Neuroimmune mechanisms of stress: sex differences, developmental plasticity, and implications for pharmacotherapy of stress-related disease.

The last decade has witnessed profound growth in studies examining the role of fundamental neuroimmune processes as key mechanisms that might form a natural bridge between normal physiology and pathological outcomes. Rooted in core concepts from psychoneuroimmunology, this review utilizes a succinct, exemplar-driven approach of several model systems that contribute significantly to our knowledge of the mechanisms by which neuroimmune processes interact with stress physiology. Specifically, we review recent evidence showing that (i) stress challenges produce time-dependent and stressor-specific patterns of cytokine/chemokine expression in the CNS; (ii) inflammation-related genes exhibit unique expression profiles in males and females depending upon individual, cooperative or antagonistic interactions between steroid hormone receptors (estrogen and glucocorticoid receptors); (iii) adverse social experiences incurred through repeated social defeat engage a dynamic process of immune cell migration from the bone marrow to brain and prime neuroimmune function and (iv) early developmental exposure to an inflammatory stimulus (carageenin injection into the hindpaw) has a lasting influence on stress reactivity across the lifespan. As such, the present review provides a theoretical framework for understanding the role that neuroimmune mechanisms might play in stress plasticity and pathological outcomes, while at the same time pointing toward features of the individual (sex, developmental experience, stress history) that might ultimately be used for the development of personalized strategies for therapeutic intervention in stress-related pathologies.

Precision Health Analytics With Predictive Analytics and Implementation Research: JACC State-of-the-Art Review.

Emerging data science techniques of predictive analytics expand the quality and quantity of complex data relevant to human health and provide opportunities for understanding and control of conditions such as heart, lung, blood, and sleep disorders. To realize these opportunities, the information sources, the data science tools that use the information, and the application of resulting analytics to health and health care issues will require implementation research methods to define benefits, harms, reach, and sustainability; and to understand related resource utilization implications to inform policymakers. This JACC State-of-the-Art Review is based on a workshop convened by the National Heart, Lung, and Blood Institute to explore predictive analytics in the context of implementation science. It highlights precision medicine and precision public health as complementary and compelling applications of predictive analytics, and addresses future research and training endeavors that might further foster the application of predictive analytics in clinical medicine and public health.

Telehealth In Health Centers: Key Adoption Factors, Barriers, And Opportunities.

Telehealth services have the potential to improve access to care, especially in rural or urban areas with scarce health care resources. Despite the potential benefits, telehealth has not been fully adopted by health centers. This study examined factors associated with and barriers to telehealth use by federally funded health centers. We analyzed data for 2016 from the Uniform Data System using a mixed-methods approach. Our findings suggest that rural location, operational factors, patient demographic characteristics, and reimbursement policies influence health centers' decisions about using telehealth. Cost, reimbursement, and technical issues were described as major barriers. Medicaid reimbursement policies promoting live video and store-and-forward services were associated with a greater likelihood of telehealth adoption. Many health centers were implementing telehealth or exploring its use. Our findings identified areas that policy makers can address to achieve greater telehealth adoption by health centers.

Reality management. Real-time data let hospital leaders base decisions on what's happening now, not last year.

Managing quality improvement, patient safety, workforce issues and strategic decision-making makes a lot more sense when you base your decisions on what's happening in the present, not the past. Real-time management is proactive and allows potential mistakes to be corrected before they can hurt patients.

Evolutionary robotics: a new scientific tool for studying cognition.

We survey developments in artificial neural networks, in behavior-based robotics, and in evolutionary algorithms that set the stage for evolutionary robotics (ER) in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments, which is an essential aspect of real cognition that is often either bypassed or modeled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion, the origins of learning, and the ontogenetic acquisition of entrainment.

Evolving controllers for a homogeneous system of physical robots: structured cooperation with minimal sensors.

We report on recent work in which we employed artificial evolution to design neural network controllers for small, homogeneous teams of mobile autonomous robots. The robots were evolved to perform a formation-movement task from random starting positions, equipped only with infrared sensors. The dual constraints of homogeneity and minimal sensors make this a non-trivial task. We describe the behaviour of a successful system in which robots adopt and maintain functionally distinct roles in order to achieve the task. We believe this to be the first example of the use of artificial evolution to design coordinated, cooperative behaviour for real robots.