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The potential for visually detectable clinical signs of anaemia and their correlation with the severity of the pathology have supported research on non-invasive prevention methods. Physical examination for a suspected diagnosis of anaemia is a practice performed by a specialist to evaluate the pallor of the exposed tissues. The aim of the research presented herein is to quantify and minimize the subjective nature of the examination of the palpebral conjunctiva, suggesting a method of diagnostic support and autonomous monitoring. Here we describe the methodology and system for extracting key data from the digital image of the conjunctiva, which is also based on analysis of the dominant colour classes. Effective features have been used herein to establish the inclusion of each image in a diagnosis probability class for anaemia. The images of the conjunctiva were taken using a new low cost and easy to use device, designed to optimize the properties of independence from ambient light. The performance of the system was tested either by extracting manually the palpebral conjunctiva from images or by extracting them in a semi-automatic way based on the SLIC Superpixel algorithm. Tests were conducted on images obtained from 102 people. The dataset was unbalanced, since many more samples of healthy people were available, as often happens in the medical field. The SMOTE and ROSE algorithms were evaluated to balance the dataset, and some classification algorithms for assessing the anaemic condition were tested, yielding very good results. Taking a photo of the palpebral conjunctiva can aid the decision whether a blood sample is needed or even whether a patient should inform a physician, considerably reducing the number of candidate subjects for blood sampling. It also could highlight the suspected anaemia, allowing screening for anaemia in a large number of people, even in resource-poor settings.

The automated detection of suspicious anomalies in electrocardiogram (ECG) recordings allows frequent personal heart health monitoring and can drastically reduce the number of ECGs that need to be manually examined by the cardiologists, excluding those classified as normal, facilitating healthcare decision-making and reducing a considerable amount of time and money. In this paper, we present a system able to automatically detect the suspect of cardiac pathologies in ECG signals from personal monitoring devices, with the aim to alert the patient to send the ECG to the medical specialist for a correct diagnosis and a proper therapy. The main contributes of this work are: (a) the implementation of a binary classifier based on a 1D-CNN architecture for detecting the suspect of anomalies in ECGs, regardless of the kind of cardiac pathology, (b) the analysis was carried out on 21 classes of different cardiac pathologies classified as anomalous, and (c) the possibility to classify anomalies even in ECG segments containing, at the same time, more than one class of cardiac pathologies. Moreover, 1D-CNN based architectures can allow an implementation of the system on cheap smart devices with low computational complexity. The system was tested on the ECG signals from the MIT-BIH ECG Arrhythmia Database for the MLII derivation. Two different experiments were carried out, showing remarkable performance compared to other similar systems. The best result showed high accuracy and recall, computed in terms of ECG segments and even higher accuracy and recall in terms of patients alerted, therefore considering the detection of anomalies with respect to entire ECG recordings.

Muscle cell fusion is a multistep process involving cell migration, adhesion, membrane remodeling and actin-nucleation pathways to generate multinucleated myotubes. However, molecular brakes restraining cell–cell fusion events have remained elusive. Here we show that transforming growth factor beta (TGFβ) pathway is active in adult muscle cells throughout fusion. We find TGFβ signaling reduces cell fusion, regardless of the cells’ ability to move and establish cell-cell contacts. In contrast, inhibition of TGFβ signaling enhances cell fusion and promotes branching between myotubes in mouse and human. Exogenous addition of TGFβ protein in vivo during muscle regeneration results in a loss of muscle function while inhibition of TGFβR2 induces the formation of giant myofibers. Transcriptome analyses and functional assays reveal that TGFβ controls the expression of actin-related genes to reduce cell spreading. TGFβ signaling is therefore requisite to limit mammalian myoblast fusion, determining myonuclei numbers and myofiber size., The fusion of muscle progenitor cells to form syncytial myofibers is required for skeletal muscle development and regeneration. Here, the authors describe a novel and specific molecular regulation of muscle cell fusion driven by transforming growth factor beta (TGFβ) signaling.

Mechanical heart valve dysfunction can develop in as little as a few weeks, often causing a heart attack or serious complications. Investigating the altered acoustic characteristics of the cardiac tones in a mechanical valve may allow the early identification of valve malfunction. Nowadays, it is essential to equip a transplanted patient with innovative tools capable of monitoring his state of health at home and, in this case, the state of operation of the mechanical valves of his heart. This study is a step towards the early identification of valve malfunction. Here, basic acoustic features will be identified and tested, showing their effectiveness in discriminating the sound of a native mitral valve from a mechanical one. This study differs significantly from other studies since it is based on the new digital stethoscope eKuore-PRO®. The experimental results are interesting. The sensitivity is 100%, the accuracy is 95%, and the specificity is 93.3%, indicating that these features can discriminate the two types of valves effectively, and above all, that it is worth investigating whether they can timely and effectively predict mechanical valve malfunction.

