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Total Hip Arthroplasty has been one of the most successful surgical procedure in terms of patient outcomes and satisfaction. However, due to increase in life expectancy and the related incidence of age-dependent bone diseases, a growing number of cases of intra-operative fractures lead to revision surgery with high rates of morbidity and mortality. Surgeons choose the type of the implant, either cemented or cementless prosthesis, on the basis of the age, the quality of the bone and the general medical conditions of the patients. Generally, no quantitative measures are available to assess the intra-operative fracture risk. Consequently, the decision-making process is mainly based on surgical operators’ expertise and qualitative information obtained from imaging. Motivated by this scenario, we here propose a mechanical-supported strategy to assist surgeons in their decisions, by giving intelligible maps of the risk fracture which take into account the interplay between the actual mechanical strength distribution inside the bone tissue and its response to the forces exerted by the implant. In the presented study, we produce charts and patient-specific synthetic “traffic-light” indicators of fracture risk, by making use of ad hoc analytical solutions to predict the stress levels in the bone by means of Computed Tomography-based mechanical and geometrical parameters of the patient. We felt that if implemented in a friendly software or proposed as an app, the strategy could constitute a practical tool to help the medical decision-making process, in particular with respect to the choice of adopting cemented or cementless implant.

Articular cartilage degeneration poses a significant public health challenge; techniques such as 3D bioprinting are being explored for its regeneration in vitro. Gelatin-based hydrogels represent one of the most promising biopolymers used in cartilage tissue engineering, especially for its collagen composition and tunable mechanical properties. However, there are no standard protocols that define process parameters such as the crosslinking method to apply. To this aim, a reproducible study was conducted for exploring the influence of different crosslinking methods on 3D bioprinted gelatin structures. This study assessed mechanical properties and cell viability in relation to various crosslinking techniques, revealing promising results particularly for dual (photo + ionic) crosslinking methods, which achieved high cell viability and tunable stiffness. These findings offer new insights into the effects of crosslinking methods on 3D bioprinted gelatin for cartilage applications. For example, ionic and photo-crosslinking methods provide softer materials, with photo-crosslinking supporting cell stretching and diffusion, while ionic crosslinking preserves a spherical stem cell morphology. On the other hand, dual crosslinking provides a stiffer, optimized solution for creating stable cartilage-like constructs. The results of this study offer a new perspective on the standardization of gelatin for cartilage bioprinting, bridging the gap between research and clinical applications.

Consuming an unbalanced diet and being overweight represent a global health problem in young people and adults of both sexes, and may lead to metabolic syndrome. The diet-induced obesity (DIO) model in the C57BL/6J mouse substrain that mimics the gradual weight gain in humans consuming a “Western-type” (WD) diet is of great interest. This study aims to characterize this animal model, using high-frequency ultrasound imaging (HFUS) as a complementary tool to longitudinally monitor changes in the liver, heart and kidney. Long-term WD feeding increased mice body weight (BW), liver/BW ratio and body condition score (BCS), transaminases, glucose and insulin, and caused dyslipidemia and insulin resistance. Echocardiography revealed subtle cardiac remodeling in WD-fed mice, highlighting a significant age–diet interaction for some left ventricular morphofunctional parameters. Qualitative and parametric HFUS analyses of the liver in WD-fed mice showed a progressive increase in echogenicity and echotexture heterogeneity, and equal or higher brightness of the renal cortex. Furthermore, renal circulation was impaired in WD-fed female mice. The ultrasound and histopathological findings were concordant. Overall, HFUS can improve the translational value of preclinical DIO models through an integrated approach with conventional methods, enabling a comprehensive identification of early stages of diseases in vivo and non-invasively, according to the 3Rs.

Smart shoes have ushered in a new era of personalised health monitoring and assistive technologies. Smart shoes leverage technologies such as Bluetooth for data collection and wireless transmission, and incorporate features such as GPS tracking, obstacle detection, and fitness tracking. As the 2010s unfolded, the smart shoe landscape diversified and advanced rapidly, driven by sensor technology enhancements and smartphones’ ubiquity. Shoes have begun incorporating accelerometers, gyroscopes, and pressure sensors, significantly improving the accuracy of data collection and enabling functionalities such as gait analysis. The healthcare sector has recognised the potential of smart shoes, leading to innovations such as shoes designed to monitor diabetic foot ulcers, track rehabilitation progress, and detect falls among older people, thus expanding their application beyond fitness into medical monitoring. This article provides an overview of the current state of smart shoe technology, highlighting the integration of advanced sensors for health monitoring, energy harvesting, assistive features for the visually impaired, and deep learning for data analysis. This study discusses the potential of smart footwear in medical applications, particularly for patients with diabetes, and the ongoing research in this field. Current footwear challenges are also discussed, including complex construction, poor fit, comfort, and high cost.

This narrative review aims to summarise the classification of vascular anomalies, their clinical presentation, and their radiological features to propose a diagnostic algorithm to approach patients with suspected soft tissue vascular anomalies of the extremities. The management of vascular anomalies necessitates a multidisciplinary approach. Clinical presentation and physical examination are sufficient in most cases to achieve a correct diagnosis. This is especially true for small congenital lesions of the skin and subcutaneous tissue. Imaging is used for accurate characterization of these lesions, especially in cases of atypical or vague clinical presentation, and to assess extension in cases of lesions that are larger and localized in deeper tissues.

Background: The current manuscript’s aim was to determine the human papillomavirus (HPV) genotype-specific prevalence and distribution among individuals, males, and females, of different ages in the region of Apulia, Italy, highlighting the possible variables involved in the carcinogenicity mechanism. In addition, we proposed two hypothetical models of HPV’s molecular dynamics, intending to clarify the impact of prevention and therapeutic strategies, explicitly modeled by recent survey data. Methods: We presented clinical data from 9647 participants tested for either high-risk (HR) or low-risk (LR) HPV at the affiliated Bari Policlinic University Hospital of Bari from 2011 to 2022. HPV DNA detection was performed using nested-polymerase chain reaction (PCR) and multiplex real-time PCR assay. Statistical analysis showed significant associations for all genders and ages and both HR- and LR-HPV types. A major number of significant pairwise associations were detected for the higher-risk types and females and lower-risk types and males. Results: The overall prevalence of HPV was 50.5% (n-4.869) vs. 49.5% (n-4.778) of the study population, of which 74.4% (n-3621) were found to be HPV high-risk (HR-HPV) genotypes and 57.7% (n-2.807) low-risk HPV (LR-HPV) genotypes, of which males were 58% and females 49%; the three most prevalent HR-HPV genotypes were HPV 53 (n707-15%), 16 (n704-14%), and 31 (n589-12%), and for LR-HPV, they were 42 (19%), 6 (16%), and 54 (13%); 56% of patients screened for HPV were ≤ 30 years old, 53% were between 31 and 40 years old, 46% were 41–50 and 51–60 years old, and finally, 44% of subjects were >60 years old. Conclusions: Our study provided comprehensive epidemiological data on HPV prevalence and genotype distribution among 9647 participants, which could serve as a significant reference for clinical practice, and it implied the necessity for more effective screening methods for HPV carcinogenesis covering the use of more specific molecular investigations. Although this is a predominantly descriptive and epidemiological study, the data obtained offer not only a fairly unique trend compared to other studies of different realities and latitudes but also lead us to focus on the HPV infection within two groups of young people and adults and hypothesize the possible involvement of dysbiosis, stem cells, and the retrotransposition mechanism.

