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Spinal cord injury (SCI) is a condition that significantly affects the quality of life (QoL) of individuals, causing motor, physiological, social, and psychological impairments. Physical exercise plays a crucial role in maintaining the health and functional capacity of these individuals, helping to minimize the negative impacts of SCI. The aim of this study was to evaluate the effect of detraining (DT) (reduction or cessation of physical exercise) during the pandemic on five individuals with thoracic SCI. We assessed muscle strength using strength tests, functional capacity using a functional agility test, mental health using anxiety and depression inventories, and body composition using dual-energy X-ray absorptiometry (DEXA). The results after 33 months of DT showed significant losses in functional agility and MS, as well as a worsening in symptoms of anxiety and depression. It was observed that total body mass and fat mass (FM) exhibited varied behaviors among the individuals. Similarly, the results for lean body mass were heterogeneous, with one participant showing significant deterioration. It is concluded that DT caused by the pandemic worsened the physical and mental condition of individuals with SCI, highlighting the importance of continuous exercise for this population and underscoring the need for individual assessments to fully understand the impacts of DT.

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Respiratory diseases are among the leading causes of death globally, with the COVID-19 pandemic serving as a prominent example. Issues such as infections affect a large population and, depending on the mode of transmission, can rapidly spread worldwide, impacting thousands of individuals. These diseases manifest in mild and severe forms, with severely affected patients requiring ventilatory support. The air-oxygen blender is a critical component of mechanical ventilators, responsible for mixing air and oxygen in precise proportions to ensure a constant supply. The most commonly used version of this equipment is the analog model, which faces several challenges. These include a lack of precision in adjustments and the inspiratory fraction of oxygen, as well as gas wastage from cylinders as pressure decreases. The research proposes a blender model utilizing only dynamic pressure sensors to calculate oxygen saturation, based on Bernoulli's equation. The model underwent validation through simulation, revealing a linear relationship between pressures and oxygen saturation up to a mixture outlet pressure of 500 cmH2O. Beyond this value, the relationship begins to exhibit non-linearities. However, these non-linearities can be mitigated through a calibration algorithm that adjusts the mathematical model. This research represents a relevant advancement in the field, addressing the scarcity of work focused on this essential equipment crucial for saving lives.

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Respiratory diseases are one of the most common causes of death in the world and this recent COVID-19 pandemic is a key example. Problems such as infections, in general, affect many people and depending on the form of transmission they can spread throughout the world and weaken thousands of people. Two examples are severe acute respiratory syndrome and the recent coronavirus disease. These diseases have mild and severe forms, in which patients gravely affected need ventilatory support. The equipment that serves as a basis for operation of the mechanical ventilator is the air-oxygen blender, responsible for carrying out the air-oxygen mixture in the proper proportions ensuring constant supply. New blender models are described in the literature together with applications of control techniques, such as Proportional, Integrative and Derivative (PID); Fuzzy; and Adaptive. The results obtained from the literature show a significant improvement in patient care when using automatic controls instead of manual adjustment, increasing the safety and accuracy of the treatment. This study presents a deep review of the state of the art in air-oxygen benders, identifies the most relevant characteristics, performs a comparison study considering the most relevant available solutions, and identifies open research directions in the topic.

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Abstract Background: There are divergences in the literature regarding the experimental model (Wistar-WIS or Wistar Kyoto-WKY) to be used as a Spontaneously Hypertensive Rat (SHR) control. The characterization of these models in terms of cardiovascular parameters provides researchers with important tools at the time of selection and application in scientific research. Objective: The aim of this study was to evaluate the use of WIS and WKY as a Spontaneously Hypertensive Rat (SHR) control by assessing the long-term behavior of blood pressure and cardiac structure and function in these strains. Methods: To this end, WIS, WKY, and SHR underwent longitudinal experiments. Blood pressure and body mass were measured every two weeks from the 8th to the 72nd. Echocardiographic analysis was performed in all groups with 16, 48, and 72 weeks of life. After having applied the normality test, the Two-Way ANOVA of repeated measures followed by the Tukey post hoc test was used. A significance level of 5% was established. Results: The WIS group showed higher body mass (p<0.05), while the WKY and SHR presented higher body mass variation over time (p<0.05). SHR exhibited increased values of systolic, diastolic, and mean blood pressure when compared to WKY and WIS, whereas the WKY generally showed higher values than WIS (p<0.05). Regarding the cardiac function, SHR showed reduced values, while the WKY presented an early decrease when compared to WIS with aging (p<0.05). Conclusion: WIS is a more suitable normotensive control for SHR than WKY in experiments to test blood pressure and cardiac structure and function.

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BACKGROUND: Mental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients' interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as digital phenotyping of mental health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies. OBJECTIVE: This article aims to identify and characterize the sensing applications and public data sets for DPMH from a technical perspective. METHODS: We performed a systematic review of scientific literature and data sets. We searched 8 digital libraries and 20 data set repositories to find results that met the selection criteria. We conducted a data extraction process from the selected articles and data sets. For this purpose, a form was designed to extract relevant information, thus enabling us to answer the research questions and identify open issues and research trends. RESULTS: A total of 31 sensing apps and 8 data sets were identified and reviewed. Sensing apps explore different context data sources (eg, positioning, inertial, ambient) to support DPMH studies. These apps are designed to analyze and process collected data to classify (n=11) and predict (n=6) mental states/disorders, and also to investigate existing correlations between context data and mental states/disorders (n=6). Moreover, general-purpose sensing apps are developed to focus only on contextual data collection (n=9). The reviewed data sets contain context data that model different aspects of human behavior, such as sociability, mood, physical activity, sleep, with some also being multimodal. CONCLUSIONS: This systematic review provides in-depth analysis regarding solutions for DPMH. Results show growth in proposals for DPMH sensing apps in recent years, as opposed to a scarcity of public data sets. The review shows that there are features that can be measured on smart devices that can act as proxies for mental status and well-being; however, it should be noted that the combined evidence for high-quality features for mental states remains limited. DPMH presents a great perspective for future research, mainly to reach the needed maturity for applications in clinical settings.

