3D-Printed Face Mask with Integrated Sensors as Protective and Monitoring Tool

The outbreak of the recent Covid-19 pandemic changed many aspects of our daily life, such as the constant wearing of face masks as protection from virus transmission risks. Furthermore, it exposed the healthcare system's fragilities, showing the urgent need to design a more inclusive model that takes into account possible future emergencies, together with population's aging and new severe pathologies. In this framework, face masks can be both a physical barrier against viruses and, at the same time, a telemedical diagnostic tool. In this paper, we propose a low-cost, 3D-printed face mask able to protect the wearer from virus transmission, thanks to internal FFP2 filters, and to monitor the air quality (temperature, humidity, CO2) inside the mask. Acquired data are automatically transmitted to a web terminal, thanks to sensors and electronics embedded in the mask. Our preliminary results encourage more efforts in these regards, towards rapid, inexpensive and smart ways to integrate more sensors into the mask's breathing zone in order to use the patient's breath as a fingerprint for various diseases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

AuNP-Coated Cotton as VOC Sensor for Disease Detection from Breath

The COVID-19 pandemic outbreak, declared in March 2020, has led to several behavioral changes in the general population, such as social distancing and mask usage among others. Furthermore, the sanitary emergency has stressed health system weaknesses in terms of disease prevention, diagnosis, and cure. Thus, smart technologies allowing for early and quick detection of diseases are called for. In this framework, the development of point-of-care devices can provide new solutions for sanitary emergencies management. This work focuses on the development of useful tools for early disease diagnosis based on nanomaterials on cotton substrates, to obtain a low-cost and easy-to-use detector of breath volatiles as disease markers. Specifically, we report encouraging experimental results concerning acetone detection through impedance measurements. Such findings can pave the way to the implementation of VOCs (Volatile Organic Compounds) sensors into smart and user friendly diagnostic devices. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Characterization of Disposable Facemasks for COVID-19 Through Colorimetric Analysis

Many aspects of the world population’s daily life have been recently changed by the events following the SARS-COV-2 pandemic outbreak. Among all the consequences, wearing face masks has become a common routine to protect from virus transmission risks. This work presents a simple colorimetric system able to detect the carbon dioxide (CO2) saturation inside a disposable face mask, which is useful to determine the level of wear and degradation and to visually provide indications on its disposal time. The experiments were carried out by wearing a FFP2 face mask externally treated with a phenolphthalein solution and including in its breathing zone a CO2 sensor. Changes in face mask color were recorded by a camera and analyzed with ImageJ. A strong correspondence was found between the high values of CO2 detected by the sensor and the analyzed data. The results are promising and suggest further efforts in developing easy-to-use colorimetric methods as a visual indicator of the life cycle of a disposable face mask.

Optimal Seating Assignment in the COVID-19 Era via Quantum Computing

In recent years, researchers have oriented their studies towards new technologies based on quantum physics that should allow the resolution of complex problems currently considered to be intractable. This new research area is called Quantum Computing. What makes Quantum Computing so attractive is the particular way with which quantum technology operates and the great potential it can offer to solve real-world problems. This work focuses on solving combinatorial optimization problems, specifically assignment problems, by exploiting this novel computational approach. A case-study, denoted as the Seating Arrangement Optimization problem, is considered. It is modeled through the Quadratic Unconstrained Binary Optimization (QUBO) paradigm and solved through two tools made available by the D-Wave Systems company, QBSolv and a quantum-classical hybrid system. The obtained experimental results are compared in terms of solution quality and computational efficiency. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Filtering efficiency model that includes the statistical randomness of non-woven fiber layers in facemasks.

Facemasks have become important tools to fight virus spread during the recent COVID-19 pandemic, but their effectiveness is still under debate. We present a computational model to predict the filtering efficiency of an N95-facemask, consisting of three non-woven fiber layers with different particle capturing mechanisms. Parameters such as fiber layer thickness, diameter distribution, and packing density are used to construct two-dimensional cross-sectional geometries. An essential and novel element is that the polydisperse fibers are positioned randomly within a simulation domain, and that the simulation is repeated with different random configurations. This strategy is thought to give a more realistic view of practical facemasks compared to existing analytical models that mostly assume homogeneous fiber beds of monodisperse fibers. The incompressible Navier-Stokes and continuity equations are used to solve the velocity field for various droplet-laden air inflow velocities. Droplet diameters are ranging from 10 nm to 1.0 µm, which covers the size range from the SARS-CoV-2 virus to the large virus-laden airborne droplets. Air inflow velocities varying between 0.1 m·s-1 to 10 m·s-1 are considered, which are typically encountered during expiratory events like breathing, talking, and coughing. The presented model elucidates the different capturing efficiencies (i.e., mechanical and electrostatic filtering) of droplets as a function of their diameter and air inflow velocity. Simulation results are compared to analytical models and particularly compare well with experimental results from literature. Our numerical approach will be helpful in finding new directions for anti-viral facemask optimization.

COVID-19 Lockdown and the Behavior Change on Physical Exercise, Pain and Psychological Well-Being: An International Multicentric Study

OBJECTIVE: To evaluate the impact of the COVID-19 pandemic and the following lockdown on physical exercise (PEx) practice, pain, and psychological well-being. METHODS: A cross-sectional multicentric study was performed using a nonrandom convenience sampling from the general population (>=18 years-old) of 6 countries (Brazil, Italy, France, Portugal, Germany, and Spain) adopting social isolation (SI). The validated self-administered online survey (PEF-COVID19) was used. The tests T-test and Chi-square with Bonferroni correction were used for statistical analysis and a multivariate logistic regression model (p < 0.05). RESULTS: We included 3194 replies and ~80% of the respondents were in SI. Brazilian sample was highly influenced by the pandemic considering PEx practice and habits, pain, anxiety, and stress (p < 0.05). Among the European countries, Italy presented the major changes. The model to predict the non-practice of PEx during SI showed that the variables countries, smoking, SI, and PEx level were significant predictors (p < 0.001). CONCLUSION: The pandemic changed the PEx practice and habits, and the psychological well-being of populations in different manners. Countries, smoking, SI, and PEx level were predictors for the non-practice of PEx. Public health strategies are suggested to avoid sedentary lifestyles and quality of life decrease.

The impact of COVID-19 pandemic in the quality of sleep by Pittsburgh sleep quality index

This article aims to evaluate the sleep quality in individuals during the COVID-19 pandemic by Pittsburgh Sleep Quality Index (PSQI). Searches were conducted in the PubMed, Embase, Web of Science, and PEDro databases, on May 22, 2020. In the publications, 208 articles were found and, considering the eligibility criteria, 10 articles were included at the end, showing the effects on sleep quality during the pandemic, in populations hospitalized, quarantined, and in frontline health professionals. The PSQI measured sleep disorders and a higher score indicated poor sleep quality. Nine articles were classified with evidence level IV and one as level III-2. Eight studies present a "serious" risk of bias and two in "moderate". The studies investigated different populations and described the results as "poor" sleep quality, considering the PSQI on quarantined individuals and frontline health professionals as the most committed. A poor sleep quality was found in the populations evaluated in the selected publications, probably, due to the COVID-19 to contribute as a risk factor for mental health. Psychological interventions must be made to minimize the consequences through social support and social capital.