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Background and objective Neurodegenerative diseases are disorders that affect neurons in the brain resulting in a debilitating condition and progressive degeneration of nerve cells. These diseases involve different aspects among which speech impairment. Vocal signal analysis is used to evaluate this impairment and to discriminate normal from pathological voices. Materials and methods In this paper, two methods of vocal signal analysis have been proposed to evaluate an anomalous condition in human speech, known as dysarthria, useful to compare pathological and healthy voices. Parkinson and Multiple Sclerosis disease have been considered and patients affected by both pathologies have been enrolled. The methods have been tested on 153 voice signals belonging to: 39 healthy subjects (HS), 60 patients with Parkinson's Disease (PD) and 54 patients with Multiple Sclerosis (MS). Acoustic (F0, jitter, shimmer, NHR) and vowel metric (tVSA, qVSA, FCR) features have been extracted. Results The results report significant differences in almost all of these features in pathological and healthy voices by performing statistical tests. F0, jitter, shimmer, NHR, tVSA and FCR are statistically significant features thus they can be used as indicators in the diagnosis of dysarthria-related diseases such as in PD and MS. The results suggest that the applied methodologies are efficient and useful in characterizing the different behavior of vocal signal in healthy and pathological subjects. Consequently, they could be a valid support for physicians in disease evaluation and progression monitoring. Conclusions The contribution aims to evaluate, support and diagnose the comorbidity in pathological patients verifying the co-occurrence of speech and neurological disorders in the same individual. The proposed solution is studied and implemented to be efficient and low cost following the model of precision medicine to customize clinical practice in disease diagnosis and treatment.

Physical therapy and rehabilitation therapy aim to support patients in dealing with the consequences of their and physical impairments in daily activities. The recent developments in biomedical sensors combined with the wireless network infrastructure will deeply transform healthcare systems and help physicians in designing better and more precise therapies with faster feedback from patients in terms of health-related measured data. Furthermore, these new systems will enable distributed healthcare services for remote patients who may live far away from health structures or who may have movement impairment. Efficiently monitoring or acquiring data from a large number of patients will cause improvements during rehabilitation and help in early diagnoses together with reducing the costs in the healthcare system with more effective prevention. We present a programmable rehabilitation device which can be useful to evaluate patients’ performances in a set of physiotherapy exercises designed to evaluate subjects by neurophysiological impairments which slow down some types of movements. The tool is able to support the definition of rehabilitation exercises involving upper limbs and hands. The presented device assesses the responsiveness and movement capacity of patients undergoing physiotherapy aiming to test and measure the mobility, strength and functional ability of the hand during prone supination exercises.

The efficient management and analysis of patient data enrolled in clinical studies is a critical factor for both supporting data management and knowledge discovery from data. Recent trends in literature present many approaches that demonstrate that the integration of multiple data sources (e.g. biochemical parameters, geographical data as well as the behaviour of patients into social networks) may improve the quality of findings. Moreover, the collection of such data may enable the development of a tailored intervention for precision medicine. All these aspects rely on the design and development of novel solutions for data management, storing and consequently, analysis. We here report the design and development of a prototype for data management and sharing introduced during a collaboration of Bioinformatics Laboratory, the Fisiopatology Unit and the University Hospital of Catanzaro. Our findings are currently under the validation of the clinicians.

Epidemiological models are relevant to study and analyze clinical as well as environmental and behavioural data, useful to support health studies. The target is to perform epidemiological analysis producing fast and reliable data access useful to guide prevention and curing processes. This is currently true in pandemic emergency as the current Covid-19 context. Epidemiological models should support in the early identification of pandemic phenomena and in making available data set for studying more accurate drug-based strategy for vaccines or virus containment.In this contribution we present an epidemiology database which integrates different types of clinical data to support research, follow-up and patient monitoring. The idea starts from an hospital databases cooperation integration where virus available data have been integrated to support statistical based studies. Starting from an available database containing 5 years data of infection related viruses (such as HPC, hepatitis) and patient anonymous data, the proposed system provide an integrated data access able to (i) extracting data filtered by means of clinical hypothesis based on patient profiles, environment and drugs and (ii) allowing to build large scale geographical data mappings in order to study correlations among chronic infection diseases and their relations with upcoming pandemic phenomena. Even if the application is in its infancy, the application is relevant with high very important applications.

