Cannabis Sativa targets mediobasal hypothalamic neurons to stimulate appetite.

The neurobiological mechanisms that regulate the appetite-stimulatory properties of cannabis sativa are unresolved. This work examined the hypothesis that cannabinoid-1 receptor (CB1R) expressing neurons in the mediobasal hypothalamus (MBH) regulate increased appetite following cannabis vapor inhalation. Here we utilized a paradigm where vaporized cannabis plant matter was administered passively to rodents. Initial studies in rats characterized meal patterns and operant responding for palatable food following exposure to air or vapor cannabis. Studies conducted in mice used a combination of in vivo optical imaging, electrophysiology and chemogenetic manipulations to determine the importance of MBH neurons for cannabis-induced feeding behavior. Our data indicate that cannabis vapor increased meal frequency and food seeking behavior without altering locomotor activity. Importantly, we observed augmented MBH activity within distinct neuronal populations when mice anticipated or consumed food. Mechanistic experiments demonstrated that pharmacological activation of CB1R attenuated inhibitory synaptic tone onto hunger promoting Agouti Related Peptide (AgRP) neurons within the MBH. Lastly, chemogenetic inhibition of AgRP neurons attenuated the appetite promoting effects of cannabis vapor. Based on these results, we conclude that MBH neurons contribute to the appetite stimulatory properties of inhaled cannabis.

Prolonged, High-Fidelity Simulation for Study of Patient Care in Resource-Limited Medical Contexts and for Technology Comparative Effectiveness Testing.

Most high-fidelity medical simulation is of limited duration, used for education and training, and rarely intended to study medical technology. U.S. caregivers working in prehospital, resource-limited settings may need to manage patients for extended periods (hours to days). This "prolonged casualty care" occurs during military, wilderness, humanitarian, disaster, and space medicine. We sought to develop a standardized simulation model that accurately reflects prolonged casualty care in order to study caregiver decision-making and performance, training requirements, and technology use in prolonged casualty care. DESIGN: Model development. SETTING: High-fidelity simulation laboratory. SUBJECTS: None. INTERVENTIONS: We interviewed subject matter experts to identify relevant prolonged casualty care medical challenges and selected two casualty types to further develop our model: a large thermal burn model and a severe hypoxia model. We met with a multidisciplinary group of experts in prolonged casualty care, nursing, and critical care to describe how these problems could evolve over time and how to contextualize the problems with a background story and clinical environment with expected resource availability. Following initial scenario drafting, we tested the models with expert clinicians. After multiple tests, we selected the hypoxia model for refinement and testing with inexperienced providers. We tested and refined this model until two research teams could proctor the scenario consistently despite subject performance variability. MEASUREMENTS AND MAIN RESULTS: We developed a 6-8-hour simulation model that represented a 14-hour scenario. This model of pneumonia evolved from presentation to severe hypoxia necessitating advanced interventions including airway, breathing, and shock management. The model included: context description, caregiver orientation scripts, hourly progressive physiology tracks corresponding to caregiver interventions, intervention/procedure-specific physiology tracks, intervention checklists, equipment lists, prestudy checklists, photographs of setups, procedure, telementor, and role player scripts, business rules, and data collection methods. CONCLUSIONS: This is the first standardized, high-fidelity simulation model of prolonged casualty care described in the literature. It may be used to assess caregiver performance and patient outcomes resulting from that performance during a complex, 14-hour prolonged casualty care scenario. Because it is standardized, the model may be used to compare differences in the impact of new technologies upon caregiver performance and simulated patient outcomes..