Efficacy of budesonide/formoterol inhalation powder in treating viral pneumonia in children.

BACKGROUND: Respiratory viruses are increasingly detected in children with community-acquired pneumonia. Further strategies to limit antibiotic use in children with viral pneumonia are warranted. AIM: To explore clinical efficacy of budesonide/formoterol inhalation powder for viral pneumonia in children and its impact on cellular immunity and inflammatory factor production. METHODS: A total of 60 children with viral pneumonia were recruited: 30 receiving budesonide/formoterol inhalation powder and 30 conventional symptomatic treatment. Outcome measures included peripheral blood levels of inflammatory cytokines, CD4+, CD8+, Th1, Th2, Th17 and Treg, clinical efficacy, and incidence of adverse reactions. RESULTS: Compared with the control group, the observation group showed a significant reduction in interleukin-6 and high-sensitivity C-reactive protein levels after treatment. Compared with the control group, the observation group showed a significant increase in CD4+/CD8+ and Th1/Th2 levels, and a decrease in Th17/Treg levels after treatment. The total effective rates in the observation group and the control group were 93.75% and 85.00%, respectively, which was a significant difference (P = 0.003). CONCLUSION: Budesonide/formoterol inhalation powder significantly improved therapeutic efficacy for viral pneumonia in children. The mechanism of action may be related to downregulation of the inflammatory response and improved cellular immune function.

Sparse-to-dense coarse-to-fine depth estimation for colonoscopy.

Colonoscopy, as the golden standard for screening colon cancer and diseases, offers considerable benefits to patients. However, it also imposes challenges on diagnosis and potential surgery due to the narrow observation perspective and limited perception dimension. Dense depth estimation can overcome the above limitations and offer doctors straightforward 3D visual feedback. To this end, we propose a novel sparse-to-dense coarse-to-fine depth estimation solution for colonoscopic scenes based on the direct SLAM algorithm. The highlight of our solution is that we utilize the scattered 3D points obtained from SLAM to generate accurate and dense depth in full resolution. This is done by a deep learning (DL)-based depth completion network and a reconstruction system. The depth completion network effectively extracts texture, geometry, and structure features from sparse depth along with RGB data to recover the dense depth map. The reconstruction system further updates the dense depth map using a photometric error-based optimization and a mesh modeling approach to reconstruct a more accurate 3D model of colons with detailed surface texture. We show the effectiveness and accuracy of our depth estimation method on near photo-realistic challenging colon datasets. Experiments demonstrate that the strategy of sparse-to-dense coarse-to-fine can significantly improve the performance of depth estimation and smoothly fuse direct SLAM and DL-based depth estimation into a complete dense reconstruction system.