COVID-19 (SARS-CoV-2) is a pandemic disease diffused throughout the world. COVID-19 is usually identified by applying Reverse transcriptase-polymerase chain reaction (RT-PCR) analysis on swab tests. The high rate of diffusion of the disease caused many problems related to the managing part of limited healthcare resources such as Intensive Care Units (ICUs) services. Assessing the real number of infected as well as early identification of the more infected zones have been defined as a relevant issue to treat pandemic. COVID-19 infected citizens are identified by swab test applied on suspected cases as well as people that have been in touch with affected ones. For these reasons, recognised numbers of COVID-19 affected patients are significantly lower than real ones. We investigate the number of COVID-19 infections and the number of deaths, through Italian regions by comparing these data with respect to diseases caused by similar viruses. We assess several infections having a higher rate of dissemination than the ones currently measured. We focus on the characterisation of the pandemic diffusion by estimating the infected number of patients versus the number of death. We believe that our model can support the healthcare system to react as COVID-19 infection rate increases.

Motivation The outbreak of novel severe acute respiratory syndrome coronavirus (SARS-CoV-2, also known as COVID-19) in Wuhan has attracted worldwide attention. SARS-CoV-2 causes severe inflammation, which can be fatal. Consequently, there has been a massive and rapid growth in research aimed at throwing light on the mechanisms of infection and the progression of the disease. With regard to this data science is playing a pivotal role in in silico analysis to gain insights into SARS-CoV-2 and the outbreak of COVID-19 in order to forecast, diagnose and come up with a drug to tackle the virus. The availability of large multiomics, radiological, bio-molecular and medical datasets requires the development of novel exploratory and predictive models, or the customisation of existing ones in order to fit the current problem. The high number of approaches generates the need for surveys to guide data scientists and medical practitioners in selecting the right tools to manage their clinical data. Results Focusing on data science methodologies, we conduct a detailed study on the state-of-the-art of works tackling the current pandemic scenario. We consider various current COVID-19 data analytic domains such as phylogenetic analysis, SARS-CoV-2 genome identification, protein structure prediction, host–viral protein interactomics, clinical imaging, epidemiological research and drug discovery. We highlight data types and instances, their generation pipelines and the data science models currently in use. The current study should give a detailed sketch of the road map towards handling COVID-19 like situations by leveraging data science experts in choosing the right tools. We also summarise our review focusing on prime challenges and possible future research directions. Contact hguzzi@unicz.it, sroy01@cus.ac.in

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.

COVID-19 is a worldwide emergency since it has rapidly spread from China to almost all the countries worldwide. Italy has been one of the most affected countries after China. North Italian regions, such as Lombardia and Veneto, had an abnormally large number of cases. COVID-19 patients management requires availability of sufficiently large number of Intensive Care Units (ICUs) beds. Resources shortening is a critical issue when the number of COVID-19 severe cases are higher than the available resources. This is also the case at a regional scale. We analysed Italian data at regional level with the aim to: (i) support health and government decision-makers in gathering rapid and efficient decisions on increasing health structures capacities (in terms of ICU slots) and (ii) define a geographic model to plan emergency and future COVID-19 patients management using reallocating them among health structures. Finally, we retain that the here proposed model can be also used in other countries.

Background COVID-19 has been declared a worldwide emergency and a pandemic by the World Health Organization. It started in China in December 2019, and it rapidly spread throughout Italy, which was the most affected country after China. The pandemic affected all countries with similarly negative effects on the population and health care structures. Objective The evolution of the COVID-19 infections and the way such a phenomenon can be characterized in terms of resources and planning has to be considered. One of the most critical resources has been intensive care units (ICUs) with respect to the infection trend and critical hospitalization. Methods We propose a model to estimate the needed number of places in ICUs during the most acute phase of the infection. We also define a scalable geographic model to plan emergency and future management of patients with COVID-19 by planning their reallocation in health structures of other regions. Results We applied and assessed the prediction method both at the national and regional levels. ICU bed prediction was tested with respect to real data provided by the Italian government. We showed that our model is able to predict, with a reliable error in terms of resource complexity, estimation parameters used in health care structures. In addition, the proposed method is scalable at different geographic levels. This is relevant for pandemics such as COVID-19, which has shown different case incidences even among northern and southern Italian regions. Conclusions Our contribution can be useful for decision makers to plan resources to guarantee patient management, but it can also be considered as a reference model for potential upcoming waves of COVID-19 and similar emergency situations.

