying diabetes comorbidities.

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.

How Well Did the Healthcare System Respond to the Healthcare Needs of Older People with and without Dementia during the COVID-19 Pandemic? The Perception of Healthcare Providers and Older People from the SI4CARE Project in the ADRION Region.

One major challenge during the COVID-19 pandemic was the limited accessibility to healthcare facilities, especially for the older population. The aim of the current study was the exploration of the extent to which the healthcare systems responded to the healthcare needs of the older people with or without cognitive impairment and their caregivers in the Adrion/Ionian region. Data were collected through e-questionnaires regarding the adequacy of the healthcare system and were anonymously administered to older individuals and stakeholder providers in the following countries: Slovenia, Italy (Calabria), Croatia, Bosnia and Herzegovina, Greece, Montenegro, and Serbia. Overall, 722 older people and 267 healthcare stakeholders participated in the study. During the COVID-19 pandemic, both healthcare stakeholders and the older population claimed that the healthcare needs of the older people and their caregivers increased dramatically in all countries, especially in Italy (Calabria), Croatia and BiH. According to our results, countries from the Adrion/Ionian regions faced significant challenges to adjust to the special needs of the older people during the COVID-19 pandemic, which was possibly due to limited accessibility opportunities to healthcare facilities. These results highlight the need for the development of alternative ways of providing medical assistance and supervision when in-person care is not possible.

Computational analysis of the sequence-structure relation in SARS-CoV-2 spike protein using protein contact networks.

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.

Temporal networks in biology and medicine: a survey on models, algorithms, and tools.

The use of static graphs for modelling and analysis of biological and biomedical data plays a key role in biomedical research. However, many real-world scenarios present dynamic behaviours resulting in both node and edges modification as well as feature evolution. Consequently, ad-hoc models for capturing these evolutions along the time have been introduced, also referred to as dynamic, temporal, time-varying graphs. Here, we focus on temporal graphs, i.e., graphs whose evolution is represented by a sequence of time-ordered snapshots. Each snapshot represents a graph active in a particular timestamp. We survey temporal graph models and related algorithms, presenting fundamentals aspects and the recent advances. We formally define temporal graphs, focusing on the problem setting and we present their main applications in biology and medicine. We also present temporal graph embedding and the application to recent problems such as epidemic modelling. Finally, we further state some promising research directions in the area. Main results of this study include a systematic review of fundamental temporal network problems and their algorithmic solutions considered in the literature, in particular those having application in computational biology and medicine. We also include the main software developed in this context.

Impaired insulin-stimulated myocardial glucose metabolic rate is associated with reduced estimated myocardial energetic efficiency in subjects with different degrees of glucose tolerance.

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 < 0.0001) as compared with subjects in III tertile. Univariate correlations showed that insulin-stimulated myocardial MrGlu was positively correlated with MEEi and whole-body glucose disposal, and negatively correlated with waist circumference, fasting plasma glucose, HbA1c and MVO2. In a multivariate regression analysis running a model including several CV risk factors, the only variable that remained significantly associated with MEEi was myocardial MrGlu (ß 0.346; P = 0.01). CONCLUSIONS: These data suggest that an impairment in insulin-stimulated myocardial glucose metabolism is an independent contributor of depressed myocardial MEEi in subjects without history of CHD.

Structural analysis of SARS-CoV-2 Spike protein variants through graph embedding.

Since December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected almost all countries. The unprecedented spreading of this virus has led to the insurgence of many variants that impact protein sequence and structure that need continuous monitoring and analysis of the sequences to understand the genetic evolution and to prevent possible dangerous outcomes. Some variants causing the modification of the structure of the proteins, such as the Spike protein S, need to be monitored. Protein contact networks (PCNs) have been recently proposed as a modelling framework for protein structures. In such a framework, the protein structure is represented as an unweighted graph whose nodes are the central atoms of the backbones (C- α ), and edges connect two atoms falling in the spatial distance between 4 and 7 Å. PCN may also be a data-rich representation since we may add to each node/atom biological and topological information. Such formalism enables the possibility of using algorithms from graph theory to analyze the graph. In particular, we refer to graph embedding methods enabling the analysis of such graphs with deep learning methods. In this work, we explore the possibility of embedding PCN using Graph Neural Networks and then analyze in the embedded space each residue to distinguish mutated residues from non-mutated ones. In particular, we analyzed the structure of the Spike protein of the coronavirus. First, we obtained the PCNs of the Spike protein for the wild-type, α , ß , and δ variants. Then we used the GraphSage embedding algorithm to obtain an unsupervised embedding. Then we analyzed the point of mutation in the embedded space. Results show the characteristics of the mutation point in the embedding space.

REHABS: An Innovative and User-Friendly Device for Rehabilitation.

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.

Design and Implementation of a New Local Alignment Algorithm for Multilayer Networks.

Network alignment (NA) is a popular research field that aims to develop algorithms for comparing networks. Applications of network alignment span many fields, from biology to social network analysis. NA comes in two forms: global network alignment (GNA), which aims to find a global similarity, and LNA, which aims to find local regions of similarity. Recently, there has been an increasing interest in introducing complex network models such as multilayer networks. Multilayer networks are common in many application scenarios, such as modelling of relations among people in a social network or representing the interplay of different molecules in a cell or different cells in the brain. Consequently, the need to introduce algorithms for the comparison of such multilayer networks, i.e., local network alignment, arises. Existing algorithms for LNA do not perform well on multilayer networks since they cannot consider inter-layer edges. Thus, we propose local alignment of multilayer networks (MultiLoAl), a novel algorithm for the local alignment of multilayer networks. We define the local alignment of multilayer networks and propose a heuristic for solving it. We present an extensive assessment indicating the strength of the algorithm. Furthermore, we implemented a synthetic multilayer network generator to build the data for the algorithm's evaluation.

PCN-Miner: an open-source extensible tool for the analysis of Protein Contact Networks.

MOTIVATION: Protein Contact Network (PCN) is a powerful method for analysing the structure and function of proteins, with a specific focus on disclosing the molecular features of allosteric regulation through the discovery of modular substructures. The importance of PCN analysis has been shown in many contexts, such as the analysis of SARS-CoV-2 Spike protein and its complexes with the Angiotensin Converting Enzyme 2 (ACE2) human receptors. Even if there exist many software tools implementing such methods, there is a growing need for the introduction of tools integrating existing approaches. RESULTS: We present PCN-Miner, a software tool implemented in the Python programming language, able to (i) import protein structures from the Protein Data Bank; (ii) generate the corresponding PCN; (iii) model, analyse and visualize PCNs and related protein structures by using a set of known algorithms and metrics. The PCN-Miner can cover a large set of applications: from clustering to embedding and subsequent analysis. AVAILABILITY AND IMPLEMENTATION: The PCN-Miner tool is freely available at the following GitHub repository: https://github.com/hguzzi/ProteinContactNetworks. It is also available in the Python Package Index (PyPI) repository.