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The immune response is a dynamic process by which the body determines whether an antigen is self or nonself. The state of this dynamic process is defined by the relative balance and population of inflammatory and regulatory actors which comprise this decision making process. The goal of immunotherapy as applied to, e.g. Rheumatoid Arthritis (RA), then, is to bias the immune state in favor of the regulatory actors - thereby shutting down autoimmune pathways in the response. While there are several known approaches to immunotherapy, the effectiveness of the therapy will depend on how this intervention alters the evolution of this state. Unfortunately, this process is determined not only by the dynamics of the process, but the state of the system at the time of intervention - a state which is difficult if not impossible to determine prior to application of the therapy. To identify such states we consider a mouse model of RA (Collagen-Induced Arthritis (CIA)) immunotherapy; collect high dimensional data on T cell markers and populations of mice after treatment with a recently developed immunotherapy for CIA; and use feature selection algorithms in order to select a lower dimensional subset of this data which can be used to predict both the full set of T cell markers and populations, along with the efficacy of immunotherapy treatment. 

Metabolites are not only involved in energy pathways but can also act as signaling molecules. Herein, we demonstrate that polyesters of alpha-ketoglutararte (paKG) can be generated by reacting aKG with aliphatic diols of different lengths, which release aKG in a sustained manner. paKG polymer-based microparticles generated via emulsion-evaporation technique lead to faster keratinocyte wound closures in a scratch assay test. Moreover, paKG microparticles also led to faster wound healing responses in an excisional wound model in live mice. Overall, this study shows that paKG MPs that release aKG in a sustained manner can be used to develop regenerative therapeutic responses.