Correct interpretation of images may be crucial for early disease detection. A growing number of medical instruments are image-oriented and produce a large quantity of image data, typically in the DICOM format, which contain spatio-temporal features together with alpha-numeric information regarding patients. Dealing with this high-dimensional datasets is a complex and time-consuming task. In addition the diffusion of smartphones and tablets requires the development of technological features enabling the medical team to check on helthcare processes on-the-go and freely access and send image and data for case analysis and collaborative diagnostic. This paper presents SIMPATICO 3D (Sistema Informativo Medico PATologIe COmplesse) a system supporting scientists and physicians by providing facilities for case studies analysis and diagnostic imaging in a shared virtual environment and details the features of SIMPATICO 3D Mobile (standing for Evolution Imaging System 3D for Mobile), that extends SIMPATICO 3D with dedicated functions for the mobile environment.

Multiple Sclerosis (MS) is one of the most common neurodegenerative disorder that presents specific manifestations among which the impaired speech (known also as dysarthria). The evaluation of the speech plays a crucial role in the diagnosis and follow-up since the identification of anomalous patterns in vocal signal may represent a valid support to physician in diagnosis and monitoring of these neurological diseases. In this contribution, we present a method to perform voice analysis of neurologically impaired patients affected by MS aiming to early detection, differential diagnosis, and monitoring of disease progression. This method integrates two well-known methodologies to support the health structure in MS diagnosis in clinical practice. Acoustic analysis and vowel metric methodologies have been considered to implement this procedure to better define the pathological voices compared to healthy voices. Specifically, the method acquires and analyzes vocal signals performing features extraction and identifying possible important patterns useful to associate impaired speech with this neurological disease. The contribution consists in furnishing to physician a guide method to support MS trend. As result, this method furnishes patterns that could be valid indicators for physician in monitoring of patients affected by MS. Moreover, the procedure is appropriate to be used in early diagnosis that is critical in order to improve the patient’s quality of life.

Electronic medical records (EMRs) store data related to patients information enrolled during their stay in health structures. Data stored into EMRs span from data crawled from biological laboratories to textual description of diseases and diagnostic device results (e.g., biomedical images). Each EMR is related to a diagnosis related group (DRG) record. A DRG record is a record associated with a citizen that has been cured in a hospital. It contains a code, called major diagnostic category (MDC), which summarizes the treated disease and allows to reimburse costs related to patient treatments during his staying in health structures. DRGs are used for administrative process (e.g., costs and reimbursement management) as well as disease monitoring. Associating diagnostic codes with external information (such as environmental and geographical data) and with information filtered from EMRs (e.g., biological results or analytes values) can be useful to monitor citizens wellness status. We propose a methodology to analyze such data based on a multistep process. First, we cross reference data by using a semantics-based clustering procedure, extract information from EMRs, and then, cluster them by looking for similar patterns of diseases. Then, biological records in each disease cluster are analyzed to evaluate intracluster similarity by selecting analytes typologies and values. Finally, biological data is related to diagnosis codes and geometrically projected in areas of interest in order to map calculated outlier patients. We applied the methodology on two case studies: 1) diagnosis codes and biochemical analytes of 20 000 biological analyses about hospitalized patients during one observation year and 2) the correlation between cardiovascular diseases and water quality in a southern Italian region. Preliminary findings show the effectiveness of our method.

Prevention is essential to counteract the onset of cancer. The analysis of clinical and environmental data as well as their integration are basic topics to prevent chronic diseases, especially neoplasms, and to identify the correlations between cancer and environmental factors. The proposed contribution aims to acquire, analyze and integrate clinical and geographical data to evaluate their possible correlations. A GIS application is here reported to correlate TSH (Thyroid-Stimulating Hormone) values with environmental data as case study.

Advanced Information and Communications Technology (ICT) systems in the agri-food sector are a promise for remarkable improvements in control mechanisms for the food supply chain, both in terms of effectiveness and efficiency. The aim is to ensure the wellness of citizens, a quick and efficient management of food processes and a positive impact in competitiveness of companies brands on both national and international markets.We here report the proposal of an information system able to model the entire food supply chain. Data is loaded in a database containing the organoleptic qualities of milk, fruit and vegetables to guarantee an easy access and insertion of information and allowing the traceability of products.