New types of patient health records aim to help physicians shift from a medical practice, often based on their personal experience, towards one of evidence based medicine, thus improving the communication among patients and care providers and increasing the availability of personal medical information. These new records, allowing patients and care providers to share medical data and clinical information, and access them whenever they need, can be considered enabling Ambient Assisted Living technologies. Furthermore, new personal disease monitoring tools support specialists in their tasks, as an example allowing acquisition, transmission and analysis of medical images. The growing interest around these new technologies poses serious questions regarding data integrity and transaction security. The huge amount of sensitive data stored in these new records surely attracts the interest of malicious hackers, therefore it is necessary to guarantee the integrity and the maximum security of servers and transactions. Blockchain technology can be an important turning point in the development of personal health records. This paper discusses some issues regarding the management and protection of health data exchanged through new medical or diagnostic devices.

Anemia is a global public health problem with major consequences for human health. About a quarter of the world population shows a hemoglobin concentration that is below the recommended thresholds. Non-invasive methods for monitoring and identifying potential risk of anemia and smartphone-based devices to perform this task are promising in addressing this pathology. We have considered some well-known studies carried out on this topic since the main purpose of this work was not to produce a review. The first group of papers describes the approaches for the clinical evaluation of anemia focused on different human exposed tissues, while we used a second group to overview some technologies, basic methods, and principles of operation of some devices and highlight some technical problems. Results extracted from the second group of papers examined were aggregated in two comparison tables. A growing interest in this topic is demonstrated by the increasing number of papers published recently. We believe we have identified several critical issues in the published studies, including those published by us. Just as an example, in many papers the dataset used is not described. With this paper we wish to open a discussion on these issues. Few papers have been sufficient to highlight differences in the experimental conditions and this makes the comparison of the results difficult. Differences are also found in the identification of the regions of interest in the tissue, descriptions of the datasets, and other boundary conditions. These critical issues are discussed together with open problems and common mistakes that probably we are making. We propose the definition of a road-map and a common agenda for research on this topic. In this sense, we want to highlight here some issues that seem worthy of common discussion and the subject of synergistic agreements. This paper, and in particular, the discussion could be the starting point for an open debate about the dissemination of our experiments and pave the way for further updates and improvements of what we have outlined.

Medical practice for determining haemoglobin (Hb) concentration, which is important for anemic patients in need of blood transfusion, requires a blood sample. In this paper, we propose a non-invasive approach to Hb estimation based on the image analysis of a specific conjunctival region. Our goal is to develop a device that is not expensive and simple to use for assessing the anemic condition; this device could be used by the physician to decide whether to take a blood sample or even by a patient at home to decide whether to inform a physician; in this way, we can avoid having the patient go frequently to the laboratory to take a blood sample. This device also allows us to rapidly screen for Anemia in a large number of persons, for example, groups of girls who are at the beginning of the menses. Here, we detail the prototype of our device and the methodology for extracting key information from the color values of the acquired image. Tests conducted on 113 anemic and healthy persons show a strong correlation between the real Hb value obtained through blood sampling and the value estimated by our device. A k-nearest neighbor classification algorithm for assessing the (non)anemic condition yielded good results and helps doctors avoid a significant number of blood tests.

In this paper, we propose the Personal Health Electronic Record (PHER), a new type of Patient Health Record. This record aims to help physicians shift from a medical practice that is often based on their personal opinion or experience towards one of evidence based medicine, thus improving the communication among patients and their care providers and increasing the availability of personal medical information. The PHER can be considered an enabling Ambient Assisted Living technology because it allows patients and care providers to file and share all types of medical data and useful clinical information so that they can access whatever they need, whenever they need it. The development of diagnostic and therapeutic algorithms that are able to support the physician in planning treatment using the data stored in the PHER is also discussed.

This paper presents a comparison between two different Computer Aided Diagnosis systems for classification of five types of leucocytes located in the tail of a Peripheral Blood Smears: Lymphocytes, Monocytes, Neutrophils, Basophils and Eosinophils. In particular, we have evaluated and compared the performance of a previous feature-based Back Propagation Neural Network classifier with the performance of two novel classifiers both based on Deep Learning using Convolutional Neural Networks introduced in this study. All the classifiers are built considering the same dataset of images acquired in a previous study. The experimental results, reported in terms of accuracy, sensitivity, specificity and precision, show that the different strategies could be compared and discussed from both clinical and technical point of view.

