Inflammasomes are multiprotein complexes that coordinate cellular inflammatory responses and mediate host defense. Following recognition of pathogens and danger signals, inflammasomes assemble and recruit and activate caspase-1, the cysteine protease that cleaves numerous downstream targets, including pro–IL-1β and pro–IL-18 into their biologically active form. In this study, we sought to develop a biosensor that would allow us to monitor the initiation, progression, and resolution of inflammation in living animals. To this end, we inserted a known caspase-1 target sequence into a circularly permuted luciferase construct that becomes bioluminescent upon protease cleavage. This biosensor was activated in response to various inflammatory stimuli in human monocytic cell lines and murine bone marrow–derived macrophages. Next, we generated C57BL/6 transgenic mice constitutively expressing the caspase-1 biosensor. We were able to monitor the spatiotemporal dynamics of caspase-1 activation and onset of inflammation in individual animals in the context of a systemic bacterial infection, colitis, and acute graft-versus-host disease. These data established a model whereby the development and progression of inflammatory responses can be monitored in the context of these and other mouse models of disease.

Background Mortality rates are unnecessarily high in developing countries due to lack of medical training or available procedures. For example, maternal deaths were estimated at 287,000 in 2010 of which 99% happened in developing countries [1]. Many of these deaths could be avoided if the physicians and nurses had a way to train and practice life-saving medical procedures like Cesarean sections or tracheotomies without risking harm to patients. A way to do this would be to have surgical simulators that they could train on. While the medical profession in the developed world has shifted to simulation-based training [2], current procedural simulators range from $500 to $300,000 [3], which is not an option for most hospitals or care providers in developing countries. We propose an open-source ultralow-cost (less than $10 USD) medical procedure simulator platform that would be made of materials available in developing countries so that they could be locally made yet provide a means to accurately train and assess skill acquisition in medical procedures. Two enabling technologies may satisfy these requirements: low-cost bioplastics to simulate tissue and two-dimensional surface potentiometers to electronically track surgical tools on tissue. The goal of this research is to assess the feasibility of such simulators by evaluating the feasibility of these two technologies for low-cost medical simulators in the developing world.

This work presents a "smart" robotic surgical grasper capable of identifying tissue during the early stages of a grasp, allowing automated prevention of grasper-induced tissue crush injuries. It employs no additional sensors beyond signals already present in surgical robots. An estimation algorithm using an extended Kalman filter (EKF) is employed for a nonlinear tissue dynamic model, which is investigated in silico as well as in vivo and in situ on porcine models. Results show that while the approach is sensitive to initial conditions, tissue can be identified during the early stage of a typical grasp.

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