Innovative ICT technologies, approaches and applications are becoming increasingly pervasive in several domains, including in medicine and healthcare [...]

Human arteries show structural and functional peculiarities according to the nutrient and oxygen needs of a specific vascular district. This architectural heterogeneity is reflected in the pathological setting of cardiovascular diseases (CVDs). Indeed, the responsiveness to cardiovascular risk factors, and the morphological and molecular patterns are discriminating factors among CVDs affecting different vascular beds. MicroRNAs (miRNAs) are endogenous regulators of gene expression and fine-tuners of vascular cell differentiation; thus, these non-coding RNAs can modulate arterial heterogeneity. The identification of an artery-specific miRNA signature would be promising in the therapy of CVDs, especially in patients who are frail and elderly. In the present review, we will provide a concise description of the arterial tree heterogeneity on a structural and cellular basis, mainly in the pathological context. Secondly, we will address the miRNA potential as crucial mediators of arterial heterogeneity, focusing on the abdominal aorta and femoral artery, with the final goal of strengthening the search for more targeted therapies in CVDs and stratification approaches in patients who are frail and elderly.

Lampedusa, the largest island of the Pelagie archipelago, Sicily, Italy, has proven to be a rich source of plants and shrubs used in folk medicine. These plants, often native to the island, have been very poorly investigated for their phytochemical composition and biological potential to be translated into pharmacological applications. To start achieving this purpose, a specimen of Limonium lopadusanum, a plant native to Lampedusa, was investigated for the first time. This manuscript reports the results of a preliminary biological assay, focused on antimicrobial activity, carried out using the plant organic extracts, and the isolation and chemical and biological characterization of the secondary metabolites obtained. Thus 3-hydroxy-4-methoxybenzoic acid methyl ester (syn: methyl isovanillate, (1), methyl syringate (2), pinoresinol (3), erythrinassinate C (4) and tyrosol palmitate (5) were isolated. Their antimicrobial activity was tested on several strains and compound 4 showed promising antibacterial activity against Enterococcus faecalis. Thus, this metabolite has antibiotic potential against the drug-resistant opportunistic pathogen E. faecalis.

Green areas and parks are places where people’s quality of life improves, places of recreation and relaxation, in which to carry out various social activities. Among these, the historic gardens represent the union between green areas and historic architecture. Light is considered one of the main factors influencing the perception of such spaces at night; therefore, lighting solutions should be properly balanced. Immersive virtual reality is a tool that allows for lighting design management from different viewpoints. This study investigates how the lighting of a monumental garden affects people’s perception of its architecture at night. With this aim, a detailed 3D model of the western part of Escorial’s monumental complex in Spain was built in DIALux evo 11 and Unreal Engine 4.27 and used to propose six different lighting scenarios. Participants viewed the scenarios through a head-mounted display and provided subjective feedback on the perceived light strength of the environment and architecture. Results highlight that illuminating surrounding areas affects the environment and building perception, as well as the order in which elements capture observer attention. In particular, lit elements between the observer and the façade can reduce façade importance.

Monoliths manufactured by Direct Ink Writing containing 60% SSZ-13 (SiO2/Al2O3 = 23) and SiO2 with 10% laponite as a binder were investigated as self-standing structured catalysts for NH3-SCR of NOx after a short (4 h) and prolonged (24 h) ion exchange with copper and then compared with pure SSZ-13 exchanged under the same conditions. The catalysts were characterized by morphological (XRD and SEM), textural (BET and pore size distribution), chemical (ICP-MS), red-ox (H2-TPR), and surface (NH3-TPD) analyses. The silica-based binder uniformly covered the SSZ-13 particles, and copper was uniformly distributed as well. The main features of the pure Cu-exchanged SSZ-13 zeolite were preserved in the composite monoliths with a negligible contribution of the binder fraction. NH3-SCR tests, carried out on both monolithic and powdered samples in the temperature range of 70–550 °C, showed that composite monoliths provided very good activity, and that the intrinsic activity of SSZ-13 was enhanced by the hierarchical structure of the composite material.

Heart failure (HF) has a global prevalence of 1–2%, and the incidence around the world is growing. The prevalence increases with age, from around 1% for those aged 10% for those aged 70 years or over. Based on studies in hospitalized patients, about 50% of patients have heart failure with reduced ejection fraction (HFrEF), and 50% have heart failure with preserved ejection fraction (HFpEF). HF is associated with high morbidity and mortality, and HF-related hospitalizations are common, costly, and impact both quality of life and prognosis. More than 5–10% of patients deteriorate into advanced HF (AdHF) with worse outcomes, up to cardiogenic shock (CS) condition. Right heart catheterization (RHC) is essential to assess hemodynamics in the diagnosis and care of patients with HF. The aim of this article is to review the evidence on RHC in various clinical scenarios of patients with HF.

This paper proposes an innovative method that exploits a complex deep learning network architecture, called Hierarchical Deep Neural Network (HDNN), specifically developed for the eXtreme Multilabel Text Classification (XMTC) task, when the label set is hierarchically organized, such as the case of the PubMed article labeling task. In detail, the topology of the proposed HDNN architecture follows the exact hierarchical structure of the label set to integrate this knowledge directly into the DNN. We assumed that if a label set hierarchy is available, as in the case of the PubMed Dataset, forcing this information into the network topology could enhance the classification performances and the interpretability of the results, especially related to the hierarchy. We performed an experimental assessment of the PubMed article classification task, demonstrating that the proposed HDNN provides performance improvement for a baseline based on a classic flat Convolution Neural Network (CNN) deep learning architecture, in particular in terms of hierarchical measures. These results provide useful hints for integrating previous and innate knowledge in a deep neural network. The drawback of the HDNN is the high computational time required to train the neural network, which can be addressed with a parallel implementation planned as a future work.