Clinical records (also known as Electronic Medical Records or EMRs) have been considered as a collection of patients health information. Elettronic Citizen Helth Records store information regarding life style, activities, habits, environment (i.e. water sources or air quality), routine health screening like blood biological analysis. Patients and providers stand to benefit from Internet of Things (IoT) in healthcare. Some uses of healthcare IoT are mobile medical applications or wearable devices that allow patients to capture their health data. Smart personal devices as well as data generator devices (i.e., internet of things) allow to provide such kind of data which may be collected in unique (cloud) database. Such information can then be related to location data within a geographic context. We use clinical anonymized data extracted from a Biological Department of University Magna Graecia Hospital. We show how to include geographic operators to statistical methods and how to analyze environmental data and citizen habits to improve wellness. We report on developing and testing a geo-based system to analyze biological data.

Phonemic alterations can indicate possible pathologies with poten- tially serious repercussions both from a psychological than a social point of view. Nowadays, the level of population awareness about this issues is rather low and mild dysfunctions are neglected. The main goal of this work is to show the design and imple- mentation of an innovative voice-awareness system able to train and monitor the voice apparatus for any individual. The system supports users in: (i) monitoring voice to avoid overload of the phonatory apparatus; (ii) giving indications to prevent upset or fatigue; (iii) analyzing vocal signals to identify possible pathologies; (iv) monitoring voice during the during rehabilitation phase in pre- and post-surgical treatments. The system has been implemented with IBM Watson services and it is designed for a wide range of users.

Background and objective Computer aided simulations are useful to support the physician in many steps of the surgical activity, but also in pre-surgical patient classification and in post-surgical diagnosis and treatment decisions. At a broader level, computerized technologies and infrastructures permeate every aspect of the medical activity, from patient management to surgery and patients’ follow up with outcomes analyses. Radiography assisted surgery is often used in hemodynamic surgery to study and support cardio-circulatory stents positioning with the use of radioscopy coupled with contrast liquid injected into the vessels. Computer based surgery instruments (both software and hardware) are used to support clinicians during interventions, e.g., to reduce radioscopy time exposure, to minimize errors and to estimate tissues and organs dimension. In this paper we present the use of a newly developed system which supports physicians during transcatheter percutaneous coronary interventions. Methods This paper presents a Java-based tool which acquires images from angiographic equipment during surgery procedures. An high performance image acquisition module has been used and a stent simulation environment module is available to simulate stent positioning and to measure vessels. Operators may acquire images, perform measurements and simulations on DICOM images. We performed tests off-line on images to validate the reliability of the tool. Real cases and on line tests have been performed by operators showing the robustness of the system to be used in surgery room. The system has been integrated in the surgery room control panel and allows (i) vascular images acquisition, (ii) vessels and coronary measurement and (iii) stent positioning simulations. The tool is an aid for the physician for both measuring tissues or lesions and for defining the stent's geometry and position before its deployment in the patient's vessels. Results Experiments have been performed on lesions and vessels by different operators using the system and an available commercial system, on both real patient cases and synthetic images designed with a CAD. It has been tested on 76 images extracted from real angiography cases and on 11 synthetic images created by using CAD. Five different operators performed 2128 measurements for the real cases images (for both Cartesio and CAAS tools) and 112 for the synthetic dataset. Results show the efficacy of the system compared with the commercial one by means of several statistical tests. Conclusions The proposed system is a reliable tool for hemodynamic surgery and can be used both for decision support in stent positioning procedures and for didactic training of new physicians.

Language is very important for communication among people. Any alteration of the phonemic functionalities, in addition to being an index of possible pathologies, it can compromise peoples interaction at various levels. This paper shows the design and implementation of a voiceawareness system able to: (i) monitor voice to avoid overload of the phonatory apparatus; (ii) give indications to prevent upset or fatigue; (iii) analyze vocal signals to identify possible pathologies; (iv) monitor voice during rehabilitation phase in pre-and postsurgical treatments.

Extracting morphological features from DICOM images is useful to obtain numerical anatomic values for population-wide studies. Currently software tools on medical devices are able to extract some parameters that can indicate the presence of diseases. Nevertheless, there still is a lot of not exploited information contained in images which can be useful for research as well as to characterize human behavior. For instance, measures for lung volume compared with reference data sets can be studied starting from clinical images. In this paper we report preliminary results on a framework for the acquisition and decomposition of DICOM images applied on a dataset containing lung exams from which we extracted information and parameters useful for disease research studies. The here proposed algorithms for images segmentation and anatomical features extraction have been tested on a clinical dataset obtained from University Hospital of Catanzaro, providing the framework validity.

