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Influenza B is one of the infective agents that can cause rapid and fatal myocarditis in children. Here, we describe a fatal case of myocarditis in a previously healthy child, after infection with an influenza B/Victoria-lineage virus during the 2022–23 epidemic season in Italy. Influenza B virus was isolated also in a second case, a younger family member showing only a mild influenza-like illness. Genotypic and phenotypic analyses have been performed on both virus samples and results showed that HA1 sequences were identical and genetically and antigenically related to other B viruses circulating in 2022–23 season in Italy. However, a D129N substitution was found in the receptor binding domain of the HA of the two viruses, not detected in other circulating viruses in Italy but only in a proportion of those circulating in other European countries. Phenotypic analyses assessed the susceptibility towards either neuraminidase inhibitors and baloxavir. Annual influenza vaccination remains one of the best interventions to prevent complications such as myocarditis, particularly in children.

Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects’ movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver’s well-being by providing information about the driver’s physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace’s design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor’s performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer’s algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads.

The objective of this study was to compare the effect of diabetes and pathologies potentially related to diabetes on the risk of infection and death from COVID-19 among people from Highly-Developed-Country (HDC), including Italians, and immigrants from the High-Migratory-Pressure-Countries (HMPC). Among the population with diabetes, whose prevalence is known to be higher among immigrants, we compared the effect of body mass index among HDC and HMPC populations. A population-based cohort study was conducted, using population registries and routinely collected surveillance data. The population was stratified into HDC and HMPC, according to the place of birth; moreover, a focus was set on the South Asiatic population. Analyses restricted to the population with type-2 diabetes were performed. We reported incidence (IRR) and mortality rate ratios (MRR) and hazard ratios (HR) with 95% confidence interval (CI) to estimate the effect of diabetes on SARS-CoV-2 infection and COVID-19 mortality. Overall, IRR of infection and MRR from COVID-19 comparing HMPC with HDC group were 0.84 (95% CI 0.82–0.87) and 0.67 (95% CI 0.46–0.99), respectively. The effect of diabetes on the risk of infection and death from COVID-19 was slightly higher in the HMPC population than in the HDC population (HRs for infection: 1.37 95% CI 1.22–1.53 vs. 1.20 95% CI 1.14–1.25; HRs for mortality: 3.96 95% CI 1.82–8.60 vs. 1.71 95% CI 1.50–1.95, respectively). No substantial difference in the strength of the association was observed between obesity or other comorbidities and SARS-CoV-2 infection. Similarly for COVID-19 mortality, HRs for obesity (HRs: 18.92 95% CI 4.48–79.87 vs. 3.91 95% CI 2.69–5.69) were larger in HMPC than in the HDC population, but differences could be due to chance. Among the population with diabetes, the HMPC group showed similar incidence (IRR: 0.99 95% CI: 0.88–1.12) and mortality (MRR: 0.89 95% CI: 0.49–1.61) to that of HDC individuals. The effect of obesity on incidence was similar in both HDC and HMPC populations (HRs: 1.73 95% CI 1.41–2.11 among HDC vs. 1.41 95% CI 0.63–3.17 among HMPC), although the estimates were very imprecise. Despite a higher prevalence of diabetes and a stronger effect of diabetes on COVID-19 mortality in HMPC than in the HDC population, our cohort did not show an overall excess risk of COVID-19 mortality in immigrants.

Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects' movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver's well-being by providing information about the driver's physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace's design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor's performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer's algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads. [ABSTRACT FROM AUTHOR]