We report a [68Ga]Ga-PSMA-11 positron emission tomography/computed tomography (PET/CT) scan of a 71-year-old man with metastatic castration-resistant prostate cancer (mCRPC) and concomitant active lumbar spondylodiscitis, both PSMA-positive on a PET/CT scan. This interesting image should advise colleagues to consider spondylodiscitis as a differential diagnosis of PSMA-positive findings in the spine, particularly if intervertebral space and soft tissue are involved.

Articular cartilage morphology and composition are essential factors in joint biomechanics, and their alteration is a crucial aspect of osteoarthritis (OA), a prevalent disease that causes pain and functional loss. This research focuses on developing patient-specific synthetic cartilage using innovative Digital Anatomy polymers. The objectives include investigating the morphology, characterizing the mechanical properties, and replicating the architecture of natural cartilage. This approach offers potential alternatives to traditional manufacturing methods and reduces the need for expensive in vivo experiments. Finite Element Analysis (FEA) validates a novel patient-specific measurement setup. It provides insights into the role of morphology in the distribution of stress and strain within cartilage. CAD design is also utilized to create standardized fiber-reinforced samples that mimic the layered micro-architecture of natural cartilage, allowing for the study of their contribution to the overall mechanical properties. The results demonstrate that 3D-printed polymers can effectively replicate the elastic properties of cartilage. The proposed patient-specific simulator produces reliable results, which have been validated through FEM analysis. While the recreated microstructure closely resembles biological cartilage samples, the elastic properties are slightly underestimated. In conclusion, designing an in silico knee joint is a feasible approach that offers numerous advantages for further development. The Young’s modulus values of our synthetic cartilage modules range from 2.43 MPa to 7.24 MPa, within the range reported in the literature. Moreover, Young´s modulus at the micro level shows the differences between surface 1.74 MPa and internal substrate 1.83 MPa depending on the fiber orientation. Finally, our model proves to be mechanically and morphologically accurate at both the macro and micro levels.

Uncrewed aerial vehicles (UAVs), commonly known as drones, have emerged as transformative tools in the healthcare sector, offering the potential to revolutionize medical logistics, emergency response, and patient care. This scoping review provides a comprehensive exploration of the diverse applications of drones in healthcare, addressing critical gaps in existing literature. While previous reviews have primarily focused on specific facets of drone technology within the medical field, this study offers a holistic perspective, encompassing a wide range of potential healthcare applications. The review categorizes and analyzes the literature according to key domains, including the transport of biomedical goods, automated external defibrillator (AED) delivery, healthcare logistics, air ambulance services, and various other medical applications. It also examines public acceptance and the regulatory framework surrounding medical drone services. Despite advancements, critical knowledge gaps persist, particularly in understanding the intricate interplay between technological challenges, the existing regulatory framework, and societal acceptance. This review highlights the need for the extensive validation of cost-effective business cases, the development of control techniques that can address time and resource savings within the constraints of real-life scenarios, the design of crash-protected containers, and the establishment of corresponding tests and standards to demonstrate their conformity.

Heart rate (HR) and respiratory rate (RR) are two vital parameters of the body medically used for diagnosing short/long-term illness. Out-of-the-body, non-skin-contact HR/RR measurement remains a challenge due to imprecise readings. “Invisible” wearables integrated into day-to-day garments have the potential to produce precise readings with a comfortable user experience. Sleep studies and patient monitoring benefit from “Invisibles” due to longer wearability without significant discomfort. This paper suggests a novel method to reduce the footprint of sleep monitoring devices. We use a single silver-coated nylon fabric band integrated into a substrate of a standard cotton/nylon garment as a resistive elastomer sensor to measure air and blood volume change across the chest. We introduce a novel event-based architecture to process data at the edge device and describe two algorithms to calculate real-time HR/RR on ARM Cortex-M3 and Cortex-M4F microcontrollers. RR estimations show a sensitivity of 99.03% and a precision of 99.03% for identifying individual respiratory peaks. The two algorithms used for HR calculation show a mean absolute error of 0.81 ± 0.97 and 0.86±0.61 beats/min compared with a gold standard ECG-based HR. The event-based algorithm converts the respiratory/pulse waveform into instantaneous events, therefore reducing the data size by 40–140 times and requiring 33% less power to process and transfer data. Furthermore, we show that events hold enough information to reconstruct the original waveform, retaining pulse and respiratory activity. We suggest fabric sensors and event-based algorithms would drastically reduce the device footprint and increase the performance for HR/RR estimations during sleep studies, providing a better user experience.

Background: Takotsubo syndrome (TTS) is an important type of acute heart failure with significant risk of acute complications and death. In this analysis we sought to identify predictors for in-hospital clinical outcome in TTS patients and present long-term outcomes. Methods: In this analysis from the Austrian national TTS registry, univariable and multivariable analyses were performed to identify significant predictors for severe in-hospital complications requiring immediate invasive treatment or leading to irreversible damage, such as cardiogenic shock, intubation, stroke, arrhythmias and death. Furthermore, the influence of independent predictors on long-term survival was evaluated. Results: A total of 338 patients (median age 72 years, 86.9% female) from six centers were included. Severe in-hospital complications occurred in 14.5% of patients. In multivariable analysis, high neutrophile-lymphocyte-ratio (NLR; OR 1.04 [95% CI 1.02–1.07], p = 0.009) and low LVEF (OR 0.92 [0.90–0.95] per %, p < 0.001) were significant predictors of severe in-hospital complications. Both the highest NLR tercile and the lowest LVEF tercile were significantly associated with reduced 5-year survival. Conclusions: Low LVEF and high NLR at admission were independently associated with increased in-hospital complications and reduced long-term survival in TTS patients. NLR is a new easy-to-measure tool to predict worse short- and long-term outcome after TTS.

The ability to measure and monitor the concentration of specific chemical and/or gaseous species (i.e., “analytes”) is the main requirement in many fields, including industrial processes, medical applications, and workplace safety management. As a consequence, several kinds of sensors have been developed in the modern era according to some practical guidelines that regard the characteristics of the active (sensing) materials on which the sensor devices are based. These characteristics include the cost-effectiveness of the materials’ manufacturing, the sensitivity to analytes, the material stability, and the possibility of exploiting them for low-cost and portable devices. Consequently, many gas sensors employ well-defined transduction methods, the most popular being the oxidation (or reduction) of the analyte in an electrochemical reactor, optical techniques, and chemiresistive responses to gas adsorption. In recent years, many of the efforts devoted to improving these methods have been directed towards the use of certain classes of specific materials. In particular, ionic liquids have been employed as electrolytes of exceptional properties for the preparation of amperometric gas sensors, while metal–organic frameworks (MOFs) are used as highly porous and reactive materials which can be employed, in pure form or as a component of MOF-based functional composites, as active materials of chemiresistive or optical sensors. Here, we report on the most recent developments relative to the use of these classes of materials in chemical sensing. We discuss the main features of these materials and the reasons why they are considered interesting in the field of chemical sensors. Subsequently, we review some of the technological and scientific results published in the span of the last six years that we consider among the most interesting and useful ones for expanding the awareness on future trends in chemical sensing. Finally, we discuss the prospects for the use of these materials and the factors involved in their possible use for new generations of sensor devices.

