Effects of dexmedetomidine on surgical site wound pain in patients undergoing laparoscopic cholecystectomy: A meta-analysis.

This study aimed to evaluate the effectiveness of dexmedetomidine as an adjuvant to local wound infiltration anaesthesia in reducing surgical site wound pain in patients undergoing laparoscopic cholecystectomy. The Cochrane Library, PubMed, EMBASE, China National Knowledge Infrastructure, and Wanfang databases were searched from the time of database creation until February 2023. We performed a randomised controlled trial on the effect of dexmedetomidine as an adjunct to local wound infiltration anaesthesia on postoperative wound pain in patients undergoing laparoscopic cholecystectomy. Two investigators independently screened the literature, extracted data, and evaluated the quality of each study. This study was performed using the Review Manager 5.4 software. Ultimately, 13 publications with 1062 patients were included. The results showed that dexmedetomidine was effective as an adjunct to local wound infiltration anaesthesia at 1 h (standardised mean difference [SMD]: -5.31, 95% confidence intervals [CIs]: -7.22 to -3.40, P < .001), 4 h (SMD: -3.40, P < .001), 12 h (SMD: -2.11, 95% CIs: -3.10 to -1.13, P < .001) and 24 h postoperatively (SMD: -1.98, 95% CIs: -2.76 to -1.21, P < .001) significantly reduced surgical site wound pain. However, there was no significant difference in the analgesic effect at 48 h postoperatively (SMD: -1.33, 95% CIs: -3.25 to -0.58, P = .17). Dexmedetomidine provided good postoperative wound analgesia at the surgical site when used for laparoscopic cholecystectomy.

Understanding Pixel-Level 2D Image Semantics With 3D Keypoint Knowledge Engine.

Pixel-level 2D object semantic understanding is an important topic in computer vision and could help machine deeply understand objects (e.g., functionality and affordance) in our daily life. However, most previous methods directly train on correspondences in 2D images, which is end-to-end but loses plenty of information in 3D spaces. In this paper, we propose a new method on predicting image corresponding semantics in 3D domain and then projecting them back onto 2D images to achieve pixel-level understanding. In order to obtain reliable 3D semantic labels that are absent in current image datasets, we build a large scale keypoint knowledge engine called KeypointNet, which contains 103,450 keypoints and 8,234 3D models from 16 object categories. Our method leverages the advantages in 3D vision and can explicitly reason about objects self-occlusion and visibility. We show that our method gives comparative and even superior results on standard semantic benchmarks.