In this paper, we present a comprehensive assessment of individuals’ mental engagement states during manual and autonomous driving scenarios using a driving simulator. Our study employed two sensor fusion approaches, combining the data and features of multimodal signals. Participants in our experiment were equipped with Electroencephalogram (EEG), Skin Potential Response (SPR), and Electrocardiogram (ECG) sensors, allowing us to collect their corresponding physiological signals. To facilitate the real-time recording and synchronization of these signals, we developed a custom-designed Graphical User Interface (GUI). The recorded signals were pre-processed to eliminate noise and artifacts. Subsequently, the cleaned data were segmented into 3 s windows and labeled according to the drivers’ high or low mental engagement states during manual and autonomous driving. To implement sensor fusion approaches, we utilized two different architectures based on deep Convolutional Neural Networks (ConvNets), specifically utilizing the Braindecode Deep4 ConvNet model. The first architecture consisted of four convolutional layers followed by a dense layer. This model processed the synchronized experimental data as a 2D array input. We also proposed a novel second architecture comprising three branches of the same ConvNet model, each with four convolutional layers, followed by a concatenation layer for integrating the ConvNet branches, and finally, two dense layers. This model received the experimental data from each sensor as a separate 2D array input for each ConvNet branch. Both architectures were evaluated using a Leave-One-Subject-Out (LOSO) cross-validation approach. For both cases, we compared the results obtained when using only EEG signals with the results obtained by adding SPR and ECG signals. In particular, the second fusion approach, using all sensor signals, achieved the highest accuracy score, reaching 82.0%. This outcome demonstrates that our proposed architecture, particularly when integrating EEG, SPR, and ECG signals at the feature level, can effectively discern the mental engagement of drivers.

In this paper, we consider the evaluation of the mental attention state of individuals driving in a simulated environment. We tested a pool of subjects while driving on a highway and trying to overcome various obstacles placed along the course in both manual and autonomous driving scenarios. Most systems described in the literature use cameras to evaluate features such as blink rate and gaze direction. In this study, we instead analyse the subjects’ Electrodermal activity (EDA) Skin Potential Response (SPR), their Electrocardiogram (ECG), and their Electroencephalogram (EEG). From these signals we extract a number of physiological measures, including eye blink rate and beta frequency band power from EEG, heart rate from ECG, and SPR features, then investigate their capability to assess the mental state and engagement level of the test subjects. In particular, and as confirmed by statistical tests, the signals reveal that in the manual scenario the subjects experienced a more challenged mental state and paid higher attention to driving tasks compared to the autonomous scenario. A different experiment in which subjects drove in three different setups, i.e., a manual driving scenario and two autonomous driving scenarios characterized by different vehicle settings, confirmed that manual driving is more mentally demanding than autonomous driving. Therefore, we can conclude that the proposed approach is an appropriate way to monitor driver attention.

Background: Although the diagnosis of new coronavirus 2019 (COVID-19) is made through the identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in respiratory secretions by means of molecular methods, a more accurate estimation of SARS-CoV-2 circulation can be obtained by seroprevalence studies. The main aim of this study was to evaluate the true epidemiology of SARS-CoV-2 infection among workers in the metal-mechanical sector who never stopped working during the pandemic period in an area with a high incidence of COVID-19 and to define whether and how they could continue the work without appreciable risks during a second wave.Methods: A total of 815 metal-mechanical workers who had never stopped working even during the pandemic period in three different factories in the Emilia-Romagna Region, Italy, and who had always used face masks during working hours, underwent a capillary blood rapid test for the determination of IgM and IgG against SARS-CoV-2 (COVID-19 IgG/IgM Rapid test, PrimaLab, Modena, Italy). In the event of a positive test, a nasopharyngeal was performed and tested for the presence of SARS-CoV-2.Results: The detection of serum IgG/IgM against SARS-CoV-2 was significantly more common among workers employed in Parma (21/345, 6.1%) than among those employed in Calerno (7/242, 2.9%) or in Spilamberto (3/228, 1.3%) (p