Three-phase motors are commonly adopted in several industrial contexts and their failures can result in costly downtime causing undesired service outages; therefore, motor diagnostics is an issue that assumes great importance. To prevent their failures and face the considered service outages in a timely manner, a non-invasive method to identify electrical and mechanical faults in three-phase asynchronous electric motors is proposed in the paper. In particular, a measurement strategy along with a machine learning algorithm based on an artificial neural network is exploited to properly classify failures. In particular, digitized current samples of each motor phase are first processed by means of FFT and PSD in order to estimate the associated spectrum. Suitable features (in terms of frequency and amplitude of the spectral components) are then singled out to either train or feed a neural network acting as a classifier. The method is preliminarily validated on a set of 28 electric motors, and its performance is compared with common state-of-the-art machine learning techniques. The obtained results show that the proposed methodology is able to reach accuracy levels greater than 98% in identifying anomalous conditions of three-phase asynchronous motors.

A systematic review on electroencephalographic (EEG)-based feature extraction strategies to diagnosis and therapy of attention deficit hyperactivity disorder (ADHD) in children is presented. The analysis is realized at an executive function level to improve the research of neurocorrelates of heterogeneous disorders such as ADHD. The Quality Assessment Tool for Quantitative Studies (QATQS) and field-weighted citation impact metric (Scopus) were used to assess the methodological rigor of the studies and their impact on the scientific community, respectively. One hundred and one articles, concerning the diagnostics and therapy of ADHD children aged from 8 to 14, were collected. Event-related potential components were mainly exploited for executive functions related to the cluster inhibition, whereas band power spectral density is the most considered EEG feature for executive functions related to the cluster working memory. This review identifies the most used (also by rigorous and relevant articles) EEG signal processing strategies for executive function assessment in ADHD.

Understanding the impact of the COVID-19 pandemic on dental emergencies. A systematic review of the literature (PubMed/Scopus) searching for articles on COVID-19 and dental abscess and a retrospective cohort study with quantitative/qualitative data analysis of our hospital E.R. patients admitted for cervico-facial abscess of dental origin were performed. Thirteen studies could be included in the review, concerning characteristics/management of patients with dental emergencies in hospitals/private practices, generally with poor evidence. For the retrospective analysis, 232 consecutive patients were included (100 study vs. 132 control). The prevalence of dental emergencies (abscess) and relative complications (mediastinitis, exitus) increased. Dental care availability was limited, with strong heterogeneity amongst regions/nations. At-risk (aerosol-generating) procedures were generally avoided, and hospitalization length reduced. Comorbidity patients and males seem less likely to restore regular dentist attendance during the post-lockdown pandemic. Despite the poor scientific evidence, COVID-19 seems to have impacted dental emergencies through limited routine dental care availability and influence on physicians’ and patients’ behaviour.

Cardiac surgery is necessary in almost 50% of patients with endocarditis. Early surgery, i.e., the surgery performed during the first hospitalization, is required in the following cases: heart failure secondary to valve regurgitation; S. aureus, fungal organism, or other highly resistant organism infection; heart block, annular or aortic abscess, or destructive penetrating lesions; evidence of persistent infection as manifested by persistent bacteremia or fevers lasting >5 days after onset of appropriate antimicrobial therapy. A 62-year-old man developed a fever (38 °C) 3 days after a transaortic electrophysiological study; blood cultures were positive for S. aureus, and were sensitive to vancomycin and ceftaroline. Antibiotic therapy was started, controlling the fever and the patient’s infective and inflammatory profiles well; however, 3 days later, acute aortic regurgitation developed. At transesophageal echocardiography (TEE), a rare condition was revealed—vegetation was attached to the aortic wall, impeding correct aortic valve closure. Cardiac operation was carried out and the time for surgery was discussed; based on the patient’s clinically stable condition, and on the infection, which was controlled well by antibiotics therapy, surgery was not performed in emergency circumstance (within 24–48 h)—rather, it was programmed during the hospitalization. A TEE surveillance was initiated, and after 7 days, TEE revealed a new picture, with images of an aortic abscess with small perforation in the right atrium, requiring emergency surgery, carried out 20 h later. In our case, the rapid evolution of the vegetation attached to the aortic wall suggested the following: (1) that the time for the surgery cannot be guided only by clinical procedure but must also be guided by imaging pictures; (2) that strictly TEE surveillance is mandatory in patients with aortic endocarditis not initially referred for emergency surgery.

For the observation of human joint cartilage, X-ray, computed tomography (CT) or magnetic resonance imaging (MRI) are the main diagnostic tools to evaluate pathologies or traumas. The current work introduces a set of novel measurements and 3D features based on MRI and CT data of the knee joint, used to reconstruct bone and cartilages and to assess cartilage condition from a new perspective. Forty-seven subjects presenting a degenerative disease, a traumatic injury or no symptoms or trauma were recruited in this study and scanned using CT and MRI. Using medical imaging software, the bone and cartilage of the knee joint were segmented and 3D reconstructed. Several features such as cartilage density, volume and surface were extracted. Moreover, an investigation was carried out on the distribution of cartilage thickness and curvature analysis to identify new markers of cartilage condition. All the extracted features were used with advanced statistics tools and machine learning to test the ability of our model to predict cartilage conditions. This work is a first step towards the development of a new gold standard of cartilage assessment based on 3D measurements.

Triage is the first interaction between a patient and a nurse/paramedic. This assessment, usually performed at Emergency departments, is a highly dynamic process and there are international grading systems that according to the patient condition initiate the patient journey. Triage requires an initial rapid assessment followed by routine checks of the patients’ vitals, including respiratory rate, temperature, and pulse rate. Ideally, these checks should be performed continuously and remotely to reduce the workload on triage nurses; optimizing tools and monitoring systems can be introduced and include a wearable patient monitoring system that is not at the expense of the patient’s comfort and can be remotely monitored through wireless connectivity. In this study, we assessed the suitability of a small ceramic piezoelectric disk submerged in a skin-safe silicone dome that enhances contact with skin, to detect wirelessly both respiration and cardiac events at several positions on the human body. For the purposes of this evaluation, we fitted the sensor with a respiratory belt as well as a single lead ECG, all acquired simultaneously. To complete Triage parameter collection, we also included a medical-grade contact thermometer. Performances of cardiac and respiratory events detection were assessed. The instantaneous heart and respiratory rates provided by the proposed sensor, the ECG and the respiratory belt were compared via statistical analyses. In all considered sensor positions, very high performances were achieved for the detection of both cardiac and respiratory events, except for the wrist, which provided lower performances for respiratory rates. These promising yet preliminary results suggest the proposed wireless sensor could be used as a wearable, hands-free monitoring device for triage assessment within emergency departments. Further tests are foreseen to assess sensor performances in real operating environments.

