Association between sleep quality and general anesthesia in teenager under elective surgery: a prospective study.

OBJECTIVE: Anesthesia was reported to be associated with lowered postoperative sleep quality in adults, but its effect on teenager was less understood. This study was conducted to explore the association between postoperative sleep quality and general anesthesia in teenagers. METHODS: A prospective study was conducted. Teenagers aged from 12 to 16 years who were treated with general anesthesia and under urologic or otolaryngologic surgery were recruited. Healthy teenagers matched by sex and age (± 3 years) with the specific case were recruited as the controls. The Sleep Habits Questionnaire was applied to assess the sleep quality of the teenagers. We applied a logistic regression analysis to evaluate the association between general anesthesia in teenagers under elective surgery and poor sleep quality. Risk ratio (RR) and its corresponding 95% confidence interval (CI) were computed. RESULTS: A total of 212 teenagers were included comprising 106 patients with general anesthesia who underwent urologic or otolaryngologic surgery and 106 healthy controls. The male participants were accounting for 47.2% (100/212). Anesthesia duration and surgery duration in the patients were 103.7 ± 14.4 min and 162.1 ± 17.0 min, respectively. Positive associations between general anesthesia and poor sleep quality in the 1st, 3rd, and 7th postoperative days were found, and RRs and their corresponding 95%CIs were 4.87 (1.72-13.79), 3.33 (1.22-9.1), and 3.26 (1.07-9.93), respectively. However, there was a lack of statistical associations before surgery and after 14 postoperative days. CONCLUSIONS: Teenagers who were treated with general anesthesia and under urologic or otolaryngologic surgery might have poor sleep quality within 7 postoperative days.

A Method of Green Citrus Detection in Natural Environments Using a Deep Convolutional Neural Network.

The accurate detection of green citrus in natural environments is a key step in realizing the intelligent harvesting of citrus through robotics. At present, the visual detection algorithms for green citrus in natural environments still have poor accuracy and robustness due to the color similarity between fruits and backgrounds. This study proposed a multi-scale convolutional neural network (CNN) named YOLO BP to detect green citrus in natural environments. Firstly, the backbone network, CSPDarknet53, was trimmed to extract high-quality features and improve the real-time performance of the network. Then, by removing the redundant nodes of the Path Aggregation Network (PANet) and adding additional connections, a bi-directional feature pyramid network (Bi-PANet) was proposed to efficiently fuse the multilayer features. Finally, three groups of green citrus detection experiments were designed to evaluate the network performance. The results showed that the accuracy, recall, mean average precision (mAP), and detection speed of YOLO BP were 86, 91, and 91.55% and 18 frames per second (FPS), respectively, which were 2, 7, and 4.3% and 1 FPS higher than those of YOLO v4. The proposed detection algorithm had strong robustness and high accuracy in the complex orchard environment, which provides technical support for green fruit detection in natural environments.