Search

Acute kidney injury (AKI) is characterized by rapid renal decline. Periodontitis, a chronic oral inflammatory disease, is increasingly associated with renal dysfunction. Although periodontitis is recognized as a contributor to kidney damage, the mechanisms linking it to AKI remain unclear. This study explored the effects of Porphyromonas gingivalis(P. gingivalis) W83‐infected periodontitis on AKI in C57BL/6J mice, using ischemia‐reperfusion injury 55 days post‐infection. Gingipain inhibitors, KYT‐1 and KYT‐36, were applied. Detection of P. gingivaliswas performed using quantitative real‐time polymerase chain reaction (qRT‐PCR) and PCR, while transcriptome sequencing, qRT‐PCR, immunohistochemistry, and immunofluorescence staining assessed renal damage. In vitro, HK‐2 cells were exposed to P. gingivalisat a multiplicity of infection of 10 for 48 h, with inhibition by gingipain or oncostatin M (OSM). Disruption of tight junctions (TJs) was quantified using qRT‐PCR, transepithelial electrical resistance, and cell counting kit‐8 assays. Periodontitis worsened AKI, linked to P. gingivalisinfection and renal TJ disruption in the kidney. P. gingivalisinfection activated OSM expression, which correlated positively with gingipain. Significantly, OSM and gingipain might collaboratively contribute to the damage of renal TJs, with the reduced expression of TJ proteins. Suppressing gingipain activity presented itself as a protective strategy against the destruction of TJs and the attendant worsening of AKI due to periodontitis. Our study enhances the understanding of the interplay between periodontitis and AKI, highlighting the harmful impact of P. gingivalisin AKI.

To mitigate the long-term spectrum crunch problem, the FCC recently opened up the 6 GHz frequency band for unlicensed use. However, the existing spectrum sharing strategies cannot support the operation of access points in moving vehicles such as cars and UAVs. This is primarily because of the directionality-based spectrum sharing among the incumbent systems in this band and the high mobility of the moving vehicles, which together make it challenging to control the cross-system interference. In this paper, we propose SwarmShare, a mobility-resilient spectrum sharing framework for swarm UAV networking in the 6 GHz band. We first present a mathematical formulation of the SwarmShare problem, where the objective is to maximize the spectral efficiency of the UAV network by jointly controlling the flight and transmission power of the UAVs and their association with the ground users, under the interference constraints of the incumbent system. We find that there are no closed-form mathematical models that can be used to characterize the statistical behaviors of the aggregate interference from the UAVs to the incumbent system. Then we propose a data-driven three-phase spectrum sharing approach, including Initial Power Enforcement, Offline-dataset Guided Online Power Adaptation, and Reinforcement Learning-based UAV Optimization. We validate the effectiveness of SwarmShare through an extensive simulation campaign. Results indicate that, based on SwarmShare, the aggregate interference from the UAVs to the incumbent system can be effectively kept below the target level without requiring the real-time cross-system channel state information. The mobility resilience of SwarmShare is also validated in coexisting networks with no precise UAV location information.