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Low-Power Wide-Area Network (LPWAN) is one of the enabling technologies of the Internet of Things (IoT), and focuses on providing long distance connectivity for a vast amount of smart devices. Currently, LoRa is one of the leading LPWAN solutions available for public use. In LPWANs, especially in LoRa, security is a major concern due to the resource constraints of the devices, the sensitivity level of the transmitted data, the large amount of connected devices, among other reasons. This paper studies the key management mechanism of LoRaWAN environments. A secure architecture for key management based on smart contracts and permissioned blockchain to enhance security and availability in LoRaWAN networks is proposed. To demonstrate the feasibility of the proposed blockchain-based LoRaWAN architecture, a working prototype has been created using open-source tools and commodity hardware. Performance analysis shows that the prototype presents similar execution time and latency values, when compared to a traditional system, especially for small and medium-sized LoRaWAN networks. We also discuss why the proposed solution can be used in environments with a large number of end-devices.

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Fog computing is a distributed infrastructure where specific resources are managed at the network border using cloud computing principles and technologies. In contrast to traditional cloud computing, fog computing supports latency-sensitive applications with less energy consumption and a reduced amount of data traffic. A fog device is placed at the network border, allowing data collection and processing to be physically close to their end-users. This characteristic is essential for applications that can benefit from improved latency and response time. In particular, in the e-Health field, many solutions rely on real-time data to monitor environments, patients, and/or medical staff, aiming at improving processes and safety. Therefore, fog computing can play an important role in such environments, providing a low latency infrastructure. The main goal of the current research is to present fog computing strategies focused on electronic-Health (e-Health) applications. To the best of our knowledge, this article is the first to propose a review in the scope of applications and challenges of e-Health fog computing. We introduce some of the available e-Health solutions in the literature that focus on latency, security, privacy, energy efficiency, and resource management techniques. Additionally, we discuss communication protocols and technologies, detailing both in an architectural overview from the edge devices up to the cloud. Differently from traditional cloud computing, the fog concept demonstrates better performance in terms of time-sensitive requirements and network data traffic. Finally, based on the evaluation of the current technologies for e-Health, open research issues and challenges are identified, and further research directions are proposed.

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Global industry is undergoing major transformations with the genesis of a new paradigm known as the Internet of Things (IoT) with its underlying technologies. Many company leaders are investing more effort and money in transforming their services to capitalize on the benefits provided by the IoT. Thereby, the decision makers in public waste management do not want to be outdone, and it is challenging to provide an efficient and real-time waste management system. This paper proposes a solution (hardware, software, and communications) that aims to optimize waste management and include a citizen in the process. The system follows an IoT-based approach where the discarded waste from the smart bin is continuously monitored by sensors that inform the filling level of each compartment, in real-time. These data are stored and processed in an IoT middleware providing information for collection with optimized routes and generating important statistical data for monitoring the waste collection accurately in terms of resource management and the provided services for the community. Citizens can easily access information about the public waste bins through the Web or a mobile application. The creation of the real prototype of the smart container, the development of the waste management application and a real-scale experiment use case for evaluation, demonstration, and validation show that the proposed system can efficiently change the way people deal with their garbage and optimize economic and material resources.

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Activation of mineralocorticoid receptor antagonists (MRAs) is cardioprotective; however, this property is lost upon blockade or inactivation of adenosine (ADO) receptor A2b. In this study, we investigated whether the effects of MRAs are mediated by an interaction between cardioprotective ADO receptors A1 and A3. Spironolactone (SPI) or eplerenone (EPL) increased ADO levels in the plasma of treated animals compared to control animals. SPI or EPL increased the protein and activity levels of ecto-5'-nucleotidase (NT5E), an enzyme that synthesizes ADO, compared to control. The levels of ADO deaminase (ADA), which degrades ADO, were not affected by SPI or EPL; however, the activity of ADA was reduced in SPI-treated rats compared to control. Using an isolated cardiomyocyte model, we found inotropic and chronotropic effects, and increased calcium transient [Ca2+]i in cells treated with ADO receptor A1 or A3 antagonists compared to control groups. Upon co-treatment with MRAs, EPL and SPI fully and partially reverted the effects of receptor A1 or A3 antagonism, respectively. Collectively, MRAs in vivo lead to increased ADO bioavailability. In vitro, the rapid effects of SPI and EPL are mediated by an interaction between ADO receptors A1 and A3.

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Internet of Things (IoT) management systems require scalability, standardized communication, and context-awareness to achieve the management of connected devices with security and accuracy in real environments. Interoperability and heterogeneity between hardware and application layers are also critical issues. To attend to the network requirements and different functionalities, a dynamic and context-sensitive configuration management system is required. Thus, reference architectures (RAs) represent a basic architecture and the definition of key characteristics for the construction of IoT environments. Therefore, choosing the best technologies of the IoT management platforms and protocols through comparison and evaluation is a hard task, since they are difficult to compare due to their lack of standardization. However, in the literature, there are no management platforms focused on addressing all IoT issues. For this purpose, this paper surveys the available policies and solutions for IoT Network Management and devices. Among the available technologies, an evaluation was performed using features such as heterogeneity, scalability, supported technologies, and security. Based on this evaluation, the most promising technologies were chosen for a detailed performance evaluation study (through simulation and deployment in real environments). In terms of contributions, these protocols and platforms were studied in detail, the main features of each approach are highlighted and discussed, open research issues are identified as well as the lessons learned on the topic.