We are in the era of Big Data. Data are everywhere! They are part of the information processing system of all sectors, from science to government, from healthcare to media, from university to real time commerce. In healthcare, in particular, the increasing use of medical devices, such as the Computed Tomography (CT) and the Magnetic Resonance Imaging (MRI) has led to the generation of large amounts of data, including image data. Bioinformatics solutions provide an effective approach for image data processing techniques whose final aim is to support scientists and physicians in diagnosis and therapies. This paper surveys bioinformatics toolkits for medical imaging.

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.

It is widely demonstrated that cardiovascular risk is reversible, that the reduction of major risk factors leads to a reduction in events, and that less serious events will occur in the future. One of the best goals in the clinical field is to help reduce the risk of subsequent cardiovascular events, thus improving the quality of life and positively influencing survival through the benefits of physical exercise. The first activity that a patient has to follow consists of a personalized prescription of physical reconditioning sessions by the physician. This activity can be highly individualized, with a correct mental algorithm of overall evaluation, based on manifold clinical, prognostic, metabolic, psychological and of physical ability variables. The Decision Support System (DSS) is a software system that provides support for the strategic decision. The purpose of this work was to plan and develop a DSS able to estimate cardiovascular risk and to optimize the therapeutic processes of physical reconditioning in the field of intensive cardiovascular rehabilitation. In this way it is possible to provide assistance to the above-mentioned decisions, thus helping improve the prescriptive process to be undertaken.

Computer based support for clinical and health-related procedures is growing in the last decades. However, the vast majority of information systems adopted in health structures are legacy systems, which do not often allow to export data easily and also are usually desktop oriented. A growing number of medical instruments are image-oriented and produce a large quantity of image data, typically in the DICOM format, which contain spatio-temporal features together with alpha-numeric information regarding patients. Dealing with this high-dimensional datasets is a complex and time-consuming task. In addition the diffusion of smartphones and tablets requires the development of technological features enabling the medical team to check on helthcare processes on-the-go and freely access and send image and data for case analysis and collaborative diagnostic. This paper presents eIMES 3D Mobile (standing for Evolution Imaging System 3D for Mobile), a system which is based on the eIMES 3D system and supports clinicians for images studies with dedicated functions for the mobile environment. The tool has been developed within a project called ReCaTuR for RAre Cancer Network (i.e., Network of Rare Cancer), aiming to define a network for the management, organization and distribution of medical information. Moreover, it has been implemented following the specifications by the oncology department of an Italian Hospital. eIMES 3D allows to start a medical interdisciplinary collaboration among different teams, geographically distributed in the network, so that obtaining the integration of skills, expertize, knowledge and experiences with the final aim of clinical case resolution. eIMES 3D provides an hardware infrastructure that allows to connect multiple devices, as well as to create workstations (WorkSpaces) that independently and asynchronously can request information to the central database containing the 3D imaging data. Il also allows to share information among a network of mobile devices. The ability to build plug-in modules enables to easily implement new features in eIMES 3D Mobile, thus ensuring its further development and its sustainability.

Electroencephalography (EEG) is a technique for the acquisition of electrical brain signals. In recent years the increase of information acquired from signal analysis has genereted a large amount of data; therefore, the development of tools for analysis has become necessary. In this paper, the mEEG prototype for EEG data managing is presented. It offers a user-friendly communication solution to exchange data between physicians and biomedical engineers. Features can be used for: (i) perform a fast diagnoses; (ii) show reports about clinical information; (iii) store and retrieve neurological data.

Parkinson's Disease (PD) is a neurodegenerative disorder that is frequently correlated with vowel articulation difficulties. The phonation problem arises in patients affected by PD is commonly known as Parkinsonian Dysarthria and identifiedby vocal signal analysis. The analysis supporte physicians and specialists in early detection and monitoring of dysarthria aiming, to increase patients life quality and to evaluate the efficacy of treatments. We investigate on vocal signal analysis correlation with speech patterns related to PD. Vowel parameters are considered as discriminant elements among PD patients and healthy subjects. Aim of this work is to define possible indicators for dysarthria in PD patients.