Complement-mediated tissue injury in humans occurs upon deposition of immune complexes, such as in autoimmune diseases and acute respiratory distress syndrome. Acute lung inflammatory injury in wild-type and C3-/- mice after deposition of IgG immune complexes was of equivalent intensity and was C5a dependent, but injury was greatly attenuated in Hc-/- mice (Hc encodes C5). Injury in lungs of C3-/- mice and C5a levels in bronchoalveolar lavage (BAL) fluids from these mice were greatly reduced in the presence of antithrombin III (ATIII) or hirudin but were not reduced in similarly treated C3+/+ mice. Plasma from C3-/- mice contained threefold higher levels of thrombin activity compared to plasma from C3+/+ mice. There were higher levels of F2 mRNA (encoding prothrombin) as well as prothrombin and thrombin protein in liver of C3-/- mice compared to C3+/+ mice. A potent solid-phase C5 convertase was generated using plasma from either C3+/+ or C3-/- mice. Human C5 incubated with thrombin generated C5a that was biologically active. These data suggest that, in the genetic absence of C3, thrombin substitutes for the C3-dependent C5 convertase. This linkage between the complement and coagulation pathways may represent a new pathway of complement activation.

Highlights d Cities possess a consistent ''core'' set of non-human microbes d Urban microbiomes echo important features of cities and city-life d Antimicrobial resistance genes are widespread in cities d Cities contain many novel bacterial and viral species

IL-6 is known to be an important pro- and anti-inflammatory cytokine, which is up-regulated during sepsis. Our previous work has suggested a role for IL-6 in the up-regulation of C5aR in sepsis. We reported earlier that interception of C5a or C5aR results in improved outcomes in experimental sepsis. Using the cecal ligation/puncture (CLP) model in mice, we now demonstrate that treatment with anti-IL-6 Ab (anti-IL-6) results in significantly improved survival, dependent on the amount of Ab infused. CLP animals showed significantly increased binding of 125I-labeled anti-C5aR to organs when compared to either control mice at 0 h or CLP animals infused with normal rabbit 125I-labeled IgG. Binding of 125I-labeled anti-C5aR to lung, liver, kidney, and heart was significantly decreased in anti-IL-6-treated animals 6 h after CLP. RT-PCR experiments with mRNA isolated from various organs obtained 3, 6, and 12 h after CLP demonstrated increased C5aR mRNA expression during the onset of sepsis, which was greatly suppressed in CLP mice treated with anti-IL-6. These data suggest that IL-6 plays an important role in the increased expression of C5aR in lung, liver, kidney, and heart during the development of sepsis in mice and that interception of IL-6 leads to reduced expression of C5aR and improved survival.

This prospective, randomized clinical study suggests an immunomodulatory role for the volatile anesthetic sevoflurane in patients undergoing OLV for thoracic surgery with significant reduction of inflammatory mediators and a significantly better clinical outcome (defined by postoperative adverse events) during sevoflurane anesthesia.

The recent research explosion around implicit neural representations, such as NeRF, shows that there is immense potential for implicitly storing high‐quality scene and lighting information in compact neural networks. However, one major limitation preventing the use of NeRF in real‐time rendering applications is the prohibitive computational cost of excessive network evaluations along each view ray, requiring dozens of petaFLOPS. In this work, we bring compact neural representations closer to practical rendering of synthetic content in real‐time applications, such as games and virtual reality. We show that the number of samples required for each view ray can be significantly reduced when samples are placed around surfaces in the scene without compromising image quality. To this end, we propose a depth oracle network that predicts ray sample locations for each view ray with a single network evaluation. We show that using a classification network around logarithmically discretized and spherically warped depth values is essential to encode surface locations rather than directly estimating depth. The combination of these techniques leads to DONeRF, our compact dual network design with a depth oracle network as its first step and a locally sampled shading network for ray accumulation. With DONeRF, we reduce the inference costs by up to 48× compared to NeRF when conditioning on available ground truth depth information. Compared to concurrent acceleration methods for raymarching‐based neural representations, DONeRF does not require additional memory for explicit caching or acceleration structures, and can render interactively (20 frames per second) on a single GPU.