UNC13D inhibits STING signaling by attenuating its oligomerization on the endoplasmic reticulum.

Stimulator of interferon genes (STING) is an essential signaling protein that is located on the endoplasmic reticulum (ER) and triggers the production of type I interferons (IFN) and proinflammatory cytokines in response to pathogenic DNA. Aberrant activation of STING is linked to autoimmune diseases. The mechanisms underlying homeostatic regulation of STING are unclear. Here, we report that UNC13D, which is associated with familial hemophagocytic lymphohistiocytosis (FHL3), is a negative regulator of the STING-mediated innate immune response. UNC13D colocalizes with STING on the ER and inhibits STING oligomerization. Cellular knockdown and knockout of UNC13D promote the production of interferon-ß (IFN-ß) induced by DNA viruses, but not RNA viruses. Moreover, UNC13D deficiency also increases the basal level of proinflammatory cytokines. These effects are diminished by an inhibitor of STING signaling. Furthermore, the domains involved in the UNC13D/STING interaction on both proteins are mapped. Our findings provide insight into the regulatory mechanism of STING, the previously unknown cellular function of UNC13D and the potential pathogenesis of FHL3.

Towards Real-World Applications of Personalized Anesthesia Using Policy Constraint Q Learning for Propofol Infusion Control.

Automated anesthesia promises to enable more precise and personalized anesthetic administration and free anesthesiologists from repetitive tasks, allowing them to focus on the most critical aspects of a patient's surgical care. Current research has typically focused on creating simulated environments from which agents can learn. These approaches have demonstrated good experimental results, but are still far from clinical application. In this paper, Policy Constraint Q-Learning (PCQL), a data-driven reinforcement learning algorithm for solving the problem of learning strategies on real world anesthesia data, is proposed. Conservative Q-Learning was first introduced to alleviate the problem of Q function overestimation in an offline context. A policy constraint term is added to agent training to keep the policy distribution of the agent and the anesthesiologist consistent to ensure safer decisions made by the agent in anesthesia scenarios. The effectiveness of PCQL was validated by extensive experiments on a real clinical anesthesia dataset we collected. Experimental results show that PCQL is predicted to achieve higher gains than the baseline approach while maintaining good agreement with the reference dose given by the anesthesiologist, using less total dose, and being more responsive to the patient's vital signs. In addition, the confidence intervals of the agent were investigated, which were able to cover most of the clinical decisions of the anesthesiologist. Finally, an interpretable method, SHAP, was used to analyze the contributing components of the model predictions to increase the transparency of the model.