Background In recent years, cytological observations in the Rhinology field are being increasingly utilized. This development has taken place over the last two decades and has proven to be fundamental in defining new nosological entities and in driving changes in the previous classification of rhinitis. The simplicity of the technique and its low invasiveness make nasal cytology a practical diagnostic tool for all rhino-allergology services. Furthermore, since it allows the monitoring of responses to treatment, this method plays an important role in guiding a more effective and less expensive diagnostic program. Microscopic observation requires prolonged effort by a specialist, but the modern scanning systems for cytological preparations and the new affordable digital microscopes allow to design a software support system, based on deep learning techniques, to relieve specialist’s tiring activity. Basic procedure By means of the system presented in this paper, it is possible to automatically identify and classify cells present on a nasal cytological preparation based on a digital image of the preparation itself. Thus, an interesting diagnostic support has been made available to the rhino-cytologist, who can quickly verify that the cells have been correctly classified by the software system: any few unclassified or incorrectly classified cells can be quickly sorted by the specialist itself, then one or more diagnosis can be suggested by this system, taking into consideration also the anamnesis of each patient. The final diagnosis can be defined by the specialist, also based on the result of the prick test and the observation of the nasal cavity. Findings In the system presented herein, image processing and image segmentation techniques have been used to find images of cellular elements within the preparation. Cell classification is based on a convolutional neural network composed of three blocks of main layers. Cell identification (first step, image segmentation) exhibits sensitivity greater than 97%, while cell classification (second step, seven cytotypes) attained a mean accuracy of approximately 99% on the test set and 94% on the validation set. Conclusions This complete system supports clinicians in the preparation of a rhino-cytogram report.

Wnt is a family of signaling molecules involved in embryogenesis, adult tissue repair, and cancer. They activate canonical and noncanonical Wnt signaling cascades in target cells. Several studies, within the last decades, showed that several Wnt ligands are involved in myogenesis and both canonical and noncanonical Wnt pathways regulate muscle formation and the maintenance of adult tissue homeostasis. In this review, we provide a comprehensive overview of the roles of Wnt signaling during muscle development and an updated description of Wnt functions during muscle repair. Lastly, we discuss the crosstalk between Wnt and TGFβ signaling pathways in skeletal muscle.

In recent years, cytological observations in the rhinological field are being increasingly utilized. This development has taken place over the last two decades and has proven to be fundamental in defining new nosological entities and in driving changes in the previous classification of rhinopathies. The simplicity of the technique and its low invasiveness make rhinocytology a practical diagnostic tool practical for all rhinoallergology services. Furthermore, since it allows the monitoring of responses to treatment, this method plays an important role in guiding a more effective and less expensive diagnostic program. Microscopic observation requires prolonged effort by a specialist, but the modern scanning systems for cytological preparations allow scanning of an entire preparation enlarged to 400x. By means of the system presented in this paper, it is possible to automatically identify and classify cells present on a rhinocytologic preparation based on a digital image of the preparation itself. Thus, pivotal diagnostic support has been made available to the rhinocytologist, who can quickly verify that the cells have been correctly classified by observation on a monitor. In the system presented herein, image processing and image segmentation techniques have been used to find images of cellular elements within the preparation. Cell classification is based on a convolutional neural network composed of three blocks of main layers. Cell identification (first step, image segmentation) exhibits accuracy greater than 90%, while cell classification (second step, seven cytotypes) attained a mean accuracy of approximately 98%. Finally, the classified cell images are shown to a specialist for rapid verification. This complete system supports clinicians in the preparation of a rhinocytogram report.

Wnt is a family of signaling molecules involved in embryogenesis, adult tissue repair, and cancer. They activate canonical and noncanonical Wnt signaling cascades in target cells. Several studies, within the last decades, showed that several Wnt ligands are involved in myogenesis and both canonical and noncanonical Wnt pathways regulate muscle formation and the maintenance of adult tissue homeostasis. In this review, we provide a comprehensive overview of the roles of Wnt signaling during muscle development and an updated description of Wnt functions during muscle repair. Lastly, we discuss the crosstalk between Wnt and TGFβ signaling pathways in skeletal muscle.