Randomized clinical trials are considered the milestones of clinical research in oncology, and guided the development and approval of new compounds so far. In the last few years, however, molecular and genomic profiling led to a change of paradigm in therapeutic algorithms of many cancer types, with the spread of different biomarker-driven therapies (or targeted therapies). This scenario of “personalized medicine” revolutionized therapeutic strategies and the methodology of the supporting clinical research. New clinical trial designs are emerging to answer to the unmet clinical needs related to the development of these targeted therapies, overcoming the “classical” structure of randomized studies. Innovative trial designs able to evaluate more than one treatment in the same group of patients or many groups of patients with the same treatment (or both) are emerging as a possible future standard in clinical trial methodology. These are identified as “master protocols”, and include umbrella, basket and platform trials. In this review, we described the main characteristics of these new trial designs, focusing on the opportunities and limitations of their use in the era of personalized medicine.

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complexity, so their usefulness should be carefully evaluated for the specific application.

Objective: The objective of this research is to demonstrate the release of SARS-CoV-2 Spike (S) antibodies in human milk samples obtained by patients who have been vaccinated with mRNABNT162b2 vaccine. Methods: Milk and serum samples were collected in 10 volunteers 20 days after the first dose and 7 seven days after the second dose of the mRNABNT162b2 vaccine. Anti-SARS-CoV-2 S antibodies were measured by the Elecsys® Anti-SARS-CoV-2 S ECLIA assay (Roche Diagnostics AG, Rotkreuz, Switzerland), a quantitative electrochemiluminescence immunometric method. Results: At first sample, anti-SARS-CoV-2 S antibodies were detected in all serum samples (103.9 ± 54.9 U/mL) and only in two (40%) milk samples with a low concentration (1.2 ± 0.3 U/mL). At the second sample, collected 7 days after the second dose, anti-SARS-CoV-2 S antibodies were detected in all serum samples (3875.7 ± 3504.6 UI/mL) and in all milk samples (41.5 ± 47.5 UI/mL). No correlation was found between the level of serum and milk antibodies; the milk antibodies/serum antibodies ratio was on average 2% (range: 0.2–8.4%). Conclusion: We demonstrated a release of anti-SARS-CoV-2 S antibodies in the breast milk of women vaccinated with mRNABNT162b2. Vaccinating breastfeeding women could be a strategy to protect their infants from COVID-19 infection.

This article presents a numerical model of an aeronautical hybrid electric propulsion system (HEPS) based on an energy method. This model is designed for HEPS with a total power of 100 kW in a parallel configuration intended for ultralight aircraft and unmanned aerial vehicles (UAV). The model involves the interaction between the internal combustion engine (ICE), the electric motor (EM), the lithium battery and the aircraft propeller. This paper also describes an experimental setup that can reproduce some flight phases, or entire missions, for the reference aircraft class. The experimental data, obtained by reproducing two different take-offs, were used for model validation. The model can also simulate anomalous operating conditions. Therefore, the tests chosen for the model validation are characterized by the EM flux weakening (“de-fluxing”). This model is particularly suitable for preliminary stages of design when it is necessary to characterize the hybrid system architecture. Moreover, this model helps with the choice of the main components (e.g., ICE, EM, and transmission gear ratio). The results of the investigation conducted for different battery voltages and EM transmission ratios are shown for the same mission. Despite the highly simplified model, the average margin of error between the experimental and simulated results was generally under 5%.

Geo-resources are widely exploited in our society, with huge benefits for both economy and communities. Nevertheless, with benefits come risks and impacts. Understanding how such risks and impacts are intrinsically borne in a given project is of critical importance for both industry and society. In particular, it is crucial to distinguish between the specific impacts related to exploiting a given energy resource and those shared with the exploitation of other energy resources. A variety of different approaches can be used to identify and assess such risks and impacts. In particular, Life Cycle Assessment (LCA) and risk assessments (RAs) are the most commonly adopted. Although both are widely used to support decision making in environmental management, they are rarely used in combination perhaps because they have been developed by largely different groups of specialists. By analyzing the structure and the ratio of the two tools, we have developed an approach for combining and harmonizing LCA and MRA; the resulting protocol envisages building MRA upon LCA both qualitatively and quantitatively. We demonstrate the approach in a case study using a virtual site (based on a real one) for geothermal energy production.

In the last few decades, a number of wearable systems for respiration monitoring that help to significantly reduce patients’ discomfort and improve the reliability of measurements have been presented. A recent research trend in biosignal acquisition is focusing on the development of monolithic sensors for monitoring multiple vital signs, which could improve the simultaneous recording of different physiological data. This study presents a performance analysis of respiration monitoring performed via forcecardiography (FCG) sensors, as compared to ECG-derived respiration (EDR) and electroresistive respiration band (ERB), which was assumed as the reference. FCG is a novel technique that records the cardiac-induced vibrations of the chest wall via specific force sensors, which provide seismocardiogram-like information, along with a novel component that seems to be related to the ventricular volume variations. Simultaneous acquisitions were obtained from seven healthy subjects at rest, during both quiet breathing and forced respiration at higher and lower rates. The raw FCG sensor signals featured a large, low-frequency, respiratory component (R-FCG), in addition to the common FCG signal. Statistical analyses of R-FCG, EDR and ERB signals showed that FCG sensors ensure a more sensitive and precise detection of respiratory acts than EDR (sensitivity: 100% vs. 95.8%, positive predictive value: 98.9% vs. 92.5%), as well as a superior accuracy and precision in interbreath interval measurement (linear regression slopes and intercepts: 0.99, 0.026 s (R2 = 0.98) vs. 0.98, 0.11 s (R2 = 0.88), Bland–Altman limits of agreement: ±0.61 s vs. ±1.5 s). This study represents a first proof of concept for the simultaneous recording of respiration signals and forcecardiograms with a single, local, small, unobtrusive, cheap sensor. This would extend the scope of FCG to monitoring multiple vital signs, as well as to the analysis of cardiorespiratory interactions, also paving the way for the continuous, long-term monitoring of patients with heart and pulmonary diseases.