Abstract Background Since 1985, two antigenically distinct lineages of influenza B viruses (Victoria-like and Yamagata-like) have circulated globally. Trivalent seasonal influenza vaccines contain two circulating influenza A strains but a single B strain and thus provide limited immunity against circulating B strains of the lineage not included in the vaccine. In this study, we describe the characteristics of influenza B viruses that caused respiratory illness in the population in Italy over 13 consecutive seasons of virological surveillance, and the match between the predominant influenza B lineage and the vaccine B lineage, in each season. Methods From 2004 to 2017, 26,886 laboratory-confirmed influenza cases were registered in Italy, of which 18.7% were type B. Among them, the lineage of 2465 strains (49%) was retrieved or characterized in this study by a real-time RT-PCR assay and/or sequencing of the hemagglutinin (HA) gene. Results Co-circulation of both B lineages was observed each season, although in different proportions every year. Overall, viruses of B/Victoria and B/Yamagata lineages caused 53.3 and 46.7% of influenza B infections, respectively. A higher proportion of infections with both lineages was detected in children, and there was a declining frequency of B/Victoria detections with age. A mismatch between the vaccine and the predominant influenza B lineage occurred in eight out of thirteen influenza seasons under study. Considering the seasons when B accounted for > 20% of all laboratory-confirmed influenza cases, a mismatch was observed in four out of six seasons. Phylogenetic analysis of the HA1 domain confirmed the co-circulation of both lineages and revealed a mixed circulation of distinct evolutionary viral variants, with different levels of match to the vaccine strains. Conclusions This study contributes to the understanding of the circulation of influenza B viruses in Italy. We found a continuous co-circulation of both B lineages in the period 2004–2017, and determined that children were particularly vulnerable to Victoria-lineage influenza B virus infections. An influenza B lineage mismatch with the trivalent vaccine occurred in about two-thirds of cases.

Nowadays in modern societies, a sedentary lifestyle is almost inevitable for a majority of the population. Long hours of sitting, especially in wrong postures, may result in health complications. A smart chair with the capability to identify sitting postures can help reduce health risks induced by a modern lifestyle. This paper presents the design, realization and evaluation of a new smart chair sensors system capable of sitting postures identification. The system consists of eight pressure sensors placed on the chair’s sitting cushion and the backrest. A signal acquisition board was designed from scratch to acquire data generated by the pressure sensors and transmit them via a Wi-Fi network to a purposely developed graphical user interface which monitors and stores the acquired sensors’ data on a computer. The designed system was tested by means of an extensive sitting experiment involving 40 subjects, and from the acquired data, the classification of the respective sitting postures out of eight possible postures was performed. Hereby, the performance of seven deep-learning algorithms was assessed. The best accuracy of 91.68% was achieved by an echo memory network model. The designed smart chair sensors system is simple and versatile, low cost and accurate, and it can easily be deployed in several smart chair environments, both for public and private contexts.

In most children, coronavirus disease 2019 (COVID-19) is a mild or moderate disease. Moreover, in a relevant number of cases, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection remains totally asymptomatic. All these findings seem to suggest that otherwise healthy children with suspected COVID-19 might be managed in the community in most cases, thus avoiding hospital admission and closely related medical, social and economic problems, including overwhelming hospitals. Unfortunately, home management of children with suspected COVID-19 rarely occurs, and many children with suspected or laboratory-confirmed SARS-CoV-2 infection are frequently hospitalized irrespective of the severity of disease. To evaluate the role of community health houses (CHHs) in the management of children with COVID-19, 1,009 children with suspected SARS-CoV-2 infection were studied in Emilia-Romagna Region, Italy. Among them, 194 (19.2%) resulted positive for SARS-CoV-2. The majority (583, 58%) were tested at home by CHHs, while 426 (42%) were brought to the hospital for testing. The patients who were managed in the hospital had a significantly lower median age than those who were managed at home (2 vs. 12 years, p < 0.001). Exposure to SARS-CoV-2 cases within the family was significantly more frequent among those who were managed at home (82 vs. 46%, p < 0.05). The clinical findings were similar between the children who were managed at home and those who were managed in the hospital. Only one of the children managed at home (0.7%) required hospitalization; in comparison, 26 (48%) of those whose swab samples were taken at the hospital were hospitalized. Our research shows for the first time the importance of CHHs in the management of COVID-19 in children; because of the high frequency of mild to moderate cases, management by CHHs can reduce the care load in hospitals, providing enormous advantages on the familial, medical, social, and economic levels. These findings could be useful for suggesting a territorial rather than hospital-based strategy in pediatrics in the case of a new wave of the epidemic.

