The few cohort and case-control studies on the association between psychosocial stress and hypertension employed variable definition of stressors and the responses, making the meta-analysis difficult. Although we found an association between chronic psychosocial stress and hypertension, more studies are needed to confirm this relationship.

Accumulating evidence has demonstrated that post-stroke physical rehabilitation may reduce morbidity. The effectiveness of post-stroke exercise, however, appears to be contingent upon exercise initiation. This study assessed the hypothesis that very early exercise exacerbates brain injury, induces reactive oxygen species (ROS) generation, and promotes energy failure. A total of 230 adult male Sprague-Dawley rats were subjected to middle cerebral artery (MCA) occlusion for 2 h, and randomized into eight groups, including two sham injury control groups, three non-exercise and three exercise groups. Exercise was initiated after 6 h, 24 h and 3 days of reperfusion. Twenty-four hours after completion of exercise (and at corresponding time points in non-exercise controls), infarct volumes and apoptotic cell death were examined. Early brain oxidative metabolism was quantified by examining ROS, ATP and NADH levels 0.5 h after completion of exercise. Furthermore, protein expressions of angiogenic growth factors were measured in order to determine whether post-stroke angiogenesis played a role in rehabilitation. As expected, ischemic stroke resulted in brain infarction, apoptotic cell death and ROS generation, and diminished NADH and ATP production. Infarct volumes and apoptotic cell death were enhanced (p < 0.05) by exercise that was initiated after 6 h of reperfusion, but decreased by late exercise (24 h, 3 days). This exacerbated brain injury at 6 h was associated with increased ROS levels (p < 0.05), and decreased (p < 0.05) NADH and ATP levels. In conclusion, very early exercise aggravated brain damage, and early exercise-induced energy failure with ROS generation may underlie the exacerbation of brain injury. These results shed light on the manner in which exercise initiation timing may affect post-stroke rehabilitation.

Mild physical hypothermia after stroke has been associated with positive outcomes. Despite the well-studied beneficial effects of hypothermia in the treatment of stroke, lack of precise temperature control, intolerance for the patient, and immunosuppression are some of the reasons which limit its clinical translation. Pharmacologically induced hypothermia has been explored as a possible treatment option following stroke in animal models. Currently, there are eight classes of pharmacological agents/agonists with hypothermic effects affecting a multitude of systems including cannabinoid, opioid, transient receptor potential vanilloid 1 (TRPV1), neurotensin, thyroxine derivatives, dopamine, gas, and adenosine derivatives. Interestingly, drugs in the TRPV1, neurotensin, and thyroxine families have been shown to have effects in thermoregulatory control in decreasing the compensatory hypothermic response during cooling. This review will briefly present drugs in the eight classes by summarizing their proposed mechanisms of action as well as side effects. Reported thermoregulatory effects of the drugs will also be presented. This review offers the opinion that these agents may be useful in combination therapies with physical hypothermia to achieve faster and more stable temperature control in hypothermia.

Microgrids are becoming popular nowadays because they provide clean, efficient, and lowcost energy. Microgrids require bulk storage capacity to use the stored energy in times of emergency or peak loads. Since microgrids are the future of renewable energy, the energy storage technology employed should be optimized to provide power balancing. Batteries play a variety of essential roles in daily life. They are used at peak hours and during a time of emergency. There are different types of batteries i.e., lithium-ion batteries, lead-acid batteries, etc. Optimal battery sizing of microgrids is a challenging problem that limits modern technologies such as electric vehicles, etc. Therefore, it is imperative to assess the optimal size of a battery for a particular system or microgrid according to its requirements. The optimal size of a battery can be assessed based on the different battery features such as battery life, battery throughput, battery autonomy, etc. In this work, the mixed-integer linear programming (MILP) based newly generated dataset is studied for computing the optimal size of the battery for microgrids in terms of the battery autonomy. In the considered dataset, each instance is composed of 40 attributes of the battery. Furthermore, the Support Vector Regression (SVR) model is used to predict the battery autonomy. The capability of input features to predict the battery autonomy is of importance for the SVR model. Therefore, in this work, the relevant features are selected utilizing the feature selection algorithms. The performance of six best-performing feature selection algorithms is analyzed and compared. The experimental results show that the feature selection algorithms improve the performance of the proposed methodology. The Ranker Search algorithm with SVR attains the highest performance with a Spearman’s rank-ordered correlation constant of 0.9756, linear correlation constant of 0.9452, Kendall correlation constant of 0.8488, and root mean squared error of 0.0525.