Radon is a major source of ionizing radiation exposure for the general population. It is known that exposure to radon is a risk factor for the onset of lung cancer. In this study, the results of a radon survey conducted in all districts of a Public Healthcare in Italy, are reported. Measurements of indoor radon were performed using nuclear track detectors, CR-39. The entire survey was conducted according to a well-established quality assurance program. The annual effective dose and excess lifetime cancer risk were also calculated. Results show that the radon concentrations varied from 7 ± 1 Bq/m3 and 5148 ± 772 Bq/m3, with a geometric mean of 67 Bq/m3 and geometric standard deviation of 2.5. The annual effective dose to workers was found to be 1.6 mSv/y and comparable with the worldwide average. In Italy, following the transposition of the European Directive 59/2013, great attention was paid to the radon risk in workplaces. The interest of the workers of the monitored sites was very high and this, certainly contributed to the high return rate of the detectors after exposure and therefore, to the presence of few missing data. Although it was not possible to study the factors affecting radon concentrations, certainly the main advantage of this study is that it was the first in which an entire public health company was monitored in regards to all the premises on the underground and ground floor.

The monitoring of heritage objects is necessary due to their continuous deterioration over time. Therefore, the joint use of the most up-to-date inspection techniques with the most innovative data processing algorithms plays an important role to apply the required prevention and conservation tasks in each case study. InfraRed Thermography (IRT) is one of the most used Non-Destructive Testing (NDT) techniques in the cultural heritage field due to its advantages in the analysis of delicate objects (i.e., undisturbed, non-contact and fast inspection of large surfaces) and its continuous evolution in both the acquisition and the processing of the data acquired. Despite the good qualitative and quantitative results obtained so far, the lack of automation in the IRT data interpretation predominates, with few automatic analyses that are limited to specific conditions and the technology of the thermographic camera. Deep Learning (DL) is a data processor with a versatile solution for highly automated analysis. Then, this paper introduces the latest state-of-the-art DL model for instance segmentation, Mask Region-Convolution Neural Network (Mask R-CNN), for the automatic detection and segmentation of the position and area of different surface and subsurface defects, respectively, in two different artistic objects belonging to the same family: Marquetry. For that, active IRT experiments are applied to each marquetry. The thermal image sequences acquired are used as input dataset in the Mask R-CNN learning process. Previously, two automatic thermal image pre-processing algorithms based on thermal fundamentals are applied to the acquired data in order to improve the contrast between defective and sound areas. Good detection and segmentation results are obtained regarding state-of-the-art IRT data processing algorithms, which experience difficulty in identifying the deepest defects in the tests. In addition, the performance of the Mask R-CNN is improved by the prior application of the proposed pre-processing algorithms.

This paper presents a virtual model of a scroll compressor developed on the one-dimensional analysis software Simcenter Amesim®. The model is semi-empirical: it needs some physical details of the modelled machine (e.g., the cubic capacity), but, on the other hand, it does not require the geometrical features of the spirals, so it needs experimental data to calibrate it. The model also requires rotational speed and the outlet temperature as boundary conditions. The model predicts the power consumption and the mass flow rate and considers leakages and mechanical losses. After the model presentation, this paper describes the test bench and the obtained data used to calibrate and validate the model. At last, the calibration process is described, and the results are discussed. The calculated values fit the experimental data also in extrapolation, despite the model is simple and performs calculations within 7 s. Due to these characteristics, the model is suitable for being used in a larger model as a sub-component.

Power supply quality and stability are critical for wearable and implantable biomedical applications. For this reason we have designed a reconfigurable switched-capacitor DC-DC converter that, aside from having an extremely small footprint (with an active on-chip area of only 0.04 mm2), uses a novel output voltage control method based upon a combination of adaptive gain and discrete frequency scaling control schemes. This novel DC-DC converter achieves a measured output voltage range of 1.0 to 2.2 V with power delivery up to 7.5 mW with 75% efficiency. In this paper, we present the use of this converter as a power supply for a concept design of a wearable (15 mm × 15 mm) 1-lead ECG front-end sensor device that simultaneously harvests power and communicates with external receivers when exposed to a suitable RF field. Due to voltage range limitations of the fabrication process of the current prototype chip, we focus our analysis solely on the power supply of the ECG front-end whose design is also detailed in this paper. Measurement results show not just that the power supplied is regulated, clean and does not infringe upon the ECG bandwidth, but that there is negligible difference between signals acquired using standard linear power-supplies and when the power is regulated by our power management chip.

There are two surgical approaches to performing total hip arthroplasty (THA): a cemented or uncemented type of prosthesis. The choice is usually based on the experience of the orthopaedic surgeon and on parameters such as the age and gender of the patient. Using machine learning (ML) techniques on quantitative biomechanical and bone quality data extracted from computed tomography, electromyography and gait analysis, the aim of this paper was, firstly, to help clinicians use patient-specific biomarkers from diagnostic exams in the prosthetic decision-making process. The second aim was to evaluate patient long-term outcomes by predicting the bone mineral density (BMD) of the proximal and distal parts of the femur using advanced image processing analysis techniques and ML. The ML analyses were performed on diagnostic patient data extracted from a national database of 51 THA patients using the Knime analytics platform. The classification analysis achieved 93% accuracy in choosing the type of prosthesis; the regression analysis on the BMD data showed a coefficient of determination of about 0.6. The start and stop of the electromyographic signals were identified as the best predictors. This study shows a patient-specific approach could be helpful in the decision-making process and provide clinicians with information regarding the follow up of patients.

Background: Endovascular aneurysm repair (EVAR) has become the primary choice of treatment for abdominal aortic aneurysm (AAA) and preventive intraoperative AAA sac embolization (SE) is an effective way to reduce endoleaks after EVAR. Preoperative planning of AAA SE has not been standardized yet, and typically requires a quite long and operator-dependent procedure using standard EVAR planning software. Methods: We introduce ‘EVAR-eaSE’, a software tool that we have developed to help vascular surgeons to plan the optimal SE procedure during EVAR, providing semi-automatic AAA segmentation and 3D modeling starting from patient scans, and then computing the aneurysm volumes. A preliminary accuracy evaluation on radiological phantoms, and a comparison with the commercial 3Mensio software for EVAR planning are carried out. Results: EVAR-eaSE software showed a good level of accuracy in aneurysm volume reconstruction and calculation (mean difference of 2.5% between real and segmented phantom volumes). A strong positive correlation (r = 0.940, r = 0.954, p < 0.001) was found between EVAR-eaSE computation of AAA Lumen and AAA Total volumes and the same measurements obtained with 3Mensio, for a database of 26 AAA cases. Conclusions: EVAR-eaSE demonstrated to be an easy-to-use, objective and accurate tool to assist the vascular surgeons in preoperative planning of SE during EVAR.

