The complement system plays an important role in the innate immune response to invading pathogens. The complement fragment C5a is one of its important effector components and exerts diverse physiological functions through activation of the C5a receptor 1 (C5aR1) and associated downstream G protein and β-arrestin signaling pathways. Dysfunction of the C5a-C5aR1 axis is linked to numerous inflammatory and immune-mediated diseases, but the structural basis for activation and biased signaling of C5aR1 remains elusive. Here, we present cryo-electron microscopy structures of the activated wild-type C5aR1–Gi protein complex bound to each of the following: C5a, the hexapeptidic agonist C5apep, and the G protein-biased agonist BM213. The structures reveal the landscape of the C5a–C5aR1 interaction as well as a common motif for the recognition of diverse orthosteric ligands. Moreover, combined with mutagenesis studies and cell-based pharmacological assays, we deciphered a framework for biased signaling using different peptide analogs and provided insight into the activation mechanism of C5aR1 by solving the structure of C5aR1I116A mutant–Gi signaling activation complex induced by C089, which exerts antagonism on wild-type C5aR1. In addition, unusual conformational changes in the intracellular end of transmembrane domain 7 and helix 8 upon agonist binding suggest a differential signal transduction process. Collectively, our study provides mechanistic understanding into the ligand recognition, biased signaling modulation, activation, and Gi protein coupling of C5aR1, which may facilitate the future design of therapeutic agents.

Electronic health records (EHRs) have been widely used to help physicians to make decisions by predicting medical events such as diseases, prescriptions, outcomes, and so on. How to represent patient longitudinal medical data is the key to making these predictions. Recurrent neural network (RNN) is a popular model for patient longitudinal medical data representation from the view of patient status sequences, but it cannot represent complex interactions among different types of medical information, i.e., temporal medical event graphs, which can be represented by graph neural network (GNN). In this paper, we propose a hybrid method of RNN and GNN, called RGNN, for next-period prescription prediction from two views, where RNN is used to represent patient status sequences, and GNN is used to represent temporal medical event graphs. Experiments conducted on the public MIMIC-III ICU data show that the proposed method is effective for next-period prescription prediction, and RNN and GNN are mutually complementary.

All viruses require host cell factors to replicate. A large number of host factors have been identified that participate at numerous points of the human immunodeficiency virus 1 (HIV-1) life cycle. Recent evidence supports a role for components of the trans-Golgi network (TGN) in mediating early steps in the HIV-1 life cycle. The Conserved Oligomeric Golgi (COG) complex is a heteroctamer complex that functions in coat protein complex I (COPI)-mediated intra-Golgi retrograde trafficking and plays an important role in the maintenance of Golgi structure and integrity as well as glycosylation enzyme homeostasis. The targeted silencing of components of lobe B of the COG complex, namely COG5, COG6, COG7 and COG8, inhibited HIV-1 replication. This inhibition of HIV-1 replication preceded late reverse transcription (RT) but did not affect viral fusion. Silencing of the COG interacting protein the t-SNARE syntaxin 5, showed a similar defect in late RT product formation, strengthening the role of the TGN in HIV replication.

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BackgroundMany studies have reported the impact of air pollution on cardiovascular disease (CVD), but few of these studies were conducted in severe haze-fog areas. The present study focuses on the impact of different air pollutant concentrations on daily CVD outpatient visits in a severe haze-fog city.MethodsData regarding daily air pollutants and outpatient visits for CVD in 2013 were collected, and the association between six pollutants and CVD outpatient visits was explored using the least squares mean (LSmeans) and logistic regression. Adjustments were made for days of the week, months, air temperature and relative humidity.ResultsThe daily CVD outpatient visits for particulate matter (PM10 and PM2.5), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) in the 90th-quantile group were increased by 30.01, 29.42, 17.68, 14.98, 29.34%, and − 19.87%, respectively, compared to those in the <10th-quantile group. Odds ratios (ORs) and 95% confidence intervals (CIs) for the increase in daily CVD outpatient visits in PM10 300- and 500-μg/m3, PM2.5 100- and 300-μg/m3 and CO 3-mg/m3 groups were 2.538 (1.070–6.020), 7.781 (1.681–36.024), 3.298 (1.559–6.976), 8.72 (1.523–49.934), and 5.808 (1.016–33.217), respectively, and their corresponding attributable risk percentages (AR%) were 60.6, 87.15, 69.68, 88.53 and 82.78%, respectively. The strongest associations for PM10, PM2.5 and CO were found only in lag 0 and lag 1. The ORs for the increase in CVD outpatient visits per increase in different units of the six pollutants were also analysed.ConclusionsAll five air pollutants except O3 were positively associated with the increase in daily CVD outpatient visits in lag 0. The high concentrations of PM10, PM2.5 and CO heightened not only the percentage but also the risk of increased daily CVD outpatient visits. PM10, PM2.5 and CO may be the main factors of CVD outpatient visits.