This paper presents an analyses of an Energy Storage System (ESS) for grid-tied photovoltaic (PV) systems, in order to harness the energy usually lost due to PV array oversizing. A real case of annual PV power generation analysis is presented to illustrate the existing problem and future solutions. Three PV modeling techniques have been applied to estimate non-measured non-harnessed PV power to provide an ESS energy and power sizing strategy. Moreover, a control strategy to store or release power from the DC-link, without modifying the Maximum Power Point Tracking (MPPT) strategy, is presented. The results show an estimation of the annual power loss caused by oversizing the PV array. The ESS sizing strategy gives insight into not only the energy requirements, but also the power requirements of the system. Simulation results show that the proposed ESS control strategy is capable of harnessing the extra power without modifying the existing power converter of the PV plant nor its control strategy.

Patients with Parkinson's disease may have difficulties in speaking because of the reduced coordination of the muscles that control breathing, phonation, articulation and prosody. Symptoms that may occur because of changes are weakening of the volume of the voice, voice monotony, changes in the quality of the voice, speed of speech, uncontrolled repetition of words. The evaluation of some of the disorders mentioned can be achieved through measuring the variation of parameters in an objective manner. It may be done to evaluate the response to the treatments with intra-daily frequency pre / post-treatment, as well as in the long term. Software systems allow these measurements also by recording the patient's voice. This allows to carry out a large number of tests by means of a larger number of patients and a higher frequency of the measurements. The main goal of our work was to design and realize Voxtester, an effective and simple to use software system useful to measure whether changes in voice emission are sensitive to pharmacologic treatments. Doctors and speech therapists can easily use it without going into the technical details, and we think that this goal is reached only by Voxtester, up to date.

This paper presents the design and the implementation of a Computer Aided Diagnosis (CAD) system for the clinical analysis of Peripheral Blood Smears (PBS also called Blood Film). The proposed system is able to count and classify the five types of leucocytes located in the tail of a PBS for computing the leukocyte formula. Image processing and segmentation techniques were used to extract 33 leucocyte's features (morphological, chromatic and texture-based). Only 7 features, selected by using the Information Gain Ranking algorithm of Weka platform, were used to evaluate the classification performance of two different classifiers: Back Propagation Neural Network (BPNN) and Decision Tree (DT). From the comparison between the two proposed approaches we can argue that the BPNN performed better than the DT on the validation set. Finally, the Neural Network classifier was evaluated with a test set composed of 1274 leucocytes obtaining good results in terms of Precision (87.9%) and Sensitivity (97.4%).

In this paper we propose the Electronic Multimedia Health Fascicle (EMHF), a truly new software system for the very large number of available electronic health records. It allows the physician to see at a glance the patient's clinical biometric measurements and biologic parameters, so as to be able to link any alarming physical status to his recent medical history. Web based, accessible from any mobile device, and easy to use by both physicians and patients, the system facilitates patient-medical interaction. Using the system can also promote better adherence to medical guidelines by the physicians and to medical prescriptions and advice by the patients.

The European Tracer Experiment (ETEX) is jointly organised by the European Commission, the International Atomic Energy Agency and the World Meteorological Organization. The project was established to evaluate the validity of long-range transport models for real time application in emergency management and to assemble a database which will allow the evaluation of long-range atmospheric dispersion models in general (Klug et al., 1993) ETEX main objectives were to conduct a European-scale tracer experiment, and test the capability of meteorological institutes responsible for emergency response to forecast in real time atmospheric dispersion (Girardi et al., 1997). Following this, the Atmospheric Transport Model Evaluation Study II (ATMES II) will simulate the tracer experiment with the same meteorological input data for all models.

As part of the European Tracer Experiment (ETEX) two successful atmospheric experiments were carried out in October and November, 1994. Perfluorocarbon (PFC) tracers were released into the atmosphere in Monterfil, Brittany, and air samples were taken at 168 stations in 17 European countries for 72 hours after the release. Upper air tracer measurements were made from three aircraft. During the first experiment a westerly air flow transported the tracer plume north-eastwards across Europe. During the second release the flow was eastwards. The results from the ground sampling network allowed the determination of the cloud evolution as far as Sweden, Poland and Bulgaria. Typical background concentrations of the tracer used are around 5 to 7 fl/l in ambient air. Concentrations in the plume ranged from 10 to above 200 fl/l.

We report a case of escape-capture bigeminy, a rare form of arrhythmia, occurring after treatment of wide-QRS tachycardia and diagnosed by esophageal electrocardiography recording of atrial activity which was not evident on the surface electrocardiogram.