Objectives To determine the timing of viral clearance (first negative RT-PCR on nasopharyngeal swab) and the probability of viral clearance confirmation (two consecutive negative swabs) in COVID-19 patients and to identify related determinants.Design Population-based prospective cohort study on archive data.Setting Preventive services and hospital care in the Reggio Emilia province, northern Italy.Participants All 1162 subjects testing positive to RT-PCR on nasopharyngeal swabs and diagnosed with COVID-19 in the Reggio Emilia province with at least 30 days of follow-up by 22 April 2020.Main outcome measures Median times from diagnosis and from symptom onset to viral clearance with IQR assessed using the Kaplan–Meier estimator, stratified by included characteristics. The probability of viral clearance confirmation, stratified by time from diagnosis and putative determinants assessed using a multivariate logistic regression model.Results Viral clearance was achieved by 60.6% (704/1162) of patients, with a median time of 30 days from diagnosis (IQR 23–40) and 36 days from symptom onset (IQR 28–45). Of those negative and retested, 78.7% (436/554) had viral clearance confirmation, suggesting one in five false negative tests. The time from symptom onset to viral clearance slightly increased with age, from 35 (IQR 26–44) days under age 50 to 38 (IQR 28–44) in over age 80, and with disease severity, from 33 (IQR 25–41) days in non-hospitalised subjects to 38 (IQR 30–47) days in hospitalised patients. The probability of confirmed viral clearance reached 86.8% after 34 days from symptom onset and increased with time, even when adjusting for age and sex (OR 1.16 95% CI 1.06 to 1.26 per day from diagnosis).Conclusions Postponing follow-up testing of clinically recovered COVID-19 patients could increase the efficiency and performance of testing protocols. Understanding viral shedding duration also has implications for containment measures of paucisymptomatic subjects.

In this paper, we designed from scratch, realized, and characterized a six-channel EEG wearable headband for the measurement of stress-related brain activity during driving. The headband transmits data over WiFi to a laptop, and the rechargeable battery life is 10 h of continuous transmission. The characterization manifested a measurement error of 6 μV in reading EEG channels, and the bandwidth was in the range [0.8, 44] Hz, while the resolution was 50 nV exploiting the oversampling technique. Thanks to the full metrological characterization presented in this paper, we provide important information regarding the accuracy of the sensor because, in the literature, commercial EEG sensors are used even if their accuracy is not provided in the manuals. We set up an experiment using the driving simulator available in our laboratory at the University of Udine; the experiment involved ten volunteers who had to drive in three scenarios: manual, autonomous vehicle with a “gentle” approach, and autonomous vehicle with an “aggressive” approach. The aim of the experiment was to assess how autonomous driving algorithms impact EEG brain activity. To our knowledge, this is the first study to compare different autonomous driving algorithms in terms of drivers’ acceptability by means of EEG signals. The obtained results demonstrated that the estimated power of beta waves (related to stress) is higher in the manual with respect to autonomous driving algorithms, either “gentle” or “aggressive”.

In this paper, we propose a relatively noninvasive system that can automatically assess the impact of traffic conditions on drivers. We analyze the physiological signals recorded from a set of individuals while driving in a simulated urban scenario in two different traffic scenarios, i.e., with traffic and without traffic. The experiments were carried out in a laboratory located at the University of Udine, employing a driving simulator equipped with a moving platform. We acquired two Skin Potential Response (SPR) signals from the hands of the drivers, and an electrocardiogram (ECG) signal from their chest. In the proposed scheme, the SPR signals are then processed through a Motion Artifact (MA) removal algorithm such that possible motion artifacts arising during the drive are reduced. An analysis considering the scalogram of the single cleaned SPR signal is presented. This signal, along with the ECG, is then fed to various Machine Learning (ML) algorithms. More specifically, some statistical features are extracted from each signal segment which, after being analyzed through a binary ML model, are labeled as corresponding to a stressful situation or not. Our results confirm the applicability of the proposed approach to identify stress in the two scenarios. This is also in accordance with our findings considering the SPR signal scalograms.

