The purpose of this study was to evaluate the effect of intracutaneous pyonex on analgesia and sedation in critically ill patients who underwent mechanical ventilation. A total of 88 critically ill patients were divided into a control group and an intervention group. Critical Care Pain Observation Tool (CPOT) and Richmond Agitation and Sedation Scale (RASS) were used to evaluate pain and agitation. The dosage and treatment period of sedative and analgesic drugs in the intervention group were notably lower than the control group (p < 0.05). Analgesia compliance time in the intervention group was superior to control group (p < 0.05). The shallow sedation compliance rate in the intervention group was significantly higher than the control group (p < 0.01). There was significant difference in blood gas analysis before and after treatment between the two groups (p < 0.05). After 2 h of sedation and analgesia, heart rate in the intervention group was lower than control group, but respiratory rate was higher than the control group (p < 0.05). The traditional analgesia and sedation combined with intracutaneous pyonex reduced the total amount and treatment period of sedative and analgesic drugs in critically ill patients throughout the treatment process, and it also decreased the adverse reactions such as blood pressure drops and respiratory depression.
Depth and ego-motion estimations are essential for the localization and navigation of autonomous robots and autonomous driving. Recent studies make it possible to learn the per-pixel depth and ego-motion from the unlabeled monocular video. A novel unsupervised training framework is proposed with 3D hierarchical refinement and augmentation using explicit 3D geometry. In this framework, the depth and pose estimations are hierarchically and mutually coupled to refine the estimated pose layer by layer. The intermediate view image is proposed and synthesized by warping the pixels in an image with the estimated depth and coarse pose. Then, the residual pose transformation can be estimated from the new view image and the image of the adjacent frame to refine the coarse pose. The iterative refinement is implemented in a differentiable manner in this paper, making the whole framework optimized uniformly. Meanwhile, a new image augmentation method is proposed for the pose estimation by synthesizing a new view image, which creatively augments the pose in 3D space but gets a new augmented 2D image. The experiments on KITTI demonstrate that our depth estimation achieves state-of-the-art performance and even surpasses recent approaches that utilize other auxiliary tasks. Our visual odometry outperforms all recent unsupervised monocular learning-based methods and achieves competitive performance to the geometry-based method, ORB-SLAM2 with back-end optimization.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
The morphological modulation and phase conversion of α- and β-Ni(OH)2 complex architectures with varying subunits from nanopetals, nanocolumns, nanocones, and nanoflakes were investigated using a facile coordination homogeneous precipitation method in the Ni(NO3)2+ urea system. Slow growth and nucleation rates due to relatively low reaction temperatures and molar ratios of CO(NH2)2 to Ni(NO3)2 induced the formation of uniform flower-like α-Ni(OH)2 architectures. Such flower-like architectures originated from subordinate nanopetals that grow perpendicular to the primordial nanopetal surface and are driven by minimum surface free energy effects. At relatively high reaction temperatures, flower-like α-Ni(OH)2 can transform into β-Ni(OH)2 microspheres assembled from nanocolumns, nanocones, and even nanoflakes by varying the reaction time. These processes could be related to the synergetic effect of the anisotropic growth and continuous increase in mass transportation along the [001] direction. Flower-like α-Ni(OH)2 exhibited better electrochemical activity for glucose oxidation compared with β-Ni(OH)2 microspheres consisting of nanocones because of its special flower-like morphology with high specific surface areas, well-ordered pores, and layered structures intercalated by water and anions. The approach in this study can be used to fabricate other metal hydroxide nanostructures. Flower-like Ni(OH)2 nanoarchitectures have potential applications in rechargeable batteries, photonic catalysis, and non-enzymatic sensors for glucose.
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