Polymerization of furfuryl alcohol carried out using ZnCl2 or CuCl2 as Lewis acid activators was investigated by exploring various synthesis parameters in order to produce activated carbons with different porosity and metal load. The temperature of polymerization was changed according to Lewis acidity strength of the two metal chlorides: 0 °C for CuCl2 and 80 °C for ZnCl2. The polymer obtained was pyrolyzed under pure He flow or under 1000 ppm O2/He flow at 600 or 850 °C in order to produce activated carbons with specific textural features. The load and nature of the residual metal after pyrolysis were determined by ICP and XRD analyses, respectively. Copper was mostly preserved even at high pyrolysis temperature in contrast to zinc, which was almost totally lost at 850 °C. A foamy structure was detected by SEM analysis for all samples. Textural properties were determined by both N2 and CO2 physisorption; surface areas and pore size distributions were evaluated according to BET, DFT and DR models. The polymerization activated by ZnCl2 produced carbons with larger surface areas were also related to the presence of some mesopores, whereas CuCl2 promoted the prevailing formation of narrow micropores, making these materials particularly suited to H2 storage applications.

This paper presents forcecardiography (FCG), a novel technique to measure local, cardiac-induced vibrations onto the chest wall. Since the 19th century, several techniques have been proposed to detect the mechanical vibrations caused by cardiovascular activity, the great part of which was abandoned due to the cumbersome instrumentation involved. The recent availability of unobtrusive sensors rejuvenated the research field with the most currently established technique being seismocardiography (SCG). SCG is performed by placing accelerometers onto the subject’s chest and provides information on major events of the cardiac cycle. The proposed FCG measures the cardiac-induced vibrations via force sensors placed onto the subject’s chest and provides signals with a richer informational content as compared to SCG. The two techniques were compared by analysing simultaneous recordings acquired by means of a force sensor, an accelerometer and an electrocardiograph (ECG). The force sensor and the accelerometer were rigidly fixed to each other and fastened onto the xiphoid process with a belt. The high-frequency (HF) components of FCG and SCG were highly comparable (r > 0.95) although lagged. The lag was estimated by cross-correlation and resulted in about tens of milliseconds. An additional, large, low-frequency (LF) component, associated with ventricular volume variations, was observed in FCG, while not being visible in SCG. The encouraging results of this feasibility study suggest that FCG is not only able to acquire similar information as SCG, but it also provides additional information on ventricular contraction. Further analyses are foreseen to confirm the advantages of FCG as a technique to improve the scope and significance of pervasive cardiac monitoring.

The study presents a Life Cycle Assessment (LCA) of Italian electricity scenarios, devised in the Integrated National Energy and Climate Plan (INECP). A fully representative LCA of the national electricity system was carried out, taking into consideration a great number of different power plant typologies for current (2016 and 2017) and future (2030) electricity mixes. The study confirms that LCA can be a powerful tool for supporting energy planning and strategies assessment. Indeed the results put in evidence not only the improvement of the environmental profile from the current to the future mix (the impacts decrease from 2016 to 2030 due to the transition towards renewables, mainly wind and photovoltaic), but also underline the difference between two scenarios at 2030 (being the scenario that includes the strategic objectives of the INECP to 2030 the one showing best environmental profile), providing an evaluation of the effect of different energy policies. For example, in the INECP scenario CO2 eq/kWh is 46% lower than current scenario and 37% lower than business as usual scenario for 2030. Moreover, considering different impact categories allowed to identify potential environmental trade-offs. The results suggest also the need of future insight on data related to photovoltaic technologies and materials and their future development.

With this paper we communicated the existence of a surface electrocardiography (ECG) recordings dataset, named WCTECGdb, that aside from the standard 12-lead signals includes the raw electrode biopotential for each of the nine exploring electrodes refereed directly to the right leg. This dataset, comprises of 540 ten second segments recorded from 92 patients at Campbelltown Hospital, NSW Australia, and is now available for download from the Physionet platform. The data included in the dataset confirm that the Wilson’s Central Terminal (WCT) has a relatively large amplitude (up to 247% of lead II) with standard ECG characteristics such as a p-wave and a t-wave, and is highly variable during the cardiac cycle. As further examples of application for our data, we assess: (1) the presence of a conductive pathway between the legs and the heart concluding that in some cases is electrically significant and (2) the initial assumption about the limbs potential stating the dominance of the left arm concluding that this is not always the case and that might requires case to case assessment.

In this paper, we present a new approach to the fusion of Sentinel 1 (S1) and Sentinel 2 (S2) data for land cover mapping. The proposed solution aims at improving methods based on Sentinel 2 data, that are unusable in case of cloud cover. This goal is achieved by using S1 data to generate S2-like segmentation maps to be used to integrate S2 acquisitions forbidden by cloud cover. In particular, we propose for the first time in remote sensing a multi-temporal W-Net approach for the segmentation of Interferometric Wide swath mode (IW) Sentinel-1 data collected along ascending/descending orbit to discriminate rice, water, and bare soil. The quantitative assessment of segmentation accuracy shows an improvement of 0.18 and 0.25 in terms of accuracy and F1-score by applying the proposed multi-temporal procedure with respect to the previous single-date approach. Advantages and disadvantages of the proposed W-Net based solution have been tested in the National Park of Albufera, Valencia, and we show a performance gain in terms of the classical metrics used in segmentation tasks and the computational time.

Just as laparoscopic surgery provided a giant leap in safety and recovery for patients over open surgery methods, robotic-assisted surgery (RAS) is doing the same to laparoscopic surgery. The first laparoscopic-RAS systems to be commercialized were the Intuitive Surgical, Inc. (Sunnyvale, CA, USA) da Vinci and the Computer Motion Zeus. These systems were similar in many aspects, which led to a patent dispute between the two companies. Before the dispute was settled in court, Intuitive Surgical bought Computer Motion, and thus owned critical patents for laparoscopic-RAS. Recently, the patents held by Intuitive Surgical have begun to expire, leading to many new laparoscopic-RAS systems being developed and entering the market. In this study, we review the newly commercialized and prototype laparoscopic-RAS systems. We compare the features of the imaging and display technology, surgeons console and patient cart of the reviewed RAS systems. We also briefly discuss the future directions of laparoscopic-RAS surgery. With new laparoscopic-RAS systems now commercially available we should see RAS being adopted more widely in surgical interventions and costs of procedures using RAS to decrease in the near future.