The emerging epidemic caused by the new coronavirus SARS-CoV-2 represents the most important socio-health threat of the 21st century. The high contagiousness of the virus, the strong impact on the health system of the various countries and the absence to date of treatments able to improve the prognosis of the disease make the introduction of a vaccine indispensable, even though there are currently no approved human coronavirus vaccines. The aim of the study is to carry out a review of the medical literature concerning vaccine candidates for the main coronaviruses responsible for human epidemics, including recent advances in the development of a vaccine against COVID-19. This extensive review carried out on the vaccine candidates of the main epidemic coronaviruses of the past has shown that the studies in animal models suggest a high efficacy of potential vaccines in providing protection against viral challenges. Similar human studies have not yet been carried out, as the main trials are aimed at assessing mainly vaccine safety and immunogenicity. Whereas the severe acute respiratory syndrome (SARS-CoV) epidemic ended almost two decades ago and the Middle East respiratory syndrome (MERS-CoV) epidemic is now better controlled, as it is less contagious due to the high lethality of the virus, the current SARS-CoV-2 pandemic represents a problem that is certainly more compelling, which pushes us to accelerate the studies not only for the production of vaccines but also for innovative pharmacological treatments. SARS-CoV-2 vaccines might come too late to affect the first wave of this pandemic, but they might be useful if additional subsequent waves occur or in a post-pandemic perspective in which the virus continues to circulate as a seasonal virus.

This paper describes the design of a two channels electrodermal activity (EDA) sensor and two channels electrocardiogram (ECG) sensor. The EDA sensors acquire data on the hands and transmit them to the ECG sensor with wireless WiFi communication for increased wearability. The sensors system acquires two EDA channels to improve the removal of motion artifacts that take place if EDA is measured on individuals who need to move their hands in their activities. The ECG channels are acquired on the chest and the ECG sensor is responsible for aligning the two ECG traces with the received packets from EDA sensors; the ECG sensor sends via WiFi the aligned packets to a laptop for real time plot and data storage. The metrological characterization showed high-level performances in terms of linearity and jitter; the delays introduced by the wireless transmission from EDA to ECG sensor have been proved to be negligible for the present application.

The evaluation of car drivers’ stress condition is gaining interest as research on Autonomous Driving Systems (ADS) progresses. The analysis of the stress response can be used to assess the acceptability of ADS and to compare the driving styles of different autonomous drive algorithms. In this contribution, we present a system based on the analysis of the Electrodermal Activity Skin Potential Response (SPR) signal, aimed to reveal the driver’s stress induced by different driving situations. We reduce motion artifacts by processing two SPR signals, recorded from the hands of the subjects, and outputting a single clean SPR signal. Statistical features of signal blocks are sent to a Supervised Learning Algorithm, which classifies between stress and normal driving (non-stress) conditions. We present the results obtained from an experiment using a professional driving simulator, where a group of people is asked to undergo manual and autonomous driving on a highway, facing some unexpected events meant to generate stress. The results of our experiment show that the subjects generally appear more stressed during manual driving, indicating that the autonomous drive can possibly be well received by the public. During autonomous driving, however, significant peaks of the SPR signal are evident during unexpected events. By examining the electrocardiogram signal, the average heart rate is generally higher in the manual case compared to the autonomous case. This further supports our previous findings, even if it may be due, in part, to the physical activity involved in manual driving.

A new biosensor for the real-time analysis of thrombus formation is reported. The fast and accurate monitoring of the individual thrombotic risk represents a challenge in cardiovascular diagnostics and in treatment of hemostatic diseases. Thrombus volume, as representative index of the related thrombotic status, is usually estimated with confocal microscope at the end of each in vitro experiment, without providing a useful behavioral information of the biological sample such as platelets adhesion and aggregation in flowing blood. Our device has been developed to work either independently or integrated with the microscopy system; thus, images of the fluorescently labeled platelets are acquired in real-time during the whole blood perfusion, while the global electrical impedance of the blood sample is simultaneously monitored between a pair of specifically designed gold microelectrodes. Fusing optical and electrical data with a novel technique, the dynamic of thrombus formation events in flowing blood can be reconstructed in real-time, allowing an accurate extrapolation of the three-dimensional shape and the spatial distribution of platelet thrombi forming and growing within artificial capillaries. This biosensor is accurate and it has been used to discriminate different hemostatic conditions and to identify weakening and detaching platelet aggregates. The results obtained appear compatible with those quantified with the traditional optical method. With advantages in terms of small size, user-friendliness and promptness of response, it is a promising device for the fast and automatic individual health monitoring at the Point of Care (POC).