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5. Effect of Sanyrene combined with Mepilex Border in prevention of pressure injuries in the perioperative period of the spinal surgery (赛肤润联合美皮康预防脊柱手术围术期压力性损伤的效果观察)

Author

HUANG Mingliang (黄明亮), ZHU Yunqian (朱芸倩), ZHANG Qiujuan (张秋娟), HUANG Shishu (黄世树), and LIANG Hui (梁辉)

Subjects
pressure injuries, dressing, sanyrene, mepilex border, spinal surgery, perioperative nursing, 压力性损伤, 敷料, 赛肤润, 美皮康, 脊柱手术, 围手术期护理, Nursing, RT1-120
Abstract

Objective To investigate the effect of Sanyrene combined with Mepilex Border in prevention of pressure injuries in the perioperative period of the spinal surgery. Methods Totally 150 patients undergoing thoracolumbar surgery in prone position were randomly divided into the observation group and control group, with 75 cases in each group. In the control group, the Mepilex Border dressing was used to prevent pressure injuries in the perioperative period. In the observation group, Sanyrene cream was smeared at high-risk areas of developing pressure injuries such as forehead, zygoma, lower jaw, anterior superior spine and knee, followed by the external use of Mepilex Border dressing. The incidence of pressure injuries in two groups was compared. Results The incidence of pressure injuries in the observation group was lower than that in the control group (P＜0. 05). Conclusion Sanyrene combined with Mepilex Border is potentially effective to prevent the pressure injuries in the perioperative period of the spinal surgery. (目的 探讨赛肤润联合美皮康预防脊柱手术围术期压力性损伤的效果。方法 俯卧位胸腰椎手术患者150例按照随机数字表分为观察组和对照组, 每组75例。观察组在摆放手术体位前使用赛肤润涂抹在易发生压力性损伤的前额、颧弓、下颌、髂前上棘、双膝关节等骨隆突出处, 涂抹2 min后贴上美皮康, 对照组仅采用美皮康进行干预。比较2组患者围术期压力性损伤发生情况。结果 观察组围术期压力性损伤的发生率低于对照组, 差异有统计学意义(P＜0. 05)。结论 赛肤润联合美皮康可有效减少脊柱胸腰椎手术中压力性损伤的发生。)

Published
2021
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42. DeepRTP: A Deep Spatio-Temporal Residual Network for Regional Traffic Prediction

Author

Zhi Ye, Zhidan Liu, Huang Mingliang, and Kaishun Wu

Subjects
050210 logistics & transportation, Artificial neural network, business.industry, Computer science, Computation, Deep learning, 05 social sciences, 020206 networking & telecommunications, 02 engineering and technology, computer.software_genre, Residual, Data modeling, Smart city, 0502 economics and business, Metric (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Data mining, Artificial intelligence, business, Baseline (configuration management), computer
Abstract

Accurate traffic prediction can benefit many smart city applications. Existing works mainly consider traffic prediction on each individual road segment, and heavily rely on some statistical or machine learning models, which suffer from either poor prediction accuracy or high computation overheads for predictions of the whole road network. In this paper, we instead consider the region-level traffic prediction that is still useful for many applications. To describe the regional traffic conditions and capture their spatio-temporal dependencies, we present a deep learning based model - DeepRTP. Specifically, we use a novel metric called Traffic State Index (TSI) to measure regional traffic conditions, and carefully classify traffic data into three categories that are used to capture hourly, daily, and weekly traffic patterns. Furthermore, we employ the convolutional and residual neural networks to model both spatial and temporal dependencies. Experimental results from real-world traffic data demonstrate that DeepRTP outperforms five baseline methods and can achieve higher prediction accuracy.

Published
2019

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