Notes

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Controlled drug release was also achieved by morphological transfer via a sensitive response to the endogenous redox and NO stimuli, which are specifically related to the microenvironment of liver tumor cells. Upon combination of these properties with the anticancer ability from the platinum acceptor, in vitro studies demonstrated that DOX-loaded 1 is able to codeliver anticancer drugs and exhibit therapeutic effectiveness to liver tumor sites via a synergistic effect, thereby revealing a potential DDS platform for precise liver cancer therapeutics.Humans and other animals use multiple strategies for making decisions. Reinforcement-learning theory distinguishes between stimulus-response (model-free; MF) learning and deliberative (model-based; MB) planning. The spatial-navigation literature presents a parallel dichotomy between navigation strategies. In "response learning," associated with the dorsolateral striatum (DLS), decisions are anchored to an egocentric reference frame. In "place learning," associated with the hippocampus, decisions are anchored to an allocentric reference frame. Emerging evidence suggests that the contribution of hippocampus to place learning may also underlie its contribution to MB learning by representing relational structure in a cognitive map. Here, we introduce a computational model in which hippocampus subserves place and MB learning by learning a "successor representation" of relational structure between states; DLS implements model-free response learning by learning associations between actions and egocentric representations of landmarks; and action values from either system are weighted by the reliability of its predictions. We show that this model reproduces a range of seemingly disparate behavioral findings in spatial and nonspatial decision tasks and explains the effects of lesions to DLS and hippocampus on these tasks. Furthermore, modeling place cells as driven by boundaries explains the observation that, unlike navigation guided by landmarks, navigation guided by boundaries is robust to "blocking" by prior state-reward associations due to learned associations between place cells. Our model, originally shaped by detailed constraints in the spatial literature, successfully characterizes the hippocampal-striatal system as a general system for decision making via adaptive combination of stimulus-response learning and the use of a cognitive map.Humans readily form social impressions, such as attractiveness and trustworthiness, from a stranger's facial features. Understanding the provenance of these impressions has clear scientific importance and societal implications. Motivated by the efficient coding hypothesis of brain representation, as well as Claude Shannon's theoretical result that maximally efficient representational systems assign shorter codes to statistically more typical data (quantified as log likelihood), we suggest that social "liking" of faces increases with statistical typicality. Combining human behavioral data and computational modeling, we show that perceived attractiveness, trustworthiness, dominance, and valence of a face image linearly increase with its statistical typicality (log likelihood). We also show that statistical typicality can at least partially explain the role of symmetry in attractiveness perception. Additionally, by assuming that the brain focuses on a task-relevant subset of facial features and assessing log likelihood of a face using those features, our model can explain the "ugliness-in-averageness" effect found in social psychology, whereby otherwise attractive, intercategory faces diminish in attractiveness during a categorization task.Autoimmune diabetes is one of the complications resulting from checkpoint blockade immunotherapy in cancer patients, yet the underlying mechanisms for such an adverse effect are not well understood. Leveraging the diabetes-susceptible nonobese diabetic (NOD) mouse model, we phenocopy the diabetes progression induced by programmed death 1 (PD-1)/PD-L1 blockade and identify a cascade of highly interdependent cellular interactions involving diabetogenic CD4 and CD8 T cells and macrophages. We demonstrate that exhausted CD8 T cells are the major cells that respond to PD-1 blockade producing high levels of IFN-γ. Most importantly, the activated T cells lead to the recruitment of monocyte-derived macrophages that become highly activated when responding to IFN-γ. These macrophages acquire cytocidal activity against β-cells via nitric oxide and induce autoimmune diabetes. Collectively, the data in this study reveal a critical role of macrophages in the PD-1 blockade-induced diabetogenesis, providing new insights for the understanding of checkpoint blockade immunotherapy in cancer and infectious diseases.Telomerase is a ribonucleoprotein complex that counteracts the shortening of chromosome ends due to incomplete replication. Telomerase contains a catalytic core of telomerase reverse transcriptase (TERT) and telomerase RNA (TER). However, what defines TERT and separates it from other reverse transcriptases remains a subject of debate. A recent cryoelectron microscopy map of Tetrahymena telomerase revealed the structure of a previously uncharacterized TERT domain (TRAP) with unanticipated interactions with the telomerase essential N-terminal (TEN) domain and roles in telomerase activity. Both TEN and TRAP are absent in the putative Tribolium TERT that has been used as a model for telomerase for over a decade. selleck kinase inhibitor To investigate the conservation of TRAP and TEN across species, we performed multiple sequence alignments and statistical coupling analysis on all identified TERTs and find that TEN and TRAP have coevolved as telomerase-specific domains. Integrating the data from bioinformatic analysis and the structure of Tetrahymena telomerase, we built a pseudoatomic model of human telomerase catalytic core that accounts for almost all of the cryoelectron microscopy density in a published map, including TRAP in previously unassigned density as well as telomerase RNA domains essential for activity. This more complete model of the human telomerase catalytic core illustrates how domains of TER and TERT, including the TEN-TRAP complex, can interact in a conserved manner to regulate telomere synthesis.
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