Emerging evidence demonstrates that the blockade of intracellular Ca2+ signals may protect pancreatic acinar cells against Ca2+ overload, intracellular protease activation, and necrosis. The ...activation of cannabinoid receptor subtype 2 (CB2R) prevents acinar cell pathogenesis in animal models of acute pancreatitis. However, whether CB(2)Rs modulate intracellular Ca2+ signals in pancreatic acinar cells is largely unknown. We evaluated the roles of CB2R agonist, GW405833 (GW) in agonist-induced Ca2+ oscillations in pancreatic acinar cells using multiple experimental approaches with acute dissociated pancreatic acinar cells prepared from wild type, CB1R-knockout (KO), and CB2R-KO mice. Immunohistochemical labeling revealed that CB2R protein was expressed in mouse pancreatic acinar cells. Electrophysiological experiments showed that activation of CB(2)Rs by GW reduced acetylcholine (ACh)-, but not cholecystokinin (CCK)-induced Ca2+ oscillations in a concentration-dependent manner; this inhibition was prevented by a selective CB2R antagonist, AM630, or was absent in CB2R-KO but not CB1R-KO mice. In addition, GW eliminated L-arginine-induced enhancement of Ca2+ oscillations, pancreatic amylase, and pulmonary myeloperoxidase. Collectively, we provide novel evidence that activation of CB(2)Rs eliminates ACh-induced Ca2+ oscillations and L-arginine-induced enhancement of Ca2+ signaling in mouse pancreatic acinar cells, which suggests a potential cellular mechanism of CB2R-mediated protection in acute pancreatitis.
Point cloud registration, which effectively coincides the source and target point clouds, is generally implemented by geometric metrics or feature metrics. In terms of resistance to noise and ...outliers, feature-metric registration has less error than the traditional point-to-point corresponding geometric metric, and point cloud reconstruction can generate and reveal more potential information during the recovery process, which can further optimize the registration process. In this paper, CFNet, a correspondence-free point cloud registration framework based on feature metrics and reconstruction metrics, is proposed to learn adaptive representations, with an emphasis on optimizing the network. Considering the correlations among the paired point clouds in the registration, a feature interaction module that can perceive and strengthen the information association between point clouds in multiple stages is proposed. To clarify the fact that rotation and translation are essentially uncorrelated, they are considered different solution spaces, and the interactive features are divided into two parts to produce a dual branch regression. In addition, CFNet with its comprehensive objectives estimates the transformation matrix between two input point clouds by minimizing multiple loss metrics. The extensive experiments conducted on both synthetic and real-world datasets show that our method outperforms the existing registration methods.
