Safety and efficacy of Endovascular Management of high-grade blunt renal injury.

Objectives: To provide data on the safety and efficacy of renal arterial embolization (RAE) in patients with high-grade blunt renal injury. Materials and methods: Fifteen patients with high-grade blunt renal injury (AAST grades IV-V) admitted to our hospital from July 2014 to December 2019 were retrospectively reviewed in this study. Their clinical success rate and complications were investigated accordingly. Results: Fifteen patients with high-grade blunt renal injury, 13 men and 2 women with an average age of 41.6 years, including 11 hemodynamically unstable patients and 4 stable patients, were treated with RAE. Among these patients, 73.3% (11 of 15) had grade IV, and 26.7% (4 of 15) had grade V injuries, while 53.3% (8 of 15) patients had concomitant injuries. One patient received main RAE and 14 patients received selective RAE. The clinical success rate after the first embolization was 93.3% (14 of 15). RAE was repeated and was successfully performed in one patient with sustained hematuria. No significant difference in creatinine levels was found before and after embolization. During the follow-up period of 2-82 months, two patients required tube drainage due to urine leaks, one patient developed renal failure requiring renal replacement therapy, and one patient developed secondary hypertension. Conclusions: RAE can provide a high success rate of hemostasis for both hemodynamically stable and unstable patients with high-grade blunt renal injury, and only minor complications are observed with this procedure.

Restoration Simulation of Image Quality Degradation of Nanosensors Based on Virtual Reality.

The texture complexity of traditional sensor image degradation restoration methods is high and the restoration effect is reduced. For this reason, a virtual reality-based image quality degradation recovery method for nanosensors is designed in this paper. First, the image quality degradation model of nanometer sensor is constructed based on virtual reality technology. Then, the noise characteristics of the degraded image are analyzed. On the premise of retaining the original image information, the diffusion coefficients in the vertical and horizontal directions are calculated to obtain the expression of adaptive filter (ADF) in the image with noise, so as to complete the image denoising process. On the basis of texture complexity analysis, singular value decomposition detection and alpha channel calculation are completed, and image quality degradation recovery of nanosensor is achieved through synthesis operation. The experimental results show that the texture complexity of the recovered images is lower than 0.54, the average absolute error percentage of the recovered images is only 10%, and the P-R value is high, which fully demonstrates the effectiveness of the offered procedure.