Ovarian cancer is the most lethal gynecological cancer, and despite years of research, with the exception of a BRCA mutation driving the use of PARP inhibitors, no new prognostic/predictive biomarkers are clinically available. Improvement in biomarker selection and validation may derive from the systematic inclusion of translational analyses into the design of clinical trials. In the era of personalized medicine, the prospective centralized collection of high-quality biological material, expert pathological revision, and association to well-controlled clinical data are important or even essential added values to clinical trials. Here, we present the academic experience of the MITO (Multicenter Italian Trial in Ovarian Cancer) group, including gynecologists, pathologists, oncologists, biostatisticians, and translational researchers, whose effort is dedicated to the care and basic/translational research of gynecologic cancer. In our ten years of experience, we have been able to collect and process, for translational analyses, formalin-fixed, paraffin-embedded blocks from more than one thousand ovarian cancer patients. Standard operating procedures for collection, shipping, and processing were developed and made available to MITO researchers through the coordinating center’s web-based platform. Clinical data were collected through dedicated electronic case report forms hosted in a web-based electronic platform and stored in a central database at the trial’s coordinating center, which performed all the analyses related to the proposed translational researches. During this time, we improved our strategies of block management from retrospective to prospective collection, up to the design of a prospective collection with a quality check for sample eligibility before patients’ accrual. The final aim of our work is to share our experience by suggesting a guideline for the process of centralized collection, revision processing, and storing of formalin-fixed, paraffin-embedded blocks for translational purposes.

The comfortable, continuous monitoring of vital parameters is still a challenge. The long-term measurement of respiration and cardiovascular signals is required to diagnose cardiovascular and respiratory diseases. Similarly, sleep quality assessment and the recovery period following acute treatments require long-term vital parameter datalogging. To address these requirements, we have developed “VitalCore”, a wearable continuous vital parameter monitoring device in the form of a T-shirt targeting the uninterrupted monitoring of respiration, pulse, and actigraphy. VitalCore uses polymer-based stretchable resistive bands as the primary sensor to capture breathing and pulse patterns from chest expansion. The carbon black-impregnated polymer is implemented in a U-shaped configuration and attached to the T-shirt with “interfacing” material along with the accompanying electronics. In this paper, VitalCore is bench tested and compared to gold standard respiration and pulse measurements to verify its functionality and further to assess the quality of data captured during sleep and during light exercise (walking). We show that these polymer-based sensors could identify respiratory peaks with a sensitivity of 99.44%, precision of 96.23%, and false-negative rate of 0.557% during sleep. We also show that this T-shirt configuration allows the wearer to sleep in all sleeping positions with a negligible difference of data quality. The device was also able to capture breathing during gait with 88.9−100% accuracy in respiratory peak detection.

Modifications of lean mass are a frequent critical determinant in the pathophysiology and progression of heart failure (HF). Sarcopenia may be considered one of the most important causes of low physical performance and reduced cardiorespiratory fitness in older patients with HF. Sarcopenia is frequently misdiagnosed as cachexia. However, muscle wasting in HF has different pathogenetic features in sarcopenic and cachectic conditions. HF may induce sarcopenia through common pathogenetic pathways such as hormonal changes, malnutrition, and physical inactivity; mechanisms that influence each other. In the opposite way, sarcopenia may favor HF development by different mechanisms, including pathological ergoreflex. Paradoxically, sarcopenia is not associated with a sarcopenic cardiac muscle, but the cardiac muscle shows a hypertrophy which seems to be “not-functional.” First-line agents for the treatment of HF, physical activity and nutritional interventions, may offer a therapeutic advantage in sarcopenic patients irrespective of HF. Thus, sarcopenia is highly prevalent in patients with HF, contributing to its poor prognosis, and both conditions could benefit from common treatment strategies based on pharmacological, physical activity, and nutritional approaches.

Stretchable conductive materials are originally conceived as radio frequency (RF) and electromagnetic interference (EMI) shielding materials, and, under stretch, they generally function as distributed strain-gauges. These commercially available conductive elastomers have found their space in low power health monitoring systems, for example, to monitor respiratory and cardiac functions. Conductive elastomers do not behave linearly due to material constraints; hence, when used as a sensor, a full characterisation to identify ideal operating ranges are required. In this paper, we studied how the continuous stretch cycles affected the material electrical and physical properties in different embodiment impressed by bodily volume change. We simulated the stretch associated with breathing using a bespoke stress rig to ensure reproducibility of results. The stretch rig is capable of providing constant sinusoidal waves in the physiological ranges of extension and frequency. The material performances is evaluated assessing the total harmonic distortion (THD), signal-to-noise ratio (SNR), correlation coefficient, peak to peak (P-P) amplitude, accuracy, repeatability, hysteresis, delay, and washability. The results showed that, among the three controlled variables, stretch length, stretch frequency and fabric width, the most significant factor to the signal quality is the stretch length. The ideal working region is within 2% of the original length. The material cut in strips of >3 mm show more reliable to handle a variety of stretch parameter without losing its internal characteristics and electrical properties.

Abnormal heart rhythms are one of the significant health concerns worldwide. The current state-of-the-art to recognize and classify abnormal heartbeats is manually performed by visual inspection by an expert practitioner. This is not just a tedious task; it is also error prone and, because it is performed, post-recordings may add unnecessary delay to the care. The real key to the fight to cardiac diseases is real-time detection that triggers prompt action. The biggest hurdle to real-time detection is represented by the rare occurrences of abnormal heartbeats and even more are some rare typologies that are not fully represented in signal datasets; the latter is what makes it difficult for doctors and algorithms to recognize them. This work presents an automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies. Although the algorithm is designed and tested on a computer, it is intended ultimately to run on a portable i.e., field-programmable gate array (FPGA) devices. Our algorithm tested on Massachusetts Institute of Technology- Beth Israel Hospital(MIT-BIH) database as per Association for the Advancement of Medical Instrumentation(AAMI) recommendations. The simulation results show the superiority of the proposed method, especially in predicting minority groups: the fusion and unknown classes with 90.4% and 100%.

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations.

Nondestructive inspection (NDI) has immensely contributed to the restoration of historic and artistic works. As one of the most common used NDI methods, active thermography is an easy-to-operate and efficient technique. Principal component thermography (PCT) has been widely used to deal with thermographic data for enhancing the visibility of subsurface defects. Unlike PCT, edge-group sparse PCT introduced herein enforces sparsity of principal component (PC) loadings by considering the spatial connectivity of thermographic image pixels. The feasibility and effectiveness of this method is illustrated by the experimental results of the defect characterization in an ancient marquetry sample with a fir wood support.