In addition, breathing rates correlated with terminal pathology measurements, such as normalized lung weights and histologic alveolar damage and edema. This study is a preliminary evaluation of the technology; our results demonstrate that continuously measured breathing rate and body motion served as physiologically relevant readouts to assess lung injury progression and drug response in a respiratory injury animal model.Technological advancements are opening the possibility of prolonged monitoring of physiological parameters under daily-life conditions, with potential applications in sport science and medicine, and in extreme environments. Among emerging wearable technologies, in-ear devices or hearables possess technical advantages for long-term monitoring, such as non-invasivity, unobtrusivity, good fixing, and reduced motion artifacts, as well as physiological advantages related to the proximity of the ear to the body trunk and the shared vasculature between the ear and the brain. The present scoping review was aimed at identifying and synthesizing the available evidence on the use and performance of in-ear monitoring of physiological parameters, with focus on applications in sport science, sport medicine, occupational medicine, and extreme environment settings. Pubmed, Scopus, and Web of Science electronic databases were systematically searched to identify studies conducted in the last 10 years and addressing the measurement of three main physiological parameters (temperature, heart rate, and oxygen saturation) in healthy subjects. Thirty-nine studies were identified, 24 performing temperature measurement, 12 studies on heart/pulse rate, and three studies on oxygen saturation. The collected evidence supports the premise of in-ear sensors as an innovative and unobtrusive way for physiological monitoring during daily-life and physical activity, but further research and technological advancement are necessary to ameliorate measurement accuracy especially in more challenging scenarios.Key tissues are dysfunctional in obesity, diabetes, cardiovascular disease, fatty liver and other metabolic diseases. Focus has centered on individual organs as though each was isolated. Attention has been paid to insulin resistance as the key relevant pathosis, particularly insulin receptor signaling. However, many tissues play important roles in synergistically regulating metabolic homeostasis and should be considered part of a network. Our approach identifies redox as an acute regulator of the greater metabolic network. Redox reactions involve the transfer of electrons between two molecules and in this work refer to commonly shared molecules, reflective of energy state, that can readily lose electrons to increase or gain electrons to decrease the oxidation state of molecules including NAD(P), NAD(P)H, and thiols. Metabolism alters such redox molecules to impact metabolic function in many tissues, thus, responding to anabolic and catabolic stimuli appropriately and synergistically. It is also important to cs regulation of all collaborating metabolic organs through changes in circulating redox metabolites, regardless of whether change was initiated exogenously or by a single organ. Validation of these predictions suggests novel ways to understand function by monitoring and impacting redox state.A dense network of blood vessels distributes blood to different regions of the brain. To meet the temporarily and spatially varying energy demand resulting from changes in neuronal activity, the vasculature is able to locally up-regulate the blood supply. However, to which extent diameter changes of different vessel types contribute to the up-regulation, as well as the spatial and temporal characteristics of their changes, are currently unknown. MZ-1 Here, we present a new simulation method, which solves an inverse problem to calculate diameter changes of individual blood vessels needed to achieve predefined blood flow distributions in microvascular networks. This allows us to systematically compare the impact of different vessel types in various regulation scenarios. Moreover, the method offers the advantage that it handles the stochastic nature of blood flow originating from tracking the movement of individual red blood cells. Since the inverse problem is formulated for time-averaged pressures and flow rates, a deterministic approach for calculating the diameter changes is used, which allows us to apply the method for large realistic microvascular networks with high-dimensional parameter spaces. Our results obtained in both artificial and realistic microvascular networks reveal that diameter changes at the level of capillaries enable a very localized regulation of blood flow. In scenarios where only larger vessels, i.e., arterioles, are allowed to adapt, the flow increase cannot be confined to a specific activated region and flow changes spread into neighboring regions. Furthermore, relatively small dilations and constrictions of all vessel types can lead to substantial changes of capillary blood flow distributions. This suggests that small scale regulation is necessary to obtain a localized increase in blood flow.Background/objective This study aimed to investigate the impacts of a 12-week training of the aerobic exercise (AE), resistance exercise (RE), and combined exercise (CE) on the serum levels of nesfatin-1, irisin-1 and some other metabolic and anthropometric indices in overweight women with metabolic syndrome. Methods Sixty overweight women with metabolic syndrome were assigned equally into four groups aerobic exercise (AE, n = 15), resistance exercise (RE, n = 15), combined exercise (CE, n = 15), and control (n = 15). All groups underwent 12 weeks of intervention. The study variables were measured before and 24 h after the intervention period. Results Twelve weeks of training resulted in an increase of irisin-1 in the AE and CE groups and nesfatin-1 in all the intervention groups. As expected, all the trained groups exhibited a positive alteration in anthropometric indices and lipid profile in comparison with the control group. Besides, compared with the control group, insulin resistance (based on the homeostatic model assessment) in AE (p = 0.